We'll start when he gives the time. Thanks to you. So good evening everyone. My name is Ajay Sik. I'm from corporate sales at Talent. Responsible for driving talent personas in ASEAN. I mean, again, I'm with this. We love this more about you guys, helping in your journey with talent, how talent can really help you. Integrating big data in real time. So, before we start, quick question. How many of you are working on talent for, say, six months or more? Raise your hand. Any experience working on talent six months or more? Anyone? No? So pretty new like me. I joined Talent a month back, so we're selling in the same boat. Okay, first of all, we keep talking about big data, Hadoop, Kafka, so many things coming up these days, right? What's the reason behind it? I mean, things were going pretty well five years back. All of a sudden, we're talking big data, Hadoop, Kafka, Spark, MapReduce. The thing is, there has been a paradigm shift in terms of how data is being generated today. There are so many different types of devices. Everyone has got a mobile, tablet. Everyone is online, right? So, 90% of today's data was generated in the last two years. Can you imagine the speed at the data being generated today? It will continue for next year, next five years, 10 years. Now, relation database or data warehouse, I mean, they are still working today. It's not obsolete. But when you're talking about unstructured data, because YouTube, hundreds of thousands of videos are uploaded every hour. If you're talking about Amazon, they're selling 450 items every second, right? People are uploading YouTube videos or using images. When it comes to unstructured data, it really becomes difficult for a traditional relation database or data warehouse to store the data and then to process it and then do it in real time. So, that's a huge challenge any IT developer, admin or ACI is facing today. Now, if you think about it, at the same time, technology is evolving every day. Talk about Hadoop was invented a few years back, then came MapReduce, now it's Spark, and it will change in every one year or two years. It brings a big dilemma for every IT executive, right? On one side, there are so many challenges they have to face.  Within six months, there is another technology. So, how do you keep a pace with this innovation? That's a big challenge. Now, if you talk about five years back, then most of the decisions are based on experience.  You will do reporting, it will go to decision makers that make a decision. The product is not doing so well. Maybe we need to do a better promotion, maybe we need to reduce the price, but that will turn into lower margin. More marketing spend. Now, if you talk about today, if you take an example of an e-commerce company, the biggest challenge e-commerce vendors are facing today is shopping card abandonment. Now, how do you deal with 90% of people are abandoning the shopping cards, right? Now, just imagine if you're talking about Amazon or Lazara, if they can improve by 1%, instead of 90%, only 89% people are abandoning the cards. It will lead to huge increase in terms of revenue for that company. Now, how do you do that? You have to make sure that you're leveraging the latest technology and you're addressing those challenges. You are processing the data in the real time. For example, if I'm trying to buy someone from Amazon, then right away, if there is a chance I might move to another website, right away Amazon will maybe pop up something for me. Okay, why don't you try this? I mean, chances are more that I'll end up buying something from Amazon, which will lead to more revenue, right? Now, why talent? So, when we talk about talent, the first thing comes into mind is its open source, so it's not that expensive. Yeah, of course, its open source, it will bring the TCO down. It will help increasing the ROI, but that's not it. Its open source means we have a big community supporting you. Whatever new innovations are there in the market will be the first to bring it to you. For example, you'll start working on talent now. You'll be part of that community. For example, Spark, we have 900 plus connectors, so you'll have access to the latest technology. Something goes wrong. You have any question. There is a community forum. You can raise your query there. Instead of, you know, working on a propriety solution where you have to rely on the vendor, here it's supported by the community. That's why talent is the market leader today in big data integration and cloud integration space as Pogartner and Forester, right? Now, of course, any vendor will talk about their company and that with that, okay, we are the best. Just to give you a few examples, since we are talking about big data real time, one of the biggest companies, GE, right? Everyone is aware of it, one of the biggest conglomerates. So they're using talent for GE Electric, GE Transportation, GE Healthcare. Now, how did they leverage talent in real time big data integration? So they have, if you talk about GE Electric, so they have wind farms, wind turbines around 22,000. Now, the challenge was how to make sure any turbine doesn't go down because if there is any downtime, it will lead to loss of revenue. So what they did is they integrated with talent to make sure that they're integrating in real time. Now, so every turbine is generating about one terabyte of data every day. Now, think about 22,000 turbines. Now, they process this with weather forecast. For example, the wind direction will be this way. So they have to adjust the direction of the turbine which will lead to more energy production. At the same time, they're doing predictive maintenance. How does that help GE? Because there won't be any downtime when it comes to any turbine. So again, GE, their objective was we just have to improve the process by 1%. They call it power of 1. If we can improve our current production by 1%, we are happy. This 1% improvement led to 1 to 2 million of savings each turbine every year, which is about $2 billion over a period of 15 years. Now, think about that. Just doing 1% of improvement led to 2 billion of savings, more revenue. Now, who doesn't want to do that? Every company wants to do that. Another example is, if you talk about Air France KLM. So they had this challenge. I mean, every company wants to go towards 360 degree view of the customer. I mean, it's a very fancy term, right? We want to know everything about the customer. We want to understand them better so that there is less customer churn. Customers are happy. They stay with us. They don't go with the competitors. By using talent, real-time, big data integration, what they did, one is if I'm flying to, say, Thailand or I'm flying to Indonesia, depending on buying behavior, one is they'll tell me which travel destination I would like to go to, and they'll give me a good pricing, right? So I don't move to another airline. That is one. Another is if I go to the airport, I'm supposed to be at the gate number five or six. Gate number changes. Right away, I'll get a text on Facebook because they have connected the social media. Right away, I'll get a text. Okay, gate number has changed. Go there. Number three, many times it happens, right? We put our stuff at the back of the seat. And at the time of landing, we forget about it because everyone is in so much hurry.  Please come and collect it. Now think of the effect of all this on the customer, how happy the customer will be. Of course, if I lose my iPad or something like that, if I get a text from KLM, at least 50% chances are more that I wouldn't go to another airline. So these are some of the examples, like how real-time, you know, big data integration helps companies in making data-driven decisions rather than relying on experience. We have facts available with our decision makers, how businesses can collaborate with a technical team, right, and make better informed decisions. I mean, so that's pretty much an overview. Now I'll just pass it on to Sangju. He is our technical consultant. We'll walk you through the technical process. Thank you. Thank you, Vijay. Okay. Okay, so what Vijay shared just now about all these Amazon, you buy things, all KLM, all this. Today what I'm going to show you is you can also do what he's saying using talent. Okay, sure. Okay, here who still do programming. Anyone? Okay, good. Okay, keep your hands up. Those of you doing programming one, doing Java.net C. Okay, do it. Okay. How about doing now, Mac Reduce? Okay, those doing here. How about Spark? Spark. Oh, Spark got more. Okay, Spark Streaming and Spark Storm. Okay, still quite a good handful. Okay, today I'm going to show you the tool. Everyone here can also do Spark Streaming and Spark Storm. Okay? Okay. Okay, overview of talent technically. Talent real-time big data. Then recommendation engine. We'll do a demo on recommendation engine like as though you go to Amazon, you buy something real-time and recommend you a product. Airplane parts maintainer alerts. We have sample data of airplane plot data. Then we send through a machine learning component. And after that, we'll do real-time recommendation based on test data. And last one, I want to create my own adventure. So today what I share with you, you all can also today go back, download and play. It's all open source in the sandbox. You just need about, thank you of RAM and i5. Then your laptop can run this thing. Okay, transforming the way we work. So just ask about the coding. So today using talent, all of you can do whatever VJ has talked about without any more coding. So we are a code generator. We generate Spark. We generate MapReduce. We generate Java code. So you know what to do on all this. You just need to know how to use all the underlying like HBase, HCatalog, Impala and stuff. Just need to know how to use this. You generate the code on your behalf. Second, learn once and apply many. So you see on the chart here, we do traditional data integration from AS4, DB2, TerraData. We also do big data integration, moving data into Hadoop, data out doing MapReduce and stuff. You also do other stuff like application integration, ESB, web services, CamelRest API, okay. We also do cloud integration, moving data into the cloud. We also have integration platform as a service and lastly, master data management for a single golden record of truth. What this point is trying to say is you do not need to install five solutions or learn five different applications or integrate five. We build everything from bottom up. So it's an Eclipse environment tool and you just want to use any of this. You just need to click, click, click. The function will change. The UI stays the same. So you just need to learn once and you can now do ESB, do big data, do MDM, do data modeling, all in one tool. It's all open source free. Next, future proof solution. We work very closely with the open source community. Our engineers also work with Google and stuff. So everybody heard about Apache Beam? Apache Beam. Okay, so Apache Beam is up and coming from Google. We also work with them closely. So end of this year, our version seven, Apache Beam will be available. So what it means to you all, you all can use Apache Beam. No need to know how to code it, but you can use it. Okay, analysis recognition, just a quick one. On the Gardner Magic Gordon, we are the leader here for data integration, the only open source guy here. Okay, for forester for big data fabric integration, this is for moving data inside big data, big data related. We are also the leader here. Okay, just summarize on the available solution that we have. We started data integration, our company 10 years ago in Paris, France. After that, we moved to US. Then we added big data, application integration, master data, blah, blah, blah. So on top here, you see, today you can download free. You can download and play on your laptop. Okay, no charge. Below here is the subscription. Similar like Red Hat Linux. These are the subscription that you get for enterprise, for your deployment, monitoring, execution, scheduling, blah, blah, blah. So you can still do your own job, but you can schedule yourself, or you can subscription, you provide this end to end. Okay, this is the Eclipse Development Tool that everyone is familiar with. So let's go through that. On the left-hand side, number one is the report story that stores your jobs, your metadata, your routine, your documentation. On the right-hand side, two, pallet. That's what I've usually shared. We have 1000 plus components and connectors, all free. No charge. You want to connect to SAP. You want to connect to Oracle, Teradata, Hadoop, Impala, Hubee, blah, blah, blah. It's all available free. Okay. Three is the perspective. You want to switch from any one of them, switch across. No need to go and uninstall or start a new application. Just select here, and you can switch between. You want to do ESB. You want to do big data. The screen will change. Okay. Four is the main design part. This is where you do your ingestion, transformation, and ingestion. Number five here is to specify the component of this guy. Okay. Question. If I want to move data out of Hadoop, how many clicks do I need? Three, five, or nine? How many steps do I need to bring data out? Five. Okay. We can do it at about three steps. Okay. First, on the right-hand side, the pallet, in this case, we find the elephant. We find Hadoop. You can select the Hadoop component out. THJF output. You can similarly do this step. You can say that you want to drag Oracle out. You want to drag Terra data out.  Whether it's red color, blue color, or green color. Anyone follow me on the red, blue, and green? Okay. Red is the map reduced. Blue is the Cloudera. Green is the Hotterwork. These are the three famous elephants that are running around. So you specify the elephant here. Then after that, you specify the version of the elephant. If you work closely with all these Hadoop distributors, we are always one version behind them. So if Cloudera now is 5.9, we are 5.8. So what it means to you is that you do not worry whatever the latest feature of the distro, you will have it just three months back. Okay.  So you today, you can get data out from HDFS, from Hadoop. No need to go and learn one month. That's the, to go and script. Okay. I also don't know how to take it out. Okay. We also have the Hadoop configuration we wizard. Okay. You know, in the Hadoop inside, there's a lot of animals running around. You've got zookeeper, Kafka, Impala, Hew, Hew base, Hitchcaddle, blah blah blah. So you don't need to configure all of them. As long as you point to the Hadoop top layer, the top guy for Hotterworks is Ambari. For Cloudera is the manager. All the underlying components, underlying animal will be surface. So you don't need to configure. So this is our Hadoop configuration we wizard. Okay. Summary of the 900 plus. Yes. Yes. Okay. The other 900 plus components, you can see here, Big Data, Business, Intelligent, Cloud, Custom, Traditional, Database, blah blah blah. Big Data components, Neo4j, NonSQL, MongoDB, Hcatalog, HDFS distribution, Amazon EMR, Cloudera, Hotterworks, Mac, Pivotal, HD. So in summary, you can see that 99.5%, you can connect anything data out there. Even if you don't find it here, we also support bringing your own JAR. If you bring your own JAR in, you just put your JAR in, you can connect. Even if you don't have the JAR, as long as the data source has its own JAR API, Java API, we can connect. Because we are Java open source. So you can move any data in and out. Okay. Common connectors. So these are examples of the 100 plus connectors that we have. We have traditional, like DB2, AS400, to NetEaser, to TerraData. So every of the components, you have input, input data in, output, take data out. I want to have slow changing dimension. I want to have stop procedure. I want to have commit close. I want to have about load. About load. And stuff. Yeah. Okay. See an example. If you want to move data from AS400, merge it with some in DB2. After that, push it to Pivotal Green Plum. So you can do it in five minutes. You don't need to go and learn how to integrate the four of them. Okay. We generate code automatically. We generate native, Hadoop, Peak, Java, or SQL code. EG, you do the component, you drag, drag, drag. The code is there you can see. So some people use these two to learn how to code. Oh, so can. Okay. No black box proprietary software. Even the subscription version or the free version, you can see all the code. No black black box. Some people take the code you want to deploy in WebSphere, WebLogic. It will still run. Okay. We build once and run anywhere. So you build already. You can run it inside different JVM also can. Any questions so far? Yes. I just want to play with the HL flag. And my brain was on collaborative work under an automated test. When did you do the work for copy data? Okay. So the question, using the open source challenges with collaborative and for test testing. Okay. In the open source version, we don't have this, but in the subscription version, we have collaboration. So we support SVN and JIT. So versioning and merging of code is there. Then for test testing in the subscription version, we support Maven and Jenkins, where you can do from deployment, testing to production. We also have debugger and we also have test creation too. But those are in the subscription. Any questions? No, I'll jump to the big data part. Okay. Talent real-time big data. Okay. Upgrade from mad reduce to spark with one click. Okay. So mad reduce has been around since Hadoop was around 10 years back. Then spark a few years back came along. So if you have already mad reduce code that you have learned, now spark came along. I need to go and learn one month to do it. No. With talent, you can do it with one click. So with one click, you can change the job that is designed here to spark. Okay. So now this is not good enough. Lately is spark streaming. So your spark batch job, if you want to convert to a streaming job, you can similarly change the property from spark streaming, spark batch to spark streaming or storm. The code will automatically regenerated as spark streaming and storm. And you can use it. Here are example where we have one on spark streaming. Okay. Big data create high quality trusted information. Okay. No Hadoop data is all there. If you want to do any of this, explore profile, monitor, past, standardized, reconcile, match and rich and share. You need to learn the spark code to how to do this. Not to worry with talent. All these components are out of the box. You just need to drag, design your job and we will generate the spark code for you or the mad reduce code and you can run. Okay. Lastly, the code that is generated is native. There is no additional black box layer whereby the code will need to be converted into something else before it runs. It's the actual Java Java code that is sent in. So you can even build the, keep it somewhere else and you can throw it in as when you want or you can use the Uzi scheduler to run it. Okay. This is the lambda architecture. I want to see this before for the real time and the batch one. Okay. So what this does is on the left-hand side here, you have the batch whereby you do all the historical click stream, the web logs. You store it here. Okay. We can process it using spark batch or mad reduce. Then after that using machine learning components, you keep the learning model here. Okay. At real time, we're coming in. The latest one now is using Kafka. So you push using Kafka. You want to store inside. No SQL, Cassandra, Baba, you can store, transform, keep it here. Real time when the click stream comes in, merging together with the recommendation of this, I will at real time recommend to the consumer. So in this case, if I'm buying a bicycle from Amazon and this bicycle is black and red, you recommend me a black glove and a red shirt. Okay. So later we'll go through this example. How you can also download and sandbox and play this yourself. Okay. Machine learning support. We also integrate closely with the machine machine learning in spark. So machine learning sparks, we have all this that is available like ALS model, classification, decision tree, K-means, K-means string. So they are scientists. They don't need to code out themselves. They can leverage, use this, throw the data in and the model is there. Then afterwards you can use the T predict to leverage on the model and the recommendation will come up. So again, no need to code. These are all out of the box. Okay. So now we come to the demo recommendation engine. Any questions? If not, I continue. Okay.  Yes. Okay. So the question is that if you need to deploy big data job, how is it deployed into Hadoop to run? Okay. So what we have here, we are a code generator for MatReduce and Spark. So in talent itself, we will generate the MatReduce and Spark code. Packages nicely, we will push it all the way to Hadoop Yarn there to go and run. So on outside it's very light. It's the ELT mode, extract, load and transform. So you do not need to have a very big server because the code generator after that you're leveraging on Hadoop to run. The validation? Visualization. Visualization. Visualization of data, yes. We do have a visualization of data. It's a newest one here. It's the data preparation, also free. So what it does, this data preparation tool allows business users to go into Hadoop, to go into Oracle, to take the data out into a nice Excel and they can do data cleansing on their own. So it's also open source, now version 2.0. You all can download and play with it. So that one exposed all the data for you to do cleansing. So what we're going to do here, we're going to do this simulation. Step number one, we will be doing the batch generating the model. After that, step 2A, we will be running this recommendation engine here. Step 2B, we will open a web socket to listen to the incoming click stream. And step 2C, after we do a matching, we will recommend it back to the user. Using AJAX. So this will be the website that we are using. So what this website is, as though you are going to a telecom, like maybe Singtel, and you want to buy some data bundle. There's a big data bundle, basic bundles and stuff like that. And the recommendation will come out here. So what this page have, you have two components. One is the web socket. When I click this, the web socket will take it and push it to the talent itself. Then after that, we have the AJAX coming back. So I'll just do a quick view code. This part here is the web socket. So later I'll run the web socket on the other side to listen. This part here is after the recommendation is completed. I'll push back the recommendation back. So this is the studio. Let me just make it more space. So this is job number one. The one is the web socket they're listening from the clicking. So we are using Apache Camel open source. So if we just click at this component here, it's listening for people clicking in. So you see there's two rows because just now I just did a test to make sure that it works. Later when you click, the rows will increase. So if we look at this component here, using Apache Camel, it's just a simple, I'm listening to my local host at 9902 with all the parameters. So this was at the website. I click our file to 9902. So after I take in the data, I process, process, process, process, I'll push it into Kafka for processing. So now I go to step number two, the real-time recommendation. So this is step number two. So step number two, what we have here. Step number one, I push the input into the Kafka topic. I extract the JSON of the Kafka. I do the recommendation product model which I did in the earlier step one. I match it. Let's check whether this customer is someone that I know. I go and check my products. Then I'll write a JSON message back to push into Kafka. So the last step is into the Kafka. I need to push it back to where the website come from. So number three, I'll extract from the Kafka. I'll do some processing. Then I'll push it back as a Ajax back to the page. Okay, I'm still here. Okay, so here you see only two rows. Let me run the demo. Okay, so I select one option. See the right-hand side here. Suggest for the product. Okay, so the plan come out here. The recommendation and stuff. So I can click something else. Here we'll change. Okay, another number change. So I click about three times. If we go back there. Okay, so five rows. Two plus three. Click three times. The input string, this is the first part. There's eight rows coming in. So yes. So you have an issue while you're running this. How will you put the bread point and divide it into one of the components to see what's happening inside this. Yes, so we have, you see this is a lock. This is the one that prints out the lock. So we can keep the lock where the information is.  When the thing die, you want to print some message. You also have lock catcher to catch wherever normal locks or you want extreme locks also have. So this, all this information will be pushed to our monitoring screen. So the you can break them. So what you can do is you can right click this guy and you have an option to deactivate. So you'll run until here and stop. We can also do that. You can also do step by step. We have a component called debug. You can do debug step by step. See here there's a debug run. So the debug run you can run. Every time let's say the message comes in, go step by step. So you can also do that. The soft code. You want to see the soft code. You can put a breakpoint inside here also can. So if you want to see the soft code, just click the code here. So this is the soft code. So this Kafka streaming thing, right? If you want to use the soft code, you can take this whole soft code, deploy it in your environment or deploy it into harddo. You will still run. So this is Java Spark code. So if it's okay with this, I'll go to the next example where VJ has shared how we can use machine maintenance, how to reduce the turbine failures and stuff. So no problem for the first recommendation, real time streaming. So you all can do it today. Later download and go and play. Can I go to the next one? Okay, next one. Airplane maintenance alert. Okay, 24,000 sensors on the fuel system help predict failure days, avoiding grounding the airplane and incurring huge cost for the, detect repairs 10 to 20 days in advance and locate repairs 5 to 6 hours. So a leading email is using this to help to keep their plane more on the air rather than on the ground. Similarly to all the G examples and turbines and stuff. Okay, so what we have here, the example we have is here number one, training set. We have a set of training data. So what does the training data look like? This is a set of training data just generated using Excel. So these are all the data that is good. Then if you scroll down, these are the data that needs to maintain. So we take this data, throw it into a machine learning model inside. So this is the data. So this is the training data. I take the training data. I put it in my own encoder. I throw it into logistic regression model. Anybody can share on this Excel learning logistic regression model. So after into this, we have the model. We use the test data here to predict whether the outcome, this is for batch testing. And the last part, we have a real time, you want to do real time testing, we send two data in. At real time, we will tell you whether these two data from the sensors are they providing any errors. So I will run this job now. So this is the same thing that you saw just now. We will send the training data in. After that, we create a model. We use this model inside the prediction here. So this prediction model will refer to this. So just like we shared in the slides, we got many models from Spark. You can just take and go. So we are using logistic regression model in this case. After that, we will take two data in. So if you look at this information here. So this information has two sample data. So based on these two sample data, prediction model, we will say whether this is a good or bad. Let's do the run. So this running will do the three things. You do the building of the model. You do the testing. Then after that, you will do the real time. So similarly, this one you can also see the code and see how it's being written. So I have a local Spark. So if you're running on the local Spark in my machine. So if you go back to look at the job, you can see at real time, how many percent is ongoing. So all is completed. All these things Spark. If we just go and look at the output. So these two data just now was being sent in at the end. And based on the model, the first one is good. The second one is go for maintenance. So now with your own IoT data, with your own web log, you can do your own data modeling and stuff. You have to ask the data scientist. Any questions for this? Yes. I mean, these connectors would help them connect the different technologies together. Then he would still, let's say he's using Spark. The developer would still have to use the, you know, go into the code level and then do the Spark, you know, the part about cleansing data and then matching. Okay. So the question is, these two very good to join all the data together. But if they want to take this do inside the Spark for all the matching cleansing and stuff, do they still need to write Spark code? Okay. So we have a lot of components on the right processing components that can do like filtering, aggregation, mapping, denormalization and all this. So those components are already Spark enabled. So you just need to drag them in form. Let's say you want to maybe filter the records for the employee, whether they are staying in Singapore or you want to normalize them based on the data and stuff. Components are on the right-hand side here. So you can just use the components. You don't need to go and code. So you see on this right, processing component, filtering, filter, map, replace, replicate, sorting, top, top, unique, unite, all this. So all these are Spark enabled. We just drag form. You can do your data cleansing, data profiling, data matching also have all inside Spark. Yes. How do you test how do you know how effective the machine learning is? Are that have to base on the data scientist? So you can change the coefficient, change the parameters. How effective have to add the data scientist? We just provide the model, the tool. Instead of the data scientist go and code R, he can just, okay, maybe I want to change the coefficient to something more. Let's look at this one. Sorry? We are using the Spark machine library. So you are using the library, the Spark MLib. So you go to Spark website, right? They have all the machine learning components that's there. You just need to learn how to connect to them manually. But if you use talent, you can skip that step. You just use. So, okay. I'm not a stats guy. So logistic regression, elastic mixing parameter, regulation, thresholds, all this. So based on the data scientist guy, he will know how to adjust. Yeah. Just that he don't need to code. Though they love to code. Yes. Again, the trauma. It's a trauma developer. So if he does the spot matching and also we can either write a single line of code or he comes and drags and drop here to kind of get back to it. Yeah. They can write it for a developer who is the Java developer. Okay. Yeah. Totally right. So for Spark developer, of course, you write a Spark code line of code. It's more efficient than using this to maybe draw a component then generate five, ten lines of code. The whole idea for the enterprise is that the developer who does this, he might be doing other jobs and the code then the next person taking over might have challenges looking at it and also the staff also move around quite a lot. So with this from a management perspective, it's more easier to manage the code. The next person comes in, look at the components and models. He can see more or less how this run. It's all written in Spark and all these Spark engines are in high demand nowadays. So the next guy comes in will also have to spend time to take a look at it. So it's more on the maintainability. But of course, those who like to code one, they'll still continue to code. Yeah. Yes. So for this Spark, do I support all the Spark underlining functions? Okay. We support most of it. So we need to check which one that you're asking for, whether we support it. Yeah. So like for example, the machine learning, there's about I think 20 plus, we now support up to 16. So we are constantly building and putting them in. Last year, January, we support 11. This last year, June, we support 16. So as and when there's more need, we convert a lot of the open source. People that do it, we convert and support it. So besides the 1000 plus components you see here, we have a community called exchange.talent.com. So the open source people just put, there's about 200 there. So ranging from like PDF conversion, you want to connect to their other stuff. Okay. So it's okay. I'll continue. Okay. Okay. So the two demo that is done, so I also want to play, how can I play? Okay. We are this talent big data sandbox. You can download this URL is open or you can just go and Google big data sandbox version two. Okay. So this sandbox is a cookbook step by step to teach you how to play the two demo that was available. So the two demo, like the inside got a few, few more. One, retail recommendation, the one that I share with you. Second one is a sports statistics using IoT. This one later I'll show a few slides on this whereby based on the guy playing softball running around throwing the ball, the camera will go and take this data and we're going to convert at real time, push it to website on REST API. Okay. But it starts from the digital part. This is example. This is how to for IoT. Next, click stream. Coming in real click stream and you can see the data. ETL off load processing your web web log using a big data rather than using the traditional data warehouses. Lastly, analyze our Apache web blog. So this got step by step. Teach you how to download. You need at least about 8 to 10 gig of memory and hard disk space, maybe about 20 gig. Because once you download this guy, it's a bit small, 3 gig. You'll ask you, which elephant do you want to download? So give it about one hour. Then you will download the relevant or on Docker so you can see everything is in stock. Kafka, Zookeeper, everything is up and you can play on this. Okay. So what you will see in the five example, there'll be step by step. Step number one, create the Kafka topic. Step number two, push to Kafka. You just need to click play, play, play, next, next, next, next. And you'll see the job runs. Then all the output will be in the bottom. So this example you'll see for the five sandbox. So the first one, retail recommendation, which I shared will include Kafka, machine learning and spark streaming. The IoT camera one, the softball guy who was throwing, that one will be using Kafka spark streaming. You'll see this nice screen. Every 10 seconds, this graph will change. Okay. So these will be the five examples that you'll see inside there. Okay. So that's the end. My last slide is a talent solution. We run natively on big data and in the cloud. It's the actual source code that are writing open source. Adaptable and extendable architecture. You can start small by just using data integration. Then if you want, you can just upgrade and the features will be there. You'll need to reinstall our stuff. Last one, one platform. One platform you can do ESB, web services, rest API, big data, data integration, master data management and all this. Traditionally, you'll need to install five. But this is the one. Last one, okay. This one is a user-based subscription. We base just on developer, not on data, not on connectors and stuff. Okay. Any questions? Yes. What version of Spark is this one? 2.0. Okay. So we support quite closely. So the latest is 2.0. Okay. We are 2.0. Yes. Let's say somebody is using this tool and you create an application. So what kind of deployment does it make?  Which we have to say deploy a group, for example. What kind of deployment? Artifacts. Artifacts. Artifacts. So you can use that example. Is it a jar or is it a jar? Yes. It's a jar. It's a jar. And you can take the jar, you can run in the even West Bay where the tree was still running. It's just a normal jar. Yes. Do you plan to explore on the Scala? Explore on Scala. So far, not yet. We are still Java based. So on the integration, we still spot at least still Java. Is it possible to integrate with Jenkins or Airflow? We can integrate with Maverick and Jenkins. Airflow. Airflow, sorry. I don't know. Maverick and Jenkins, yes. And how we handle version control? Okay, version control, we connect support, SVN and GIT. So, this is only the sufficient part. We have an admin server. So the admin server will control the user, the project. We can connect to SVN or connect to GIT. Once it's connected, all the developers that are logged in, all their versions will be controlled or will be checked in, check out all SVN and GIT. What is the prerequisites to set up this ID? Okay, what is the prerequisites to set up this ID? Okay. If you download the open source version, everything is there. There is no prerequisites except you have enough memory, 64-bit, i5, 20-gig hard disk, everything is in. Spark is also inside there. Spark local. We have a local Spark. So local Spark is there also for easy testing. No need to every time send to hardware to test. So just now you are running on my local machine. So my is a 16-gig. Yes. But if you're still getting a very set up, then we cannot connect to the internet or would it install the libraries that are required? Okay, good question. So if we install in the enterprise and we cannot connect to internet, how can we download the library? Okay, for the enterprise, we have one link. Download is about 11-gig. Continue all the JA.  Local reports three, yes. Okay, one thing good. We in the open source community, every six months we are updating. So look out for the next version coming out in June. We have more new features. And in the end of this version 7, this is currently 6.3, 6.4 in June. End of year version 7, we will be integrated with Apache Beam. So those ones looking forward to use Apache Beam. Sorry? Documentation. Documentation, okay. If you want to have like the components on the right hand side, the documentations are all available there. You download it's about 13,000 page or you can just go online, each every component. If you want to document the particular job, we can have a very good feature. Right click, document the job. Then the engineers don't need to go and document manually. So let's say I have a job. Okay, let's say I have a CDC job. It's a simple CDC job. After I completed this CDC job, I close this job. Right click, generate.shtml. It's a nice zip file. HTML zip file, finish. Okay. I go to my D drive, which I just now, this was the guy that was extract all. Extract, yes, overwrite, yes, overwrite. Okay, copy overwrite. Yes. Okay. So you have the nice job being documented. Okay. So documentation on the job itself, documentation of all the components all available online. Yes. How frequent do we release new version? So we release new version every six months. So every six months new features that is requested by customers and also in the open source community, we put it in. So in the upcoming 6.4, we will be more fully integrated with Azure and also Google. Now we are quite integrated with AWS. So example, if you want to spin off an EC2 or RDS from here, we can do it.  Is the API open? Yes, all the API on the right-hand side, if you want, we are using JavaJet, JavaJet technology. So you can create your own API and you can create your own API and put it here by yourself or you can go to our community exchange.talent.com, go and download and put it in. So one example, we don't have PEDF here, but in the community, somebody did PEDF, Tiki PEDF, based on Java, so you bring the component in, then you can un-PDF. To un-PDF, we don't have it. So you can develop your own component and put it in. If not, thank you very much. We are still here. You can come and talk to us. Do you have any questions?


The holes that were within your proverbial boat taught all of you how to first notice where those holes were. And secondly, how to start working yourself upon that energy. So now you're wide open to create those design effects within your creative collective consciousness. And if you choose to fix the holes that you found inside of your boat, and work towards a growing and fastly connecting co-creator connective consciousness starting at community level, going outward to state, regency or territorial level, going past country level into world unity, then you'll start seeing massive forms of co-creating with humans on that level.


Good morning, everyone. My name is Virginia Robert, and I'm a reporter for Lizzy Co. It's the French business daily. Today we're here to talk about the main challenges of the global economy. And here are words that we thought we would never hear again, or almost hardly ever, like isolationism or protectionism. In its latest world economic outlook, the IMF chief economist Maurice Opsfeld warned that turning back the clock on trade can only deepen and prolong the world economy's current daldrums. Yet we see less and less support for trade agreement in the United States as in Europe. And meanwhile, the pace of exchange is slowing down. It will be a mere 1.7% this year, half of what it was in 2015. Worse, its growth will be inferior to the one of the world economy that is expected at 2.2% in 2016. In addition, as you well very well know, geopolitical factors are bringing much instability. President-elect Trump, for instance, brings a whole new level of uncertainties, as did the Brexit earlier last summer. Add to this the refugee crisis. And we now wonder if there is a real threat of deconstructing Europe. Even more so if populist leaders find their way in the upcoming elections. The central banks, although keen on giving their support, will be slowly tightening their policies. The Fed, as you know, should probably raise interest rates as early as December. And it's almost the end of free money. In Europe, the Commission has given a little bit of leeway to the states so they can add more stimulus. But will that be enough? As you perhaps know, income per capita in Europe is still beyond its pre-crisis level. And in Asia, the situation is not very glorious either. China's growth is slowing and that impacts the whole region, if not the whole world. So to discuss the main challenge of the worldwide economy, we have today with us Mr. Bark, who is professor at Seoul National University and was former minister of trade at the Republic of Korea. Mr. Jean-Claude Trichet, who used to be the former president of the European Central Bank. John Lipsky, former first deputy magic editor of the IMF and also at John Hopkins University nowadays. And Mr. Chow, who's vice president and secretary general at the Shanghai Development Research Foundation. So we're gonna start right away. So to stay the news, because we like that in the newspapers, there was a little video somebody talked to me about this morning. We're talking with John Lipsky. I don't know if you had a chance to see it. It's a little over two minutes and Donald Trump gave his line, his directions for his upcoming presidency. We wonder really what are gonna be the effect of a Trump presidency on the global economy. John, we were discussing that earlier. Can you give us a hint of what the President-elect said this morning? Yes, thank you, Virginia. Thanks the organizers for the invitation to be here. Mr. Trump's video address suggested one, it was released through YouTube, indicating that we will be receiving presidential information through non-standard or through alternative media from now on between Twitter and YouTube. The idea was to announce the actions that he was planning to take on day one of his presidency. And one of those items was to notify the withdrawal from the Trans-Pacific Partnership Agreement. He also said that in the few, he put this in the context of saying that the principle will be America first and a goal of bringing essentially manufacturing, he mentioned autos and high tech back, he said, to the US, which is a little uncertain but slight ominous tone because that may hint at protection. And he said that henceforth he would focus on bilateral trade deals, which adds a new complexity. He announced some other things, a two for one regulatory program in which every new regulation promulgated would have to be accompanied by the extinction of two prior existing regulations and some other measures. But let me just say that in addition to his pronouncements, it's clear that financial markets, participants have made some assumptions about the effect of Mr. Trump's plans, President Trump's plans on the economy. There has been a reiteration of the intention to begin a very large-scale infrastructure program, although with no details are yet available. But markets seem to assume that there will be, along with tax cuts, I should say, infrastructure spending and tax cuts, markets have concluded at least to some degree that U.S. fiscal policy will turn in a more expansionary direction at a time in which it's possible that the economy is approaching full capacity, at least in many areas. And as a result, the outlook for both Fed policy and long-term interest rates, not just short-term interest rates, has become much more uncertain. Quickly go around. Do you have any gut feeling of what this means for the global economy? Mr. Vark? I think I'm the only trade expert, other panelists are more in finance. But we thought TPP, the conclusion of TPP, was a very welcome news to the world trading environment because nothing is going well these days.  And the other mega RTAs, so-called Korea-China-CJK, trilateral FTA negotiations and the R7 negotiations and TTIP, they have been moving okay, but very slowly without much progress. So we thought maybe TPP will be a kind of epoch-making event which can, you know, push for other negotiations. But since the day one, Trump already said that he will, I mean, U.S. will withdraw from the TPP, which is adding very much, you know, uncertainty and unpredictability to the world trading system because right now, current state of the world trading system is facing tremendous challenges and most difficult challenges since the establishment of multilateral trading system in 1948. So as a trade economist, we are really concerned. And on top of that, he mentioned he'll do many, many, you know, protectionist kind of policy majors in the future. So, you know, it really hurt the current state. So I don't know whether really, even though he said that he will withdraw from TPP, I think if you look at TPP, I mean, it's originally, I want to give some pressure to China. So I think it's in line with Trump's, you know, remarks on negative things about China. So maybe he will change his mind, I hope. But it may take quite some time through renegotiation and other things. So I think this is really bad. At the same time, I would react on China right away, if you don't mind, Mr. Chow, because there was this APEC meeting earlier and apparently to the Chinese who were not included in the TPP, they find here a lot of new opportunities for them that they would like to size. I would say yes. The Chinese government, the position of Chinese government toward TPP have kind of a little bit of change. From two or three years ago, Chinese government have a strong against TPP, thinking the TPP targeting against China. But in past one or two years, they just say we open to see what happened. Of course, now it's given more opportunity for Chinese government to carry other program, like RCEP. I guess some country have already expressed interest in negotiation on another program. That's something I'm thinking.  At the same time, he said he would raise tariffs incredibly high on China. I guess there's a lot of bargaining space there. Yeah. I'm not sure finally where the new administration of the United States really will have a high tariff on Chinese export. If President Trump did that, I guess China will bring the case to WTO at the same time will take action, retaliate against US. The results will be disordered for whole world trade, whole world economy. So there would be a very strong reaction into the multiple instances. Did you think we're going toward trade wars? We learned the lesson from the past near the Second World War to avoid this kind of trade war. We had before, that's why we established a multilateral trading system. But if things are going like this, retaliation after retaliation by big players, then maybe we are virtually entering into some kind of trade war which we should avoid. So maybe through this kind of conference, we should give some suggestions to the leaders or the leaders not to have this kind of situation. Yes, it seems that we wouldn't want to be premature in concluding what is really going to happen here. Mr. Trump is a negotiator, so this is an opening position. And he noticed, I believe, Prime Minister Abe has already responded by saying that Japan would not be interested in going forward at this time with TPP without US participation, which was interpreted as at least the Japanese authorities want to wait and see what Mr. Trump's real intentions are in the longer term. You would hope that the dangers of active protectionist and retaliatory measures in the trade arena that the dangers would be well understood and avoided, I would hope. First of all, I think that we should take very seriously, of course, what's going on, not only in the United States, but also in Europe, certainly, and in other advanced economy, where we have exactly the same phenomenon of frustration of a large part of the working population, feeling the stress of the, I would say, intense competition, the need for restructuring and reshaping in the productive sector, the impact of science and technology and the IT and the other advances, which is also calling for abrupt and sharp changes. So this frustration is expressed politically now very, very clearly. And I would interpret that as something like, could you slow down the process of advances in all domain because it hurts us and it obliged us to change our job, accept restructuring, accept a lot of hard thing to do, particularly when you have a low level of education and when you have a major difficulty to re-engineer your own skills. So that's the main political problem I see in the US and in the UK, in a way, because Brexit says the same and also in continental Europe, including in our country, if I may. That certainly would translate in more or less blocking the trade agreement that we are mentioned. Whether it will translate in high level of new tariffs, really protectionist measure, I would say if it goes that far, it could be extremely dangerous. Already, of course, blocking the trade agreement has an impact on the global economy, which was stressed by the panellists, which would be very bad, obviously. But again, if we are really told in the advanced economy, not in the emerging economy, but in the advanced economy that things are going a little bit too fast, then we have to take that into account. So what I would say that I hope very, very much that we will not embark on new explicit protectionist measures that would be extremely dangerous for global growth, I take as a given, unfortunately, that we will not proceed with new big trade agreements. Well, that's interesting what you're saying because the fact that people are very afraid of the impact of globalization today makes it difficult for them to agree with those large trade agreements. The fact that we would go through more bilateral agreements, is this something? Actually, it gives you pace, the pace they're asking for to negotiate things country by country. At the same time, it's complicated and cumbersome. So do you think it's a good idea like China is probably gonna do it now and the states are eager to do it, is go one country again, buy one country and forget completely about multilateral during that four year timeframe we have, which is the mandate for President Trump. Yes, of course. Well, actually, because the multilateral setting is not moving very well, countries are responding to have more bilateral FDA. But recently, we have a so-called RTA, bigger mega RTA, which consists of many countries. In other words, if you have a series of bilateral FDA, the pressure or adjustment cost or the additional cost to the business people is increasing. In other words, if you have one bilateral FDA, it's good to each other, but you are discriminating against all rest of the countries. If you had one more FDA, one more FDA, then marginal gain to the player is decreasing while the administration cost is increasing. So that's why at some point of time, they want to have a bigger RTA, which is much more efficient. But TPP has a very bad negative kind of impression to the general public. So that's why at the moment, US is trying to have a more individual bilateral FDA. I don't think this is a good strategy or good direction. But at the same time, as I mentioned earlier, the pace of trade is really slowing. I'm not sure those big multilateral agreements would really boost them considerably, but they should bring some effect. If we don't have that, what do we have to dynamize our growth in emerging and advanced economies? Because we can't rely on trade agreements anymore for at least a couple of years. So is it big infrastructure programs like Donald Trump, you know, as mentioning in the States, about one trillion, sorry, investment in Canada, they're planning, I think it's around 120 billion investment planned in infrastructure. Is this what we're gonna have to rely on to give a booster to our economies? Or is there ways to improve our investments more efficiently? Because the problem is that since 2008, the pace of growth has been extremely slow. Who wants to pitch in? If I may say a word on that, it's absolutely clear that the pace of the global trade has slowed considerably, and it is undoubtedly linked to the problem that we had in the advanced economy in the time of the crisis, the legacy of the crisis. It's also clearly, clearly a very, very bad move. Since I would like to ask colleagues whether there is some merit in the explanation that in any case the expansion of global trade, which was absolutely incredible, say in the beginning of the 2000s, was a little bit linked to a profound transformation of the value chain at a global level. And that perhaps part of the slowing down is due to the fact that this phenomenon is itself slowing down because you cannot go too far in the value chain reshaping, resorting, if I may, at a global level. So there are elements that are undoubtedly very alarming in what we have observed during the recent period, and perhaps elements that we can explain. We can explain partially, perhaps, what we have observed. But that does not diminish in any respect the judgment that, of course, global trade is closely associated with global growth and the slowing down of global trade is bad. And the intention of many, it seems, in the advanced economy, not only Mr. Trump, but others too, and I could see that, again, in Europe also, where even in Germany, which is very surprising, as well as in France, you had an opposition to the trade agreement with the US. So we have something which is very serious there and is bad for global growth undoubtedly, and bad for the emerging economies also. Mr. Lipsky? Yes, I'm sure what Jean-Claude has said is, it has truth to it, clearly the, first for the advanced economies, the particular problem of growth reflects the persistent weakness of business investment, and a shortfall in business investment, which is linked directly to the slowing of productivity growth in the advanced economies. The, in the case of the European Union, it's particularly notable. If we look at the IMF figures on net capital formation in the European Union since the global financial crisis, annual growth has been only between one and 2%. So in that context, in the context of relatively stable labor force participation, that means how could growth be much faster than one or 2% if that's all that is going to be happening with regard to investment and productivity growth? So the, this is a particular problem for the advanced economies, and one in which there is no clear and simple explanation for why this, why this persistent weakness of capital spending, I don't want to drag it out or go on, but when you think especially in the US context, outside of the energy market, there have been a series of developments in the last two years that should have boosted non-oil or non-energy capital spending that has not produced that result. And then finally turning to the emerging markets, I think it has to be seen, the slowing of growth means that the growth in trade or growth in exports from the emerging markets as a group is now slower than their GDP growth. In other words, international trade, which was the driver of their rapid expansion has now turned into a drag on their growth. Yes, Mr. Chow. I guess I agree, large-scale investment in infrastructure is one of ways to solve low growth rates, but it's not solve all of the problem, maybe just one. You look at the experience in China, in past three, four decades, Chinese government spent quite a lot of money in investment in infrastructure, which give the incentive to Chinese economy. But what I try to say, the how to make investments depend different environments in different countries with different development stage. Here I want to share my private conversation with Professor Gorbis, who now is teaching at the Chicago University. He used to be the chairman of President Economic Council. I guess a couple years ago in the meeting in Beijing, I asked him, I say, I just have checked back from US. I'm wondering whether you don't do any high speed trade from New York to Boston to Washington DC. Each side take four hours, but in China maybe one or two hours, why you don't do that? He said to me, the problem is very hard negotiating the price of land. He said, we cannot spend quite a lot of time, money to get a land. Also current track is not very straight, so high speed cannot use existing track. That's the problem. So even Trump say he want to make one trading investment, I finally, I thought he can really finish. That gives me the opportunity to say that a French company, he just want a huge contract at a stop for high speed trains between Boston and New York, so this should improve considerably in the near future. But regarding investment, as you're saying, okay, it's sometimes difficult to negotiate, you have the time for the space to land, but to make it efficient, China has invested considerably and yet your growth is really slowing down. You also have a long-term vision. You invest in infrastructure, the Silk Road is really something long-term for the whole region, and yet we see the pace of growth slowing down. So is there something you're not doing right? I don't think so, because that's the, I call development dilemma. At one hand, along with the rapid economic growth, at the same time, you create, you raise the cost of labor cost, of land cost, which will reduce your growth rate. So that's nature will happen. You cannot always growth to digit. So in this case, I think it's nature. Now, the growth rate of Chinese economy around 6.5%, which is still very high relative to other countries. Even down the road, maybe we'll go down to, in the next two or three years, we'll go down between 5% to 6% annual growth rate, which is not bad in some way. Of course, slowing down of Chinese economy do have some impact on a global economy, which varies with different countries. If you are low-material exporting country, yes. The slowing down of Chinese economy have negative impact on these countries. But at the same time, the domestic market expanded rapidly, which will provide more opportunity for foreign investor, for foreign service. Also, China now in the stage, the overseas direct investment grows faster, which will create more job, more opportunity for other countries. Mr. Park, you wanted to react. And after that, Mr. Trichet. Yeah. I think one of the reason Chinese economy's growth is slowing down is because of the world economic recession. I mean, China used to be a world factory to supply everything. But because of world recession, China is slowing down their exports. And now Chinese government is changing its policy focus on more domestic market consumption and also inland economic development. And through that, China want to maintain growth rate of 6.5, which is, as Professor says, that is very high. So what I'm saying is here, Korea, for example, our export to China is decreasing rapidly. This year, during the last nine months, our export to China decreased by 10%. The reason is this, many Korean companies operating in China, they are doing the same thing as Chinese companies, namely, assembling things and export to the world market rather than supply to the Chinese domestic market. So our parent company, most of the parent company exporting to their subsidiaries, parts and components for that assembly for final good and export to the outside. So I think right now, Korean companies are adjusting their own strategy also. In other words, they want to exploit 6.5% growth of China of domestic market and inland development, which will provide huge opportunity for neighboring countries. So in that context, I think free trade regime is much more important. I mean, US is investing a lot of money on infrastructure. How can they supply all the materials? We have to import from outside. That in, through that kind of activities, you have a positive impact on the world economy. But if you close your door, then you cannot have a positive impact on the world economy, that's what I want to say. No, what was just said is very important, of course, because we see precisely how the development of the Chinese economy itself makes that they produce things that were produced in Korea and imported in China.  So we see a good reason for global trade to diminishing a little bit. There are very bad reasons, and there are some reasons that can be explained. No, but I would like to go back to China, because we have also to understand that China has now, more or less, the same restructuring problem that we had ourselves dozens of years ago, the restructuring of the steel industry, for instance, is absolutely incredibly large and creates problems of very, very big magnitude. And are necessary because, again, China has now a level of a development which makes that it is no more appropriate to be that big and larger producer of steel. And it is true for many other production where the value added of the Chinese production is going up and up and up and as is the case in any economies that is transforming itself into an advanced economy. And this is clearly the case in China in many domain. So I trust that we can explain pretty easily if you take into account those necessary restructuring slowing down of the growth in China. And maybe we will see that it is even difficult, in my opinion, to reach the goal of 6.5 or something like seven in the years to come because precisely the impact, the drag on the economy of the necessary restructuring of the state-owned enterprise of steel production and mass production in many domain will be very difficult, obviously, to reach today and tomorrow, if I may, during the next two or three years to reach 6.5 or seven means that the service sector must literally explode. Which, of course, would be good, but maybe difficult to reach. Do you agree with this, Mr. Lipski, and then maybe Mr. Chow? Yeah, I just wanted to add before we heard about China that IMF figures in the latest world economic outlook note that the growth in capital spending or investment in China as a percent of GDP is dramatically higher than virtually any other economy. And yet the latest figures show growth in labor productivity in China is essentially about equal to the average in emerging markets. So at this moment, huge amounts of investment with relatively modest growth in productivity. I assume that the restructuring would aim at increasing the efficiency of capital spending. Would you like to comment, Mr. Chow, on the fact that the Chinese economy is gonna have to change, too? I agree with both of what you say. Yes, not only capital formation coming down, particularly the investment from private enterprise dramatically decreased due to many reasons. I guess major reason is for private companies, they don't see a great opportunity now in China. So they have been reluctant to make more investments in capital formation. I guess the Chinese government now is, what they're taking is still encourage public investment in infrastructure because China is still in the stage of urbanization. The people living in the city in China only a little bit more than 50% also due to special system, we call Hukou system. Even they work living in the city, some of them originally immigrant from countryside cannot totally enjoy the benefits of originally resident of city. So that's the part of Chinese government should carry the reform. The dilemma Chinese government is facing, more and more people still want to move to large city rather than small city because the large city will provide a good opportunity of job. So that way we'll push up the price of housing. So that's the difficulty a Chinese government try to resolve. Thank you. Yes, sure, Mr. Park. Well, China, the government is trying to improve their service sector because their service sector productivity is very low. So they make a lot of commitment to gradually open up service sector to foreign companies and foreign services. But this is a little bit technical but in Geneva, 20 some countries are negotiating for trading services agreement. Major countries are all participating but China is not participating because I thought the US is blocking the Chinese participation. I think and Chinese government want to show its willingness to participate also. I think the major countries like EU and United States, Japan should consider seriously to invite China into this negotiations because it's in line with their own government policies. Mr. Chao, do you think it would be a good idea for the Chinese to join those discussions in Geneva? Yes, definitely. If we talk to Europe now, we have a different situation. It's been a trying year. The Brexit was really a big surprise and created a lot of new uncertainties. The level of growth is not very high in Europe especially in countries like France. We just had the commission reviewing the budgets of every states of the European Union and they're giving us a little leeway to do some stimulus. And it's very much asked by all the central banks because the central banks have been really, especially the CB has been really doing a lot to have the economy grow but it's not sufficient. Do you think that maybe you and Mr. Trichet that the governments now have enough will and power to, in a substantive way, the boost of economies in Europe given the political context which is more difficult today? Well, I have to say that I am a little bit uneasy with the presentation that there is now a single recommendation for all countries in Europe which is more or less the way the message of the commission or the message of the authorities is conveyed. But there is absolutely, I would say, a need for activation of the domestic economy in Europe. That's clear. We are posting now something like 3% current account surplus at the level of the OREA as a whole. I would be very satisfied with the zero, with a balanced current account in the present circumstances where we have clearly a need for growth and jobs and certainly domestic economy slacks that are obvious. But that being said, it doesn't mean that all countries might embark on new public spending and new deficit. It is true for Germany, true probably for the Netherlands, perhaps true for one or two other countries. For the other country, I would say structural reforms are of the essence and they have to be very prudent because I have the memory of what happened in 10 and 11 and 12 which was totally dramatic with a loss of credibility or loss of creditworthiness calling for extraordinary difficult measures. Both I would say from the central bank and I have the memory of purchasing treasuries of Greece, Portugal, Ireland in 10, purchasing treasuries of Spain and Italy in 11 which were very, very difficult decision to take very, I would say bold undoubtedly, very swift because it had to be taken in a very short span of time with the drama which was unfolding and calling for dramatic changes in the macro policies of the countries concerned. What we are characterizing as austerity in Europe was austerity on part of Europe, on the countries that were in major difficulty and it was necessary to embark on appropriate new measures to regain credibility and to regain competitiveness. I would say in the case of my own country, I don't think that augmenting public spending when you are already spending something like 57% or 57.5% of your GDP in public spending, it's not good to augment. What is very good is to rebalance, to reshape the public spending in order to have much more growth and much more job creation but to augment it or to augment the deficit would not be appropriate neither for Spain nor for Italy nor for Portugal, that's clear. So again, I think we have to understand that it is true in Europe. You have different countries, different situations. Germany is not in a situation of Italy. So you should have recommendation that are not alike. You have countries that are extremely competitive and countries that are not competitive, countries that have a very good cost competitiveness, countries that have a bad cost competitiveness. The job of the commission is precisely to adapt the recommendation to each country. It is exactly what the stability and growth pact is supposed to do and it is exactly what the macroeconomic imbalance procedure which is a new pillar to monitor competitiveness is supposed to do too. We have to understand that very, very, and it's towards the global level in my opinion. I don't know what John thinks of that but it seems to me that you cannot set the global level. The new motto is, I don't know, spend, spend, spend and embark on deficit. No, you have to adapt that to the various countries concerned. Thank you for that. Do you agree though that Germany should do more for the rest of Europe considering all its surpluses? Yes, indeed. Germany has a credit council plus which is around 8.5% of the GDP tonight. Germany behave extremely properly since the setting up of the euro. The macro policies and the structural policies were undoubtedly good. The success is there, full employment is there and it is the main goal. The main goal of any economy in the advanced economy but also I trust in the emerging economy is to have full employment and not let the country or the economy in a situation of mass unemployment. So Germany did very well but the normal functioning of a market economy when you are posting such enormous surplus of savings such enormous surplus of profits of the companies and such enormous credit card surplus normally it should activate domestic demand both through investment as John said and also of course with wages and salaries which would be more dynamic. I have to say that the rhetoric of the government of Germany has changed quite considerably because they have encouraged more dynamism for wages and salaries. They have encouraged minimum wages which was new in Germany and is certainly a signal which is very important and they have said that they would diminish taxation so part of the room for maneuvering will be utilized. I expect that wages and salaries will augment much more dynamically. That investment will also be more dynamic in Germany where you have also a need for infrastructure investment in particular and that we will see a diminishing of these current accounts surplus which would be good for Germany in my opinion and of course good for Europe as a whole. Very good for Europe as a whole obviously. Mr. Hipski. Of course I agree with what John Claude has just said and would just reflect of course the current account surplus is a reflection of the excess of savings over investment and when we look at what is unusual it's not the level of savings. It's the very low level of investment and so the question has to be asked what is standing in the way of investment and I suspect if you spoke with a lot of executives of the principal corporations and many Eurozone economies they would tell you we're investing plenty just not here because the opportunities are better elsewhere so the question has to be asked well what are the barriers within the Eurozone and certainly one has to be it's just to emphasize in practical terms what John Claude said structural reforms meaning the perfection or increasing the openness of markets and the flexibility of markets both labor and product but also an aspect that has to be addressed which is the bank-based Eurozone financial sector is not as healthy as it needs to be to support that growth and we can see that in the dramatic fall in Eurozone bank stocks so far this year and that many of the European banks are not investable at this time and as a result it's clear there's more work to be done on both these aspects to improve the outlook within the Eurozone. Yeah about this one of the big worries today is on Italian banks and the Italian banking system and we always have a black sheep in Europe used to be in Spain, Portugal, Greece now the worries are geared toward Italy. Monsieur Trichet what is your assessment of that? Is there a systemic risk coming from Italian banks? Well first of all we have to replace the issue of the European banks within the larger context. When you look at the financing of the US economy you have, you had before the crisis because it changed a little bit but you had before the crisis 25% of the financing of the US economy through banks only and 75% through market it was exactly the country in Europe where the banks were playing a decisive role in the financing of the economy. We had the crisis, we of course in the crisis for us the problem was to maintain the liquidity of the banks and to maintain the financing of our economy but the effort that we had to make on our own banks as a proportion of the economy was three times the efforts that the US had to make on its own bank because simply of the size of the banks in Europe and in the US and the I would say proportion of financing that they had to organize. So again we have to understand that it is not abnormal unfortunately that the problem of the banks in Europe is larger than the problem of the banks in the US and very often we are heavily criticized in Europe with the argument you didn't do what was necessary as early and I would say aggressively as the United States and my response is we had not the same size of a problem and again and in the US of course the markets had their liquidity because the government took very, very major decisions with two institutions in particular Freddie Mac and Fannie Mae which are specialized in the mortgages and could I would say save the banks from having risks that they take in Europe because there is not such a system. So all that being said of course it's extremely important that the banks problem is as clean as possible in Europe. The banking union is a major reform which goes in this direction and we have as you know now a supervision authority which is I would say full speed acting. Of course because the system was terribly segmented and is still very largely segmented we might have problems in some countries. We don't speak of the Spanish banks because the Spanish banks were restructured in time with the help of the European through help which was given to the state of Spain. It was not the same in Italy and that the reason why we are speaking quite a lot of the Italian bank. I think that of course the system has to be cleared in Italy where there is a special problem of NPL non-performing loans and so forth. I have you know I'm sure that it will be done. I don't think that there is a lot of capital to invest in those banks. I don't think that there are problems that could not be solved but it's true that it goes against decisions which were taken at the global level and at the Italian level namely that for banks problem in the future and we are now in that future you should normally not embark on help from the state but you should ask creditors to take their own losses and it is what we call in the jargon bailing in but that is good when you do not have a systemic problem but that cannot be done if you have a systemic problem and in many cases we have systemic problems. So again I trust that we will have to find out the appropriate ways with the help of the Italian government with the help of the commission, with the help of the other countries to adapt to a circumstance where you have to understand that probably some help of the state is still necessary even if the ultimate goal is that the taxpayer should always be protected from banking problems in the future. After the 2008 crisis a lot of reforms were set up at the governance level in the world. Do you think some years later that we are more protected against systemic risk? Have all the reforms being achieved or is there still more to do? And also if you take into consideration the balance sheet of central banks today who were you know or firemen who came in I mean they're bloated, overloaded and maybe they don't have that much oil left or fuel left to help out if another crisis comes along. So could you each of us assess today if you feel more comfortable in the way the world governance has acted to prevent big crisis like we had in 2008? Or do you think there's still more to do? Whoever, maybe, Mr. Trich, you stand ready. Either first or as the last one. Mr. Vark? Well, I'm not an expert on this subject but in the case of Korea we are much more prepared for this kind of crisis not because of 2008 global financial crisis but because of the 1997 and 2008 East Asian financial crisis. So I think corporations and financial institutions and even central bank are much more prepared to deal with this kind of thing. But one thing which was very interesting is that IMF gave us a very stringent recommendation in 1997. In other words, we have a financial crisis. IMF recommended that we should do more tight monetary policy having high interest rate and government should not spend more money. But in 2008, exactly opposite kind of policy recommendation, low interest rate, print money, and stimulate the economy by using government expenditures.  But in any case, at the time, a lot of innocent small companies, healthy companies collapsed because of high interest rate. IMF quickly adjusted in having low interest rate right away but in any case, we are much more prepared. We are suffering from world recession, not the systemic kind of crisis in Korea. Mr. Lipski? Sure, there are a number of things to say. I don't have to feel defensive about that period. I was not in the IMF. And in fact, was perhaps a little critical of some of the actions of the fund at that time. But in fact, two things to say about the Asian crisis or three. One, it's so little recognized. The IMF had at that time no instruments available to it that could serve usefully as crisis prevention instruments but only crisis resolution instruments. It did not have the tools. Secondly, part of the problem of the seemingly inappropriate initial diagnosis was on the basis of a consensus view that the downturn was going to be quite mild and when it became evident that the downturn was actually severe, all the policy advice was recast. And so we shouldn't miss the point. In 1996, fastest growing region of the world was Asia. 1997 recession, 1998 recession and talk about structural feet of clay. 1999 fastest growing area of the world, Asia. So something went right. Now, are we still susceptible to the kind of crisis then or 2008, 2009? I hope the answer is no. The principal institutional response to the global financial crisis was the creation of the G20 leaders summit process and the creation of four principal goals of the G20. First, to restore global growth. Secondly, to repair and reform the financial system. Third, to prevent protectionism, trade protectionism and promote new trade liberalization. And fourth, reform the international financial institutions. On the one hand, and in each case, there was a clear assignment of duties, if you will. For growth, they created the framework for strong, sustainable and balanced growth, which was a new process of cooperative setting of macroeconomic policy. Secondly, they created the FSB, the financial stability board to reform the financial system, enhance systemic stability and create a level playing field. For trade, they focused on the adoption of the Doha, the completion of the Doha development round. And fourth, essentially IMF reform. All of those remain either unattained or incomplete at this time. And the commitment of the principal G20 countries to that cooperative framework process of macroeconomic policy setting seems to have been, I would say, a big disappointment. However, be that as it may, I think you'd have to say that in the financial, the risks of financial instability have been reduced by the actions of the FSB, increase in capital. Certainly there are still risks, but I think we're in better shape than we were before. And the G20 framework exists. At least it's there clicking over, if you will, in case there's a need for larger scale collective action. The one final point I would make in this context, the IMF still lacks sufficient crisis prevention instruments to be able to work effectively and as effectively as it should and needs to. I fully agree with what John said. Based on what he described, the progress we have all achieved after the outbreak of global financial crisis, we still have a long way to go to totally exclude the possibility of full financial crisis occur. But we do achieve some degree of progress. For example, I can mention after the global financial crisis, we have a new concept occur. We call global financial asset network. People recognize if you only account one level, whatever, institution or resource, it is not enough to prevent occurrence of financial crisis. So currently, global financial asset network includes four components. First of all, self-insurance. You have to, for developing countries, you have to have some kind of falling result. Second is the currency swap. If something happened, you can do something currency swap. I guess case in South Korea, I guess finally US have a currency swap with your country. So finally, give you a lot of help. Third one is the regional financial arrangements like the Qingmei initiative, like the ESM. At the top, of course, IMF. So leader of G20 recognize IMF should play more important role. I guess on March 31 in the conference in Paris, Madame Lagarde in the lunch speech, I'm very impressive. She said IMF should take a more positive role to have kind of arrangement with the regional financial arrangement. I was told they have already jointly IMF with the Qingmei initiative jointly do some exercises to prepare, assuming some crisis case happen, how they can do jointly. That kind of things I guess has been positive. We should continue to carry these kind of things. So to respond to your question, first of all, I would agree with all what has been said by the panelists. We proved that we were able at a global level to embark on a crisis resolution quite well, obviously, because we were threatened by a depression of the type of 29, 30s in the 20th century. That was clearly what was threatening the advanced economy and by way of consequence, the global economy. It was a terrible threat. And we could, by a combination of bold and swift measures, by the combination of institutional arrangement and the creation of the G20 at the level of heads of state and government was something which was really decisive, I trust, in the heat of the crisis. And I ask you to note that it is something of extreme importance because the informal cooperation, governance of the global economy was given to the G20, namely to all systemic emerging economies also and not only to the advanced economy, which was the case before. The G7 passed the baton, if I may, to the G20. And the G20 did a very good job, in my opinion, in the crisis, avoiding, again, a great depression along the four dimensions which have been mentioned. But I have to say that I agree very much with what was said on the fact that we are exactly at the middle of the road. We did not really, and I cannot myself say, we have all the elements which permit us to guarantee that we will not have a new crisis of the size of that crisis we had. When I look at a number of indicators, I see that in particular we have an augmentation of leverage at the global level. You know, part of the crisis came out of the piling up of debt, public and private. And then we had the explosion of 2007, 2008. But now we are in a situation in terms of pure leverage, namely debt outstanding as a proportion of GDP, which is higher. So from that standpoint, I think we have no reason for being complacent in any respect. I have also to say that there are other elements where we can say we didn't do the job yet. All what is the non-banks, the shadow banking, has not been touched until now in a way which would be very convincing. The banks, yes, on the banking institution, and I have to say also on the insurance company, one can say that the new credentials are there and they are quite important. So again, I would say we are at the middle of the road. Of course, in terms of trade, we are back-paddling and we already addressed this. It was one of the four dimensions that was reaffirmed by the entire international community, emerging countries, as well as advanced economy in the time of the crisis. And that is one of the reasons why we have to be very cautious and prudent on the consequences on the global economy of the new move on trade, which we already discussed. As regards, because your question was also on the side of the central banks, as regards the central banks, I would say that they were extremely bold, obviously, and swift also. A lot of decisions were taken extremely rapidly and are still considered today as necessary, which, by the way, means that we still have big problems in the advanced economy. We are not out of the wood from that standpoint. But of course, the message of the central banks to the other partners is very, very clear and it is the same in Japan or in Europe or in the US in the advanced economy. It is we cannot be the only game in town. You have to step in. You governments, you parliaments, you private sector and social partners. We are doing a lot. But if what we are doing in Japan, in Europe, in the US is only to permit the other partners not to do their job, then we are only paving the way for the next crisis. And the kind of job which should be done, of course, is structural reforms in all countries. There is not a single country where you could say the job is done from that standpoint. And again, these are also different messages. You can tell some countries you have room for maneuvering. You should embark on the appropriate activation of your economy through perhaps public finance deficit, more public finance spending and so forth. That would be correct in some countries, not in all. And I am very, very afraid of the fact that the interpretation of the message of the central bank could be, yes, indeed, we have to embark into generalized lose fiscal policies, for instance, would be a major mistake because we have already extremely lose monetary policy. We did not just, I think my neighbor was right in saying that in the Latin America crisis, in the African and Middle East crisis, I'm speaking of the sovereign crisis of the year 80s and 90s, in the Asian crisis, we were asking the countries concerned to adjust. And adjustment was not easy. In this crisis, we asked only the countries that had a major, major loss of credit worthiness to adjust, namely the Euro area countries, the five Euro area countries that had difficulty, not the other and certainly not the other advanced economy. In my opinion, it is the right and proper, I would say attitude should be in between. Vis-à-vis the advanced economy, we have been in this present crisis extremely gentle and kind. I'm speaking of the international institution as a whole. Of course, the advanced economy being called to adjust dramatically would have triggered the entire world to be in a dramatic situation. So there are some reasons why the international community was more kind and gracious with the advanced economy than it was for the emerging economies in the 80s and 90s. Still, I think we have to reflect on that. It doesn't go without saying that you tell Asian countries you have to adjust in the sharpest and abruptest way at the end of the 90s. And it was, it doesn't go without saying that you have to say to the advanced economy, please have the most accommodating policies possible because it's necessary in this situation. The right and proper message or recommendation should perhaps be a little bit in between. And as I said, we did not adjust at a global level and at the level of the advanced economy since the crisis leverage is augmenting when you take the simplest indicator of leverage that you could take. And that is not reassuring. Thank you for this long explanation. I see a little bit of your German side here when you don't want us to spend too much. We have about 10 minutes left. So if there are any questions in the room, please raise your hands. I do see some questions. If somebody could pass around with a mic, that would be very helpful. And the mic is coming. There's one person here. I see there was one in the front too, please. Thank you very much. So as mentioned already, you mentioned, the speakers here mentioned that the US is like there can't be even a partial isolation for the US economic in the world. So if the Trump administration, as he predicted or as he mentioned in his campaign, go ahead and eliminate the TPP, the NAFTA agreement, and the other agreements that he mentioned that he would eliminate, how could that affect the economic as a challenge in the whole world? And how could the other countries take advantage of that? And who exactly could take advantage of the elimination of this agreement? And could there be an alternative? As we know, there can't be an isolation for the United States as a part in the world economical stage or society. So what could be an alternative? And could the United States as well propose another agreement with its term and could it be accepted? Especially by China, as you mentioned here, it's already dropping below, its growth is dropping below 6.5% and how could it actually accept or deny the new agreements, especially with the TPP and the other agreements? Thank you very much. Well, I will respond to the part of your questions. I think Trump government will abandon TPP and maybe dramatically change the NAFTA. What will be the impact? I think for TPP, simply you are losing the so-called potential gain you can earn from the TPP because you are not implementing it. So it's not that much, you know, damaged. But NAFTA, I think Mexico will be, if they abandon NAFTA or renegotiate NAFTA, then maybe Mexico will be tremendously hurt. But Mexico doesn't mean Mexico alone. All the multinational companies who are operating in, including US companies, who are operating in Mexico will have a hard time by the high tariffs or whatever. So I think it is damaging if Trump pursues actually and implement this kind of new policies. But I want to say just one thing, alternative for this kind of environment is that it's about time for world leaders to seriously consider to save the multilateral tuning system. It may not, they cannot do it immediately, but we should open another track for multilateral trade negotiations. Nobody pay attention to multilateral trade negotiations, especially the Doha round. So I want to emphasize that, you know, we have to go back to the basics, which is multilateral trade negotiations. And secondly, many news media and trade experts are saying maybe because of, you know, because the US is revolting back to the protectionism at all levels, maybe China can lead the trade integration or world trading system. It makes sense, but it also requires very strong leadership from China to open up more, especially in the R7 negotiations. It is not making any progress. I think we need very strong leadership, which is different from the past because we are entering into new kind of environments. I also hope that China should show some leadership in the R7 negotiations. Yes, just a question for the panel. I was struck that in this session on the main issues facing the world economy, none of you raised the issue of the impact of adjusting to lower oil prices, lower commodity prices. It's obviously a big issue for the region, but do you think that from a global point of view, whatever cost there was of this adjustment has now been borne and going forward, this is no longer a major issue? Mr. Trissier? The, I would say, panel had to address all possible questions. I don't think you should interpret that on the fact that we consider the price of oil and commodity as a very, very meager problem. It's a major, major issue. And from my standpoint, I would say not only a major issue because if the price is too volatile to a rally, it creates a lot of uncertainties in terms of investment, in particular, as John said. It also is an explanation of the lack of investment the world over in the present period because I don't see any real investment now at the present level of oil in this domain and it is part of the global problem. But seen, I would say, from the central bank standpoint, it's absolutely clear that part of our problem, namely very low inflation, desperately low inflation, and by way of consequence, very low interest rates and nominal interest rates and so forth, comes also out of the abnormally low price of oil and commodity. And we were expecting that the price of oil and commodity and oil in particular would remain at the somewhat higher level that it had attained in the past and then that we could progressively eliminate in the CPI, in the measure of inflation, the impact of the very low price. But unfortunately, it appears that it is not the case and I would say it has a lot of consequences, not only on the real economy, not only on the systemic stability of the world but also seen from the central bank perspective in terms of maintaining this very abnormal situation of very low inflation rate. Just to say very quickly, of course, the especially energy prices have been very much affected by technology. It was only a few years ago that folks were talking about peak oil and of course things change very dramatically. So I'm looking forward to the subsequent presentation on the hybrid carbon market to give us some insight as to where things might be going. John Lipsky pointed out that one of the problem was a low level of investment and as a consequence, a low level of productivity increase. And I think he said it was not clear why we had that situation. And I was wondering, don't you think that the behavior of the financial markets and of the investors is one of the reasons for that low investment? Because for listed companies, except for high-tech, high-tech companies can walk on water, but for other companies, investors are more asking for dividends and share buybacks that for any entrepreneurial behavior or any risk-taking investment. And if there's a case, what can we do to change that behavior? Yes, for sure. Well, I think I said there's no single simple explanation and there have been some factors recently that should have been favorable, decline in energy prices for investment in the non-oil sector, very historically low real interest rates, relatively especially in place like the US, very high record, high corporate profit share of GDP, high corporate liquidity, et cetera. But I think when you're looking for explanations among other things, the lingering uncertainty from the crisis, secondly, the aging of populations may have affected investors' preference for safe assets over risk-taking, and as you point out, the financial sector, I think is part of the problem. So no simple single answer, but let me tie it for the final statement to the theme of this conference, which is global governance. And I would say the G20, as we've been talking about, the G20 actually produced a very good result in a moment of crisis, collective action, effective collective action. Since that time, I think we've seen a drift away from that commitment to coherent and cooperative action, especially at the level of macroeconomic policy, and I think that has undermined, or at least not helped boost, the kind of confidence that would be necessary for strengthening, among other things, investment. I would also just like to say, I think we're gonna get a new test coming in the immediate future of the resilience of global governance because we're moving into, clearly we're about to move into a new era in which there's perceived divergence of policy plans and policy focus that could tend to further undermine uncertainty, if not well controlled. We're gonna have the Fed raising interest rates in a context in which there's markets are being suspect that the US could be moving to more expansionary policies in a context in which the ECB has remained very committed to its loose policies. Bank of Japan has committed to new easing. China has eased its credit policies. This is going to be potentially a real test. So that will be the final, thank you very much all for listening and thank you for your insights this morning.