# BIG DATA ANALYSIS USING HACE THEOREM

Save this PDF as:

Size: px
Start display at page:

## Transcription

2 Figure 1. Sources of BIG DATA 3. Big Data Characteristic(HACE Theorem) HACE theorem is theorem to model the BIG DATA characteristics. Big Data starts with large-volume, Heterogeneous, Autonomous sources with distributed and decentralized control, and seeks to explore Complex and Evolving relationships among data each blind man may have his own information sources that tell him about subjective knowledge about the elephant (e.g., one blind man may exchange his feeling about the elephant with another blind man, where the exchanged knowledge is intrinsically subjected). Exploring the Big Data in this scenario is equivalent to form various information from different sources (blind men) to help to draw a best possible illustration to uncover the actual sign of the elephant in a actual way. Certainly, this job is not as simple as enquiring each blind man to designate his spirits about the camel and then getting an skilled to draw one single picture with a joint opinion, regarding that each separate may express a different language (varied and diverse information sources) and they may even have confidentiality concerns about the messages they measured in the information exchange procedure. The term Big Data literally concerns about data volumes, HACE theorem suggests that the key characteristics of the Big Data are Figure 2. The blind men and the enormous elephant: the restricted view of each blind man leads to a biased conclusion. These characteristics make it an intense challenge fordiscovering useful knowledge from the Big Data. In a native sense, we can imagine that a number of blind menare trying to size up a giant elephant (see Fig. 2), whichwill be the Big Data in this context. The goal of each blindman is to extract conclusion of the elephantaccording to the part of information he collects during the procedure. Because each individual s opinion is restricted to his native area, it is expected that the blind men will each conclude independently that the elephant feels like a rope, a wall, a tree, a mat, or a snake depending on the part each of them is limited to. To make the problem even more complex, let us accept that 1) the elephant is increasing quickly and its posture varies continually, and 2) A. Huge with various and miscellaneous data sources:-one of the fundamental characteristics of the Big Data is the huge volume of data represented by various and miscellaneous dimensionalities. This huge volume of data comes from various sites like Twitter, MySpace, Orkut and LinkedIn etc. This is because different information collectors prefer their own representation or procedure for data recording, and the nature of different applications also results in various data representations B. Autonomous Sources with circulated & disperse Control: - Autonomous Sources with circulated & disperse Control are a main characteristic of Big Data applications. Being autonomous, each data source is able to produce and collect information without connecting any centralized control. This is similar to the World Wide Web (WWW) setting where each web server provides a certain amount of information and each server is able to fully function without necessarily depending on other servers. On the other hand, the massive volumes of the data also make an application susceptible to attacks or failure, if the whole system has to depend on any centralized control unit. For example, Asian markets of Wal-Mart are inherently different from its North American markets in terms of seasonal promotions, top sell items, and customer behaviors. More specifically, the local government regulations also impact on the wholesale management process and result in 19

4 warehouses, this insight can be used to help revamp supply chains, improve program planning, track sales and marketing activities, measure performance across channels, and transform into an on-demand business. A Big Data strategy gives businesses the capability to better analyze this data with a goal of accelerating profitable growth. 5. Data Mining Challenges With Big Data For an intelligent knowledge database system [13] to handle Big Data, the essential key is to scale up to the extremely large volume of data and provide actions for the characteristics featured by the HACE theorem. Figure. 4 shows a conceptual view of the Big Data processing framework, which includes three tiers from inside out with considerations on data accessing and computing (Tier I), data isolation and domain knowledge (Tier II), and Big Data mining algorithms (Tier III). query from a database with billions of records, is divided into many small tasks each of which is running on one or multiple cluster. 5.2 Tier II: data isolation and domain knowledge In Big Data, Semantic & Application knowledge refer to several aspect related to the rules, policies, user information & application information. The most important aspect in this tier contain 1) Information sharing and its confidentiality; and 2) domain and application knowledge Information Sharing and its confidentiality Figure 4. a conceptual view of the Big Data processing framework 5.1. Tier I: Big Data Mining Platform (Data Accessing & Computing): In typical data mining systems, the mining procedures require computational thorough computing units for data analysis and comparisons. For Big Data mining, because amount of data is massive so that a single personal computer (PC) cannot handle, a typical Big Data processing framework will depend on cluster computers with a high-performance computing platform, with a data mining task being executed by running some parallel Computing tools, such as MapReduce or Enterprise Control Language (ECL), on a large number of clusters. The function of the software module is to make sure that a single data mining task, such as finding the best match of a Information sharing is an crucial goal for all systems relating multiple parties [7]. While the Goal of sharing is clear, a real-world concern is that Big Data applications are related to sensitive information, such as banking transactions and medical records. Simple data interactions do not resolve privacy concerns [6], [8], [11]., but public revelation of an individual s personal locations/movements over time can have serious repercussion for privacy. To protect privacy, two common approaches are to 1) limit access to the data, such as adding certification or access control to the data entries, so sensitive information is accessible by a limited group of users only, and 2) Remove data fields such that sensitive information cannot be pinpointed to an individual record [5] Domain and Application Knowledge Domain and application knowledge [9] provides necessary information for designing Big Data mining algorithms and systems. In a simple case, Application knowledge can help to identify right features for modeling the essential data. The domain and application knowledge can also help design feasible business objectives by using Big Data analytical techniques. 5.3 Tier III: Big Data Mining Algorithms Local knowledge and Model synthesis for Multiple Information Sources As Big Data applications are featured with independent sources and decentralized controls, collecting all distributed data sources to a centralized site for mining is thoroughly excessive due to the 21

6 that formless data can be linked through their difficult relationships to make useful patterns, and the growth of data volumes and item relationships should help form legal patterns to guess the trend and future. References [1] R. Ahmed and G. Karypis, Algorithms for Mining the Evolutionof Conserved Relational States in Dynamic Networks, Knowledgeand Information Systems, vol. 33, no. 3, pp , Dec [2] M.H. Alam, J.W. Ha, and S.K. Lee, Novel Approaches to Crawling Important Pages Early, Knowledge and Information Systems, vol. 33, no. 3, pp , Dec [3] Y.-C. Chen, W.-C. Peng, and S.-Y. Lee, Efficient Algorithms forinfluence Maximization in Social Networks, Knowledge and Information Systems, vol. 33, no. 3, pp , Dec [4] G. Cormode and D. Srivastava, Anonymized Data: Generation,Models, Usage, Proc. ACM SIGMOD Int l Conf. Management Data,pp , [5] G. Duncan, Privacy by Design, Science, vol. 317, pp , [6] D. Howe et al., Big Data: The Future of Biocuration, Nature, vol. 455, pp , Sept [7] B. Huberman, Sociology of Science: Big Data Deserve a Bigger Audience, Nature, vol. 482, p. 308, [8] I. Kopanas, N. Avouris, and S. Daskalaki, The Role of Domain Knowledge in a Large Scale Data Mining Project, Proc. Second Hellenic Conf. AI: Methods and Applications of Artificial Intelligence, I.P. Vlahavas, C.D. Spyropoulos, eds., pp , [9] W. Liu and T. Wang, Online Active Multi-Field Learning for Efficient Spam Filtering, Knowledge and Information Systems, vol. 33, no. 1, pp , Oct [10] E. Schadt, The Changing Privacy Landscape in the Era of Big Data, Molecular Systems, vol. 8, article 612, [11] A. da Silva, R. Chiky, and G. He brail, A Clustering Approach for Sampling Data Streams in Sensor Networks, Knowledge and Information Systems, vol. 32, no. 1, pp. 1-23, July [12] X. Wu, Building Intelligent Learning Database Systems, AI Magazine, vol. 21, no. 3, pp , 2000 Author Profile Deepak S. Tamhane, Received the Bachelor degree (B.E.) in Information Technology in 2009 from SVPM COE, Malegaon (Bk). He is now pursuing Master s degree in Computer Science Engineering at MLR, Institute of Technology, Hyderabad. He is currently working as a Lecturer in PGM College of Engineering, Wagholi, Pune -Id: Sultana N. Sayyad, Received the Bachelor degree (B.E.) in Information Technology in 2009 from SVPM COE, Malegaon (Bk). She is now pursuing Master s degree in Computer Science Engineering at MLR, Institute of Technology, Hyderabad. She is currently working as a Lecturer in Al-Ameen College of Engineering, Koregaon Bhima, Pune -Id: 23

### A Study on Effective Business Logic Approach for Big Data Mining

A Study on Effective Business Logic Approach for Big Data Mining T. Sathis Kumar Assistant Professor, Dept of C.S.E, Saranathan College of Engineering, Trichy, Tamil Nadu, India. ABSTRACT: Big data is

### Mining With Big Data Using HACE

Mining With Big Data Using HACE 1 R. M. Shete, 2 Snehal N. Kathale 1 CSE Department, DMIETR, Sawangi (M), Wardha, 2 CSE/IT Department, GHRIETW, Nagpur 2 1,2 RTMNU, Nagpur Email: 1 shete_rushikesh@yahoo.com,

### Volume 3, Issue 8, August 2015 International Journal of Advance Research in Computer Science and Management Studies

Volume 3, Issue 8, August 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com An

### Data Mining with Big Data e-health Service Using Map Reduce

Data Mining with Big Data e-health Service Using Map Reduce Abinaya.K PG Student, Department Of Computer Science and Engineering, Parisutham Institute of Technology and Science, Thanjavur, Tamilnadu, India

### International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 4, Jul-Aug 2015

RESEARCH ARTICLE OPEN ACCESS Data Mining Approach To Big Data Jyothiprasanna Jaladi [1], B.V.Kiranmayee [2], S.Nagini [3] Student of M.Tech(SE) [1], Associate Professor Département Computer Science and

### Of all the data in recorded human history, 90 percent has been created in the last two years. - Mark van Rĳmenam, Think Bigger, 2014

What is Big Data? Of all the data in recorded human history, 90 percent has been created in the last two years. - Mark van Rĳmenam, Think Bigger, 2014 Data in the Twentieth Century and before In 1663,

### Big Data Analytic and Mining with Machine Learning Algorithm

International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 4, Number 1 (2014), pp. 33-40 International Research Publications House http://www. irphouse.com /ijict.htm Big Data

### Big Data: Study in Structured and Unstructured Data

Big Data: Study in Structured and Unstructured Data Motashim Rasool 1, Wasim Khan 2 mail2motashim@gmail.com, khanwasim051@gmail.com Abstract With the overlay of digital world, Information is available

### Data Mining With Application of Big Data

Data Mining With Application of Big Data Aqeel Abbood Rahmah Master of Science (Information System), Nizam College (Autonomous),O.U, Basheer Bagh, Hyderabad. Abstract: Big Data concern large-volume, complex,

### Data Refinery with Big Data Aspects

International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 655-662 International Research Publications House http://www. irphouse.com /ijict.htm Data

### International Journal of Advanced Engineering Research and Applications (IJAERA) ISSN: 2454-2377 Vol. 1, Issue 6, October 2015. Big Data and Hadoop

ISSN: 2454-2377, October 2015 Big Data and Hadoop Simmi Bagga 1 Satinder Kaur 2 1 Assistant Professor, Sant Hira Dass Kanya MahaVidyalaya, Kala Sanghian, Distt Kpt. INDIA E-mail: simmibagga12@gmail.com

### A REVIEW REPORT ON DATA MINING

Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 4, April 2015,

### Index Terms Big Data, data mining, autonomous sources, complex and evolving associations

Volume 5, Issue 4, April 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Big Data and the

### Danny Wang, Ph.D. Vice President of Business Strategy and Risk Management Republic Bank

Danny Wang, Ph.D. Vice President of Business Strategy and Risk Management Republic Bank Agenda» Overview» What is Big Data?» Accelerates advances in computer & technologies» Revolutionizes data measurement»

### Why is BIG Data Important?

Why is BIG Data Important? March 2012 1 Why is BIG Data Important? A Navint Partners White Paper May 2012 Why is BIG Data Important? March 2012 2 What is Big Data? Big data is a term that refers to data

### INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK A SURVEY ON BIG DATA ISSUES AMRINDER KAUR Assistant Professor, Department of Computer

### Ashish R. Jagdale, Kavita V. Sonawane, Shamsuddin S. Khan

International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1156 Data Mining and Data Pre-processing for Big Data Ashish R. Jagdale, Kavita V. Sonawane, Shamsuddin S. Khan

### ENHANCING DATA PRIVACY IN DATA EXTRACTION WITH BIG DATA

ISSN 2278-3091 International Journal of Advanced Trends in Computer Science and Engineering, Vol. 3, No.1, Pages : 122 127 (2014) ENHANCING DATA PRIVACY IN DATA EXTRACTION WITH BIG DATA MELWIN DEVASSY

### Dynamic Data in terms of Data Mining Streams

International Journal of Computer Science and Software Engineering Volume 2, Number 1 (2015), pp. 1-6 International Research Publication House http://www.irphouse.com Dynamic Data in terms of Data Mining

### Introduction to Data Mining

Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:

### ANALYTICS BUILT FOR INTERNET OF THINGS

ANALYTICS BUILT FOR INTERNET OF THINGS Big Data Reporting is Out, Actionable Insights are In In recent years, it has become clear that data in itself has little relevance, it is the analysis of it that

### Data Mining with Big Data

Data Mining with Big Data Xindong Wu 1,2, Xingquan Zhu 3, Gong-Qing Wu 2, Wei Ding 4 1 School of Computer Science and Information Engineering, Hefei University of Technology, China 2 Department of Computer

### MLg. Big Data and Its Implication to Research Methodologies and Funding. Cornelia Caragea TARDIS 2014. November 7, 2014. Machine Learning Group

Big Data and Its Implication to Research Methodologies and Funding Cornelia Caragea TARDIS 2014 November 7, 2014 UNT Computer Science and Engineering Data Everywhere Lots of data is being collected and

### A Study on Security and Privacy in Big Data Processing

A Study on Security and Privacy in Big Data Processing C.Yosepu P Srinivasulu Bathala Subbarayudu Assistant Professor, Dept of CSE, St.Martin's Engineering College, Hyderabad, India Assistant Professor,

### Trends and Research Opportunities in Spatial Big Data Analytics and Cloud Computing NCSU GeoSpatial Forum

Trends and Research Opportunities in Spatial Big Data Analytics and Cloud Computing NCSU GeoSpatial Forum Siva Ravada Senior Director of Development Oracle Spatial and MapViewer 2 Evolving Technology Platforms

### Big Data. What is Big Data? Over the past years. Big Data. Big Data: Introduction and Applications

Big Data Big Data: Introduction and Applications August 20, 2015 HKU-HKJC ExCEL3 Seminar Michael Chau, Associate Professor School of Business, The University of Hong Kong Ample opportunities for business

### Introduction to Engineering Using Robotics Experiments Lecture 17 Big Data

Introduction to Engineering Using Robotics Experiments Lecture 17 Big Data Yinong Chen 2 Big Data Big Data Technologies Cloud Computing Service and Web-Based Computing Applications Industry Control Systems

### Research Note What is Big Data?

Research Note What is Big Data? By: Devin Luco Copyright 2012, ASA Institute for Risk & Innovation Keywords: Big Data, Database Management, Data Variety, Data Velocity, Data Volume, Structured Data, Unstructured

### CAP4773/CIS6930 Projects in Data Science, Fall 2014 [Review] Overview of Data Science

CAP4773/CIS6930 Projects in Data Science, Fall 2014 [Review] Overview of Data Science Dr. Daisy Zhe Wang CISE Department University of Florida August 25th 2014 20 Review Overview of Data Science Why Data

### Big Data a threat or a chance?

Big Data a threat or a chance? Helwig Hauser University of Bergen, Dept. of Informatics Big Data What is Big Data? well, lots of data, right? we come back to this in a moment. certainly, a buzz-word but

### Keywords Big Data; OODBMS; RDBMS; hadoop; EDM; learning analytics, data abundance.

Volume 4, Issue 11, November 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analytics

### Data Mining with Parallel Processing Technique for Complexity Reduction and Characterization of Big Data

Data Mining with Parallel Processing Technique for Complexity Reduction and Characterization of Big Data J.Josepha Menandas Assistant Professor(Grade-I), Panimalar Engineering College, Chennai, India J.Jakkulin

### BIG DATA FUNDAMENTALS

BIG DATA FUNDAMENTALS Timeframe Minimum of 30 hours Use the concepts of volume, velocity, variety, veracity and value to define big data Learning outcomes Critically evaluate the need for big data management

### Big Data Driven Knowledge Discovery for Autonomic Future Internet

Big Data Driven Knowledge Discovery for Autonomic Future Internet Professor Geyong Min Chair in High Performance Computing and Networking Department of Mathematics and Computer Science College of Engineering,

### Buyer s Guide to Big Data Integration

SEPTEMBER 2013 Buyer s Guide to Big Data Integration Sponsored by Contents Introduction 1 Challenges of Big Data Integration: New and Old 1 What You Need for Big Data Integration 3 Preferred Technology

### Smarter Planet evolution

Smarter Planet evolution 13/03/2012 2012 IBM Corporation Ignacio Pérez González Enterprise Architect ignacio.perez@es.ibm.com @ignaciopr Mike May Technologies of the Change Capabilities Tendencies Vision

### An Analysis and Review on Various Techniques of Mining Big Data

Journal homepage: http://www.journalijar.com INTERNATIONAL JOURNAL OF ADVANCED RESEARCH RESEARCH ARTICLE An Analysis and Review on Various Techniques of Mining Big Data Hitesh Kumar Bhatia Masters of Computer

### Sunnie Chung. Cleveland State University

Sunnie Chung Cleveland State University Data Scientist Big Data Processing Data Mining 2 INTERSECT of Computer Scientists and Statisticians with Knowledge of Data Mining AND Big data Processing Skills:

### Statistics for BIG data

Statistics for BIG data Statistics for Big Data: Are Statisticians Ready? Dennis Lin Department of Statistics The Pennsylvania State University John Jordan and Dennis K.J. Lin (ICSA-Bulletine 2014) Before

### Transforming the Telecoms Business using Big Data and Analytics

Transforming the Telecoms Business using Big Data and Analytics Event: ICT Forum for HR Professionals Venue: Meikles Hotel, Harare, Zimbabwe Date: 19 th 21 st August 2015 AFRALTI 1 Objectives Describe

### Data Centric Computing Revisited

Piyush Chaudhary Technical Computing Solutions Data Centric Computing Revisited SPXXL/SCICOMP Summer 2013 Bottom line: It is a time of Powerful Information Data volume is on the rise Dimensions of data

### A New Era Of Analytic

Penang egovernment Seminar 2014 A New Era Of Analytic Megat Anuar Idris Head, Project Delivery, Business Analytics & Big Data Agenda Overview of Big Data Case Studies on Big Data Big Data Technology Readiness

### A Strategic Approach to Unlock the Opportunities from Big Data

A Strategic Approach to Unlock the Opportunities from Big Data Yue Pan, Chief Scientist for Information Management and Healthcare IBM Research - China [contacts: panyue@cn.ibm.com ] Big Data or Big Illusion?

### Are You Ready for Big Data?

Are You Ready for Big Data? Jim Gallo National Director, Business Analytics February 11, 2013 Agenda What is Big Data? How do you leverage Big Data in your company? How do you prepare for a Big Data initiative?

### 5 Keys to Unlocking the Big Data Analytics Puzzle. Anurag Tandon Director, Product Marketing March 26, 2014

5 Keys to Unlocking the Big Data Analytics Puzzle Anurag Tandon Director, Product Marketing March 26, 2014 1 A Little About Us A global footprint. A proven innovator. A leader in enterprise analytics for

### Review on Data Mining with Big Data

Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,

### Big Data Introduction, Importance and Current Perspective of Challenges

International Journal of Advances in Engineering Science and Technology 221 Available online at www.ijaestonline.com ISSN: 2319-1120 Big Data Introduction, Importance and Current Perspective of Challenges

### Big Data. Fast Forward. Putting data to productive use

Big Data Putting data to productive use Fast Forward What is big data, and why should you care? Get familiar with big data terminology, technologies, and techniques. Getting started with big data to realize

### How In-Memory Data Grids Can Analyze Fast-Changing Data in Real Time

SCALEOUT SOFTWARE How In-Memory Data Grids Can Analyze Fast-Changing Data in Real Time by Dr. William Bain and Dr. Mikhail Sobolev, ScaleOut Software, Inc. 2012 ScaleOut Software, Inc. 12/27/2012 T wenty-first

### The Big Deal about Big Data. Mike Skinner, CPA CISA CITP HORNE LLP

The Big Deal about Big Data Mike Skinner, CPA CISA CITP HORNE LLP Mike Skinner, CPA CISA CITP Senior Manager, IT Assurance & Risk Services HORNE LLP Focus areas: IT security & risk assessment IT governance,

### Surfing the Data Tsunami: A New Paradigm for Big Data Processing and Analytics

Surfing the Data Tsunami: A New Paradigm for Big Data Processing and Analytics Dr. Liangxiu Han Future Networks and Distributed Systems Group (FUNDS) School of Computing, Mathematics and Digital Technology,

### Big Data / FDAAWARE. Rafi Maslaton President, cresults the maker of Smart-QC/QA/QD & FDAAWARE 30-SEP-2015

Big Data / FDAAWARE Rafi Maslaton President, cresults the maker of Smart-QC/QA/QD & FDAAWARE 30-SEP-2015 1 Agenda BIG DATA What is Big Data? Characteristics of Big Data Where it is being used? FDAAWARE

### International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April-2014 442 ISSN 2229-5518

International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April-2014 442 Over viewing issues of data mining with highlights of data warehousing Rushabh H. Baldaniya, Prof H.J.Baldaniya,

### Data Centric Systems (DCS)

Data Centric Systems (DCS) Architecture and Solutions for High Performance Computing, Big Data and High Performance Analytics High Performance Computing with Data Centric Systems 1 Data Centric Systems

### PARC and SAP Co-innovation: High-performance Graph Analytics for Big Data Powered by SAP HANA

PARC and SAP Co-innovation: High-performance Graph Analytics for Big Data Powered by SAP HANA Harnessing the combined power of SAP HANA and PARC s HiperGraph graph analytics technology for real-time insights

### BIG DATA: BIG BOOST TO BIG TECH

BIG DATA: BIG BOOST TO BIG TECH Ms. Tosha Joshi Department of Computer Applications, Christ College, Rajkot, Gujarat (India) ABSTRACT Data formation is occurring at a record rate. A staggering 2.9 billion

### What happens when Big Data and Master Data come together?

What happens when Big Data and Master Data come together? Jeremy Pritchard Master Data Management fgdd 1 What is Master Data? Master data is data that is shared by multiple computer systems. The Information

### International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

### USING BIG DATA FOR INTELLIGENT BUSINESSES

HENRI COANDA AIR FORCE ACADEMY ROMANIA INTERNATIONAL CONFERENCE of SCIENTIFIC PAPER AFASES 2015 Brasov, 28-30 May 2015 GENERAL M.R. STEFANIK ARMED FORCES ACADEMY SLOVAK REPUBLIC USING BIG DATA FOR INTELLIGENT

### CSC590: Selected Topics BIG DATA & DATA MINING. Lecture 2 Feb 12, 2014 Dr. Esam A. Alwagait

CSC590: Selected Topics BIG DATA & DATA MINING Lecture 2 Feb 12, 2014 Dr. Esam A. Alwagait Agenda Introduction What is Big Data Why Big Data? Characteristics of Big Data Applications of Big Data Problems

### The disruptive power of big data

Business white paper How big data analytics is transforming business The disruptive power of big data Table of contents 3 Executive overview: The big data revolution 4 The big data imperative 4 Why big

### Big Data in Transportation Engineering

Big Data in Transportation Engineering Nii Attoh-Okine Professor Department of Civil and Environmental Engineering University of Delaware, Newark, DE, USA Email: okine@udel.edu IEEE Workshop on Large Data

### Are You Ready for Big Data?

Are You Ready for Big Data? Jim Gallo National Director, Business Analytics April 10, 2013 Agenda What is Big Data? How do you leverage Big Data in your company? How do you prepare for a Big Data initiative?

Hadoop for Enterprises: Overcoming the Major Challenges Introduction to Big Data Big Data are information assets that are high volume, velocity, and variety. Big Data demands cost-effective, innovative

### Keywords Big Data, NoSQL, Relational Databases, Decision Making using Big Data, Hadoop

Volume 4, Issue 1, January 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Transitioning

### Certification In SAS Programming. Introduction to SAS Program

Certification In SAS Programming Introduction to SAS Program What Lies Ahead In this session, you will gain answers to: Overview of Analytics Careers in Analytics Why Use SAS? Introduction to SAS System

### Big Data Executive Survey

Big Data Executive Full Questionnaire Big Date Executive Full Questionnaire Appendix B Questionnaire Welcome The survey has been designed to provide a benchmark for enterprises seeking to understand the

### Is Big Data a Big Deal? What Big Data Does to Science

Is Big Data a Big Deal? What Big Data Does to Science Netherlands escience Center Wilco Hazeleger Wilco Hazeleger Student @ Wageningen University and Reading University Meteorology PhD @ Utrecht University,

### Next-Gen Analytics: Conversing with Big Data

Next-Gen Analytics: Conversing with Big Data Next-Gen Analytics: Conversing with Big Data Enterprises should never lose sight of the endgame of Big Data: improving business decisions based on actionable,

### International Journal of Advancements in Research & Technology, Volume 3, Issue 5, May-2014 18 ISSN 2278-7763. BIG DATA: A New Technology

International Journal of Advancements in Research & Technology, Volume 3, Issue 5, May-2014 18 BIG DATA: A New Technology Farah DeebaHasan Student, M.Tech.(IT) Anshul Kumar Sharma Student, M.Tech.(IT)

### Taking Data Analytics to the Next Level

Taking Data Analytics to the Next Level Implementing and Supporting Big Data Initiatives What Is Big Data and How Is It Applicable to Anti-Fraud Efforts? 2 of 20 Definition Gartner: Big data is high-volume,

### Big Data & Analytics: Your concise guide (note the irony) Wednesday 27th November 2013

Big Data & Analytics: Your concise guide (note the irony) Wednesday 27th November 2013 Housekeeping 1. Any questions coming out of today s presentation can be discussed in the bar this evening 2. OCF is

### SPATIAL DATA CLASSIFICATION AND DATA MINING

, pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal

### Collaborations between Official Statistics and Academia in the Era of Big Data

Collaborations between Official Statistics and Academia in the Era of Big Data World Statistics Day October 20-21, 2015 Budapest Vijay Nair University of Michigan Past-President of ISI vnn@umich.edu What

### Beyond Watson: The Business Implications of Big Data

Beyond Watson: The Business Implications of Big Data Shankar Venkataraman IBM Program Director, STSM, Big Data August 10, 2011 The World is Changing and Becoming More INSTRUMENTED INTERCONNECTED INTELLIGENT

### How Big Data is Different

FALL 2012 VOL.54 NO.1 Thomas H. Davenport, Paul Barth and Randy Bean How Big Data is Different Brought to you by Please note that gray areas reflect artwork that has been intentionally removed. The substantive

### Big Data Analytics: 14 November 2013

www.pwc.com CSM-ACE 2013 Big Data Analytics: Take it to the next level in building innovation, differentiation and growth 14 About me Data analytics in the UK Forensic technology and data analytics in

### Information Management course

Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)

### Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012. Viswa Sharma Solutions Architect Tata Consultancy Services

Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012 Viswa Sharma Solutions Architect Tata Consultancy Services 1 Agenda What is Hadoop Why Hadoop? The Net Generation is here Sizing the

### Big Data Analytics. Prof. Dr. Lars Schmidt-Thieme

Big Data Analytics Prof. Dr. Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany 33. Sitzung des Arbeitskreises Informationstechnologie,

### HP Vertica at MIT Sloan Sports Analytics Conference March 1, 2013 Will Cairns, Senior Data Scientist, HP Vertica

HP Vertica at MIT Sloan Sports Analytics Conference March 1, 2013 Will Cairns, Senior Data Scientist, HP Vertica So What s the market s definition of Big Data? Datasets whose volume, velocity, variety

### BIG DATA: IT MAY BE BIG BUT IS IT SMART?

BIG DATA: IT MAY BE BIG BUT IS IT SMART? Turning Big Data into winning strategies A GfK Point-of-view 1 Big Data is complex Typical Big Data characteristics?#! %& Variety (data in many forms) Data in different

### Business Challenges and Research Directions of Management Analytics in the Big Data Era

Business Challenges and Research Directions of Management Analytics in the Big Data Era Abstract Big data analytics have been embraced as a disruptive technology that will reshape business intelligence,

### Research trends relevant to data warehousing and OLAP include [Cuzzocrea et al.]: Combining the benefits of RDBMS and NoSQL database systems

DATA WAREHOUSING RESEARCH TRENDS Research trends relevant to data warehousing and OLAP include [Cuzzocrea et al.]: Data source heterogeneity and incongruence Filtering out uncorrelated data Strongly unstructured

### The Next Wave of Data Management. Is Big Data The New Normal?

The Next Wave of Data Management Is Big Data The New Normal? Table of Contents Introduction 3 Separating Reality and Hype 3 Why Are Firms Making IT Investments In Big Data? 4 Trends In Data Management

### Business Analytics and the Nexus of Information

Business Analytics and the Nexus of Information 2 The Impact of the Nexus of Forces 4 From the Gartner Files: Information and the Nexus of Forces: Delivering and Analyzing Data 6 About IBM Business Analytics

### BIG DATA CHALLENGES AND PERSPECTIVES

BIG DATA CHALLENGES AND PERSPECTIVES Meenakshi Sharma 1, Keshav Kishore 2 1 Student of Master of Technology, 2 Head of Department, Department of Computer Science and Engineering, A P Goyal Shimla University,

### Government Technology Trends to Watch in 2014: Big Data

Government Technology Trends to Watch in 2014: Big Data OVERVIEW The federal government manages a wide variety of civilian, defense and intelligence programs and services, which both produce and require

### Data Isn't Everything

June 17, 2015 Innovate Forward Data Isn't Everything The Challenges of Big Data, Advanced Analytics, and Advance Computation Devices for Transportation Agencies. Using Data to Support Mission, Administration,

### Problems to store, transfer and process the Big Data 6/2/2016 GIANG TRAN - TTTGIANG2510@GMAIL.COM 1

Problems to store, transfer and process the Big Data COURSE: COMPUTING CLUSTERS, GRIDS, AND CLOUDS LECTURER: ANDREY SHEVEL ITMO UNIVERSITY SAINT PETERSBURG 6/2/2016 GIANG TRAN - TTTGIANG2510@GMAIL.COM

### BIG DATA & ANALYTICS. Transforming the business and driving revenue through big data and analytics

BIG DATA & ANALYTICS Transforming the business and driving revenue through big data and analytics Collection, storage and extraction of business value from data generated from a variety of sources are

### Keywords: Big Data, HDFS, Map Reduce, Hadoop

Volume 5, Issue 7, July 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Configuration Tuning

### Enhance Collaboration and Data Sharing for Faster Decisions and Improved Mission Outcome

Enhance Collaboration and Data Sharing for Faster Decisions and Improved Mission Outcome Richard Breakiron Senior Director, Cyber Solutions Rbreakiron@vion.com Office: 571-353-6127 / Cell: 803-443-8002

### Exploiting Data at Rest and Data in Motion with a Big Data Platform

Exploiting Data at Rest and Data in Motion with a Big Data Platform Sarah Brader, sarah_brader@uk.ibm.com What is Big Data? Where does it come from? 12+ TBs of tweet data every day 30 billion RFID tags

### Big Data Mining: Challenges and Opportunities to Forecast Future Scenario

Big Data Mining: Challenges and Opportunities to Forecast Future Scenario Poonam G. Sawant, Dr. B.L.Desai Assist. Professor, Dept. of MCA, SIMCA, Savitribai Phule Pune University, Pune, Maharashtra, India

### Survey of Big Data Benchmarking

Page 1 of 7 Survey of Big Data Benchmarking Kyle Cooper, kdc1@wustl.edu (A paper written under the guidance of Prof. Raj Jain) Download Abstract: The purpose of this paper is provide a survey of up to