Big Data Analysis in Smart Manufacturing: A Review
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paper_title: The Fourth Industrial Revolution

paper_content:

World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine smart factories in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better futureone in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress.
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paper_title: Handling smart environment devices, data and services at the semantic level with the FI-WARE core platform

paper_content:

The development of IoT (Internet of Things) applications poses a number of scientific and technological challenges which stem from the characteristics of the IoT domain itself. These include the huge and increasing number of connected entities (devices, physical objects, people,…) populating the physical environment, the variety of their types which leads to heterogeneity of the data produced or consumed by those connected entities. In this paper we argue that semantic modeling takes up many of these challenges and explain how the core platform developed by the FI-WARE project supports IoT application developers. After a short introduction of the characteristics and requirements of IoT applications we identify the contribution of semantic technologies to address some of them. We describe the FI-WARE platform enablers which support these technologies and illustrate through a real application how these enablers help developers satisfy these requirements.
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paper_title: Data mining with big data

paper_content:

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
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paper_title: MapReduce: simplified data processing on large clusters

paper_content:

MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. ::: ::: Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. ::: ::: Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day.
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paper_title: Yahoo! as an ontology: using Yahoo! categories to describe documents

paper_content:

We suggest that one (or a collection) of names of Yahoo! (or any other WWW indexer's) categories can be used to describe the content of a document. Such categories offer a standardized and universal way for referring to or describing the nature of real world objects, activities, documents and so on, and may be used (we suggest) to semantically characterize the content of documents. WWW indices, like Yahoo! provide a huge hierarchy of categories (topics) that touch every aspect of human endeavors. Such topics can be used as descriptors, similarly to the way librarians use for example, the Library of Congress cataloging system to annotate and categorize books. In the course of investigating this idea, we address the problem of automatic categorization of webpages in the Yahoo! directory. We use Telltale as our classifier; Telltale uses n-grams to compute the similarity between documents. We experiment with various types of descriptions for the Yahoo! categories and the webpages to be categorized. Our findings suggest that the best results occur when using the very brief descriptions of the Yahoo! categorized entries; these brief descriptions are provided either by the entries' submitters or by the Yahoo! human indexers and accompany most Yahoo! -indexed entries.
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paper_title: The Google file system

paper_content:

We have designed and implemented the Google File System, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients. While sharing many of the same goals as previous distributed file systems, our design has been driven by observations of our application workloads and technological environment, both current and anticipated, that reflect a marked departure from some earlier file system assumptions. This has led us to reexamine traditional choices and explore radically different design points. The file system has successfully met our storage needs. It is widely deployed within Google as the storage platform for the generation and processing of data used by our service as well as research and development efforts that require large data sets. The largest cluster to date provides hundreds of terabytes of storage across thousands of disks on over a thousand machines, and it is concurrently accessed by hundreds of clients. In this paper, we present file system interface extensions designed to support distributed applications, discuss many aspects of our design, and report measurements from both micro-benchmarks and real world use.
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paper_title: Trends in big data analytics

paper_content:

Abstract One of the major applications of future generation parallel and distributed systems is in big-data analytics. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size. Beyond their sheer magnitude, these datasets and associated applications’ considerations pose significant challenges for method and software development. Datasets are often distributed and their size and privacy considerations warrant distributed techniques. Data often resides on platforms with widely varying computational and network capabilities. Considerations of fault-tolerance, security, and access control are critical in many applications (Dean and Ghemawat, 2004; Apache hadoop). Analysis tasks often have hard deadlines, and data quality is a major concern in yet other applications. For most emerging applications, data-driven models and methods, capable of operating at scale, are as-yet unknown. Even when known methods can be scaled, validation of results is a major issue. Characteristics of hardware platforms and the software stack fundamentally impact data analytics. In this article, we provide an overview of the state-of-the-art and focus on emerging trends to highlight the hardware, software, and application landscape of big-data analytics.
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paper_title: Top 10 algorithms in data mining

paper_content:

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification,
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paper_title: Mobile agent based artificial immune system for machine condition monitoring

paper_content:

Machine condition monitoring is a process of continuously observing the status of a machine to ensure that proactive measures are taken to prevent damage due to abnormal operating conditions. Generally, industrial units such as mining, oil and gas etc, are located in geographically remote areas, so a large amount of data need to be acquired for fault diagnosis and prognosis remotely. To achieve this, certain resources such as stable communication network and adequate bandwidth are required. Furthermore, it is not always feasible to dispatch human resources simultaneously over large areas of operation to perform on-site maintenance. To overcome these issues, a mobile agent based system architecture is proposed for machine condition monitoring by imitating human immune system (ACMIS), which is also known as artificial immune system. The experiment results are presented to evaluate the performance of the proposed system in terms of fault detection accuracy and bandwidth allocation. Overall performance evaluation of the proposed framework suggests that our proposed scheme not only provides excellent fault detection accuracy but also a flexible and reliable machine condition monitoring system with reduced network and computational resources. Further our approach provides cost effective solution in building a practical machine condition monitoring system.
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paper_title: Graph Analysis and Visualization: Discovering Business Opportunity in Linked Data

paper_content:

Introduction xvii PART 1 Overview Chapter 1 Why Graphs? 3 Chapter 2 A Graph for Every Problem 27 PART 2 Process and Tools Chapter 3 Data--Collect, Clean, and Connect 55 Chapter 4 Stats and Layout 87 Chapter 5 Visual Attributes 125 Chapter 6 Explore and Explain 157 Chapter 7 Point-and-Click Graph Tools 187 Chapter 8 Lightweight Programming 223 PART 3 Visual Analysis of Graphs Chapter 9 Relationships 275 Chapter 10 Hierarchies 293 Chapter 11 Communities 315 Chapter 12 Flows 351 Chapter 13 Spatial Networks 389 PART 4 Advanced Techniques Chapter 14 Big Data 419 Chapter 15 Dynamic Graphs 449 Chapter 16 Design 473 Glossary 497 Index 501
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paper_title: Pregel: a system for large-scale graph processing

paper_content:

Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs - in some cases billions of vertices, trillions of edges - poses challenges to their efficient processing. In this paper we present a computational model suitable for this task. Programs are expressed as a sequence of iterations, in each of which a vertex can receive messages sent in the previous iteration, send messages to other vertices, and modify its own state and that of its outgoing edges or mutate graph topology. This vertex-centric approach is flexible enough to express a broad set of algorithms. The model has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier. Distribution-related details are hidden behind an abstract API. The result is a framework for processing large graphs that is expressive and easy to program.
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paper_title: Monitoring of wind farms’ power curves using machine learning techniques

paper_content:

The estimation of a wind farm’s power curve, which links the wind speed to the power that is produced by the whole wind farm, is a challenging task because this relationship is nonlinear and bounded, in addition to being non-stationary due for example to changes in the site environment and seasonality. Even for a single wind turbine the measured power at different wind speeds is generally different than the rated power, since the operating conditions on site are generally different than the conditions under which the turbine was calibrated (the wind speed on site is not uniform horizontally across the face of the turbine; the vertical wind profile and the air density are different than during the calibration; the wind data available on site are not always measured at the height of the turbine’s hub).
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paper_title: INTRODUCTION TO MACHINE LEARNINGIntroduction to Machine Learning

paper_content:

This chapter introduces the foundations of machine learning that are necessary to motivate its combination to evolutionary algorithms and image processing. In the chapter, we will describe several machine learning tools, suitable for a wide variety of data and tasks. The goal is not to describe the total number of techniques, but instead to present a general view of the field through the description of the mail problems considered in machine learning.
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paper_title: A bridging model for parallel computation

paper_content:

The success of the von Neumann model of sequential computation is attributable to the fact that it is an efficient bridge between software and hardware: high-level languages can be efficiently compiled on to this model; yet it can be effeciently implemented in hardware. The author argues that an analogous bridge between software and hardware in required for parallel computation if that is to become as widely used. This article introduces the bulk-synchronous parallel (BSP) model as a candidate for this role, and gives results quantifying its efficiency both in implementing high-level language features and algorithms, as well as in being implemented in hardware.
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paper_title: Machine learning in automated text categorization

paper_content:

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.
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paper_title: An Introduction to MCMC for Machine Learning

paper_content:

This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.
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paper_title: Hadoop-HBase for large-scale data

paper_content:

Today we are inundated with digital data. Yet we are very poor in managing and processing it. It is becoming increasingly difficult to store and analyze data efficiently and economically via conventional database management tools. Not only that, type of data, appearing in the databases, are also changing. Now a day, binary large objects are a standard integral part of any database. Researchers, all over the globe, are baffling with analysis of these ultra large databases. Apache HBase is one such attempt. HBase is a noSQL distributed database developed on top of Hadoop Distributed File System (HDFS). In this paper, we present an evaluation of hybrid architecture where HDFS contains the non-textual data like images and location of such data is stored in HBase. This hybrid architecture enables faster search and retrieval of the data which is a growing need in any organization who are flooded with data. The paper aims at evaluating the performance of random reads and random writes of data storage location information to HBase and retrieving and storing data in HDFS respectively. We also present a comparative study of HBase-HDFS architecture with MySQL-HDFS architecture.
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paper_title: Machine Learning for Information Extraction in Informal Domains

paper_content:

We consider the problem of learning to perform information extraction in domains where linguistic processing is problematic, such as Usenet posts, email, and finger plan files. In place of syntactic and semantic information, other sources of information can be used, such as term frequency, typography, formatting, and mark-up. We describe four learning approaches to this problem, each drawn from a different paradigm: a rote learner, a term-space learner based on Naive Bayes, an approach using grammatical induction, and a relational rule learner. Experiments on 14 information extraction problems defined over four diverse document collections demonstrate the effectiveness of these approaches. Finally, we describe a multistrategy approach which combines these learners and yields performance competitive with or better than the best of them. This technique is modular and flexible, and could find application in other machine learning problems.
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paper_title: Big Data Analysis to Ease Interconnectivity in Industry 4.0—A Smart Factory Perspective

paper_content:

Thriving and challenging market trends led to changes in the manufacturing industry. Production lines that need to adapt to customisable products on the fly emerged. By applying communication and sensors to the shop-floor, along with Industry 4.0 principles, this became a possibility. The growing amount of sensors led to an exponential boom of the amount of data available, creating the concept of Smart Factory. By applying Big Data Analysis to this data, it may be possible to optimise Smart Factories. There are technologies capable of doing this, even though only some are capable of guaranteeing Smart Factory requirements, such as real-time. A study of these technologies, based on SME’s experts’ opinion, is hereby presented to assess the most suitable ones to analyse Big Data in a Smart Factory environment.
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paper_title: NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison

paper_content:

Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as ‘Big Data’ that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can’t be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as - NoSQL, NewSQL and Search-based systems. This paper motivation is to provide - classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.
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paper_title: Rattle : A Data Mining GUI for R by

paper_content:

Data mining delivers insights, pat- terns, and descriptive and predictive models from the large amounts of data available today in many organisations. The data miner draws heavily on methodologies, techniques and al- gorithms from statistics, machine learning, and computer science. R increasingly provides a powerful platform for data mining. However, scripting and programming is sometimes a chal- lenge for data analysts moving into data mining. The Rattle package provides a graphical user in- terface specifically for data mining using R. It also provides a stepping stone toward using R as a programming language for data analysis.
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paper_title: The WEKA data mining software: an update

paper_content:

More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
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paper_title: Encyclopedia of Systems Biology

paper_content:

Systems biology refers to the quantitative analysis of the dynamic interactions among several components of a biological system and aims to understand the behavior of the system as a whole. Systems biology involves the development and application of systems theory concepts for the study of complex biological systems through iteration over mathematical modeling, computational simulation and biological experimentation. Systems biology could be viewed as a tool to increase our understanding of biological systems, to develop more directed experiments, and to allow accurate predictions.The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology a one-stop shop for someone seeking information on key concepts of systems biology. As a result, the Encyclopedia comprises a broad range of topics relevant in the context of systems biology. The audience targeted by the Encyclopedia includes researchers, developers, teachers, students and practitioners who are interested or working in the field of systems biology. Keeping in mind the varying needs of the potential readership, we have structured and presented the content in a way that is accessible to readers from wide range of backgrounds. In contrast to encyclopedic online resources, which often rely on the general public to author their content, a key consideration in the development of the Encyclopedia of Systems Biology was to have subject matter experts define the concepts and subjects of systems biology.
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paper_title: Orange: Data Mining Toolbox in Python

paper_content:

Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design of components, we focus on the flexibility of their reuse: our principal intention is to let the user write simple and clear scripts in Python, which build upon C++ implementations of computationally-intensive tasks. Orange is intended both for experienced users and programmers, as well as for students of data mining.
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paper_title: A survey of open source tools for machine learning with big data in the Hadoop ecosystem

paper_content:

With an ever-increasing amount of options, the task of selecting machine learning tools for big data can be difficult. The available tools have advantages and drawbacks, and many have overlapping uses. The world’s data is growing rapidly, and traditional tools for machine learning are becoming insufficient as we move towards distributed and real-time processing. This paper is intended to aid the researcher or professional who understands machine learning but is inexperienced with big data. In order to evaluate tools, one should have a thorough understanding of what to look for. To that end, this paper provides a list of criteria for making selections along with an analysis of the advantages and drawbacks of each. We do this by starting from the beginning, and looking at what exactly the term “big data” means. From there, we go on to the Hadoop ecosystem for a look at many of the projects that are part of a typical machine learning architecture and an understanding of how everything might fit together. We discuss the advantages and disadvantages of three different processing paradigms along with a comparison of engines that implement them, including MapReduce, Spark, Flink, Storm, and H2O. We then look at machine learning libraries and frameworks including Mahout, MLlib, SAMOA, and evaluate them based on criteria such as scalability, ease of use, and extensibility. There is no single toolkit that truly embodies a one-size-fits-all solution, so this paper aims to help make decisions smoother by providing as much information as possible and quantifying what the tradeoffs will be. Additionally, throughout this paper, we review recent research in the field using these tools and talk about possible future directions for toolkit-based learning.
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paper_title: Large Scale Distributed Data Science using Apache Spark

paper_content:

Apache Spark is an open-source cluster computing framework for big data processing. It has emerged as the next generation big data processing engine, overtaking Hadoop MapReduce which helped ignite the big data revolution. Spark maintains MapReduce's linear scalability and fault tolerance, but extends it in a few important ways: it is much faster (100 times faster for certain applications), much easier to program in due to its rich APIs in Python, Java, Scala (and shortly R), and its core data abstraction, the distributed data frame, and it goes far beyond batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming, machine learning, and graph processing. This tutorial will provide an accessible introduction to Spark and its potential to revolutionize academic and commercial data science practices.
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paper_title: Encyclopedia of Systems Biology

paper_content:

Systems biology refers to the quantitative analysis of the dynamic interactions among several components of a biological system and aims to understand the behavior of the system as a whole. Systems biology involves the development and application of systems theory concepts for the study of complex biological systems through iteration over mathematical modeling, computational simulation and biological experimentation. Systems biology could be viewed as a tool to increase our understanding of biological systems, to develop more directed experiments, and to allow accurate predictions.The Encyclopedia of Systems Biology is conceived as a comprehensive reference work covering all aspects of systems biology, in particular the investigation of living matter involving a tight coupling of biological experimentation, mathematical modeling and computational analysis and simulation. The main goal of the Encyclopedia is to provide a complete reference of established knowledge in systems biology a one-stop shop for someone seeking information on key concepts of systems biology. As a result, the Encyclopedia comprises a broad range of topics relevant in the context of systems biology. The audience targeted by the Encyclopedia includes researchers, developers, teachers, students and practitioners who are interested or working in the field of systems biology. Keeping in mind the varying needs of the potential readership, we have structured and presented the content in a way that is accessible to readers from wide range of backgrounds. In contrast to encyclopedic online resources, which often rely on the general public to author their content, a key consideration in the development of the Encyclopedia of Systems Biology was to have subject matter experts define the concepts and subjects of systems biology.
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paper_title: Big Data Challenges

paper_content:

Big Data Challenges ::: Massive, fast and diverse data moving quickly everywhere creating what is known as “Big Data” era. This data becomes very important source for valuable insights and ultimately helping to make more informed decision. However this data with very special attributes can’t be managed and processed by the current traditional software systems, which became a real problem. This study will discuss all different challenges of Big Data categorized into three main groups: Data, process and management challenges. Data challenges are the group of the challenges relates to the characteristics of the data itself. Process group includes all the challenges encountered while processing the Big Data; started with capture step and ended with presenting the output to clients.
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paper_title: SAMOA: a platform for mining big data streams

paper_content:

Social media and user generated content are causing an ever growing data deluge. The rate at which we produce data is growing steadily, thus creating larger and larger streams of continuously evolving data. Online news, micro-blogs, search queries are just a few examples of these continuous streams of user activities. The value of these streams relies in their freshness and relatedness to ongoing events. However, current (de-facto standard) solutions for big data analysis are not designed to deal with evolving streams. In this talk, we offer a sneak preview of SAMOA, an upcoming platform for mining dig data streams. SAMOA is a platform for online mining in a cluster/cloud environment. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as S4 and Storm. SAMOA includes algorithms for the most common machine learning tasks such as classification and clustering. Finally, SAMOA will soon be open sourced in order to foster collaboration and research on big data stream mining.
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paper_title: An Approach for Implementing ISA 95-Compliant Big Data Observation, Analysis and Diagnosis Features in Industry 4.0 Vision Following Manufacturing Systems

paper_content:

Current trends are showing a technological evolution to an unified Industrial Internet of Things network where smart manufacturing devices are loosely coupled over a cloud to realize comprehensive collaboration and analysis possibilities, and to increase the dynamic and volatile of manufacturing environments. This rising complexity generates also higher ranges of error possibilities and analog a growing demand of new diagnostic approaches to handle also those highly complex systems as manufacturing systems which are following the Industry 4.0 vision. This is an ISA’95 compliant approach of a Big Data analytics methodology for analysis and observation in Industry 4.0 vision following manufacturing systems.
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paper_title: The Fourth Industrial Revolution

paper_content:

World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine smart factories in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better futureone in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress.
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Title: Big Data Analysis in Smart Manufacturing: A Review

Section 1: Introduction
Description 1: Provide an overview of smart manufacturing and the relevance of Big Data analysis in this domain.

Section 2: Roadmaps and Related Research Activities
Description 2: Discuss selected European research roadmaps and related projects to identify research activities, challenges, and goals in Big Data analysis for smart manufacturing.

Section 3: European Research Roadmaps
Description 3: Present and summarize critical European research roadmaps concerning Big Data analysis in smart manufacturing.

Section 4: State of the Art
Description 4: Provide an in-depth review of the current state of various technologies and methodologies related to Big Data analysis in smart manufacturing.

Section 5: Smart Manufacturing Infrastructures
Description 5: Discuss the infrastructure needed for smart manufacturing, including network technologies, communication protocols, and architectures.

Section 6: Big Data in Smart Manufacturing
Description 6: Explain the characteristics of Big Data in smart manufacturing, including data sources, requirements, and methods for data storage and analysis.

Section 7: Big Data Analysis Processes
Description 7: Discuss the processes involved in Big Data analysis, including methodologies like CRISP-DM and data mining techniques.

Section 8: Technological Review (Tools)
Description 8: Review the tools, frameworks, and algorithms available for Big Data analysis, including data storage and processing technologies.

Section 9: Use Cases and Exploitation
Description 9: Identify and discuss potential use cases for Big Data analysis in smart manufacturing, including monitoring, diagnosis, decision making, and optimization.

Section 10: Research Challenges
Description 10: Highlight the existing challenges in Big Data analysis within smart manufacturing and propose future directions for addressing these challenges.

Section 11: Future Work
Description 11: Introduce upcoming projects and future directions that aim to address the identified research gaps in Big Data analysis for smart manufacturing.

Section 12: Conclusions
Description 12: Summarize the findings of the review, emphasizing the importance and potential of Big Data analysis in smart manufacturing, along with the identified challenges and future work.