BIG EMITLAB & CIDSE. K. Selçuk Candan

Size: px
Start display at page:

Download "BIG DATA @ EMITLAB & CIDSE. K. Selçuk Candan candan@asu.edu"

Transcription

1 BIG EMITLAB & CIDSE K. Selçuk Candan

2 Name: K. Selçuk Candan! Professor of Computer Science and Engineering at (CIDSE) ASU! Director, Enterprise, Media, and Information Technologies Labs (EmitLab)! Fulton Schools of Engineering Exemplar Faculty! Senior Sustainability Scientist- Global Institute of Sustainability

3 EmitLab Xiaolan Wang Ex-MS (now at U.Mass) Sriram Rathinavelu Ex-MS Mijung Kim ; Ex-PhD (now at HP Labs) Aneesha Bhat M S Jung Hyun Kim PhD Mithila Nagendra Ex-PhD (now at Akamai) Yash Garg M S Parth Nagarkar PhD Xinsheng Liu PhD Sicong Liu PhD Marco Berchiatti MS (U. Torino) Shengyu Huang PhD Adam Tse Undergrad Xilun Chen PhD Leonardo Allisio MS (U. Torino) Silvestro Poccia Research Technologist Ilaria Dal Grande MS (U. Torino) Rosaria Rossini PhD (U. Torino) KSC Maria Luisa Sapino Professor (U. Torino) Claudio Schifanella Ex. Post-doc. (now at RAI) Antonio Penta Post-doc. (U. Torino)

4 Research Overview Recent Relevant Grants/Projects: [NSF] National Science Digital Library (NSDL) Middleware for Network- and Context-aware Recommendations [KRA] A Framework for Real-time Context Monitoring in Sensor-rich Personal Mobile Environments [NSF] AURA: Design of Dense RFID Systems for Indexing in the Physical World across Space, Time, and Human Experience Ongoing Grants/Projects: [with SHESC, NSF] Management for Real-Time Driven Epidemic Simulations [with SHESC, NSF] Understanding the Evolution Patterns of the Ebola Outbreak in West- Africa and Supporting Real-Time Decision Making and Hypothesis Testing through Large Scale Simulations [NSF] RanKloud: Partitioning and Resource Allocation Strategies for Scalable Multimedia and Social Media Analysis [with JCI, NSF] Analysis and Optimization for Building Energy Management NSF: An Infrastructure to Support Complex Financial Patterns (CFP) based Real-Time Services Delivery and Visual Analytics [NSF] One Size Does Not Fit All: Empowering the User with User-Driven Integration NSF-IGERT: Person-centered Technologies and Practices for Individuals with Disabilities

5 What do I do?? Executive Committee member, ACM Special Interest Group on Management of (SIGMOD) Associate editor, ACM Transactions on base Systems (TODS) Associate editor, IEEE Transactions on Multimedia Associate editor, the Very Large Bases journal ( ) Associate editor, Journal of Multimedia General Chair, IEEE International Conference on Cloud Engineering (IC2E) Workshops Chair, International Conference on Extending base Technology (EDBT) 2014 Organizing Committee Member, ACM SIG Multimedia Conference 2013 Panels Chair, Very Large bases (VLDB) Conference 2012 Publicity Chair, ACM SIG Multimedia Conference 2012 General Chair, ACM SIGMOD Conference 2012 General Chair, ACM SIG Multimedia Conference 2011 Program Group leader, ACM SIG Management of (SIGMOD) Conference 2010 PC Chair, the ACM International Conference on Image and Video Retrieval (CIVR) 2010 PC Chair, Workshop on Information & Software as Services. (WISS) 2010 Chair,Workshop on Information & Software as Services. (WISS) 2009 Chair, Workshop on Real-Time Business Intelligence (RTBI) 2009 PC Chair, ACM Workshop on Ambient Media Computing (iwam) PC Chair, ACM SIG Multimedia Conference 2008

6 Today, the amount of data being generated is massive. This necessitates engineering of new data architectures with lots of processing power and tools that can match the scale of the data and support split second decision making, through data fusion and integration and analysis and forecasting algorithms, to help non-data-experts (both government and commercial) make decisions and generate value. "Hunting for the Value Gaps in Management, Services, and Analytics ACM SIGMOD blog;

7 Challenges Cisco estimates we ll see a 1.3 zettabytes of traffic annually over the internet in 2016 Sensors from a Boeing jet engine create 20 terabytes of data every hour. 500 terabytes of new data of all forms are ingested in Facebook every day ISQP 3Vs HMLE [I]mprecision [S]parsity [Q]uality [P]rivacy [V]olume [V]elocity [V]ariety [H]igh-dimensional [M]ulti-modal inter-[l]inked [E]volving

8 Manage ment Analytic s Dimensi onality reductio n/feature selection Classific ation, clusterin g Summar ization Visual analytics Feature extractio n/media analysis Tempor al/spatial analysis Text Analysis /NLP Web/ social network s Recom mender systems Scalable /real time Perform ance and Scalabili ty Consiste ncy, quality, cleaning models Organiz ation and Schema Integrati on Cloud, DaaS Streami ng Parallel/ Distribut ed DM MapRede ce/ Hadoop Pregel/ Hama Other parallel DBMS Multitenant, Virtualiz ation Security, privacy, assuran ce Mobile, Sensor Visualiz ation Extractio n, filtering Rowstores Column Stores Key-value stores NoSql Relational OO XML Spatial Temporal Sequence Graph Fuzzy/ uncertain Text, image, video

9 Sequence Spatial management/mining techniques for supporting scalable, real-time, distributed analysis and retrieval systems Rowstores Key-value stores Fuzzy/ uncertain Column Stores NoSql and Schema Integrati on Graph Text, image, video Organiz ation Cloud, DaaS Multitenant, Virtualiz ation Temporal models Manage ment Streami ng XML Relational Mobile, Sensor Security, privacy, assuran ce systems for scalable data/query processing data streaming/mining/fusion OO Perform ance and Scalabili ty Parallel/ Distribut ed DM Visualiz ation Consiste ncy, quality, cleaning Extractio n, filtering MapRede ce/ Hadoop Pregel/ Hama Other parallel DBMS Feature extractio n/media analysis Tempor al/spatial analysis Visual analytics Text Analysis /NLP Summar ization Analytic s Web/ social network s Classific ation, clusterin g Scalable /real time Recom mender systems Dimensi onality reductio n/feature selection

10 Rowstores Key-value stores Most data in the real world are Spatial Sequence imprecise, multi-modal, and subjective Temporal anyhow Column Stores NoSql and Schema Integrati on Graph Organiz ation Cloud, DaaS Multitenant, Virtualiz ation Manage ment Streami ng XML So can we leverage techniques Fuzzy/ uncertain models from data and Text, media analysis Relational to image, video tackle the so called traditional data management/mining challenges?? Mobile, Sensor Security, privacy, assuran ce OO Perform ance and Scalabili ty Parallel/ Distribut ed DM Visualiz ation Consiste ncy, quality, cleaning Extractio n, filtering MapRede ce/ Hadoop Pregel/ Hama Other parallel DBMS Feature extractio n/media analysis Tempor al/spatial analysis Visual analytics Text Analysis /NLP Summar ization Analytic s Web/ social network s Classific ation, clusterin g Scalable /real time Recom mender systems Dimensi onality reductio n/feature selection

11 CENTER/CONSORTIUM FOR ASSURED AND SCALABLE DATA ENGINEERING (CASCADE) (CONSTRUCTION STAGE)

12 Focus and vision

13 CASCADE NSF I/UCRC Center (Proposal) Academic Partners Arizona State Univ. (KS Candan, H Davulcu, G Ahn, M Sapino) University of Maryland, College Park (Louiqa Raschid) The potential industrial members to the proposed NSF I/UCRC Center for Assured and SCAlable Engineering (CASCADE includes ASU site members: American Express, Early Warning, JCI, HP Labs, MapR, NEC America Labs, Oracle, Computational Analysis & Network Enterprise Solutions (CAaNES), Arizona Cyber Threat Response Alliance (ACTRA) UMD site members: Unscrambl, Leidos, JP Morgan Chase, Applied Communication Sciences (ACS), John Bottega, State Street, IBM Other potential partners Rengen Orion Health

14 Core CS Faculty working on Name Title Area(s) of Specialization as they relate to proposed concentration K. Selcuk Candan Professor Scalable data management and analysis Hasan Davulcu Assoc. Professor bases and data extraction Huan Liu Professor mining and analysis Ross Maciejewski Assistant Professor visualization Baoxin Li Professor Statistical machine learning, visual data Rao Kambhampati Professor integration, data cleaning Chitta Baral Professor Knowledge representation, NLP Dijuang Huang Associate Professor clouds Hanghang Tong Assistant Professor Graph structured data Mohamed Sarwat Assistant Professor management systems Jingrui He Assistant Professor analysis and sparse learning Paolo Shakarian Assistant Professor and network analysis

15 Relevant faculty at CIDSE/ASU 1. Gail- Joon Ahn risk management, access control, and security architecture for distributed systems 2. Ron Askin scheduling, opera?ons research; applied sta?s?cs 3. ChiCa Baral knowledge representa?on, bioinforma?cs, and text analysis 4. Rida Bazzi distributed compu?ng, fault tolerance, dynamic schema update in data clouds 5. K. Selcuk Candan scalable data management, integra?on and retrieval, data management and processing systems, mul?media retrieval, accessibility 6. Partha Dasgupta distributed systems, security, and resilience 7. Sandeep Gupta parallel and distributed compu?ng, data centers, energy- efficient, reliable data dissemina?on, and caching 8. Dijang Huang security, virtualiza?on, mobile cloud compu?ng 9. Subbarao Kambhampa? data integra?on, data cleaning, and planning 10. Baoxin Li sta?s?cal inference for visual tracking, feature selec?on for data/sensor fusion, image/video retrieval 11. Huan Liu data mining, machine learning, feature selec?on, classifica?on, subspace clustering, and social compu?ng 12. Ross Maciejewski geo- spa?al and spa?o- temporal visualiza?on, visual analy?cs for healthcare/pandemics, law enforcement 13. Pitu Mirchandhani water distribu?on systems, urban planning, transporta?on, forecas?ng, dynamic systems, remote sensing 14. Sethuraman Panchanathan ubiquituous mul?media analyis, accesibility 15. Andrea Richa adhoc networks, algorithms, self organizing systems, wireless communica?on 16. George Runger sta?s?cal learning, process control, data mining for massive, mul?variate data sets 17. Arunabha Sen network analysis, social, biological, transporta?on, communica?on networks 18. Esma Gel applied probability techniques for modeling, design and control of produc?on systems and supply chain 19. Hari Sundaram mul?- media and social- media analy?cs 20. Yalin Wang data visualiza?on, medical imaging, sta?s?cal pacern recogni?on 21. Peter Wonka data visualiza?on, geo- spa?al visualiza?on, modelling, image analysis 22. Teresa Wu decision making under uncertainty, biomedical informa?cs 23. Guoliang Xue privacy, smart grid, cloud compu?ng, network science 24. Steve Yau service- based systems, informa?on assurance, security, qos monitoring 25. Jieping Ye machine learning, data mining, dimensionality reduc?on, biomedical informa?cs 26. Nong Ye cyber- and network security

16 Relevant faculty at CIDSE/ASU

17 Big Systems Concentration for MS in Computer Science

18 CIDSE MS/MCS Concentration in Big Systems 15 credits of coursework in data engineering and data analytics Required base Management System (DBMS) Implementation Distributed and Parallel Systems Mining Elective (2 out of 5) Virtualization and Cloud Computing Semantic Web Mining Visualization Multimedia and Web bases Statistical Machine Learning

19 Key knowledge gaps.. Six most critical knowledge competency groups (in terms of the value gap i.e., the difference between current and desired states of the knowledge area) temporal and spatial analyses, summarization, cleaning, visualization, anomaly detection, real-time processing for streaming data, media analytics representations and fusion for unstructured/structured data, semantic Web, make unstructured data queriable, prioritize and rank data, correlate and identify the gaps in the data graph-based models, social networks, entity analytics, (social and other) network analytics, performance and scalability, distributed architectures. performance and scalability, distributed architectures. "Hunting for the Value Gaps in Management, Services, and Analytics ACM SIGMOD blog;

20 Key Tools.. Tools that can support federated and scalable data storage, analysis, and modeling make unstructured data queriable, prioritize and rank data, correlate and identify the gaps in the data entity analytics, (social and other) network analytics, and media analytics take into account for known models, but also adapt to new emerging patterns going back in history to validate models and going forward into future to support forecasting and if-then hypothesis testing.

21 Engineers.must have solid algorithmic and mathematical background, complemented with excellent data management, programming, and system development/integration skills

22 Engineers..should be able make informed architectural decisions based on a MapReduce/Hadoop Clustering/ classification RanKloud good understanding on how Reduce available technologies differ and complement each other Spark Mango-DB Map Map Map Map Feature extraction NetworkX GraphLab MADLib Hadoop-Online

23 Engineers.should also be able to identify data that is important, restructure data to make it useful, interpret data, formulate observation strategies and relevant data queries, and ask new questions based on the observations and results including what happened?, why did it happen?, and what happens next?.

24 Engineers..need to have the necessary skills to communicate with non data scientist/engineer co-workers, including domain experts business executives

25 Key learning outcomes make informed architectural decisions based on a good understanding on how available technologies differ and complement each other and what scalability/consistency trade-offs they provide. be able to pick and deploy the appropriate data management, processing, and analysis systems (including commercial and open-source) with the suitable structured or unstructured data model for the particular task and domain application needs. make informed decisions regarding data storage, indexing, querying, and retrieval. reason about optimization and execution alternatives and will be able to plan within the trade-offs introduces by concurrency control, transaction management, and recovery protocols and algorithms.

26 Key learning outcomes use tools and develop frameworks for federated and cloud based data storage, analysis, and modeling and mediated data services delivery. use as well as develop high performance distributed and/ or parallel data architectures that can match the scale of the data and support split second decision making, through data fusion and integration and analysis and forecasting algorithms. use as well as develop real-time, on-line data processing systems for temporally and spatially distributed observations for data in motion in applications, including those that include mobile applications, location-aware services, and human behavior modeling at individual and population scales. use as well as develop scalable batch processing systems for data at rest.

27 Key learning outcomes have knowledge regarding cutting-edge algorithms and systems for temporal and spatial data analyses, summarization, cleaning, anomaly detection, representations and fusion for unstructured/structured data, semantic Web, graph-based models, social networks, and multi-dimensional data visualization, use as well as develop tools that support entity analytics, (social and other) network analytics, text analytics, and media analytics not only for traditional applications like monitoring and security, but also for emerging applications, including enabling interest detection for retail/advertisement, social media, energy, healthcare, and finance.

28 Key learning outcomes use and develop algorithms, techniques, and tools for reducing the size and/or dimensionality of the data to make data amenable to analysis. make unstructured data queriable, prioritize and rank data, correlate and identify the gaps in the data, highlight what is normal and not normal, and automate the ingest of the data.

29 Key learning outcomes The graduates will be able to design and develop adaptive systems that take into account known models, but also adapt the models to new emerging patterns. use tools and develop systems that can go back in history to validate models and go forward into future to support forecasting and if-then hypothesis testing. The graduates will have the necessary skills to communicate with technical and non-technical co-workers

BIG DATA @ EMITLAB & CIDSE. K. Selçuk Candan

BIG DATA @ EMITLAB & CIDSE. K. Selçuk Candan BIG DATA @ EMITLAB & CIDSE K. Selçuk Candan Name: K. Selçuk Candan Professor of computer science and engineering at (CIDSE) ASU Senior Sustainability Scientist- Global Institute of Sustainability Director,

More information

NEW GRADUATE CONCENTRATION PROPOSALS ARIZONA STATE UNIVERSITY

NEW GRADUATE CONCENTRATION PROPOSALS ARIZONA STATE UNIVERSITY NEW GRADUATE CONCENTRATION PROPOSALS ARIZONA STATE UNIVERSITY GRADUATE EDUCATION This form should be used for academic units wishing to propose a new concentration for existing graduate degrees. A concentration

More information

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

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

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

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 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

More information

Big Data and Complex Networks Analytics. Timos Sellis, CSIT Kathy Horadam, MGS

Big Data and Complex Networks Analytics. Timos Sellis, CSIT Kathy Horadam, MGS Big Data and Complex Networks Analytics Timos Sellis, CSIT Kathy Horadam, MGS Big Data What is it? Most commonly accepted definition, by Gartner (the 3 Vs) Big data is high-volume, high-velocity and high-variety

More information

SURVEY REPORT DATA SCIENCE SOCIETY 2014

SURVEY REPORT DATA SCIENCE SOCIETY 2014 SURVEY REPORT DATA SCIENCE SOCIETY 2014 TABLE OF CONTENTS Contents About the Initiative 1 Report Summary 2 Participants Info 3 Participants Expertise 6 Suggested Discussion Topics 7 Selected Responses

More information

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON Overview * Introduction * Multiple faces of Big Data * Challenges of Big Data * Cloud Computing

More information

IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper

IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper IEEE International Conference on Computing, Analytics and Security Trends CAST-2016 (19 21 December, 2016) Call for Paper CAST-2015 provides an opportunity for researchers, academicians, scientists and

More information

COMP9321 Web Application Engineering

COMP9321 Web Application Engineering COMP9321 Web Application Engineering Semester 2, 2015 Dr. Amin Beheshti Service Oriented Computing Group, CSE, UNSW Australia Week 11 (Part II) http://webapps.cse.unsw.edu.au/webcms2/course/index.php?cid=2411

More information

Big Data and Analytics: Challenges and Opportunities

Big Data and Analytics: Challenges and Opportunities Big Data and Analytics: Challenges and Opportunities Dr. Amin Beheshti Lecturer and Senior Research Associate University of New South Wales, Australia (Service Oriented Computing Group, CSE) Talk: Sharif

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

BIG DATA Alignment of Supply & Demand Nuria de Lama Representative of Atos Research &

BIG DATA Alignment of Supply & Demand Nuria de Lama Representative of Atos Research & BIG DATA Alignment of Supply & Demand Nuria de Lama Representative of Atos Research & Innovation 04-08-2011 to the EC 8 th February, Luxembourg Your Atos business Research technologists. and Innovation

More information

Data-intensive HPC: opportunities and challenges. Patrick Valduriez

Data-intensive HPC: opportunities and challenges. Patrick Valduriez Data-intensive HPC: opportunities and challenges Patrick Valduriez Big Data Landscape Multi-$billion market! Big data = Hadoop = MapReduce? No one-size-fits-all solution: SQL, NoSQL, MapReduce, No standard,

More information

Are You Ready for Big 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?

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

Sustainable Development with Geospatial Information Leveraging the Data and Technology Revolution

Sustainable Development with Geospatial Information Leveraging the Data and Technology Revolution Sustainable Development with Geospatial Information Leveraging the Data and Technology Revolution Steven Hagan, Vice President, Server Technologies 1 Copyright 2011, Oracle and/or its affiliates. All rights

More information

III Big Data Technologies

III Big Data Technologies III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution

More information

Smarter Planet evolution

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

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

Search and Real-Time Analytics on Big Data

Search and Real-Time Analytics on Big Data Search and Real-Time Analytics on Big Data Sewook Wee, Ryan Tabora, Jason Rutherglen Accenture & Think Big Analytics Strata New York October, 2012 Big Data: data becomes your core asset. It realizes its

More information

Where is... How do I get to...

Where is... How do I get to... Big Data, Fast Data, Spatial Data Making Sense of Location Data in a Smart City Hans Viehmann Product Manager EMEA ORACLE Corporation August 19, 2015 Copyright 2014, Oracle and/or its affiliates. All rights

More information

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

More information

The Masters of Science in Information Systems & Technology

The Masters of Science in Information Systems & Technology The Masters of Science in Information Systems & Technology College of Engineering and Computer Science University of Michigan-Dearborn A Rackham School of Graduate Studies Program PH: 1-59-561; FAX: 1-59-692;

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

Big Data Storage Architecture Design in Cloud Computing

Big Data Storage Architecture Design in Cloud Computing Big Data Storage Architecture Design in Cloud Computing Xuebin Chen 1, Shi Wang 1( ), Yanyan Dong 1, and Xu Wang 2 1 College of Science, North China University of Science and Technology, Tangshan, Hebei,

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

Embedded inside the database. No need for Hadoop or customcode. True real-time analytics done per transaction and in aggregate. On-the-fly linking IP

Embedded inside the database. No need for Hadoop or customcode. True real-time analytics done per transaction and in aggregate. On-the-fly linking IP Operates more like a search engine than a database Scoring and ranking IP allows for fuzzy searching Best-result candidate sets returned Contextual analytics to correctly disambiguate entities Embedded

More information

Big Data Challenges and Success Factors. Deloitte Analytics Your data, inside out

Big Data Challenges and Success Factors. Deloitte Analytics Your data, inside out Big Data Challenges and Success Factors Deloitte Analytics Your data, inside out Big Data refers to the set of problems and subsequent technologies developed to solve them that are hard or expensive to

More information

Center for Dynamic Data Analytics (CDDA) An NSF Supported Industry / University Cooperative Research Center (I/UCRC) Vision and Mission

Center for Dynamic Data Analytics (CDDA) An NSF Supported Industry / University Cooperative Research Center (I/UCRC) Vision and Mission Photo courtesy of Justin Reuter Center for Dynamic Data Analytics (CDDA) An NSF Supported Industry / University Cooperative Research Center (I/UCRC) Vision and Mission CDDA Mission Mission of our CDDA

More information

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization

Course 803401 DSS. Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Oman College of Management and Technology Course 803401 DSS Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization CS/MIS Department Information Sharing

More information

Chapter 5. Warehousing, Data Acquisition, Data. Visualization

Chapter 5. Warehousing, Data Acquisition, Data. Visualization Decision Support Systems and Intelligent Systems, Seventh Edition Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization 5-1 Learning Objectives

More information

Play with Big Data on the Shoulders of Open Source

Play with Big Data on the Shoulders of Open Source OW2 Open Source Corporate Network Meeting Play with Big Data on the Shoulders of Open Source Liu Jie Technology Center of Software Engineering Institute of Software, Chinese Academy of Sciences 2012-10-19

More information

Are You Ready for Big Data?

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?

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

Professional Organization Checklist for the Computer Science Curriculum Updates. Association of Computing Machinery Computing Curricula 2008

Professional Organization Checklist for the Computer Science Curriculum Updates. Association of Computing Machinery Computing Curricula 2008 Professional Organization Checklist for the Computer Science Curriculum Updates Association of Computing Machinery Computing Curricula 2008 The curriculum guidelines can be found in Appendix C of the report

More information

Research at the Department of Computer Science and Software Engineering. Professor Yong Yue BEng, PhD, CEng, FIET, FIMechE 17 October 2014

Research at the Department of Computer Science and Software Engineering. Professor Yong Yue BEng, PhD, CEng, FIET, FIMechE 17 October 2014 Research at the Department of Computer Science and Software Engineering Professor Yong Yue BEng, PhD, CEng, FIET, FIMechE 17 October 2014 Research Areas Ar%ficial intelligence Robo%cs Data mining Image

More information

Industry Impact of Big Data in the Cloud: An IBM Perspective

Industry Impact of Big Data in the Cloud: An IBM Perspective Industry Impact of Big Data in the Cloud: An IBM Perspective Inhi Cho Suh IBM Software Group, Information Management Vice President, Product Management and Strategy email: inhicho@us.ibm.com twitter: @inhicho

More information

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 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

More information

Towards a Thriving Data Economy: Open Data, Big Data, and Data Ecosystems

Towards a Thriving Data Economy: Open Data, Big Data, and Data Ecosystems Towards a Thriving Data Economy: Open Data, Big Data, and Data Ecosystems Volker Markl volker.markl@tu-berlin.de dima.tu-berlin.de dfki.de/web/research/iam/ bbdc.berlin Based on my 2014 Vision Paper On

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

Deploying Big Data to the Cloud: Roadmap for Success

Deploying Big Data to the Cloud: Roadmap for Success Deploying Big Data to the Cloud: Roadmap for Success James Kobielus Chair, CSCC Big Data in the Cloud Working Group IBM Big Data Evangelist. IBM Data Magazine, Editor-in- Chief. IBM Senior Program Director,

More information

Introduction. A. Bellaachia Page: 1

Introduction. A. Bellaachia Page: 1 Introduction 1. Objectives... 3 2. What is Data Mining?... 4 3. Knowledge Discovery Process... 5 4. KD Process Example... 7 5. Typical Data Mining Architecture... 8 6. Database vs. Data Mining... 9 7.

More information

BigData at UI CS. Hasan Jamil Department of Computer Science University of Idaho

BigData at UI CS. Hasan Jamil Department of Computer Science University of Idaho BigData at UI CS Hasan Jamil Department of Computer Science University of Idaho BigData Four Vs of BigData Volume: Unprecedented size 40 zecabytes by 2020; 2.5 quinjllion bytes each day; 100 terabytes

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

Firebird meets NoSQL (Apache HBase) Case Study

Firebird meets NoSQL (Apache HBase) Case Study Firebird meets NoSQL (Apache HBase) Case Study Firebird Conference 2011 Luxembourg 25.11.2011 26.11.2011 Thomas Steinmaurer DI +43 7236 3343 896 thomas.steinmaurer@scch.at www.scch.at Michael Zwick DI

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

Big Data Driven Knowledge Discovery for Autonomic Future Internet

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,

More information

From Big Data to Smart Data Thomas Hahn

From Big Data to Smart Data Thomas Hahn Siemens Future Forum @ HANNOVER MESSE 2014 From Big to Smart Hannover Messe 2014 The Evolution of Big Digital data ~ 1960 warehousing ~1986 ~1993 Big data analytics Mining ~2015 Stream processing Digital

More information

How to Leverage Big Data in the Cloud to Gain Competitive Advantage

How to Leverage Big Data in the Cloud to Gain Competitive Advantage How to Leverage Big Data in the Cloud to Gain Competitive Advantage James Kobielus, IBM Big Data Evangelist Editor-in-Chief, IBM Data Magazine Senior Program Director, Product Marketing, Big Data Analytics

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

Oracle Big Data SQL Technical Update

Oracle Big Data SQL Technical Update Oracle Big Data SQL Technical Update Jean-Pierre Dijcks Oracle Redwood City, CA, USA Keywords: Big Data, Hadoop, NoSQL Databases, Relational Databases, SQL, Security, Performance Introduction This technical

More information

Hadoop. MPDL-Frühstück 9. Dezember 2013 MPDL INTERN

Hadoop. MPDL-Frühstück 9. Dezember 2013 MPDL INTERN Hadoop MPDL-Frühstück 9. Dezember 2013 MPDL INTERN Understanding Hadoop Understanding Hadoop What's Hadoop about? Apache Hadoop project (started 2008) downloadable open-source software library (current

More information

Developing Scalable Smart Grid Infrastructure to Enable Secure Transmission System Control

Developing Scalable Smart Grid Infrastructure to Enable Secure Transmission System Control Developing Scalable Smart Grid Infrastructure to Enable Secure Transmission System Control EP/K006487/1 UK PI: Prof Gareth Taylor (BU) China PI: Prof Yong-Hua Song (THU) Consortium UK Members: Brunel University

More information

Challenges for Data Driven Systems

Challenges for Data Driven Systems Challenges for Data Driven Systems Eiko Yoneki University of Cambridge Computer Laboratory Quick History of Data Management 4000 B C Manual recording From tablets to papyrus to paper A. Payberah 2014 2

More information

The Internet of Things and Big Data: Intro

The Internet of Things and Big Data: Intro The Internet of Things and Big Data: Intro John Berns, Solutions Architect, APAC - MapR Technologies April 22 nd, 2014 1 What This Is; What This Is Not It s not specific to IoT It s not about any specific

More information

The 4 Pillars of Technosoft s Big Data Practice

The 4 Pillars of Technosoft s Big Data Practice beyond possible Big Use End-user applications Big Analytics Visualisation tools Big Analytical tools Big management systems The 4 Pillars of Technosoft s Big Practice Overview Businesses have long managed

More information

Doctor of Philosophy in Computer Science

Doctor of Philosophy in Computer Science Doctor of Philosophy in Computer Science Background/Rationale The program aims to develop computer scientists who are armed with methods, tools and techniques from both theoretical and systems aspects

More information

Big Data Mining: Challenges and Opportunities to Forecast Future Scenario

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

More information

2013-2014. school of computing, informatics, engineering SCAN SCAN THIS PAGE SCAN THIS PAGE WITH LAYAR WITH LAYAR SCAN WITH LAYAR WITH LAYAR

2013-2014. school of computing, informatics, engineering SCAN SCAN THIS PAGE SCAN THIS PAGE WITH LAYAR WITH LAYAR SCAN WITH LAYAR WITH LAYAR SCAN WITH LAYAR 2013-2014 SCAN WITH LAYAR school of computing, informatics, SCAN THIS PAGE SCAN THIS PAGE and WITH decision LAYAR systems WITH LAYAR engineering school of computing, informatics, and decision

More information

Smart Data THE driving force for industrial applications

Smart Data THE driving force for industrial applications Smart Data THE driving force for industrial applications European Data Forum Luxembourg, siemens.com The world is becoming digital User behavior is radically changing based on new business models Newspaper,

More information

International Journal of Engineering Research ISSN: 2348-4039 & Management Technology November-2015 Volume 2, Issue-6

International Journal of Engineering Research ISSN: 2348-4039 & Management Technology November-2015 Volume 2, Issue-6 International Journal of Engineering Research ISSN: 2348-4039 & Management Technology Email: editor@ijermt.org November-2015 Volume 2, Issue-6 www.ijermt.org Modeling Big Data Characteristics for Discovering

More information

Session 1: IT Infrastructure Security Vertica / Hadoop Integration and Analytic Capabilities for Federal Big Data Challenges

Session 1: IT Infrastructure Security Vertica / Hadoop Integration and Analytic Capabilities for Federal Big Data Challenges Session 1: IT Infrastructure Security Vertica / Hadoop Integration and Analytic Capabilities for Federal Big Data Challenges James Campbell Corporate Systems Engineer HP Vertica jcampbell@vertica.com Big

More information

Unlocking the Intelligence in. Big Data. Ron Kasabian General Manager Big Data Solutions Intel Corporation

Unlocking the Intelligence in. Big Data. Ron Kasabian General Manager Big Data Solutions Intel Corporation Unlocking the Intelligence in Big Data Ron Kasabian General Manager Big Data Solutions Intel Corporation Volume & Type of Data What s Driving Big Data? 10X Data growth by 2016 90% unstructured 1 Lower

More information

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

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

More information

ANALYTICS CENTER LEARNING PROGRAM

ANALYTICS CENTER LEARNING PROGRAM Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals

More information

IEEE JAVA Project 2012

IEEE JAVA Project 2012 IEEE JAVA Project 2012 Powered by Cloud Computing Cloud Computing Security from Single to Multi-Clouds. Reliable Re-encryption in Unreliable Clouds. Cloud Data Production for Masses. Costing of Cloud Computing

More information

BIG DATA & DATA SCIENCE

BIG DATA & DATA SCIENCE BIG DATA & DATA SCIENCE ACADEMY PROGRAMS IN-COMPANY TRAINING PORTFOLIO 2 TRAINING PORTFOLIO 2016 Synergic Academy Solutions BIG DATA FOR LEADING BUSINESS Big data promises a significant shift in the way

More information

Sanjeev Kumar. contribute

Sanjeev Kumar. contribute RESEARCH ISSUES IN DATAA MINING Sanjeev Kumar I.A.S.R.I., Library Avenue, Pusa, New Delhi-110012 sanjeevk@iasri.res.in 1. Introduction The field of data mining and knowledgee discovery is emerging as a

More information

Integrating a Big Data Platform into Government:

Integrating a Big Data Platform into Government: Integrating a Big Data Platform into Government: Drive Better Decisions for Policy and Program Outcomes John Haddad, Senior Director Product Marketing, Informatica Digital Government Institute s Government

More information

The Next Big Thing in the Internet of Things: Real-time Big Data Analytics

The Next Big Thing in the Internet of Things: Real-time Big Data Analytics The Next Big Thing in the Internet of Things: Real-time Big Data Analytics Dale Skeen CTO and Co-Founder 2014. VITRIA TECHNOLOGY, INC. All rights reserved. Internet of Things (IoT) Devices > People In

More information

Big Data Executive Survey

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

More information

Complexity and Scalability in Semantic Graph Analysis Semantic Days 2013

Complexity and Scalability in Semantic Graph Analysis Semantic Days 2013 Complexity and Scalability in Semantic Graph Analysis Semantic Days 2013 James Maltby, Ph.D 1 Outline of Presentation Semantic Graph Analytics Database Architectures In-memory Semantic Database Formulation

More information

ANALYTICS BUILT FOR INTERNET OF THINGS

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

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

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

The 3 questions to ask yourself about BIG DATA

The 3 questions to ask yourself about BIG DATA The 3 questions to ask yourself about BIG DATA Do you have a big data problem? Companies looking to tackle big data problems are embarking on a journey that is full of hype, buzz, confusion, and misinformation.

More information

Getting Started Practical Input For Your Roadmap

Getting Started Practical Input For Your Roadmap Getting Started Practical Input For Your Roadmap Mike Ferguson Managing Director, Intelligent Business Strategies BA4ALL Big Data & Analytics Insight Conference Stockholm, May 2015 About Mike Ferguson

More information

Building the Internet of Things Jim Green - CTO, Data & Analytics Business Group, Cisco Systems

Building the Internet of Things Jim Green - CTO, Data & Analytics Business Group, Cisco Systems Building the Internet of Things Jim Green - CTO, Data & Analytics Business Group, Cisco Systems Brian McCarson Sr. Principal Engineer & Sr. System Architect, Internet of Things Group, Intel Corp Mac Devine

More information

Information Infrastructure for Archiving & Integrating Primary Archaeological Data

Information Infrastructure for Archiving & Integrating Primary Archaeological Data Information Infrastructure for Archiving & Integrating Primary Archaeological Data Keith W. Kintigh kintigh@asu.edu Arizona State University Tempe, Arizona 85287-2402, US Principal Collaborators: K. Selçuk

More information

Dept. of Financial Information Security

Dept. of Financial Information Security Dept. of Financial Information Security Department of Financial Information Security offers an excellent education and interdisciplinary cutting-edge research programs to train future leaders and innovators

More information

Addressing Open Source Big Data, Hadoop, and MapReduce limitations

Addressing Open Source Big Data, Hadoop, and MapReduce limitations Addressing Open Source Big Data, Hadoop, and MapReduce limitations 1 Agenda What is Big Data / Hadoop? Limitations of the existing hadoop distributions Going enterprise with Hadoop 2 How Big are Data?

More information

Professional Organization Checklist for the Computer Information Systems Curriculum

Professional Organization Checklist for the Computer Information Systems Curriculum Professional Organization Checklist f the Computer Infmation Systems Curriculum Association of Computing Machinery and Association of Infmation Systems IS 2002 Model Curriculum and Guidelines f Undergraduate

More information

Big Data Analytics Platform @ Nokia

Big Data Analytics Platform @ Nokia Big Data Analytics Platform @ Nokia 1 Selecting the Right Tool for the Right Workload Yekesa Kosuru Nokia Location & Commerce Strata + Hadoop World NY - Oct 25, 2012 Agenda Big Data Analytics Platform

More information

Big Data and Semantic Web in Manufacturing. Nitesh Khilwani, PhD Chief Engineer, Samsung Research Institute Noida, India

Big Data and Semantic Web in Manufacturing. Nitesh Khilwani, PhD Chief Engineer, Samsung Research Institute Noida, India Big Data and Semantic Web in Manufacturing Nitesh Khilwani, PhD Chief Engineer, Samsung Research Institute Noida, India Outline Big data in Manufacturing Big data Analytics Semantic web technologies Case

More information

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data

Alexander Nikov. 5. Database Systems and Managing Data Resources. Learning Objectives. RR Donnelley Tries to Master Its Data INFO 1500 Introduction to IT Fundamentals 5. Database Systems and Managing Data Resources Learning Objectives 1. Describe how the problems of managing data resources in a traditional file environment are

More information

Big Data R&D Initiative

Big Data R&D Initiative Big Data R&D Initiative Howard Wactlar CISE Directorate National Science Foundation NIST Big Data Meeting June, 2012 Image Credit: Exploratorium. The Landscape: Smart Sensing, Reasoning and Decision Environment

More information

The Future of Business Analytics is Now! 2013 IBM Corporation

The Future of Business Analytics is Now! 2013 IBM Corporation The Future of Business Analytics is Now! 1 The pressures on organizations are at a point where analytics has evolved from a business initiative to a BUSINESS IMPERATIVE More organization are using analytics

More information

PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS.

PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS. PSG College of Technology, Coimbatore-641 004 Department of Computer & Information Sciences BSc (CT) G1 & G2 Sixth Semester PROJECT DETAILS Project Project Title Area of Abstract No Specialization 1. Software

More information

Master of Science in Computer Science

Master of Science in Computer Science Master of Science in Computer Science Background/Rationale The MSCS program aims to provide both breadth and depth of knowledge in the concepts and techniques related to the theory, design, implementation,

More information

Big-Data Computing with Smart Clouds and IoT Sensing

Big-Data Computing with Smart Clouds and IoT Sensing A New Book from Wiley Publisher to appear in late 2016 or early 2017 Big-Data Computing with Smart Clouds and IoT Sensing Kai Hwang, University of Southern California, USA Min Chen, Huazhong University

More information

Beyond Watson: The Business Implications of Big Data

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

More information

Graduate Co-op Students Information Manual. Department of Computer Science. Faculty of Science. University of Regina

Graduate Co-op Students Information Manual. Department of Computer Science. Faculty of Science. University of Regina Graduate Co-op Students Information Manual Department of Computer Science Faculty of Science University of Regina 2014 1 Table of Contents 1. Department Description..3 2. Program Requirements and Procedures

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 platform for IoT Cloud Analytics. Chen Admati, Advanced Analytics, Intel

Big data platform for IoT Cloud Analytics. Chen Admati, Advanced Analytics, Intel Big data platform for IoT Cloud Analytics Chen Admati, Advanced Analytics, Intel Agenda IoT @ Intel End-to-End offering Analytics vision Big data platform for IoT Cloud Analytics Platform Capabilities

More information

What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy

What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy What do Big Data & HAVEn mean? Robert Lejnert HP Autonomy Much higher Volumes. Processed with more Velocity. With much more Variety. Is Big Data so big? Big Data Smart Data Project HAVEn: Adaptive Intelligence

More information

Big Data & Security. Aljosa Pasic 12/02/2015

Big Data & Security. Aljosa Pasic 12/02/2015 Big Data & Security Aljosa Pasic 12/02/2015 Welcome to Madrid!!! Big Data AND security: what is there on our minds? Big Data tools and technologies Big Data T&T chain and security/privacy concern mappings

More information

3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India

3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-2016) March 10-11, 2016 VIT University, Chennai, India Call for Papers Cloud computing has emerged as a de facto computing

More information

BIG Data Analytics Move to Competitive Advantage

BIG Data Analytics Move to Competitive Advantage BIG Data Analytics Move to Competitive Advantage where is technology heading today Standardization Open Source Automation Scalability Cloud Computing Mobility Smartphones/ tablets Internet of Things Wireless

More information

Amplify Serviceability and Productivity by integrating machine /sensor data with Data Science

Amplify Serviceability and Productivity by integrating machine /sensor data with Data Science Data Science & Big Data Practice INSIGHTS ANALYTICS INNOVATIONS Manufacturing IoT Amplify Serviceability and Productivity by integrating machine /sensor data with Data Science What is Internet of Things

More information