...The Situation of Big Data Technology Yu Liu International American University BUS 530: Management Information Systems Matthew Keogh 2015 Summer 2 - Section C Introduction In this paper, I will list the main technologies related to big data. According to the life cycle of the data processing, big data technology can be divided into data collection and pre-processing, data storage and management, data analysis and data mining, data visualization and data privacy and security, and so on. The reason I select topic about big data My major is computer science and I have taken a few courses about data mining before. Nowadays more and more job positions about big data are showing at job seeking website, such as Monster.com. I am planning to learn some mainstream big data technologies like Hadoop. Therefore, I choose big data as my midterm paper topic. Big data in Google Google's big data analytics intelligence applications include customer sentiment analysis, risk analysis, product recommendations, message routing, customer losing prediction, the classification of the legal copy, email content filtering, political tendency forecast, species identification and other aspects. It is said that big data will generate $23 million every day for Google. Some typical applications are as follows: Based on MapReduce, Google's traditional applications include data storage, data analysis, log analysis, search quality and other data analytical applications. Based on Dremel system...
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...ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords: Business intelligence and analytics, big data analytics...
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...AND ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords: Business intelligence and analytics, big data analytics, Web 2.0 ...
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...INTRODUCTION TO BUSINESS ANALYTICS Sumeet Gupta Associate Professor Indian Institute of Management Raipur Outline • Business Analytics and its Applications • Analytics using Data Mining Techniques • Working with R BUSINESS ANALYTICS AND ITS APPLICATIONS What is Business Analytics? Analytics is the use of: data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions. Evolution of Business Analytics? • Operations research • Management science • Business intelligence • Decision support systems • Personal computer software Application Areas of Business Analytics • Management of customer relationships • Financial and marketing activities • Supply chain management • Human resource planning • Pricing decisions • Sport team game strategies Why Business Analytics? • There is a strong relationship of BA with: • profitability of businesses • revenue of businesses • shareholder return • BA enhances understanding of data • BA is vital for businesses to remain competitive • BA enables creation of informative reports Global Warming Poll Winner Sales Revenue Predicting Customer Churn Credit Card Fraud Loan Default Prediction Managing Employee Retention Market Segmentation Medical Imaging Analyzing Tweets stylus ...
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...SAS Global Forum 2008 Reporting and Information Visualization Paper 264-2008 PROC TABULATE® and the Neat Things You Can Do With It Wendi L. Wright, CTB / McGraw-Hill, Harrisburg, PA ABSTRACT This paper starts with an introduction to PROC TABULATE®. It looks at the basic syntax, and then builds on this syntax by using examples on how to produce one-, two-, and three-dimensional tables using the TABLE statement. Some of the examples cover how to choose statistics for the table, labeling variables and statistics, how to add totals and subtotals, working with percents and missing data, and how to clean up the table. The presentation then shows several examples using the ODS STYLE= option in PROC TABULATE to customize tables and improve their attractiveness. This option is versatile and, depending on where the option is used, has the ability to justify cells or row and column headings, change colors for both the foreground and background of the table, modify borders, add a flyover text box in ODS HTML, or add GIF figures to the row or column headings. INTRODUCTION PROC TABULATE is a procedure that displays descriptive statistics in tabular format. It computes many statistics that other procedures compute, such as MEANS, FREQ, and REPORT and displays these statistics in a table format. TABULATE will produce tables in up to three dimensions and allows, within each dimension, multiple variables to be reported one after another hierarchically. There are also some very nice mechanisms...
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...6, June 2013 ISSN 2250-3153 1 Big Data Landscape Shubham Sharma Banking Product Development Division, Oracle Financial Services Software Ltd. Bachelor of Technology Information Technology, Maharishi Markandeshwar Engineering College Abstract- “Big Data” has become a major source of innovation across enterprises of all sizes .Data is being produced at an ever increasing rate. This growth in data production is driven by increased use of media, fast developing organizations, proliferation of web and systems connected to it. Having a lot of data is one thing, being able to store it, analyze it and visualize it in real time environment is a whole different ball game. New technologies are accumulating more data than ever; therefore many organizations are looking forward to optimal ways to make better use of their data. In a broader sense, organizations analyzing big data need to view data management, analysis, and decision-making in terms of “industrialized” flows and processes rather than discrete stocks of data or events. To handle these aspects of large quantities of data various open platforms had been developed. Index Terms- Big Technologies,Tools Data, Landscape,Open Platforms, nearly 500 exabytes per day .To put the numbers in perspective this is equivalent to 5×1020 bytes per day. Almost 200 times higher than all the sources combined together in the world. To handle this huge chunk of data will be hard with the existing data management technologies. Hence the technology...
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...exploration of an organization’s data with emphasis on statistical analysis. It describes the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics is used by companies committed to data-driven decision making. It focuses on developing new insights and understanding of business performance based on data and statistical methods. BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Business analytics makes extensive use of statistical analysis, including explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Data-driven companies treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to data-driven decision making. Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, cleansing, and integration into a single repository such as a data warehouse or data mart. The analysis is typically...
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...Data Mining: Concepts and Techniques (3rd ed.) Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University ©2011 Han, Kamber & Pei. All rights reserved. Adapted for CSE 347-447, Lecture 1b, Spring 2015 1 1 Introduction n n n n n n n n n n Why Data Mining? What Is Data Mining? A Multi-Dimensional View of Data Mining What Kind of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Technologies Are Used? What Kind of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society Summary 2 Why Data Mining? n The Explosive Growth of Data: from terabytes to petabytes n Data collection and data availability n Automated data collection tools, database systems, Web, computerized society n Major sources of abundant data n n n Business: Web, e-commerce, transactions, stocks, … Science: Remote sensing, bioinformatics, scientific simulation, … Society and everyone: news, digital cameras, YouTube n n We are drowning in data, but starving for knowledge! “Necessity is the mother of invention”—Data mining—Automated analysis of massive data sets 3 Evolution of Sciences: New Data Science Era n n Before 1600: Empirical science 1600-1950s: Theoretical science n Each discipline has grown a theoretical component. Theoretical models often motivate experiments and generalize our understanding...
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...An Introduction to Data Mining Kurt Thearling, Ph.D. www.thearling.com 1 Outline — Overview of data mining — What is data mining? — Predictive models and data scoring — Real-world issues — Gentle discussion of the core algorithms and processes — Commercial data mining software applications — Who are the players? — Review the leading data mining applications — Presentation & Understanding — Data visualization: More than eye candy — Build trust in analytic results 2 1 Resources — Good overview book: — Data Mining Techniques by Michael Berry and Gordon Linoff — Web: — My web site (recommended books, useful links, white papers, …) > http://www.thearling.com — Knowledge Discovery Nuggets > http://www.kdnuggets.com — DataMine Mailing List — majordomo@quality.org — send message “subscribe datamine-l” 3 A Problem... — You are a marketing manager for a brokerage company — Problem: Churn is too high > Turnover (after six month introductory period ends) is 40% — Customers receive incentives (average cost: $160) when account is opened — Giving new incentives to everyone who might leave is very expensive (as well as wasteful) — Bringing back a customer after they leave is both difficult and costly 4 2 … A Solution — One month before the end of the introductory period is over, predict which customers will leave — If you want to keep a customer that is predicted to churn, offer them something based on their predicted...
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...Data Mining D t Mi i Module 1 Introduction to Data Mining Dr. Jason T.L. Wang, Professor Department of Computer Science New Jersey Institute of Technology / Data Management: Its Evolution 1960s: – File management and network DBMS 1970s: – Relational DBMS 1980s: 980s – Non-first normal form, extended-relational, OO, deductive databases and application-oriented DBMS pp (spatial, scientific, CAD/CAM, etc.) 1990s - present: p – Data mining, digital library, and Web databases – Cloud databases, data science, and Big Data Data Mining © Jason Wang 2 Data Mining: Its Definition Data mining (knowledge discovery in databases): ) – Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from data in large databases Alternative names: – Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, analysis data archeology, data dredging archeology dredging, information harvesting, etc. Data Mining © Jason Wang 3 Data Mining: A Multidisciplinary Field Pattern Recognition Machine Learning Databases St ti ti Statistics Information Visualization Data Mining © Jason Wang 4 Data to be mined Text databases Web databases Scientific and biological databases Transactional databases Data Mining © Jason Wang 5 Knowledge to be discovered K l d t b di d Association (correlation) ...
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...85% detected computer security breaches in the preceding 12 months Financial losses due to security breaches topped $377 million 70% reported that Internet connections were frequent points of attack Only 31% said that internal systems were frequent points of attack. Theft by computer Theft is the most common form of computer crime. Computers are used to steal Money Goods Information Computer resources One common type of computer theft today is the actual theft of computers, such as notebook and PDAs Notebook and PDAs are expensive Data stored on a computer can be more valuable Denial o f Service ( DOS) Attacks bombard servers and Web sites with so much bogus traffic that they are effectively shut down networks, denying service to legitimate customers and clients. Computer security Protecting computer systems and the information they contain against unwanted access, damage, modification, or destruction Two inherent characteristics A computer does exactly what it is programmed to do, including reveal sensitive information Can be reprogrammed Any computer can do only with it is programmed to do Cannot protect itself from malfunctions or deliberate attacks Physical access restrictions...
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...decision-making areas and the techniques used to make them. Advantages and benefits are then discussed and one major problem is described in detail. Computer hacking is becoming more popular as the future comes closer and it is a larger problem than most think outside of the business world. The conclusion offers an insight into the future of BI and identifies the problem of hacking as its major threat. 1. Introduction Business intelligence (BI) has become one of the most successful and popular ways that an organization uses to answer specific questions or problems over the past 25 years and is vital to success. BI is a term that covers a multitude of different concepts, theories, and methods that are related to data that has been kept in software designed for this specific purpose. The...
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...Business Performance Measurement At the Crossroads of Strategy, Decision-Making, Learning and Information Visualization February 2003 Vince Kellen CIO & Faculty,School of CTI DePaul University Chicago, IL U.S.A. http://www.depaul.edu Abstract Business Performance Measurement (BPM) systems have grown in use and popularity over the past twenty years. Firms adopt BPM systems for a variety of reasons, but chiefly to improve control over the firm in ways that traditional accounting systems have not allowed. Several approaches, or frameworks, for building and managing BPM systems have evolved with the balanced scorecard as the dominant framework in use today. Despite the growing use of BPM systems in organizations of all kinds, significant problems cause firms to experience difficulty in implementing BPM systems. The problems range across a variety of topics: excessive diversity in the field of study, data quality and information system integration problems, lack of linkage to strategy, fundamental differences in how a strategy is formulated and executed in the firm, ill-defined metrics identification processes, high levels of change in BPM systems, analytical skills challenges, knowledge as a social and non-deterministic phenomenon, judgment and decision biases (from prospect theory literature) and organizational defenses that can undermine successful BPM systems use. To help address these problems, a set of critical...
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... . . . . . . . . . . . . . .7 Benefits and Barriers to Implementation 10 Business Benefits Sought from Customer Analytics . . . . . . . . . . 10 Barriers to Adoption of Customer Analytics . . . . . . . . . . . . . . 12 role of Analytics in Increasing Marketing roI . . . . . . . . . . . . . 13 Analytics Tools, Data Sources, and Techniques 17 BI, olAP, and data discovery for Customer Analytics . . . . . . . . . 18 In-Memory Computing for More rapid discovery Analysis . . . . . . . 20 Predictive Analytics, data Mining, and Advanced Statistics Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 filling the role of the data Scientist for Customer Analytics . . . . . . 23 Applying Technologies for Social Media Data Analysis 24 Applying Analytics to find and Influence the Influencers . . . . . . . . 26 Selecting and Accessing Internal and External Social Media data . . . 27 finding the right role for Hadoop and Mapreduce . . . . . . . . . . 28 Data Management and Integration...
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...512 Use of Data Mining in the field of Library and Information Science : An Overview Roopesh K Dwivedi Abstract Data Mining refers to the extraction or “Mining” knowledge from large amount of data or Data Warehouse. To do this extraction data mining combines artificial intelligence, statistical analysis and database management systems to attempt to pull knowledge form stored data. This paper gives an overview of this new emerging technology which provides a road map to the next generation of library. And at the end it is explored that how data mining can be effectively and efficiently used in the field of library and information science and its direct and indirect impact on library administration and services. R P Bajpai Keywords : Data Mining, Data Warehouse, OLAP, KDD, e-Library 0. Introduction An area of research that has seen a recent surge in commercial development is data mining, or knowledge discovery in databases (KDD). Knowledge discovery has been defined as “the non-trivial extraction of implicit, previously unknown, and potentially useful information from data” [1]. To do this extraction data mining combines many different technologies. In addition to artificial intelligence, statistics, and database management system, technologies include data warehousing and on-line analytical processing (OLAP), human computer interaction and data visualization; machine learning (especially inductive learning techniques), knowledge representation, pattern recognition...
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