A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> 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. <s> BIB001 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> 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. <s> BIB002 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> A practical, step-by-step approach to making sense out of data Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data. Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including: Problem definitions Data preparation Data visualization Data mining Statistics Grouping methods Predictive modeling Deployment issues and applications Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project. From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining. <s> BIB003 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Abstract The relationship between data, knowledge, and wisdom is explained in this article. Codifying knowledge will help to disseminate good practice and provide a sound basis for developing clinical decision-support systems. Stimulated by increasing access to the Internet, there is a tension between traditional sources of knowledge (e.g. books, individuals recognized as experts), and sources facilitated by ready access to the Internet (e.g. wikis, blogs and other social networking resources). <s> BIB004 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> The amount of data in our world has been exploding, and analyzing large data sets—so-called big data— will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future. <s> BIB005 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> BI technologies are essential to running today's businesses and this technology is going through sea changes. <s> BIB006 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> For scientists, collisions at the world's most powerful particle collider are just the start. Nature follows the torrent of data on its circuitous journey around the world. <s> BIB007 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Building on the legacy of the Sloan Digital Sky Survey (SDSS-I and II), SDSS-III is a program of four spectroscopic surveys on three scientific themes: dark energy and cosmological parameters, the history and structure of the Milky Way, and the population of giant planets around other stars. In keeping with SDSS tradition, SDSS-III will provide regular public releases of all its data, beginning with SDSS DR8 (which occurred in Jan 2011). This paper presents an overview of the four SDSS-III surveys. BOSS will measure redshifts of 1.5 million massive galaxies and Lya forest spectra of 150,000 quasars, using the BAO feature of large scale structure to obtain percent-level determinations of the distance scale and Hubble expansion rate at z 100 per resolution element), H-band (1.51-1.70 micron) spectra of 10^5 evolved, late-type stars, measuring separate abundances for ~15 elements per star and creating the first high-precision spectroscopic survey of all Galactic stellar populations (bulge, bar, disks, halo) with a uniform set of stellar tracers and spectral diagnostics. MARVELS will monitor radial velocities of more than 8000 FGK stars with the sensitivity and cadence (10-40 m/s, ~24 visits per star) needed to detect giant planets with periods up to two years, providing an unprecedented data set for understanding the formation and dynamical evolution of giant planet systems. (Abridged) <s> BIB008 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> The paper is focused on so called NoSQL databases. In context of cloud computing, architectures and basic features of these databases are studied, particularly their horizontal scalability and concurrency model, that is mostly weaker than ACID transactions in relational SQL-like database systems. Some characteristics like a data model and querying capabilities are discussed in more detail. The paper also contains an overview of some representatives of NoSQL databases. <s> BIB009 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> This paper is trying to comment on the various NoSQL (Not only Structured Query Language) systems and to make a comparison (using multiple criteria) between them. The NoSQL databases were created as a mean to offer high performance (both in terms of speed and size) and high availability at the price of loosing the ACID (Atomic, Consistent, Isolated, Durable) trait of the traditional databases in exchange with keeping a weaker BASE (Basic Availability, Soft state, Eventual consistency) feature. Remains to be seen which of the multiple solutions created since the official appearance of the NoSQL concept (which was defined in 1998 and reintroduced in 2009, around which moment several NoSQL solutions emerged; at the present moment there are known over 120 such solutions) are really delivering on these promises of higher performance (although several of them are already used with very good results). <s> BIB010 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> With the development of the Internet and cloud computing, there need databases to be able to store and process big data effectively, demand for high-performance when reading and writing, so the traditional relational database is facing many new challenges. Especially in large scale and high-concurrency applications, such as search engines and SNS, using the relational database to store and query dynamic user data has appeared to be inadequate. In this case, NoSQL database created. This paper describes the background, basic characteristics, data model of NoSQL. In addition, this paper classifies NoSQL databases according to the CAP theorem. Finally, the mainstream NoSQL databases are separately described in detail, and extract some properties to help enterprises to choose NoSQL. <s> BIB011 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of "Big Data," including the recent announcement from the White House about new funding initiatives across different agencies, that target research for Big Data. While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is no clear consensus on what is Big Data. In fact, there have been many controversial statements about Big Data, such as "Size is the only thing that matters." In this panel we will try to explore the controversies and debunk the myths surrounding Big Data. <s> BIB012 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> The CAP theorem asserts that any networked shared-data system can have only two of three desirable properties. However, by explicitly handling partitions, designers can optimize consistency and availability, thereby achieving some trade-off of all three. The featured Web extra is a podcast from Software Engineering Radio, in which the host interviews Dwight Merriman about the emerging NoSQL movement, the three types of nonrelational data stores, Brewer's CAP theorem, and much more. <s> BIB013 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive datasets. Architectures for big data usually range across multiple machines and clusters, and they commonly consist of multiple special purpose sub-systems. Coupled with the knowledge discovery process, big data movement offers many unique opportunities for organizations to benefit (with respect to new insights, business optimizations, etc.). However, due to the difficulty of analyzing such large datasets, big data presents unique systems engineering and architectural challenges. In this paper, we present three system design principles that can inform organizations on effective analytic and data collection processes, system organization, and data dissemination practices. The principles presented derive from our own research and development experiences with big data problems from various federal agencies, and we illustrate each principle with our own experiences and recommendations. <s> BIB014 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> SummaryMachine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the BookA machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.Purchase includes free PDF, ePub, and Kindle eBooks downloadable at manning.com. What's InsideA no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos=================================== Table of ContentsPART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: nave Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce <s> BIB015 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> This paper demonstrates ETLMR, a novel dimensional Extract--Transform--Load (ETL) programming framework that uses Map-Reduce to achieve scalability. ETLMR has built-in native support of data warehouse (DW) specific constructs such as star schemas, snowflake schemas, and slowly changing dimensions (SCDs). This makes it possible to build MapReduce-based dimensional ETL flows very easily. The ETL process can be configured with only few lines of code. We will demonstrate the concrete steps in using ETLMR to load data into a (partly snowflaked) DW schema. This includes configuration of data sources and targets, dimension processing schemes, fact processing, and deployment. In addition, we also present the scalability on large data sets. <s> BIB016 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Business intelligence and analytics (BIA) is about the development of technologies, systems, practices, and applications to analyze critical business data so as to gain new insights about business and markets. The new insights can be used for improving products and services, achieving better operational efficiency, and fostering customer relationships. In this article, we will categorize BIA research activities into three broad research directions: (a) big data analytics, (b) text analytics, and (c) network analytics. The article aims to review the state-of-the-art techniques and models and to summarize their use in BIA applications. For each research direction, we will also determine a few important questions to be addressed in future research. <s> BIB017 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Your guide to the world and technology of NoSQL Overview First hand, detailed information about NoSQL technology Learn the differences between NoSQL and RDBMS and where each is useful Understand the various data models for NoSQL Compare and contrast some of the popular NoSQL databases on the market Think outside the box; learn to design, construct, and implement using NoSQL In Detail Relational databases have been used for decades, and in the last few years NoSQL has been a growing choice for large-scale web applications. Non-relational databases provide the scale and speed that you may need for your application. To switch you must know the options available, the advantages and drawbacks, and scenarios which it is suited to the most and where it should be avoided at all costs. Getting Started with NoSQL is a from-the-ground up guide that takes you from the very first steps to a real-world NoSQL application. It provides you with a step-by-step approach to design and implement a NoSQL application that will help you make clear decisions on database choices and database model choices. The book is suited for a developer, an architect, as well as a CTO. This book is a comprehensive guide to working with NoSQL. You will learn to make key decisions, and to design and implement NoSQL applications. You will learn about NoSQL jargon, data models, and databases on the market. The case studies and comparisons presented will help you to make a decision on whether or not to use NoSQL, and if so which model and product to use. This book is an indispensable resource for you to have in your library. You will learn everything you need to know about understanding and working with NoSQL and how to implement an application with the correct NoSQL for you. What you will learn from this book Discover what NoSQL is and what it is not Master the jargon and vocabulary around NoSQL Understand the pros and cons of NoSQL Learn where to implement NoSQL and where not to Know the differences between various NoSQL data models Explore advantages and limitations of popular NoSQL databases Comprehend the differences between the NoSQL databases available How to write code specific to data models and NoSQL databases Discover the correct way of thinking for designing entities with NoSQL Approach As with all Packt books this guide will use practical examples to impart information to the reader. Who this book is written for Getting Started with NoSQL is a great resource for someone starting with NoSQL and an indispensable guide for technology decision makers. It is assumed that you have a background in RDBMS modeling and SQL and have had exposure to at least one of the programming languages Java or JavaScript. <s> BIB018 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data. The Big Data challenge is becoming one of the most exciting opportunities for the years to come. We present in this issue, a broad overview of the topic, its current status, controversy, and a forecast to the future. We introduce four articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining. <s> BIB019 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. Youll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your companys data science projects. Youll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organizationand how you can use it for competitive advantage Treat data as a business asset that requires careful investment if youre to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates <s> BIB020 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> This paper explores the convergence of Data Warehousing, OLAP and data-intensive Cloud Infrastructures in the context of so-called analytics over Big Data. The paper briefly reviews some state-of-the-art proposals, highlights open research issues and, finally, it draws possible research directions in this scientific field. <s> BIB021 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as dataintensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing. <s> BIB022 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Learn efficient ways to harness and manage your data storage networks Whether you're preparing for the CompTIA Storage+ exam or simply seeking a deeper understanding of data storage networks, this Sybex guide will help you get there. This book covers data storage from the basics to advanced topics, and provides practical examples to show you ways to deliver world-class solutions. In addition, it covers all the objectives of the CompTIA Storage+ exam (SG0-001), including storage components, connectivity, storage management, data protection, and storage performance. Focuses on designing, implementing, and administering storage for today's evolving organizations, getting under the hood of the technologies that enable performance, resiliency, availability, recoverability, and simplicity Covers virtualization, big data, cloud storage, security, and scalability as well as how storage fits in to the wider technology environments prevalent in today's cloud era Provides advice and real-world examples that storage administrators in the trenches can actually use An excellent study aid for the CompTIA Storage+ exam (SG0-001), covering all the exam objectives Data Storage Networking: Real World Skills for the CompTIA Storage+ Certification and Beyond provides a solid foundation for data storage administrators and a reference that can be consulted again and again. <s> BIB023 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest. <s> BIB024 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Although Business Intelligence and analytics (BI&A) is one of the most essential technologies to be purchased, the implementation of many BI&A projects fail. They do not fulfill the expectations of organizations. The reasons for this failure are not clear and still not well investigated. Resource-based View (RBV) and dynamic capability theory could help to overcome this gap and to provide an appropriate theoretical basis for future research in BI&A area. The research objectives for this study are: (1) conceptualization and discussion on BI&A dynamic capability, (2) building the comprehensive framework of BI&A capabilities. In order to address these objectives, the remainder of the paper is structured as follows: The first sections provide the theoretical foundations of BI&A, RBV and dynamic capability theory. Next, the BI&A capability was conceptualized. Finally, a model of BI&A as a dynamic capability was proposed. The study was based mainly on (1) a critical analysis of literature and (2) interviews with managers and experts in BI&A. The results of this study can be used by IT and business leaders as they plan and develop BI&A capabilities in their organizations. <s> BIB025 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Evidence based medicine (EBM) is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. Each year, a significant number of research studies (potentially serving as evidence) are reported in the literature at an ever-increasing rate outpacing the translation of research findings into practice. Coupled with the proliferation of electronic health records, and consumer health information, researchers and practitioners are challenged to leverage the full potential of EBM. In this paper we present a research agenda for leveraging business intelligence and big data analytics in evidence based medicine, and illustrate how analytics can be used to support EBM. <s> BIB026 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Big data is an opportunity in the emergence of novel business applications such as “Big Data Analytics” (BDA). However, these data with non-traditional volumes create a real problem given the capacity constraints of traditional systems. The aim of this paper is to deal with the impact of big data in a decision-support environment and more particularly in the data integration phase. In this context, we developed a platform, called P-ETL (Parallel-ETL) for extracting (E), transforming (T) and loading (L) very large data in a data warehouse (DW). To cope with very large data, ETL processes under our P-ETL platform run on a cluster of computers in parallel way with MapReduce paradigm. The conducted experiment shows mainly that increasing tasks dealing with large data speeds-up the ETL process. <s> BIB027 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> Extract-Transform-Load (ETL) programs process data into data warehouses (DWs). Rapidly growing data volumes demand systems that scale out. Recently, much attention has been given to MapReduce for parallel handling of massive data sets in cloud environments. Hive is the most widely used RDBMS-like system for DWs on MapReduce and provides scalable analytics. It is, however, challenging to do proper dimensional ETL processing with Hive; e.g., the concept of slowly changing dimensions (SCDs) is not supported (and due to lacking support for UPDATEs, SCDs are complex to handle manually). Also the powerful Pig platform for data processing on MapReduce does not support such dimensional ETL processing. To remedy this, we present the ETL framework CloudETL which uses Hadoop to parallelize ETL execution and to process data into Hive. The user defines the ETL process by means of high-level constructs and transformations and does not have to worry about technical MapReduce details. CloudETL supports different dimensional concepts such as star schemas and SCDs. We present how CloudETL works and uses different performance optimizations including a purpose-specific data placement policy to co-locate data. Further, we present a performance study and compare with other cloud-enabled systems. The results show that CloudETL scales very well and outperforms the dimensional ETL capabilities of Hive both with respect to performance and programmer productivity. For example, Hive uses 3.9 times as long to load an SCD in an experiment and needs 112 statements while CloudETL only needs 4. <s> BIB028 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> 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. <s> BIB029 </s> A Review on Business Intelligence and Big Data <s> DW, Online Transactional Processing (OLTP), and Online Analytical Processing (OLAP) <s> “Big Data” can mean different things to different people. The scale and challenges of Big Data are often described using three attributes, namely volume, velocity, and variety (3Vs), which only reflect some of the aspects of data. In this chapter, we review historical aspects of the term “big data” and the associated analytics. We augment the 3Vs with additional attributes of big data to make it more comprehensive and relevant. We show that Big Data is not just the 3Vs, but actually 3 2 Vs; that is, 9Vs covering the fundamental motivation behind Big Data, which is to incorporate business intelligence based on different hypothesis or statistical models so that Big Data analytics (BDA) can enable decision makers to make useful predictions for making some crucial decisions or researching results. History of Big Data has demonstrated that the most cost-effective way of performing BDA is to employ machine learning (ML) on the cloud computing (CC)-based infrastructure or simply, ML + CC → BDA. This chapter is devoted to help decision makers by defining BDA as a solution and opportunity to address their business needs. <s> BIB030
It is widely accepted that DW is a core component of BI solutions BIB017 . A DW enables saving historical data and trend analysis as well as data mining on clean and integrated data. A DW is subject oriented, integrated, time-variant, and non-volatile collection of data that supports decision making [54] . It connects different databases one to another in order to produce more inclusive information for management information needs [55] . Online Transaction Processing (OLTP) systems are used for operational transactions. In the relational database world, OLTP databases are designed to optimize insert, update and delete queries whereas DW is designed to optimize select queries. So, OLTP is not suited for decision making due to poor select query performances. Besides using directly OLTP sources for business decision making will surely cause slow down business operations. A traditional DW is mostly fed by organization's OLTP databases. The data is extracted, cleaned, confirmed, transformed and loaded into a DW. In other words, data quality is increased before loading to DW. Since its data quality is improved through ETL procedures, a DW is expected to have better data quality than OLTP database, and hence enables better data mining results. However, it is not easy to establish a DW which is consistent with OLTP database and to store high-quality data. There are some inconsistencies between DW and OLTP databases due to poor ETL processes and DW establishment and execution. Moreover, relational DWs have some limitations and disadvantages; they are not the optimal environment for non-structured data; it is hard to gain real-time insight due to change capture and ETL processes; they have additional costs, and organization's information culture and technology using skills may not benefit from DW potential. In terms of DW architectures, among many architectures, two come to the forefront which is dimensional modeling advocated by Ralph Kimball and enterprise DW by Bill Inmon. Essential differences between two are development methodologies, data modeling, and DW architecture.  ,  ,  ,  ,  , BIB005 ,  , BIB006 , BIB022 , BIB012 Big Data Value ChainData Generation  , BIB005 , BIB022 , BIB007 , BIB008 ,  Big Data Value ChainData Acquisition  ,  Big Data Value ChainData Storage BIB022 ,  , BIB023 ,  , BIB013 , BIB009 , BIB001 , [26] , BIB010 , BIB011 ,  , BIB018 , BIB002 ,  Big Data Value ChainBig Data Analytics BIB014 , BIB019 ,  ,  ,  ,  , BIB024 , BIB029 ,  , BIB015 , BIB030 ,  , BIB020 , BIB003 Business Intelligence BIB006 , BIB025 ,  ,  , BIB004 ,  ,  , BIB017 , [54] , [55] ,  ,  , BIB026 ,  ,  , BIB027 , BIB016 ,  , BIB028 , BIB021 Reviewing data from DW and gaining insight quickly is a quite cumbersome business. Especially when DW grows beyond a certain volume. OLAP enables examining quickly of huge relational datasets. OLAP uses data from DW, precomputes it and stores on its own format in order to provide faster access than DW. In another saying, data is aggregated, historical data, stored in multi-dimensional schemas thereby processing becomes faster. While the size of data which is collected for BI in DW is rapidly growing, traditional DW solutions have reached its limits. Traditional DWs mainly depend on structured data and address smaller volumes, expects predictable and arranged data loads, use a single server, and need separate data repository  . 