In the previous video we saw the major tasks in data processing that is cleaning, integration, reduction and transformation. In this lecture we are trying to understand some of the major tasks in data processing in depth. Say for example, what is missing data? Let us start with missing data. So what is missing data? Missing data is defined as the value or the data that is not stored or present for some variables in the given dataset that is called missing data. Now how to find a missing data? If the dataset is too small, if the data size is too small, then we can find the missing value manually that is one way and if it is a little large then we can write a code like is null not null, nn is not a number in pandas you can directly check it use it and is it sufficient doing manual or writing this sort of simple codes to understand the missing data. The answer is not always. So these are only useful if you have a limited set of missing values or the data size is too small. So if it is very large, then how to address this issue? There are various ways in which we address the missing data. To understand that we need to know why this missing data will come into picture. Now why there is a missing data in a database? There are various reasons with which this can happen. It can happen as the data may get corrupted due to the improper maintenance that is comes under system error. Now it can also happen that we fail to record a certain point due to human error. So as we discussed earlier slip mistake and violations. So there can be a slip or mistake or there can be a violation where actually the user does not provide the information intentionally. So these are the three major reasons where actually it causes a missing data in a given dataset. How to handle it? To understand that we have to know why we have to handle the missing data. Most of the algorithms that is the machine learning algorithms fails if the dataset contains missing values. Some algorithms are there like K nearest neighbor and Naive base where actually even if the data is missing it works. But most of the algorithms fails if the data does not contain proper information or it contains a missing value in the dataset. Also you may end up building a biased machine learning model where it may not give you the result which is actually the accurate or the optimal one. There are cases where if it does not get trained properly there are chances that the entire efficiency of the model may go down. For example there can be a lack of precision or the accuracy or the recall of the given algorithm because there was a huge amount of missing data in the algorithm. So these are the reasons why we have to handle the missing data. By handling the missing data we will get a model or a algorithm which gets trained well which is more optimal, robust and also there are cases that it can make it efficient as well. Now with this we will try to see how best we can handle the missing data. We can do two things one is either we can delete or we can add the data. Deleting the data we cannot delete the data if it is too small. Similarly there are various cases in which deleting is not a best option. Now when deleting when we now when we cannot delete the data what we can do is we can fill the missing values that is imputing the missing value. Imputing means assigning a value to something by inference. So there are two cases. If the data set is extremely large then we can delete the data and missing values are very less then we can do this. If it is small we cannot do that. Now imputing so the other way around if it is too small then we can impute if it is too large also we can impute if the imputing techniques makes more sense for the given data. So what are the various ways in which we can do the imputing. Deleting we know in the data cleaning phase where actually we delete the data which is not required. Now imputing how to do that. There are several ways in which we can do imputing. Before we understand what are the various ways in which we can do the imputing we have to know what type of data is missing. There are three categories what is MNAR, MCAR and MER. These are the three types of missingness. We have to understand for our data or the given data what is MNAR which variable is MCAR or which attribute or variable is MER. MCAR means there is no relationship between the missing data and other values observed or unobserved the data which is not recorded within the given dataset. So the missing values are completely independent of other data and there will be no pattern that is MCR missing completely at random. For example let us say the results obtained from a student based on the place he lived before. So it does not make more sense right. So most of the cases where we do the analysis it is independent of the region where he is born or something like that. So this can be one of the example for MCR missing completely at random. Now what is MER, MER is missing at random. So means the reason for missing values can be explained by a variable on which you have complete information as there is some relationship between the missing data and the other values of the data. What you mean by that is say for example you have a demographic data. In the demographic data you have asked various questions. In one of the questions is gender and one more question is age. And you observe that in gender if it is female they might not have filled the age one. It is not always true but there is a dependency. So females does not fill age always. So there is a dependency. This dependency comes under MER that there is a some relationship between the missing data and other values of the data that is MER. Now the third one is MNR, MNR stands for missing not at random. So the missing values depend on the observed data and if some structure or pattern in the missing data and other observed data cannot explain it then it is missing not at random. For example there is an income field in one of the forms where they want to know how many people get the income from how many sources. What are the different sources they get the income let us say. And if most of the information provided by them contains the missing there are chances that they may be having many income sources. So we cannot neglect this type of data nor we can impute from anything else. So in this case what we have to do for that we have a classification say for example the complexity. So this is taken from one of the literature where they say if the complexity of the given problem is very less in that case for MCR and MER we can ignore missing at random miss some completely at random we can ignore. If it is MNR when we cannot ignore then we have to do something. If the complexity is very high then you cannot even ignore the missing at random part. So this is missing at random. If there is such case we have to do some imputation maybe a single value or a multiple value or attribute value or attribute will be there so we have to do the imputation. If it is MCR in any case we are not worried we just we can just delete the data and then we can proceed. If it is an MNAR then either you have to find the data recollect the data or you have to see what is the best possible way in which you can address it. So even the data pre-processing technique may not be sufficient enough in some of the MNAR cases where actually the data has huge dependency on the problem which you are solving. And if that particular variable or the attribute itself is missing you have to find a way to find the best data itself rather than doing some pre-processing techniques. Now we will see what are the imputations we can do for MER and MNAR. In MNAR also you can do some pre-processing if the complexity is less or the dependency is little less. We will see what are the various ways in which we can fill the missing data. Now if you have a missing value if the data set is extremely large then what you can do? One of the instance does not contain the required information. You can delete the entire instance that is deleting the entire row this is one possibility. Then if one of the attributes is missing for all the instances in that case we can delete the entire column that is the second case. If you have rows and columns both some data in rows are also missing some data in columns are also missing then it is fine if it is less we can do. But if it is more you have to make sure that you will not end up deleting the whole data. You can do it to some extent where it is still meaningful if the entire data becomes biased or it conveys some other meaning then we have to stop doing that. If it is one or two instances we can delete the entire row or we can delete the entire column if very less if very less instance are there we can delete both rows and columns. But we have to make sure that we do not end up deleting the entire data. Now that is about the deleting. Deleting is quite simple. Now let us see what is imputing. Imputing is adding a data adding some value or filling some value in that missing value place by using some methods. There are various methods not all methods are correct for a given set of data or a problem which you are solving. Say for example replacing with an arbitrary value you have a data in that one of the columns is almost having a missing data but it is important and what you can do is say for example in this case where actually the corporate work experience of an under graded student we can set it as 0. It is rare that they will have the experience at that level. So we can directly set it to the 0 and then we can use it. So here it makes sense. Whenever we can use such type of arbitrary values we can replace it with a arbitrary values. Similarly for example the number of married students in a high school we can arbitrarily set to 0 because that is the general case it is very rare that we get some numbers in that. So in such cases we can fill with an arbitrary values. Apart from that if the data is missing in some numerical columns then we can use mean. We can replace the missing value with a mean. If it is a categorical feature, categorical feature means so we have a categories like we are classifying dog, cat and elephant so these are the categories then we can use mode. We can replace the data with a mode. If your data contains out layers, if you do not solve the out layer issue then you can use median. You can simply use the median and fill the data. These four cases cannot be used in few cases like for example when there is a time series data what you mean by time series data the temperature change in one hour or the temperature change in a 24 hour duration from 12 o'clock midnight to next day 12 o'clock. So if you are calculating the temperature or if you are storing the temperature then you cannot fill with a mean, median mode or some arbitrary values then we use the previous data or we use the data which is next. From the next value we can take the previous value or from the previous value you can fill. So if you are taking the previous value and filling it it is called forward fill. So if you are taking the next value from that if you are filling it is called backward fill. So in the time series data generally we use forward fill or backward fill. So we saw how to replace the data. There are various ways one is with arbitrary value then with mean then with mode and then with medium and then with forward fill or the backward fill. Now with this we will see what else we can do when it is a categorical data. As I told earlier one of the ways to do it is use the most frequent value and use it in a categorical data. This is one way which we already discussed say for example if you have three classes in a category and then if you are filling it you can use the most frequent value. Apart from this if the data is huge and you are unable to do that then we can also do one separate category for these missing values. There is something called data encoding which we learn in the coming lectures where I will try to explain how the categorical data is converted into numerical format. So in that case what happens is if you are using a separate category for a missing data then one more column will get created in one of the encoding techniques that is one hot encoding. You just remember this in this slide I will connect it back when we discuss the one hot encoding. Now this can be done for one attribute or this can be done for multiple attribute or this can be one done for one instance or it can be done for multiple instance. Based on that we have a division univariate and multivariate. Univariate is just one and multivariate is more than one. When you have to use the various methods of missing values you have to see what you are addressing whether you are addressing a univariate or you are addressing a multivariate. Based on that there are several approaches we have to choose the best approach for the given data or the given problem. Now each one has its own merits and demerits. Let us discuss this. If you are deleting then what are the positives? The complete removal of data with missing value results in robust and highly accurate model that is quite obvious right. So we do not have any ambiguous or we do not have any inconsistency because all those data are deleted that is one case. And also deleting a particular row or column with no specific information is better since it does not have a high weightage. There is some loss of information though this is the demerit. So some loss will be there in the information as well as the data. We may not know whether it is really making sense or not at that point of time but loss will be there. Now the next thing is it works poorly if the missing value is very high. So as I told earlier if the missing values are very large or if it is a very small data set and you are deleting huge amount of data in that then it will not make sense. That is the demerit. Now the next one is replacing it with mean median mode. One of the merits is this is a better approach when the data size is small and also it prevents the data loss which results in removal of rows and columns. The demerits is imputing the approximation adds variance and bias and it works poorly when compared to the multiple imputation methods. Now here there are two terms adds, variances and bias. This you will understand in the separate set of video lectures additional videos where they will explain what is variance and bias. Just to give you a brief view so this is the data and this is the collected data. Now the predicted data and the collected data are in the same one. If it is same then it is low variance and low bias. Now the data moves ahead from the actual data this becomes low bias but high variance. Now they take loss but they are too much away from the actual data that becomes high bias low variance and in this case you can see high bias and high variance. So our algorithm should be very optimal in this bias and variance. We have to understand that bias and variance should be exactly as the data is. So if it is too high too low or if it is too much different then the method may not work properly. Now we will see what is the demerits of assigning a unique category. Now you can see assigning a unique category actually brings in less variance. So it brings less variance. So to understand that we will understand in the multimodal learning analytics video lecture where we will see adding one more category how it actually decreases the variance and adds another feature to the model while encoding which may result in the poor performance. So this is the demerit of adding a unique category. So I told you the example of one heart encoding where he adds one more column to that. So when we discuss that part I will discuss again this in detail and what are the merits less possibilities with one extra category resulting in how low variance after one heart encoding since it is a categorical one. It negates the loss of data by adding a unique category. For each case we will try to understand with an example when we clearly learn the classification, multimodal analytics and affect computing videos where I will take the same techniques and tell you what does this merits and demerits mean in detail. Now the next one is predicting the missing value. So if you predict a missing value what happens is it yields an unbiased estimate for sure that is the one of the merit. Also imputing the missing variable is an important as long as the bias from the same is smaller than the omitted variable bias. So these are the two cases if it is there then we can use this or else what happens is it always consider itself as a proxy for the true variables. The second thing is the bias also arises when an incomplete conditioning set is used for a categorical variable. We can also use some support based method to fill the missing values. What happens with this is it is very time consuming process and also the choice of distance functions that can be Euclidean or Manhattan which will not yield a robust result. But there is always a merit when you use such type of data where it requires support mechanism. So it does not require creation of a predictive model for each attribute within the missing data set. So you are not creating your own predictive model which runs again with some time based on the data and also the correlation of the data is always neglected when you use the support missing value. What do you mean by prediction and support? Since I did not discuss how to predict the values and also how to use the support system I am trying to give you the notion of how it works. This is from a research article where actually they tell what are the various ways in which we can handle the missing data. We saw the conventional methods also we saw some univariate methods median mode and all those things but there are several other methods which are also used to fill the missing values. Say for example there are statistical methods, there are some machine learning methods, also there are likelihood based methods and there are similarity methods. So now we can use any type of method to fill the missing data but we have to see what is the best for our data or for our problem and using this should not take a lot of time, the data pre-processing itself should not take a huge amount of time so that the entire processing becomes slow. So we have to choose the best possible way to address the missing data. If the data collected by you or the problem which you are solving requires any such type of algorithms which are apart from the basic conventional methods or the univariate methods you can use machine learning methods or the likelihood based methods or the similarity methods but it will take its own time you need to understand each and every method the pros and cons of that method and then you can use those methods. So I will not be discussing all those but I will give you the brief idea what are the various methods available just to fill the missing value. To summarize in this lecture we saw what is missing data, why to handle missing data and also we discussed about the what are the types of missingness MNAR, MAR and MCAR. So based on the complexity we can ignore or we can use some imputation or we might have to collect the data again and what are the various ways to handle the missing data. I have shown you the entire image of what are the various methods available from the literature you can also choose the best possible method for a given data or problem and then accordingly you can use the missing data.


what we're going to do in this video is have a read through Comical Primer number two, and it's another anthology sort of a collection of works, and this wasn't an independent comic book, and well it is an independent comic book that came out and actually I'm not sure what the exact date on this is, we'll look it up as soon as we crack it open and look inside, and he was, this character is created by Matt Wagner and Matt Wagner is, well he's a legend in comic books, he's he's huge, he's one of the main main players, he's produced a lot of work, and a lot of different artists and writers have worked on Grendel, they've gotten permission to work on Grendel and produce Grendel's work, or stories based on Grendel, and this is one of the sought-after books from that period. As far as grade of this issue goes, this is probably around, probably around the seven or so, it's a nice copy, good for me, I like it, it's got some creases on the on the edge here on the spine, the corners are fairly sharp, so what we're going to do is have a flip through this and take a look at, take a look at what it's all about, who this character is, and see what his first appearance, what the story of the first appearance is all about, okay, so if you can tell, you know, there's little bits of creases here along the edge, it lies flat, which is really nice, the corners are fairly sharp, I would definitely agree this is a little ding here, let's fold it a little bit, I'm not sure if you can see it, and again it lies fairly flat, it's a nice copy, definitely give this, you know, around the seven, seven and a half, great, and as far as value goes, this thing's worth, you know, it's probably worth around 75 bucks, 80 bucks or so, that's a nice collectible for me, I like it, I like this character a lot, there's a lot of different people have worked on it and different stories have been told, some better than others, and it's a fantastic, fantastic little book in my collection, the print runs for these were pretty low because they were independent, so their funds were limited, and this is Comical Primer is also the series that gave us the first appearance of the Max that we've done a reading for, here's Primer number five, we actually read this one, I got two copies of this one, it's a short little first appearance of Sam Keith's Max, so we're about to look at Matt Wagner's Grendel and these two issues, Primer number five and Primer number two are the most sought after issues for this series, for these anthologies, so let's take a look at this thing, again it's a black and white print, and there's a message from, who is this, Pleasant Dreams? Jerry Juvencio, I guess it's Italian guy, Juvencio, I'm bad with pronouncing names, so let's look at the fine print here, this is Primer volume number one, volume number one, issue number two, it's published four times a year by Comico, the comic company, and they're based in north-town Pennsylvania, Primer's copyright 1982, Comico and the comic company characters and their stories are the properties of the creators of each all rights reserved, and that's one of the beautiful things about the independent publishers in from that period, all the characters were copyright, they were their sole property of the creators, which is a lot different from a lot of them, what the main comic book publishers were doing, which is one reason that in the early 1990s there was a huge shake-up in the industry with valiant comics and image and people breaking off and creating their own their own companies and writing their own characters, we already read the little introduction for this primary story, but we're gonna go ahead and go find Grendel, we're gonna flip through this thing though first, but we already read the little introduction that Matt Wagner had written for Primer number five, so we'll skip this for now and flip through this, this is my brother's keeper, Andrew Murphy 1982, so I guess what year was this, yeah this is copyright 1982, so it was definitely mistaken on being in the late 80s or early 90s, I should have known this actually, 1982, I'm not sure why I slipped my mind, I don't know the story, I haven't read this, and I actually have the, after this one I believe the next three issues, well I'm pretty sure it's the next three issues of Grendel, we're Grendel number one, two and three, and I actually have this in my collection as well and maybe someday we'll get a chance to read them on sale now, you can tell the independent comic book publishers, they had the layouts were much simpler, it's black and white and they're extremely appealing from that period, they're amazing really, all of this independent publishing stuff really came, the catalyst in large parts was a Canadian publisher, artist creator Dave Sim with Cerebus the Art Vark, what he was able to do with that series was absolutely amazing, he came out in the late 1970s with the character and decided to do a 300 issues series and he did it and it is actually brilliant and one day I'll go ahead and start collecting those, they're pretty expensive, they're really issues, what's this Judas kiss that's too strong to be denied, who's this by, this is by, there we go, 1982 Jim Alderman, you can see 1982 Jim Alderman is the creator of this one, what is the illusion too intense to be defined, why is the Judas kiss that's too strong to be denied, I might actually come back and read these later after we finish Grendel, we're not putting any stress on the spot and the book sits really well, it's got a nice curve on it right now so it still has its integrity with the pages so great wise the cover was a little bit nicer, I would give this an 8.5 but you know it's got a couple of things in there, actually this could be around an 8, I'm happy with it, very happy with it, maybe I'll try to grab a better copy, hard to find them in mint and they're very very expensive in mint condition, grated mint they run a couple hundred bucks, two or three hundred bucks or higher, higher grades and Matt Wagner considers these issues to be rough while he was just starting out right and here it is here's some information about the company order information copies of primer number one and two are still available, still available back then, what's the price tag on this, it was a $1.50 fantastic investment if you bought a whole ton of these back in 1982 for a $1.50 you kept them in mint condition you could sell them for a few hundred bucks a pop right are still available and if you're going to maybe obtain individual copies maybe a thing through Comic Hall, Declab Street, North Sun Pennsylvania, $1.50 per postage so primer is also available in quantity at discounts from these direct comic wholesalers, capital city this was one of the diamond distributors, diamond comics, diamond distributors in capital city were two of the main comic book distributors back in the day but there was a distribution war in their early 1990s, mid 1990s almost all the independent distributors went bankrupt and basically the only one left that distributes everywhere is diamond comics so let's just flip through this and then we'll come back and have a read through it yeah I don't consider this to be too rough I haven't read this one by the way I bought it and I decided to make these videos so I waited anxiously until I was ready to do it to read this I read a lot of other Grendel stories and loved them there's some amazing Grendel stories around not just by Matt Wagner but by other people as well let's see what the ad says first before we get into the Grendel gee these guys at Comico really know how much fun how much fun it is to create comics they love every minute of it and are glad they have the chance to bring their stories to you now that they've established themselves they want to give others the chance to experience the joy of creating comics that's why they created primer primer will introduce new characters and talent on a quarterly basis all rights belong to the creators isn't that marvelous if you'd like your story to be printed in primer contact comical fast primer next issue is not yet full wow so that's comical primer number this is number two so they're saying comical primer number three is not full which is probably it's probably comical number four five they were talking about maybe that's how Sam Keith got his work max into primer number five hey who is that guy a bit of advice refer to comical before you actually start your work so that requirements can be specified good luck and happy comic making cool very cool I have a story to tell about this about myself and getting into publishing but all reserved that for later I got into publishing comic books in the late 1990s or sorry early 1990s I sort of did the same type of deal took an advertisement in in over street price guide and you know told people I published their work if they said that to me enough I liked it and I went ahead and I actually did and published about 16 issues of different comics so let's go read Grendel's first appearance yeah nice copy nice book feels good yes my child it was a gala event on a most extravagant evening party at the home of the city councilman council chairman Everett Christie alas evil was afoot unknown to all among the guests was that sworn enemy of our noble chair champion champion agent that scourge of all we hold dear the evil ring argent how good to hear your snarl argent how good to hear your snarl police 13th precinct grendel you smile who's this such a charmer just calling to let you know that the city council can elect a new chairman i'm going to kill everett christy tonight i'll stop you this guy says or this creature says well this is grendel by the way well i was hoping you try you know in the name of the heroics goodness and all those other ridiculous concepts you're as good as dead clink puts on his uh tuxedo matt Wagner grendel and so grendel his ego satisfied set about to commit his atrocious act got to admit is a pretty sharp party i would have expected expected it out of slug like christy i would i wouldn't i wouldn't wouldn't have expected it out of a slug like christy well shouldn't have long to wait dear friends please don't write me off as poor don't write me off as a poor host if i excuse myself to attend a few pressing matters in my study enter the evil grendel beware chuckle he's putting on his mask out i hate the social garbage i need a snort so bad it feels like my nose is gonna fall off he's talking about powder click and he's got this rod grendel grendel ah finally he took christy by surprise let it snow let it snow oh he's gonna do some blow let it snow ah there's grendels of staff double-bladed let it snow choke he's freaking out grendel he's named himself the evil grendel and don't look so bug-eyed he twists his staff around i just stopped by for a chat oh he's not gonna kill him he just stopped by for a chat what does that say he took christy by surprise dot dot dot flips over and proceeded to terrorize him ah chat about what the ring of course i don't know what this ring is ring what ring christy in the underworld even the most well-kept secrets aren't safe from grendel you're the invisible head of the largest prostitution ring in the city oh my all right but we never crossed you christy admits it not directly but it has come to my attention that most of your girls are rather young not even 14 i know like that but but why don't question me warm and that's the thing with grendel he's an assassin but he has uh he's got his own code right okay okay i'm sorry i promise no more kitty kitty stuff oh really g okay oh when he kills him he just didn't talk to him he killed him look at this i guess the double-bladed sword here took out his head having killed having killed the hideous grendel attained tomes he sought and made his way to rendezvous with his waiting minions so he escapes comes down hello gentlemen so glad you could make it on time so he's grabbed something i don't know what that is yes troops in my grubby grubby little hands i hold evered christies personal notes and ledgers a very successful evening now let's be off looks like agent agent is too late again there's a hand coming around this guy's neck i guess that's agent suddenly hope arrived as agent the wondrous wolf exploded onto the scene and by his neck throws him as the truck head first you have one chance run i want your boss but would be only too happy to rip your throats out these guys they look scared what this guy does anyway i don't know this character the battle broke out bam bam we just grabbed one throw him across yard idiot i tried but now it's your fault grabs the stick beside him smacks the guy in the head he's pissed oh when he splatters him and soon only two opponents were left oh dear look at this guy's holding his dripping blood i wish you wouldn't do that got any idea how hard it is for me to get good help when you keep destroying them so i guess they're serious opponents setting up the world well here we are again your brutal power versus my electric fork oh it's an electric piece electric staff that gets let's get this over with that's what we're about to fight the struggle was powerful frightening in its intensity his zapped ground a little bit tore off a piece of his clothes it's rose out of the way jumps on him leaped grindle jumps out of the way and he's zapped some argh great i've stuck a wounded animal right between me and the notebook he's right wounded animals are dangerous well not exactly what i had planned but the cops can take care of the ring run away time so he was going to give the exposed prostitution ring this guy stays at the truck and so through though wounded the valiant wolf succeeded in driving off the evil grindle but grindle remained free and here's the kicker this guy seems to be a hero and he's the anti hero but after this issue grindle is the guy that becomes the main character as far as i know anyway i haven't wrecked too much stuff with this guy or not nothing from this guy for his time maybe i have maybe look different but grindle is the key and that's the first issue or first appearance of grindle the comical primer number two awesome maybe we'll come back and read the grindle one two and three that have in my collection but there are um really amazing grindle stories that have come out after that and they're definitely worth the read


The Cabinet State Governor, Artico Baguado, says a total of 27 abducted students and three teachers of the Federal Government College-Burning Yarré in Cabinet State have regained to freedom after four months in captivity. Addressing the students, Baguado thanked God for their release while urging them to take heart over their abduction and four months' captivity as a trial from God. The governor directed that the students should undergo medical screening and be supported while being reunited with their families.


Hi, FosAsia, happy 10th birthday from OpenFest. The roll-troll! Hello, FosAsia. My name is John Haldewanger, and I'm a student in New Media. Hi, FosAsia. I'm Nico from Romania. I'm with the Fedora Design Team. Hello, FosAsia. My name is Helen Vali-Jamison, and I am part of the Upstage Project. Hi, FosAsia. I'm Ginger Q. I'm the lead developer on the OpenKiller standard. Hello, FosAsia. My name is Tom Lochner from Portland, Oregon and the United States. Hello, FosAsia. My name is Klaus. I work for the Rostridium Project. Hello, FosAsia. My name is Tim O'Clock. I'm a designer and a teacher from Rotterdam in England. Hi, FosAsia. It's Anthony Grasso. I work for Canon, and I'm from Sydney, Australia. My name is German Davidson. I'm an animator on the durian project. Hello, FosAsia. I'm Stephanie from England. I'm from France. I'm from Russia. Hello, FosAsia. I'm a graphic designer. I come from France. Hello, FosAsia. I'm Pierre from Brussels. Hi, FosAsia. I'm Martin from Czech Republic. I work for the Fedora Design Team. Hello, FosAsia. I'm Felipe Sanchez. I'm from Brazil, and I work with the Inkscape Development Team. Hello, FosAsia. My name is Jacob. I'm from Poland. I'm Dave Rostridium, and I'm working on free Libra fonts. My name is Ben Blanen, and I'm from the DGIV fonts. Hello, FosAsia. My name is Amastu, and I also work for the Rostridium Projects. Hello, FosAsia. I wish you honest good luck for your conference. And I wish you good luck with your event. I wish you good fortune. I wish I was there. I wish you the best of luck for the next conference. I wish you all the best. I wish you good luck with your event. Good luck on your conference. I wish you all the best. Good luck with your event. I want to say good luck. Good luck. Good luck. Bye, good luck.


Hello, I'm Catherine and I'm Jeanette and this is the Product Roundup giving you a summary of the main use and improvements in your Supernova CRM solution as well as a hint of what's coming. Now today is the last Roundup before summer and we will share with everybody Jeanette. Well we have to talk about service since the new version is now fully released and available on demand. We have some updates on mobile CRM and then we will share a little about the upcoming resource centre inside SupernovaeS where you can easily access information of all news FAQs, help documentation and tours to help you master the features in SupernovaeS and we will also take a sneak peek at the upcoming notification feature. Excellent, we of course start with the new service version. Now we have talked and blogged about it in various channels but in case you haven't heard there is a new version of Supernova Service available. Now this version has taken a new look at how people want to work when responding to all incoming questions IE requests that your company receive every day through various channels. Now working with requests are much more streamlined and the navigation is simplified and information is a lot more accessible and what's more because it's fully embedded into SupernovaeS it works well not just for the expert but for everyone. Meaning that more people in your company are able to use the features in the request and handle all the incoming engagements with your customers. Like us in marketing for example who typically follow up on incoming leads and people ending up in a form on our web channels. Now we have been really impressed with how much more intuitive it is for everyone to work with requests at the same time as it works for the more advanced workflows. So in SupernovaeS ERM you access the new service solution via the main menu. Clicking on requests this opens up the main screen where you see the shared inboxes of incoming requests organizing to queues. And for those who already work with SupernovaeS Service they will find that the features they are used to are still there but they may present itself differently because in this version we have taken a good look at how we can improve the most common workflows so that you can work more effectively and faster when handling requests. For example the way each request opens in a tab and now you can navigate between these as you typically work on more than one request at a time. Or the way you can organize the view the way you prefer it to be. And search and find relevant information on the company or person you're interacting with or search for past similar requests to help you respond in the best possible way. Now there's plenty of documentation available on the news and changes versus the old version in our community so we'll be sure to link to all related content in the product article. That's right for those who have already migrated and are using the new version you will notice or you'll soon notice there are some new updates just released too. Yes so among them I can mention that there's a new preference available to close a tab automatically when the ticket status is set to closed so this will save you some clicks. Another new update is that if you have many requests open you can now close them all at the same time in order to clean up your request apps. Another great update is the ability to archive attachments and that way you can save important documents to a company card you can link it to a sale or a project which is super helpful because it's easy to search and resurface later and it makes it also more accessible for everybody in your company. That's truly one of the many benefits of having service merged with the core serum features. And there are of course more and brand new features on the way for this version. So one of them I know is that the ability to get live updates on request so that you know if somebody else is working on the same request as you that's quite exciting. Now you mentioned at the very start up this is now fully available but on demand for existing customers. Can you explain what we mean by that Schmidt? Yes so it means that if you have service today we're in the process of upgrading everyone. At the moment we upgrade customers on demand. So if you want to get started then you can contact us by completing the form available on the link in the article and then someone will get in touch and discuss how and when we can get you on this on to this new version. And if you have yet to use service but would like to know more then contact your Supervisor responsible who will be more than happy to help you. But before moving on to the next set of news Catherine I would like to mention one more thing that may have gone a bit under the radar and that's that you can connect sales and projects to requests. So which is especially powerful if you want to support all of your processes from service to delivery to after sales. Yeah that is a good reminder. But what about if you're updating a request using your mobile CRM is it also possible there since given that mobile CRM is our next topic. So if you have these fields available in your desktop version then you will also be able to see them in your mobile CRM app. So you can link to a request or link a request to a project or a sale whilst on the move. Which is great because you don't have to do updates like this afterwards because then it's easy to forget. So in the same way you would also see a request tab in the sale and project screens inside your app so that you have immediate access to the information you need all the time. And that's not all we also have something I think sales and manager will appreciate and that's the ability to drill down in your dashboard via the mobile CRM. So far you've been able to just see your dashboards but now we can also drill down into the data within the dashboard. That means that you can click on parts of a chart and then see the details behind it. That's a really good improvement. I see I often want to drill down to look behind the charts and of course I think it will make updating individual data such as ourselves a lot faster too. Now next up we want to share a little bit about some upcoming new features and the first one is that sometime over the summer you'll be able to access information like this product run up as well as FAQs, help documentation, visual tips, and guided tours in a brand new resource center inside Superfills. Yes so this will make it easier for everyone to master current and use Superfills features so that you can get more out of your solution. Now we all know there are so many possibilities to do things faster, better, or easier if we only knew how so this new feature aims to help you do just that. So more information will arrive in your inbox and later inside Superfills over the summer so do keep an eye out for that. Now it's important to say that this will become available for our cloud customers only but we have another cool upcoming feature that we can mention. It is still in pilot but it will soon be released and that is the new notification of meeting invitations that happens through Superfills and has changed its appearance quite significantly and simplified a lot. That's right so we'll be the first to say that the new way is a lot smoother and easier to see what meetings you're invited to. So new invitations will appear via the bell like before but when you click on it the invitations appear in a panel. So you can accept or reject invitation directly in this panel or you can click on each invitation to let you see the details. So for those using service you will also get notifications for requests and updates and messages and so on in the same notification panel. So you'll be able to define what types of notifications you want to see by selecting your preferences in the settings panel here. I really love this improvement. Less clicks easier to see what's coming and without interrupting what else I'm doing so great stuff. Yes I really look forward to everyone getting access to this one. Before we round up today we should mention that there are also continuously improvements to the many apps available in our App Store including the SAP integration, Get Accept for Superfills and a lot more. So do take a look in our App Store for all the news. Now that's right in fact we have an app roundup published as well introducing you to the three recent additions to the App Store so take a look at that. But that's it from us. Enjoy the summer wherever you are and we'll see you again in the fall with more news. Have a super day. Bye! Bye!