Big Data Analytics. Genoveva Vargas-Solar French Council of Scientific Research, LIG & LAFMIA Labs

Size: px
Start display at page:

Download "Big Data Analytics. Genoveva Vargas-Solar http://www.vargas-solar.com/big-data-analytics French Council of Scientific Research, LIG & LAFMIA Labs"

Transcription

1 1 Big Data Analytics Genoveva Vargas-Solar French Council of Scientific Research, LIG & LAFMIA Labs Montevideo, 22 nd November 4 th December, 2015 INFORMATIQUE

2 2

3 Collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processingapplications 3

4 + The V s & the needs of Big Data 4 n n n n n increasing volume (amount of data) Velocity (speed of data in and out) Variety (range of data types and sources) Veracity (data consistency) Value (which is the real value of data?)

5 + Big Data processing at glance 5

6 6

7 + Internet of Things 7

8 + Big Data at a bronto scale 8 1 bit Binary digit 8 bits 1 byte 1000 bytes 1 Kilobyte We will no longer have the luxury of dealing 1000 Kilobytes 1 Megabyte Helluva with just lot big of data!! 1000 Yottabytes 1 Brontobyte 1000 Megabytes 1 Gigabyte 1000 Brontobytes 1 Geopbyte Gigabytes 1 Terabyte 1000 Terabytes 1 Petabyte 1000 Petabytes 1Exabyte 1000 Exabyte 1 Zettabyte 1000 Zettabytes 1 Yottabyte

9 + New types of huge data collections 9 n Thick data: combines both quantitative and qualitative analysis, n Long data: extends back in time hundreds or thousands of years n Hot data: used constantly, meaning it must be easily and quickly accessible n Cold data: used relatively infrequently, so it can be less readily available

10 What about analytics? 10

11 Capturing value from advanced analytics 11 Big Data Based on three guiding principles Organizational transformation Predictive & Optimization Models n Decision backwards n Step by step n Test and learn

12 12 Data was not stored Beginning of the use of BDs & basic reports Great variety of visual resources to analyse data

13 13

14 Direct mailing campaign 14

15 15

16 16

17 17

18 18

19 19

20 Next product to buy 20

21 21

22 22

23 23

24 24

25 Assortment optimization 25

26 26

27 27

28 28

29 29

30 30

31 31

32 32

33 33

34 Optimizing branch networks 34

35 35

36 36

37 37

38 38

39 39

40 40

41 41 What is Data Mining? Knowledge discovery from data From the Jure Leskovec, Anand Rajaraman, Jeff Ullman Stanford University

42 J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, 42

43 + Data contains value & knowledge 43 J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

44 + Knowledge extraction 44 n Data needs to be n Stored n Managed n ANALYZED ß this class Data Mining Big Data Predictive Analytics Data Science J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

45 + Demand for Data Mining 45 J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

46 + Principle 46 n Given lots of data n Discover patterns and models that are: n Valid: hold on new data with some certainty n Useful: should be possible to act on the item n Unexpected: non-obvious to the system n Understandable: humans should be able to interpret the pattern J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

47 + Data Mining Tasks 47 n Descriptive methods n Find human-interpretable patterns that describe the data n Example: Clustering n Predictive methods n Use some variables to predict unknown or future values of other variables n Example: Recommender systems J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

48 + Meaningfulness of Analytic Answers 48 n A risk with Data mining is that an analyst can discover patterns that are meaningless n Statisticians call it Bonferroni s principle: n Roughly, if you look in more places for interesting patterns than your amount of data will support, you are bound to find crap J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

49 + Meaningfulness of Analytic Answers 49 n We want to find (unrelated) people who at least twice have stayed at the same hotel on the same day n n 10 9 people being tracked 1,000 days n Each person stays in a hotel 1% of time (1 day out of 100) n Hotels hold 100 people (so 10 5 hotels) n If everyone behaves randomly (i.e., no terrorists) will the data mining detect anything suspicious? n Expected number of suspicious pairs of people: n 250,000 n too many combinations to check we need to have some additional evidence to find suspicious pairs of people in some more efficient way J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

50 + What matters when dealing with data? 50 Usage Quality Context Streaming Scalability J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

51 + Data Mining: Cultures 51 n Data mining overlaps with: n n n Databases: Large-scale data, simple queries Machine learning: Small data, Complex models CS Theory: (Randomized) Algorithms n Different cultures: n n To a DB person, data mining is an extreme form of analytic processing queries that examine large amounts of data n Result is the query answer To a ML person, data-mining is the inference of models n Result is the parameters of the model n In this class we will do both! J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets, CS Theory Data Mining Database systems Machine Learning

52 + What will we learn? 52 n We will learn to mine different types of data: n Data is high dimensional n Data is a graph n Data is infinite/never-ending n Data is labeled n We will learn to use different models of computation: n MapReduce n Streams and online algorithms n Single machine in-memory J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

53 How It All Fits Together High dim. data Graph data Infinite data Machine learning Apps Locality sensitive hashing PageRank, SimRank Filtering data streams SVM Recommender systems Clustering Community Detection Web advertising Decision Trees Association Rules Dimensionality reduction Spam Detection Queries on streams Perceptron, knn Duplicate document detection 53 J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

54 54 How do you want that data? J. Leskovec, A. Rajaraman, J. Ullman: Mining of Massive Datasets,

55 55

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 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

More information

Big Data Analytics. Lucas Rego Drumond

Big Data Analytics. Lucas Rego Drumond Big Data Analytics Lucas Rego Drumond Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany Big Data Analytics Big Data Analytics 1 / 36 Outline

More information

Big Data Management and Analytics

Big Data Management and Analytics Big Data Management and Analytics Lecture Notes Winter semester 2015 / 2016 Ludwig-Maximilians-University Munich Prof. Dr. Matthias Renz 2015 Based on lectures by Donald Kossmann (ETH Zürich), as well

More information

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

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

More information

Information Management course

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)

More information

CIS 4930/6930 Spring 2014 Introduction to Data Science Data Intensive Computing. University of Florida, CISE Department Prof.

CIS 4930/6930 Spring 2014 Introduction to Data Science Data Intensive Computing. University of Florida, CISE Department Prof. CIS 4930/6930 Spring 2014 Introduction to Data Science Data Intensive Computing University of Florida, CISE Department Prof. Daisy Zhe Wang Data Science Overview Why, What, How, Who Outline Why Data Science?

More information

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 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,

More information

Source: Tutorial: Introduction to Big Data Marko Grobelnik, Blaz Fortuna, Dunja Mladenic Jozef Stefan Institute, Slovenia

Source: Tutorial: Introduction to Big Data Marko Grobelnik, Blaz Fortuna, Dunja Mladenic Jozef Stefan Institute, Slovenia Source: Tutorial: Introduction to Big Data Marko Grobelnik, Blaz Fortuna, Dunja Mladenic Jozef Stefan Institute, Slovenia http://ailab.ijs.si/~blazf/bigdatatutorial-grobelnikfortunamladenic- ISWC2013.pdf

More information

BIG DATA CHALLENGES AND PERSPECTIVES

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,

More information

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

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

More information

Majed Al-Ghandour, PhD, PE, CPM Division of Planning and Programming NCDOT 2016 NCAMPO Conference- Greensboro, NC May 12, 2016

Majed Al-Ghandour, PhD, PE, CPM Division of Planning and Programming NCDOT 2016 NCAMPO Conference- Greensboro, NC May 12, 2016 Big Data! Majed Al-Ghandour, PhD, PE, CPM Division of Planning and Programming NCDOT 2016 NCAMPO Conference- Greensboro, NC May 12, 2016 Big Data: Data Analytical Tools for Decision Support 2 Outline Introduce

More information

Big Data and Analytics (Fall 2015)

Big Data and Analytics (Fall 2015) Big Data and Analytics (Fall 2015) Core/Elective: MS CS Elective MS SPM Elective Instructor: Dr. Tariq MAHMOOD Credit Hours: 3 Pre-requisite: All Core CS Courses (Knowledge of Data Mining is a Plus) Every

More information

DATA MINING AND WAREHOUSING CONCEPTS

DATA MINING AND WAREHOUSING CONCEPTS CHAPTER 1 DATA MINING AND WAREHOUSING CONCEPTS 1.1 INTRODUCTION The past couple of decades have seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation

More information

Let the data speak to you. Look Who s Peeking at Your Paycheck. Big Data. What is Big Data? The Artemis project: Saving preemies using Big Data

Let the data speak to you. Look Who s Peeking at Your Paycheck. Big Data. What is Big Data? The Artemis project: Saving preemies using Big Data CS535 Big Data W1.A.1 CS535 BIG DATA W1.A.2 Let the data speak to you Medication Adherence Score How likely people are to take their medication, based on: How long people have lived at the same address

More information

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume

More information

B490 Mining the Big Data. 0 Introduction

B490 Mining the Big Data. 0 Introduction B490 Mining the Big Data 0 Introduction Qin Zhang 1-1 Data Mining What is Data Mining? A definition : Discovery of useful, possibly unexpected, patterns in data. 2-1 Data Mining What is Data Mining? A

More information

Teaching Scheme Credits Assigned Course Code Course Hrs./Week. BEITC802 Big Data 04 02 --- 04 01 --- 05 Analytics. Theory Marks

Teaching Scheme Credits Assigned Course Code Course Hrs./Week. BEITC802 Big Data 04 02 --- 04 01 --- 05 Analytics. Theory Marks Teaching Scheme Credits Assigned Course Code Course Hrs./Week Name Theory Practical Tutorial Theory Practical/Oral Tutorial Tota l BEITC802 Big Data 04 02 --- 04 01 --- 05 Analytics Examination Scheme

More information

CS 207 - Data Science and Visualization Spring 2016

CS 207 - Data Science and Visualization Spring 2016 CS 207 - Data Science and Visualization Spring 2016 Professor: Sorelle Friedler sorelle@cs.haverford.edu An introduction to techniques for the automated and human-assisted analysis of data sets. These

More information

Big Data Management in the Clouds. Alexandru Costan IRISA / INSA Rennes (KerData team)

Big Data Management in the Clouds. Alexandru Costan IRISA / INSA Rennes (KerData team) Big Data Management in the Clouds Alexandru Costan IRISA / INSA Rennes (KerData team) Cumulo NumBio 2015, Aussois, June 4, 2015 After this talk Realize the potential: Data vs. Big Data Understand why we

More information

Transforming the Telecoms Business using Big Data and Analytics

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

More information

A Professional Big Data Master s Program to train Computational Specialists

A Professional Big Data Master s Program to train Computational Specialists A Professional Big Data Master s Program to train Computational Specialists Anoop Sarkar, Fred Popowich, Alexandra Fedorova! School of Computing Science! Education for Employable Graduates: Critical Questions

More information

Introduction to 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:

More information

BIG DATA: ARE YOU READY? Andy Kyiet Demand Flow Intelligence May, 2013

BIG DATA: ARE YOU READY? Andy Kyiet Demand Flow Intelligence May, 2013 BIG DATA: ARE YOU READY? Andy Kyiet Demand Flow Intelligence May, 2013 PERSONAL BACKGROUND Founder of the first specialist Service Management & Helpdesk System provider in Europe Past President of AFSMI

More information

Big Data Analytics CSCI 4030

Big Data Analytics CSCI 4030 High dim. data Graph data Infinite data Machine learning Apps Locality sensitive hashing PageRank, SimRank Filtering data streams SVM Recommen der systems Clustering Community Detection Web advertising

More information

Marko Grobelnik marko.grobelnik@ijs.si Jozef Stefan Institute

Marko Grobelnik marko.grobelnik@ijs.si Jozef Stefan Institute Marko Grobelnik marko.grobelnik@ijs.si Jozef Stefan Institute Kalamaki, May 25 th 2012 Introduction What is Big data? Why Big-Data? When Big-Data is really a problem? Techniques Tools Applications Literature

More information

Big Data Analytics. An Introduction. Oliver Fuchsberger University of Paderborn 2014

Big Data Analytics. An Introduction. Oliver Fuchsberger University of Paderborn 2014 Big Data Analytics An Introduction Oliver Fuchsberger University of Paderborn 2014 Table of Contents I. Introduction & Motivation What is Big Data Analytics? Why is it so important? II. Techniques & Solutions

More information

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

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,

More information

Data Mining and Machine Learning in Bioinformatics

Data Mining and Machine Learning in Bioinformatics Data Mining and Machine Learning in Bioinformatics PRINCIPAL METHODS AND SUCCESSFUL APPLICATIONS Ruben Armañanzas http://mason.gmu.edu/~rarmanan Adapted from Iñaki Inza slides http://www.sc.ehu.es/isg

More information

CSE 427 CLOUD COMPUTING WITH BIG DATA APPLICATIONS

CSE 427 CLOUD COMPUTING WITH BIG DATA APPLICATIONS CSE 427 CLOUD COMPUTING WITH BIG DATA APPLICATIONS COURSE OVERVIEW & STRUCTURE Fall 2015 Marion Neumann ABOUT Marion Neumann email: m dot neumann at wustl dot edu office: Jolley Hall 403 office hours:

More information

Clustering Big Data. Anil K. Jain. (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012

Clustering Big Data. Anil K. Jain. (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012 Clustering Big Data Anil K. Jain (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012 Outline Big Data How to extract information? Data clustering

More information

SoSe 2014: M-TANI: Big Data Analytics

SoSe 2014: M-TANI: Big Data Analytics SoSe 2014: M-TANI: Big Data Analytics Lecture 4 21/05/2014 Sead Izberovic Dr. Nikolaos Korfiatis Agenda Recap from the previous session Clustering Introduction Distance mesures Hierarchical Clustering

More information

Introduction to Data Science: CptS 483-06 Syllabus First Offering: Fall 2015

Introduction to Data Science: CptS 483-06 Syllabus First Offering: Fall 2015 Course Information Introduction to Data Science: CptS 483-06 Syllabus First Offering: Fall 2015 Credit Hours: 3 Semester: Fall 2015 Meeting times and location: MWF, 12:10 13:00, Sloan 163 Course website:

More information

How To Learn Data Analytics

How To Learn Data Analytics COURSE DESCRIPTION Spring 2014 COURSE NAME COURSE CODE DESCRIPTION Data Analytics: Introduction, Methods and Practical Approaches INF2190H The influx of data that is created, gathered, stored and accessed

More information

SQLSaturday #399 Sacramento 25 July, 2015. Big Data Analytics with Excel

SQLSaturday #399 Sacramento 25 July, 2015. Big Data Analytics with Excel SQLSaturday #399 Sacramento 25 July, 2015 Big Data Analytics with Excel Presenter Introduction Peter Myers Independent BI Expert Bitwise Solutions BBus, SQL Server MCSE, SQL Server MVP since 2007 Experienced

More information

Doing Multidisciplinary Research in Data Science

Doing Multidisciplinary Research in Data Science Doing Multidisciplinary Research in Data Science Assoc.Prof. Abzetdin ADAMOV CeDAWI - Center for Data Analytics and Web Insights Qafqaz University aadamov@qu.edu.az http://ce.qu.edu.az/~aadamov 16 May

More information

Estimating PageRank Values of Wikipedia Articles using MapReduce

Estimating PageRank Values of Wikipedia Articles using MapReduce Estimating PageRank Values of Wikipedia Articles using MapReduce Due: Sept. 30 Wednesday 5:00PM Submission: via Canvas, individual submission Instructor: Sangmi Pallickara Web page: http://www.cs.colostate.edu/~cs535/assignments.html

More information

Big Data Analytics. What to Do with Big Data? V. CHRISTOPHIDES. Department of Computer Science University of Crete. Data contains value and knowledge

Big Data Analytics. What to Do with Big Data? V. CHRISTOPHIDES. Department of Computer Science University of Crete. Data contains value and knowledge Big Data Analytics V. CHRISTOPHIDES Department of Computer Science University of Crete 1 What to Do with Big Data? Data contains value and knowledge But to extract the knowledge data needs to be Stored

More information

Data mining for prediction

Data mining for prediction Data mining for prediction Prof. Gianluca Bontempi Département d Informatique Faculté de Sciences ULB Université Libre de Bruxelles email: gbonte@ulb.ac.be Outline Extracting knowledge from observations.

More information

Information Security, PII and Big Data

Information Security, PII and Big Data ITU Workshop on ICT Security Standardization for Developing Countries (Geneva, Switzerland, 15-16 September 2014) Information Security, PII and Big Data Edward (Ted) Humphreys ISO/IEC JTC 1/SC 27 (WG1

More information

Mining Big Data. Pang-Ning Tan. Associate Professor Dept of Computer Science & Engineering Michigan State University

Mining Big Data. Pang-Ning Tan. Associate Professor Dept of Computer Science & Engineering Michigan State University Mining Big Data Pang-Ning Tan Associate Professor Dept of Computer Science & Engineering Michigan State University Website: http://www.cse.msu.edu/~ptan Google Trends Big Data Smart Cities Big Data and

More information

Changing the face of Business Intelligence & Information Management

Changing the face of Business Intelligence & Information Management 1300 530 335 info@c3businessolutions.com www.c3businesssolutions.com GPO Box 589 Melbourne VIC 3001 Australia ABN 35 122 885 465 White Paper Big Data Changing the face of Business Intelligence & Information

More information

Customized Report- Big Data

Customized Report- Big Data GINeVRA Digital Research Hub Customized Report- Big Data 1 2014. All Rights Reserved. Agenda Context Challenges and opportunities Solutions Market Case studies Recommendations 2 2014. All Rights Reserved.

More information

Tutorial: Big Data Algorithms and Applications Under Hadoop KUNPENG ZHANG SIDDHARTHA BHATTACHARYYA

Tutorial: Big Data Algorithms and Applications Under Hadoop KUNPENG ZHANG SIDDHARTHA BHATTACHARYYA Tutorial: Big Data Algorithms and Applications Under Hadoop KUNPENG ZHANG SIDDHARTHA BHATTACHARYYA http://kzhang6.people.uic.edu/tutorial/amcis2014.html August 7, 2014 Schedule I. Introduction to big data

More information

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 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,

More information

What is Big Data? The three(or four) Vs in Big Data In 2013 the total amount of stored information is estimated to be Volume.

What is Big Data? The three(or four) Vs in Big Data In 2013 the total amount of stored information is estimated to be Volume. 8/26/2014 CS581 Big Data - Fall 2014 1 8/26/2014 CS581 Big Data - Fall 2014 2 CS535/CS581A BIG DATA What is Big Data? PART 0. INTRODUCTION 1. INTRODUCTION TO BIG DATA 2. COURSE INTRODUCTION PART 0. INTRODUCTION

More information

Architectures for massive data management

Architectures for massive data management Architectures for massive data management Apache Kafka, Samza, Storm Albert Bifet albert.bifet@telecom-paristech.fr October 20, 2015 Stream Engine Motivation Digital Universe EMC Digital Universe with

More information

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 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

More information

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

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

More information

Exploring Big Data in Social Networks

Exploring Big Data in Social Networks Exploring Big Data in Social Networks virgilio@dcc.ufmg.br (meira@dcc.ufmg.br) INWEB National Science and Technology Institute for Web Federal University of Minas Gerais - UFMG May 2013 Some thoughts about

More information

NZ BI User Group Auckland 18 September, 2013. Big Data Analytics with PowerPivot and Power View

NZ BI User Group Auckland 18 September, 2013. Big Data Analytics with PowerPivot and Power View NZ BI User Group Auckland 18 September, 2013 Big Data Analytics with PowerPivot and Power View Presenter Introduction Peter Myers BI Expert Bitwise Solutions BBus, SQL Server MCSE, MCT, SQL Server MVP

More information

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 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

More information

Big Data Systems CS 5965/6965 FALL 2015

Big Data Systems CS 5965/6965 FALL 2015 Big Data Systems CS 5965/6965 FALL 2015 Today General course overview Expectations from this course Q&A Introduction to Big Data Assignment #1 General Course Information Course Web Page http://www.cs.utah.edu/~hari/teaching/fall2015.html

More information

Decision Support Optimization through Predictive Analytics - Leuven Statistical Day 2010

Decision Support Optimization through Predictive Analytics - Leuven Statistical Day 2010 Decision Support Optimization through Predictive Analytics - Leuven Statistical Day 2010 Ernst van Waning Senior Sales Engineer May 28, 2010 Agenda SPSS, an IBM Company SPSS Statistics User-driven product

More information

Data Refinery with Big Data Aspects

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

More information

ENGINE(S) BEHIND BI. Sam Tawfik sam.tawfik@teradata.com @teradata_sam

ENGINE(S) BEHIND BI. Sam Tawfik sam.tawfik@teradata.com @teradata_sam ENGINE(S) BEHIND BI Sam Tawfik sam.tawfik@teradata.com @teradata_sam Transactions and Interactions BIG DATA 2 Transaction Interaction Data Growth 10 24 10 21 10 18 10 15 10 12 10 9 Yottabyte Zettabyte

More information

Big Data. Lyle Ungar, University of Pennsylvania

Big Data. Lyle Ungar, University of Pennsylvania Big Data Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. McKinsey Data Scientist: The Sexiest Job of the 21st Century -

More information

Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料

Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料 Big Data: Opportunities & Challenges, Myths & Truths 資 料 來 源 : 台 大 廖 世 偉 教 授 課 程 資 料 美 國 13 歲 學 生 用 Big Data 找 出 霸 淩 熱 點 Puri 架 設 網 站 Bullyvention, 藉 由 分 析 Twitter 上 找 出 提 到 跟 霸 凌 相 關 的 詞, 搭 配 地 理 位 置

More information

Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing

Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing Architecting for Big Data Analytics and Beyond: A New Framework for Business Intelligence and Data Warehousing Wayne W. Eckerson Director of Research, TechTarget Founder, BI Leadership Forum Business Analytics

More information

Introduction to Predictive Analytics. Dr. Ronen Meiri ronen@dmway.com

Introduction to Predictive Analytics. Dr. Ronen Meiri ronen@dmway.com Introduction to Predictive Analytics Dr. Ronen Meiri Outline From big data to predictive analytics Predictive Analytics vs. BI Intelligent platforms What can we do with it. The modeling process. Example

More information

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

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

More information

Big Data & Scripting Part II Streaming Algorithms

Big Data & Scripting Part II Streaming Algorithms Big Data & Scripting Part II Streaming Algorithms 1, Counting Distinct Elements 2, 3, counting distinct elements problem formalization input: stream of elements o from some universe U e.g. ids from a set

More information

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

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

More information

Mining Large Datasets: Case of Mining Graph Data in the Cloud

Mining Large Datasets: Case of Mining Graph Data in the Cloud Mining Large Datasets: Case of Mining Graph Data in the Cloud Sabeur Aridhi PhD in Computer Science with Laurent d Orazio, Mondher Maddouri and Engelbert Mephu Nguifo 16/05/2014 Sabeur Aridhi Mining Large

More information

BIG DATA, BIOBANKS AND PREDICTIVE ANALYTICS FOR A BETTER CLINICAL OUTCOME

BIG DATA, BIOBANKS AND PREDICTIVE ANALYTICS FOR A BETTER CLINICAL OUTCOME BIG DATA, BIOBANKS AND PREDICTIVE ANALYTICS FOR A BETTER CLINICAL OUTCOME Π. Ε. Βάρδας MD, PhD(London) DISCLOSURES My great love to innovative ideas BIG DATA It is a broad term for data sets, so large

More information

Age of Big data. Presented by: Mohammad Iqbal BCM -2014

Age of Big data. Presented by: Mohammad Iqbal BCM -2014 Age of Presented by: Mohammad Iqbal BCM -2014 Agenda Big? Big evolution from Big? Name Symbol Value Kilobyte KB 10^3 BIG DATA Megabyte MB 10^6 Gigabyte GB 10^9 Terabyte TB 10^12 Petabyte PB 10^15 So large

More information

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database

Managing Big Data with Hadoop & Vertica. A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Managing Big Data with Hadoop & Vertica A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Copyright Vertica Systems, Inc. October 2009 Cloudera and Vertica

More information

Big Data Analytics. The Hype and the Hope* Dr. Ted Ralphs Industrial and Systems Engineering Director, COR@L Laboratory

Big Data Analytics. The Hype and the Hope* Dr. Ted Ralphs Industrial and Systems Engineering Director, COR@L Laboratory Big Data Analytics The Hype and the Hope* Dr. Ted Ralphs Industrial and Systems Engineering Director, COR@L Laboratory * Source: http://www.economistinsights.com/technology-innovation/analysis/hype-and-hope/methodology

More information

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition

More information

Log Mining Based on Hadoop s Map and Reduce Technique

Log Mining Based on Hadoop s Map and Reduce Technique Log Mining Based on Hadoop s Map and Reduce Technique ABSTRACT: Anuja Pandit Department of Computer Science, anujapandit25@gmail.com Amruta Deshpande Department of Computer Science, amrutadeshpande1991@gmail.com

More information

Statistics for BIG data

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

More information

Big Data: What s the Big Deal?

Big Data: What s the Big Deal? Big Data: What s the Big Deal? Ellen D. Wagner Ph.D. Chief Research and Strategy Officer PAR Framework @edwsonoma edwsonoma@parframework.org Common Definitions for Today Data are bits of information, everywhere.

More information

Martin Sůra, Managing Director & Enterprise Group Lead, Hewlett-Packard Slovakia

Martin Sůra, Managing Director & Enterprise Group Lead, Hewlett-Packard Slovakia Solutions for the New Style of IT Martin Sůra, Managing Director & Enterprise Group Lead, Hewlett-Packard Slovakia 25. 3. 2014, Bratislava To remain static is to lose ground. David Packard Copyright 2013

More information

INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY

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

More information

RevoScaleR Speed and Scalability

RevoScaleR Speed and Scalability EXECUTIVE WHITE PAPER RevoScaleR Speed and Scalability By Lee Edlefsen Ph.D., Chief Scientist, Revolution Analytics Abstract RevoScaleR, the Big Data predictive analytics library included with Revolution

More information

Intro to Big Data and Business Intelligence

Intro to Big Data and Business Intelligence Intro to Big Data and Business Intelligence Anjana Susarla Eli Broad College of Business What is Business Intelligence A Simple Definition: The applications and technologies transforming Business Data

More information

Machine Learning, Data Mining, and Knowledge Discovery: An Introduction

Machine Learning, Data Mining, and Knowledge Discovery: An Introduction Machine Learning, Data Mining, and Knowledge Discovery: An Introduction AHPCRC Workshop - 8/17/10 - Dr. Martin Based on slides by Gregory Piatetsky-Shapiro from Kdnuggets http://www.kdnuggets.com/data_mining_course/

More information

Distributed Computing and Hadoop in Statistics

Distributed Computing and Hadoop in Statistics Distributed Computing and Hadoop in Statistics Xiaoling Lu and Bing Zheng Center For Applied Statistics, Renmin University of China, Beijing, China Corresponding author: Xiaoling Lu, e-mail: xiaolinglu@ruc.edu.cn

More information

Software Engineering for Big Data. CS846 Paulo Alencar David R. Cheriton School of Computer Science University of Waterloo

Software Engineering for Big Data. CS846 Paulo Alencar David R. Cheriton School of Computer Science University of Waterloo Software Engineering for Big Data CS846 Paulo Alencar David R. Cheriton School of Computer Science University of Waterloo Big Data Big data technologies describe a new generation of technologies that aim

More information

Machine Learning over Big Data

Machine Learning over Big Data Machine Learning over Big Presented by Fuhao Zou fuhao@hust.edu.cn Jue 16, 2014 Huazhong University of Science and Technology Contents 1 2 3 4 Role of Machine learning Challenge of Big Analysis Distributed

More information

Hadoop Cluster Applications

Hadoop Cluster Applications Hadoop Overview Data analytics has become a key element of the business decision process over the last decade. Classic reporting on a dataset stored in a database was sufficient until recently, but yesterday

More information

Volume 3, Issue 6, June 2015 International Journal of Advance Research in Computer Science and Management Studies

Volume 3, Issue 6, June 2015 International Journal of Advance Research in Computer Science and Management Studies Volume 3, Issue 6, June 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com Image

More information

Analytics Data Discovery QlikView

Analytics Data Discovery QlikView Analytics Data Discovery QlikView 3 rd -5 th September 2014 KS Gopinath Narayan, IAAS CIA, CFE, PMP Pr. Director (IT Audit) Office of the CAG of India narayanksg@cag.gov.in Presentation Outline About Data

More information

Data Aggregation and Cloud Computing

Data Aggregation and Cloud Computing Data Intensive Scalable Computing Harnessing the Power of Cloud Computing Randal E. Bryant February, 2009 Our world is awash in data. Millions of devices generate digital data, an estimated one zettabyte

More information

ANALYTICS IN BIG DATA ERA

ANALYTICS IN BIG DATA ERA ANALYTICS IN BIG DATA ERA ANALYTICS TECHNOLOGY AND ARCHITECTURE TO MANAGE VELOCITY AND VARIETY, DISCOVER RELATIONSHIPS AND CLASSIFY HUGE AMOUNT OF DATA MAURIZIO SALUSTI SAS Copyr i g ht 2012, SAS Ins titut

More information

Introduction to the Mathematics of Big Data. Philippe B. Laval

Introduction to the Mathematics of Big Data. Philippe B. Laval Introduction to the Mathematics of Big Data Philippe B. Laval Fall 2015 Introduction In recent years, Big Data has become more than just a buzz word. Every major field of science, engineering, business,

More information

Big Data and Marketing

Big Data and Marketing Big Data and Marketing Professor Venky Shankar Coleman Chair in Marketing Director, Center for Retailing Studies Mays Business School Texas A&M University http://www.venkyshankar.com venky@venkyshankar.com

More information

AppSymphony White Paper

AppSymphony White Paper AppSymphony White Paper Secure Self-Service Analytics for Curated Digital Collections Introduction Optensity, Inc. offers a self-service analytic app composition platform, AppSymphony, which enables data

More information

Big Data Analytics: Collecting, Analyzing and Decision Making

Big Data Analytics: Collecting, Analyzing and Decision Making Big Data Analytics: Collecting, Analyzing and Decision Making Defining Big Data Jennifer Jones, Senior Indirect Sales Manager, CBTS Thought Leader Definitions Oracle - Derivation of value from traditional,

More information

BIG DATA What it is and how to use?

BIG DATA What it is and how to use? BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14

More information

Big Data Challenges. technology basics for data scientists. Spring - 2014. Jordi Torres, UPC - BSC www.jorditorres.

Big Data Challenges. technology basics for data scientists. Spring - 2014. Jordi Torres, UPC - BSC www.jorditorres. Big Data Challenges technology basics for data scientists Spring - 2014 Jordi Torres, UPC - BSC www.jorditorres.eu @JordiTorresBCN Data Deluge: Due to the changes in big data generation Example: Biomedicine

More information

Big Data and Hadoop. Sreedhar C, Dr. D. Kavitha, K. Asha Rani

Big Data and Hadoop. Sreedhar C, Dr. D. Kavitha, K. Asha Rani Big Data and Hadoop Sreedhar C, Dr. D. Kavitha, K. Asha Rani Abstract Big data has become a buzzword in the recent years. Big data is used to describe a massive volume of both structured and unstructured

More information

Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms

Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms Data Mining Techniques forcrm Data Mining The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. Extremely large datasets Discovery of the non-obvious Useful knowledge

More information

Big Data and Open Data

Big Data and Open Data Big Data and Open Data Bebo White SLAC National Accelerator Laboratory/ Stanford University!! bebo@slac.stanford.edu dekabytes hectobytes Big Data IS a buzzword! The Data Deluge From the beginning of

More information

Email: justinjia@ust.hk Office: LSK 5045 Begin subject: [ISOM3360]...

Email: justinjia@ust.hk Office: LSK 5045 Begin subject: [ISOM3360]... Business Intelligence and Data Mining ISOM 3360: Spring 2015 Instructor Contact Office Hours Course Schedule and Classroom Course Webpage Jia Jia, ISOM Email: justinjia@ust.hk Office: LSK 5045 Begin subject:

More information

BIG DATA: STORAGE, ANALYSIS AND IMPACT GEDIMINAS ŽYLIUS

BIG DATA: STORAGE, ANALYSIS AND IMPACT GEDIMINAS ŽYLIUS BIG DATA: STORAGE, ANALYSIS AND IMPACT GEDIMINAS ŽYLIUS WHAT IS BIG DATA? describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information

More information

Statistical Challenges with Big Data in Management Science

Statistical Challenges with Big Data in Management Science Statistical Challenges with Big Data in Management Science Arnab Kumar Laha Indian Institute of Management Ahmedabad Analytics vs Reporting Competitive Advantage Reporting Prescriptive Analytics (Decision

More information

Big Data: Image & Video Analytics

Big Data: Image & Video Analytics Big Data: Image & Video Analytics How it could support Archiving & Indexing & Searching Dieter Haas, IBM Deutschland GmbH The Big Data Wave 60% of internet traffic is multimedia content (images and videos)

More information

QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM

QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM QLIKVIEW DEPLOYMENT FOR BIG DATA ANALYTICS AT KING.COM QlikView Technical Case Study Series Big Data June 2012 qlikview.com Introduction This QlikView technical case study focuses on the QlikView deployment

More information

Massive Labeled Solar Image Data Benchmarks for Automated Feature Recognition

Massive Labeled Solar Image Data Benchmarks for Automated Feature Recognition Massive Labeled Solar Image Data Benchmarks for Automated Feature Recognition Michael A. Schuh1, Rafal A. Angryk2 1 Montana State University, Bozeman, MT 2 Georgia State University, Atlanta, GA Introduction

More information

Data Science Center Eindhoven. Big Data: Challenges and Opportunities for Mathematicians. Alessandro Di Bucchianico

Data Science Center Eindhoven. Big Data: Challenges and Opportunities for Mathematicians. Alessandro Di Bucchianico Data Science Center Eindhoven Big Data: Challenges and Opportunities for Mathematicians Alessandro Di Bucchianico Dutch Mathematical Congress April 15, 2015 Contents 1. Big Data terminology 2. Various

More information