...amount of data being handled and processed has increased tremendously. Big Data analytics plays a very significant part in reducing the size of the data as well as the complexity in applications that are being used for Big Data. Big Data Visualization is an important approach in creating meaningful visuals and graphical representations from the Big Data that help in better decision making and that give a clear insight into the data. Visualization, Big Data, Big Data Visualization, data visualization techniques are some of the topics that are discussed in this paper and examples for visualizations have been presented as well. Keywords— Visualization, Data processing, Data analytics, Big Data, Interactive visualizations. I. VISUALIZATION...
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...Comparison of Social Network Analysis Tools What is Social Network :? Social network is a social structure made up of many actors, for example firms, or people which are all tied up in relationships, connections, or interactions(1). The social network perspective is made up to employ the structure of a social group, how they interact with each other, how this structure has an influence on other variables and how it changes as time passes. What is Social Networking Analysis? Social network analysis is the mapping and measuring of all the factors that make up the social network, it is the measuring of relationships and flows between people, groups, organizations, computers, URL, and other connected information entries(3). The nodes in the network are represented as people and the links show their direct relationships with each other. To have deeper understanding of networks and their participants , we evaluate the location of actors in the network which basically means finding the centrality of a node . These measures give us insight into the various roles and groupings in a network -- who are the connectors, mavens, leaders, bridges, isolates, where are the clusters and who is in them, who is in the core of the network, and who is on the periphery? In order to evaluate and understand these networks and the relationships between their actors we use social network analysis tools. We will be discussing three different SNA tools, compare between them, talk about their...
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...Northeastern University D’Amore-McKim School of Business Supply Chain Data Visualization by Mapping and Geographic Analytics (GA) Sandeep Kumar Karumuru 04/19/2016 Image1 Research Paper submission for Supply chain management (Spring 2016) To Distinguished Professor of Supply Chain Management Dr. Nada R. Sanders 1 Table of Contents Abstract 3 Overview 4 Background 5 Supply Chain Visualization 6 Supply Chain Mapping 7 Geographic Analytics 8-11 Business Example 12 Future Trends 13 Benefits and Challenges 14 Conclusion 15 Bibliography 16 2 Abstract The focus of this research paper in on the process of how workflow is handled in a typical supply chain environment. There are numerous areas of focus that come to mind when we talk about improvements for a supply chain but the process itself is not given enough significance. The research paper covers the most popular process in use, from spreadsheets to its immediate future evolution i.e. visualization tools for supply chain data. There are several tools that exist in the market, each of them have their advantages and disadvantages when used in a certain environment. Supply chain mapping is one such tool that many companies are already utilizing but the mapping tool which gives a visual representation of the entire supply chain network is only an abstract network map and so it has its shortcomings. In contrast, supply chain...
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...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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...Top Data Management Terms to Know Fifteen essential definitions you need to know Fifteen Essential Data Management Terms We know it’s not always easy to keep up-to-date Contents with the latest data management terms. That’s why we have put together the top fifteen terms and definitions that you and your peers need to know. OLAP (online analytical processing) Star schema What is OLAP (online analytical processing) Fact table OLAP (online analytical processing) is computer processing that enables a Big data analytics Data modeling Ad hoc analysis user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company's beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Data visualization Extract, transform, load (ETL) Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time Association rules (in data mining) Relational database period) as a separate "dimension." OLAP software can locate the intersection of dimensions (all products sold in the...
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...Tableau Software Overview: A new generation of business intelligence and visual analysis software puts data into the hands of the people who need it to quickly connect to any data, find meaningful insights, easily create map views, and add data from multiple sources at any time. Slow, rigid systems are no longer good enough for business users or the IT teams that support them. Competitive pressures and new sources of data are creating new requirements. Users are demanding the ability to answer their questions quickly and easily. People see and understand data, reports and dashboards faster through unique, easy-to-use visual analytics technology. Vendors and Software Products in this space QlikView: QlikView is business intelligence software to turn data into knowledge. It enables users to consolidate, search and visually analyze data using QlikView’s simplicity. GoodData: GoodData allows clients to track changes in pipeline with detailed graphs and charts. Analysis can be done across dimensions, set indicators to measure performance. IBM Cognos: IBM Cognos is web-based, integrated BI software which provides toolset for reporting, analyzing, scorecarding and monitoring of events and metrics of any amount of data or scenario’s. Spotfire: TIBCO Spotfire is a business intelligence software platform that allows users to analyze data using statistics. It has the ability to develop dynamic analytic applications that run on web. Overview of Tableau: Tableau software...
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...Thought Leadership White Paper 2 Reengineering IT discovery with analytics and visualization Contents 2 Introduction 3 The inevitable push towards greater efficiency 3 The need for better IT discovery 4 Building a more comprehensive snapshot of the data center 6 Changing the parameters for IT discovery 6 How ALDM works 8 Identifying issues that hinder operational efficiency and resilience 9 Compiling affinity groups automatically 11 Identifying the best candidates for virtualization 12 Extending insights with data visualization 12 The confluence of discovery analytics and human analysis 15 Conclusion 16 For more information Introduction An intimate knowledge of IT assets and dependencies has always been imperative to mitigating the risk of data center migrations and improving the resiliency of the IT environment. But the IT discovery process can be slow, costly and prone to error. And for all their value in helping organizations determine where and how to plan a migration or improve IT resiliency, traditional asset inventories and dependency maps provide only part of the picture. With modern IT infrastructures an intricate web of interdependencies, uncovering the total IT environment, including the logical relationships between physical, virtual and cloud elements, has never been more important—or more complex. IBM’s analytics for logical dependency mapping, ALDM, reengineers the IT discovery process...
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...Modeling, Visualization, and Assessment Modeling, visualization, and assessment are three tools that when incorporated properly, can make any classroom successful. Visualization is now being used in several careers. Disciplines such as mechanical design and architecture have traditional utilized drawings such as plans, sections, and elevations as the primary medium for design communication as well as documentation (Guidera, 2010). Highway design engineers now use visualization as an opportunity to improve the entire planning, design, and construction process for all types of projects, big and small, and from start to finish (Taylor, & Moler, 2010). The following sections will discuss incorporating modeling activities, creative ways to use visualization tools, technologies for assessing student progress, and difficulties expected with the incorporation of modeling, visualization, and assessment. Incorporating Modeling Activities Modeling activities can be tailored to fit any classroom situation. There are several things to keep in mind when using or creating modeling activities for instruction. One thing to keep in mind is that the activity is should be interactive. The modeling should also provide opportunities for them to experiment with the model or modify. The second thing to keep in mind is that the purpose of using a model is to help bridge the gap between observations and the real world. The final thing to keep in mind is that modeling can introduce students...
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...(including, but not limited to, software libraries, interfaces, source codes, documentation and training materials) described herein. The content of this document is furnished for informational use only, is subject to change without notice, may contain techni cal inaccuracies or typographical errors, and should not be construed as a representation, warranty or commitment by Exigen Insurance Solutions, Inc. or any other person or entity. Exigen Insurance Solutions, Inc. may make improvements and/or changes in the software products and programs described in this document at any time without notice. Exigen Insurance Solutions, Inc. is not responsible or liable for any changes made to this document. In particular, modifications in or to the data model described...
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...Training Curriculum Data Warehousing: • Introduction to Business Objects Enterprise Reporting • Fundamentals of Data warehouse Concepts • Introduction to Dimensional Modeling • Developing a Star Schema Reporting: • Building and editing queries with Web Intelligence • Performing on report analysis with Web Intelligence • Filtering Queries using conditions, prompts etc., • Using Combined Queries and merging dimensions • Displaying data in various formats (Ex: Tables, Charts etc.,) Advanced Reporting: • Calculations, Formulas and variables • Ranking Data, using Alerters to highlight data, Formatting numbers and Dates • Understanding Calculation Contexts • Web Intelligence Functions, Operators and Keywords • Calculating values with Smart Measures Universe Designer: • Designer and Universe Fundamentals • Creating a schema with Tables and Joins • Resolving Join problems in a schema • Defining Classes, Objects, hierarchies, using cascading list of values for hierarchies • Testing the universe • Working with OLAP universes Xcelsius 2008: • Application Overview • Creating and Updating Xcelsius visualizations • Using Xcelsius components ( Chart, Containers, Selectors etc.,) • Exporting Xcelsius visualizations to various applications (Power point, PDF, Flash • Creating templates, Alerts and Dynamic visibility • Using Data Manager ( Creating and configuring connections) • Live Office Connections, Query As A Web Service (QWAAS), XML data Connections Crystal...
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...things both on the dataset and on data visualization through my contribution to the project. The works in both the first and second stages of the project were divided equally and efficiently among the three group members. For the stage 1 of the project, we first met together to brainstorm all of our ideas, find an interesting dataset to be used for the next parts and divide all the work load equally. One other group member and me contributed to the database design using star schema and the planning of data visualization in the second stage. Furthermore, in the second stage of the project, through a group meeting, we again divided the work equally among the three of us and discussed the visualization types that we are going to use for our data. To make it equals, each member chose one type of visualization method to discuss in the report. In this part, I did the part to whole visualization method, where I used it to visualize two important data of the dataset through a...
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...I was always drawn to math and computers, ever since I was a kid playing computer games on my Sinclair ZX81. When I attended university, I had a special interest in numerical analysis, a field that I felt combines math and computers ideally. During my career, I learned of MATLAB, widely popular for digital signal processing, numerical analysis, and feedback and control. MATLAB’s strong suits include a high-level programming language, excellent graphing capabilities, and numerous packages from almost every imaginable engineering field. But I found that MATLAB wasn’t enough. I worked with very large files and needed the ability to manipulate both text and data. So I combined Perl, AWK, and Bash scripts to write programs that automate data analysis and visualization. And along the way, I’ve developed practices and ideas involving the organization of data—for example, ways to ensure file names are unique and self-explanatory. With the increasing popularity of the Internet, I learned of GNU/Linux and the open source movement. I made an effort to use open source software whenever possible, and so I’ve learned of GNU-Octave and gnuplot, which together provide excellent scientific computing functionality. That fit well on my Linux machine: Bash scripts, Perl and AWK, GNU-Octave and gnuplot. Knowing I was interested in programming languages and open source software, a friend suggested I give Python a try. My first impression was that it’s just another programming language:...
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...Business Intelligence: Business intelligence (BI) is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions. Business intelligence tools are a type of application software designed to retrieve, analyze, transform and report data for business intelligence. The tools generally read data that have been previously stored, often, though not necessarily, in a data warehouse or data mart. BI Tools Analyzed: When preparing the analysis of the business intelligence tools, I made a personal preference of analyzing the tools leading in the Gartner chart, and that these tools must have efficient service and customer usage. The 5 business intelligence tools which I have selected are as follows: 1. IBM Cognos Link: http://www-01.ibm.com/software/analytics/cognos/ 2. Oracle BI 12c Link: http://www.oracle.com/us/solutions/business-analytics/business-intelligence/foundation-suite/overview/index.html 3. SAS Enterprise Intelligence Platform Link: http://support.sas.com/documentation/onlinedoc/intellplatform/index.html#intell92 4. Microsoft SQL Server + MS SharePoint Server Link: https://technet.microsoft.com/en-us/library/ee210689(v=sql.105).aspx 5. MicroStrategy Link: http://www.microstrategy.com/us Criteria used for Evaluation: I have researched for multiple criteria and then grouped them into 5 main groups based...
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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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...President &CMO 1 Session Agenda Why business analytics? Review the different types of analytics & common misconceptions Review the delivery methods for the operational users Propose holistic approach to expand enterprise analytics Value of integration and data quality to analytics Discussion 2 Analytic Quiz What do beer and business analytics have in common? In 1900 W.S. Gossett, an analyst at Guinness invented a distribution to analyze production processes and employment problems. Guinness decided to keep it a trade secret. He published it under the pseudonym Student. Hence the name of this popular analytic method became Student’s t-test. Analytic Quiz Who made a killing by applying it on a massive scale? 4 Four Types of Analytics Information, Analysis And Decisions: The Basics Diagnostic Analytics Why Did it Happen? Descriptive Analytics What is Happening? Analysis Predictive Analytics What is Likely To Happen? Prescriptive Analytics What Should I Do About It? Information Analytic Excellence Leads to Better Decisions 5 Information Builders’ View The Five Types of Analytics Prescriptive Predictive Skills Levels Discovery Diagnostic Descriptive Value 6 The Five Types of Analytics It is not about more data! It is about deeper look! 100% 13% Core Analytics Advanced Analytics The Five Types of Analytics Descriptive: What Happened? 8 The Five Types of Analytics Descriptive: What Happened? Too many reports in the enterprise Too many ad hoc requests to...
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