How To Create A Social Data Science Lab In The Belgium
|
|
- Scott Gregory
- 3 years ago
- Views:
Transcription
1 Crowdsourcing in Enterprise Environments Dr. ir. Alessandro Bozzon Delft University of Technology Web Information Systems Delft Social Data Science Lab Kick-off KIVI Leerstoel Big Data Science Den Haag - June 10th 2015
2 My Why
3 Complex Systems Created By People Populated by people Kowloon Walled City, Hong Kong
4 Complex Systems Created By People Driven by people
5 Intelligent = IT Prescriptive, centralised design The Environment should fit the software Data should fit the software Users should fit the software Computing science => Efficiency Efficient Software => Efficient Systems Credits: Geert-Jan Houben, Dies Natalis 2015, Tu Delft
6 A Web-driven Cultural Shift Decentralisation Openness & Linking Personalisation Adaptation Credits:
7 Intelligent = Data Data Machines Scale Speed Sustainability
8 Intelligent = Data Semantics Data Machines Scale Speed Sustainability Semantics
9 Intelligent = Social Data Semantics People Understanding Create Analyse Interpret Engage & Retain Data Describe People Machines Semantics
10 Social Data Science From the people By the people For the people Creation Implicit Vs. Explicit Organically Vs. On Demand Sources Mobile Phones Social Media (Personal) Sensors Annotations To train machines Analysis When machine cannot Interpretation Culture, Context Multiple Domains Knowledge Generation Well-being City Life Enterprise In The Real World The World is My Lab HCI Network Analysis Sociology Cognitive Psychology Knowledge Discovery Data Mining Behavioural Economics Collective Intelligence Security & Privacy Software Engineering Domain Specific Expertise
11 Scientific Challenge How can humans and machines better collaborate in computation problems?
12 Takeaway Message More Machines - Scalability Big Data Big Computation Conventional Computation Social Computers Human Computation More People - Human Intelligence
13 Societal Challenges How will technology influence the creation and distribution of wealth and well-being? What will be the role of humans in the (near) future?
14 Crowdsourcing
15 The global opportunity in online outsourcing. June The World Bank The contracting of third-party workers and providers to supply services or perform tasks via Internet-based marketplaces or platforms.
16 Workers Infrastructure Payment Clients Online Outsourcing Firms
17 Crowdsourcing Types Microwork Online Freelancing Task of seconds/minutes Basic literacy and numeracy skills High Availability Fast Response Times Contract Services Task can take days/weeks Technical or Professional Skills More similar to traditional outsourcing
18 Crowdsourcing Market Million Workers 2B Gross Service Revenue B Gross Service Revenue B Gross Service Revenue Microwork => driven mainly by medium/large enterprises Online Freelancing => small/medium enterprises Workers are mainly millenials Educated (or being educated) Driven by income generation
19 Crowdsourcing and Data Science
20 Completely Automated Public Turing Test To Tell Computers and Humans Apart Luis von Ahn, Manuel Blum, Nicholas J. Hopper, and John Langford. Eurocrypt, 2000
21 100 million CAPTCHAs every day 100 million users typing words How can this insane amount of work be exploited?
22 Does it work? As of 2012, thirty years of The New York Times had been digitalised
23 Which data (creation / analysis / interpretation) tasks cannot satisfactorily performed by computers (yet)?
24 Object Detection, Recognition, Identification Ordering Image Annotation Clustering
25 Emulating Human Computers Alan Turing wrote in 1950: The idea behind digital computers may be explained by saying that these machines are intended to carry out any operations which could be done by a human computer Computer scientists (in the artificial intelligence field) have been trying to emulate human abilities Language Visual processing Reasoning Now we need humans again for AI-complete tasks
26 The Human Advantage Electronic Fast Determinist Arithmetic Human Slow Inconsistent/Noisy But better at Perception, Preference / Aesthetic Judgment,Creativity, Emotions
27 Algorithm INPUT OUTPUT Human Computation Computation performed by humans to help computers executed computational tasks they cannot efficiently and/or effectively solve yet
28 Applications in Data Science Information Extraction Schema Matching Entity Resolution Data spaces Building structured KBs Sorting Top-k Graph Search Mining and Classification Social Media Analysis NLP Text Summarisation Sentiment Analysis Search
29 A vision of Social Computers Humans as First Class Computational Units How to employ human computational resources efficiently and effectively to accommodate large volumes of heterogeneous data with variable quality?
30 What How hard is the problem? Is it efficiently solvable? Trade-off between human versus machine? How Who Is the human computation algorithm correct and efficient? How do to coordinate the work of many human computers? More machines Machines using people e.g., human computation People using machines e.g., collective action To whom do we route each task, and how? How to motivate participation, and incentive truthful outputs? More people
31 Infrastructures for efficient and effective hybrid data management systems Challenge Crowdsearcher Framework (with API) Query Answer Specification paradigm SE Access Interface Query Interface Search Execution Engine Local Source Access Interface Human Access Interface Human Interaction Management Social Networks Q&A Crowdsource platforms Reactive execution and control environment Hybrid computation flows Crowds from heterogeneous systems US PATENT US B2 - Method and system of management of queries for crowd searching
32 Pervasive Crowdsourcing Vision Perform tasks every time everywhere
33 Experts retrieval for knowledge-intensive data creation, analysis and interpretation tasks Challenge Novel metrics and strategies for expertise retrieval, assessment, creation
34 Bias / Veracity Challenge Social Data is nuanced by culture, context, background uncertain in expression and content inconsistent, Ambiguous, Deceptive Lack of Veracity is a challenge Hampers reliability of analysis Supports wrong interpretations But often it is an opportunity Reality can be perceived different ways Bias and diversity can be desirable data properties
35 Systematically create capacity for long-running human data management activities Challenge Workers Work Novel metrics and strategies for (crowd) engagement and retainment Sourcing
36 Veracity By Design Social'Data'Source Vision Crowd Creation Analysis Interpretation Task Modeling Crowd Modeling Skills Expertise Availability Sense5 making Personality Knowledge need Routing The$right crowd Workflow Modeling Control3& Optimization Money Fun Glory Duty Motivations Knowledge
37 Crowdsourcing in the Enterprise
38
39 How Can Crowdsourcing and Human Computation be Employed in your Company?
40 Examples Launching innovation activities Developing / Testing Software IT Inventory Management and Support Develop Business Strategies Assessing resumes of Job Candidates Support document and translation management E-commerce and the Internet Advertising and PR
41 Cultural Heritage Annotation Identification and engagement with niches of people with the right expertise for content annotation
42 Intelligent Cities www://social-glass.org Provide actionable insights about people in urban environments Offer extensible social sensing and social interaction tools Targets Urban phenomena Mobility Environment Social City Life
43 Enterprise (Social) Networks Professional Social Networks Enterprise Directory Enterprise Social Networks Personal Social Networks Enterprise Blogs Personal Blogs Communications Enterprise Q&A Examples of Applications Our study Expertise Elicitation and Retrieval Discovery of (latent) Relationship Networks Topical Bus Factor Environmental Sensing Source: Bozzon, Efstathiades, Houben, Sips. A study of the online profile of enterprise users in professional social networks. WWW 2014.
44 Employee Engagement Only 30% of US workforce is engaged in their work Learning Goal Foster positive behaviour in employees to achieve important business needs Social online interaction Spread awareness outside the company Sense of Belonging
45 Next?
46 Research Agenda Goal Employ human computational resources efficiently and effectively to accommodate large volumes of heterogeneous data with variable quality What? Novel methods and tools for Social Data Science User Modelling (expertise, hard-skills, soft-skills) Hybrid Computation Systems (combine machines and humans) User Engagement (motivation and incentives) Crowd Sensing How? Empirical, data-driven research Strong focus on value creation (e.g. prototypes)
47 Help us improving the state-of-the-art Real-world uses cases: Difficult data analysis problems (at scale) Data sense-making problems (at scale) Domain Expertise, to Develop new models Create optimised methods
48 Together Advance the state-of-the-art Create societal and business impact Educate the next generation of engineers
49
50 Contact Dr. ir. Alessandro Bozzon Web : Mail : a.bozzon@tudelft.nl
Social Data Science for Intelligent Cities
Social Data Science for Intelligent Cities The Role of Social Media for Sensing Crowds Prof.dr.ir. Geert-Jan Houben TU Delft Web Information Systems & Delft Data Science WIS - Web Information Systems Why
More informationThe Science of Social Data. Geert-Jan Houben. TU Delft. Web Information Systems & Delft Data Science. WIS - Web Information Systems.
The Science of Social Data Geert-Jan Houben TU Delft Web Information Systems & Delft Data Science 1 Intelligent typically information technology, computing science, and a natural focus on software Intelligent
More informationBig Data Science. Prof.dr.ir. Geert-Jan Houben. TU Delft Web Information Systems Delft Data Science KIVI chair Big Data Science
Big Data Science Prof.dr.ir. Geert-Jan Houben TU Delft Web Information Systems Delft Data Science KIVI chair Big Data Science 1 big data: it s there, it s important it is interesting to study it, to understand
More informationCSC384 Intro to Artificial Intelligence
CSC384 Intro to Artificial Intelligence What is Artificial Intelligence? What is Intelligence? Are these Intelligent? CSC384, University of Toronto 3 What is Intelligence? Webster says: The capacity to
More informationBig 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 informationCrowdsourcing for Big Data Analytics
KYOTO UNIVERSITY Crowdsourcing for Big Data Analytics Hisashi Kashima (Kyoto University) Satoshi Oyama (Hokkaido University) Yukino Baba (Kyoto University) DEPARTMENT OF INTELLIGENCE SCIENCE AND TECHNOLOGY
More informationProposal for the Theme on Big Data. Analytics. Qiang Yang, HKUST Jiannong Cao, PolyU Qi-man Shao, CUHK. May 2015
Proposal for the Theme on Big Data Analytics May 2015 Qiang Yang, HKUST Jiannong Cao, PolyU Qi-man Shao, CUHK Motivation The world's technological per-capita capacity to store information doubled every
More informationHow To Make Sense Of Data With Altilia
HOW TO MAKE SENSE OF BIG DATA TO BETTER DRIVE BUSINESS PROCESSES, IMPROVE DECISION-MAKING, AND SUCCESSFULLY COMPETE IN TODAY S MARKETS. ALTILIA turns Big Data into Smart Data and enables businesses to
More informationCOMP9321 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 informationMaster of Science in Marketing Analytics (MSMA)
Master of Science in Marketing Analytics (MSMA) COURSE DESCRIPTION The Master of Science in Marketing Analytics program teaches students how to become more engaged with consumers, how to design and deliver
More informationSocial Media Implementations
SEM Experience Analytics Social Media Implementations SEM Experience Analytics delivers real sentiment, meaning and trends within social media for many of the world s leading consumer brand companies.
More informationIEEE IoT IoT Scenario & Use Cases: Social Sensors
IEEE IoT IoT Scenario & Use Cases: Social Sensors Service Description More and more, people have the possibility to monitor important parameters in their home or in their surrounding environment. As an
More informationIC05 Introduction on Networks &Visualization Nov. 2009. <mathieu.bastian@gmail.com>
IC05 Introduction on Networks &Visualization Nov. 2009 Overview 1. Networks Introduction Networks across disciplines Properties Models 2. Visualization InfoVis Data exploration
More informationDelft Data Science Seminar January 26, 2015
Delft Data Science Seminar January 26, 2015 Big Data Analytics for Cyber Situational Awareness 1 100 billions in economic and societal value Content Creation millions of new jobs and millions of new talent
More informationWhat 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 informationInternship Opportunities Xerox Research Centre India (XRCI), Bangalore Analytics Research Group
Analytics Research Group The Analytics Research Group in Xerox Research Centre India (XRCI) is seeking bright Undergraduate, Masters and PhD students for research internships to participate in exciting
More informationEPSRC Cross-SAT Big Data Workshop: Well Sorted Materials
EPSRC Cross-SAT Big Data Workshop: Well Sorted Materials 5th August 2015 Contents Introduction 1 Dendrogram 2 Tree Map 3 Heat Map 4 Raw Group Data 5 For an online, interactive version of the visualisations
More informationA Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks
A Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks Text Analytics World, Boston, 2013 Lars Hard, CTO Agenda Difficult text analytics tasks Feature extraction Bio-inspired
More informationBIG 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 informationMultistep Dynamic Expert Sourcing
+33 1 69 33 59 59 MULTISTEP DYNAMIC EXPERT SOURCING 1 A Novel Approach for Open Innovation Platforms Multistep Dynamic Expert Sourcing Albert Meige & Boris Golden August 2010 X- Technologies Ecole Polytechnique
More informationManjula Ambur NASA Langley Research Center April 2014
Manjula Ambur NASA Langley Research Center April 2014 Outline What is Big Data Vision and Roadmap Key Capabilities Impetus for Watson Technologies Content Analytics Use Potential use cases What is Big
More informationVeracity of data. New approaches are emerging to account for uncertainty in data at a giant scale. 2013 IBM Corporation
Veracity of data 1. The degree to which data is accurate, reliable, certain 2. An emerging platform for organizing, understanding and deriving value from big data Introduction Financial decisions require
More informationTowards 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 informationThe Big Data Paradigm Shift. Insight Through Automation
The Big Data Paradigm Shift Insight Through Automation Agenda The Problem Emcien s Solution: Algorithms solve data related business problems How Does the Technology Work? Case Studies 2013 Emcien, Inc.
More informationCOURSE DESCRIPTIONS 科 目 簡 介
COURSE DESCRIPTIONS 科 目 簡 介 COURSES FOR 4-YEAR UNDERGRADUATE PROGRAMMES PSY2101 Introduction to Psychology (3 credits) The purpose of this course is to introduce fundamental concepts and theories in psychology
More informationI D C A N A L Y S T C O N N E C T I O N. C o g n i t i ve C o m m e r c e i n B2B M a rketing a n d S a l e s
I D C A N A L Y S T C O N N E C T I O N Dave Schubmehl Research Director, Cognitive Systems and Content Analytics Greg Girard Program Director, Omni-Channel Retail Analytics Strategies C o g n i t i ve
More informationBCS HIGHER EDUCATION QUALIFICATIONS Level 6 Professional Graduate Diploma in IT. March 2013 EXAMINERS REPORT. Knowledge Based Systems
BCS HIGHER EDUCATION QUALIFICATIONS Level 6 Professional Graduate Diploma in IT March 2013 EXAMINERS REPORT Knowledge Based Systems Overall Comments Compared to last year, the pass rate is significantly
More informationTHE BRITISH LIBRARY. Unlocking The Value. The British Library s Collection Metadata Strategy 2015-2018. Page 1 of 8
THE BRITISH LIBRARY Unlocking The Value The British Library s Collection Metadata Strategy 2015-2018 Page 1 of 8 Summary Our vision is that by 2020 the Library s collection metadata assets will be comprehensive,
More informationWhy big data? Lessons from a Decade+ Experiment in Big Data
Why big data? Lessons from a Decade+ Experiment in Big Data David Belanger PhD Senior Research Fellow Stevens Institute of Technology dbelange@stevens.edu 1 What Does Big Look Like? 7 Image Source Page:
More informationData Catalogs for Hadoop Achieving Shared Knowledge and Re-usable Data Prep. Neil Raden Hired Brains Research, LLC
Data Catalogs for Hadoop Achieving Shared Knowledge and Re-usable Data Prep Neil Raden Hired Brains Research, LLC Traditionally, the job of gathering and integrating data for analytics fell on data warehouses.
More informationText Mining - Scope and Applications
Journal of Computer Science and Applications. ISSN 2231-1270 Volume 5, Number 2 (2013), pp. 51-55 International Research Publication House http://www.irphouse.com Text Mining - Scope and Applications Miss
More informationUNIVERSITY OF INFINITE AMBITIONS. MASTER OF SCIENCE COMPUTER SCIENCE DATA SCIENCE AND SMART SERVICES
UNIVERSITY OF INFINITE AMBITIONS. MASTER OF SCIENCE COMPUTER SCIENCE DATA SCIENCE AND SMART SERVICES MASTER S PROGRAMME COMPUTER SCIENCE - DATA SCIENCE AND SMART SERVICES (DS3) This is a specialization
More informationFogbeam Vision Series - The Modern Intranet
Fogbeam Labs Cut Through The Information Fog http://www.fogbeam.com Fogbeam Vision Series - The Modern Intranet Where It All Started Intranets began to appear as a venue for collaboration and knowledge
More informationSeminar Intelligent Cities: Sustainability and Big Data
Seminar Intelligent Cities: Sustainability and Big Data BK City, 9 th of January 2015 1 29/01/2015 Seminar at BK City 2 29/01/2015 Seminar at BK City Andy van den Dobbelsteen Past generations Stone, Bronze,
More informationInternational 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 informationComputer Science Electives and Clusters
Course Number CSCI- Computer Science Electives and Clusters Computer Science electives belong to one or more groupings called clusters. Undergraduate students with the proper prerequisites are permitted
More informationBUDT 758B-0501: Big Data Analytics (Fall 2015) Decisions, Operations & Information Technologies Robert H. Smith School of Business
BUDT 758B-0501: Big Data Analytics (Fall 2015) Decisions, Operations & Information Technologies Robert H. Smith School of Business Instructor: Kunpeng Zhang (kzhang@rmsmith.umd.edu) Lecture-Discussions:
More informationCombining Social Data and Semantic Content Analysis for L Aquila Social Urban Network
I-CiTies 2015 2015 CINI Annual Workshop on ICT for Smart Cities and Communities Palermo (Italy) - October 29-30, 2015 Combining Social Data and Semantic Content Analysis for L Aquila Social Urban Network
More informationMEDICAL DATA MINING. Timothy Hays, PhD. Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012
MEDICAL DATA MINING Timothy Hays, PhD Health IT Strategy Executive Dynamics Research Corporation (DRC) December 13, 2012 2 Healthcare in America Is a VERY Large Domain with Enormous Opportunities for Data
More informationWhy Big Data Analytics?
An ebook by Datameer Why Big Data Analytics? Three Business Challenges Best Addressed Using Big Data Analytics It s hard to overstate the importance of data for businesses today. It s the lifeline of any
More informationI N T E L L I G E N T S O L U T I O N S, I N C. DATA MINING IMPLEMENTING THE PARADIGM SHIFT IN ANALYSIS & MODELING OF THE OILFIELD
I N T E L L I G E N T S O L U T I O N S, I N C. OILFIELD DATA MINING IMPLEMENTING THE PARADIGM SHIFT IN ANALYSIS & MODELING OF THE OILFIELD 5 5 T A R A P L A C E M O R G A N T O W N, W V 2 6 0 5 0 USA
More informationInformation Visualization WS 2013/14 11 Visual Analytics
1 11.1 Definitions and Motivation Lot of research and papers in this emerging field: Visual Analytics: Scope and Challenges of Keim et al. Illuminating the path of Thomas and Cook 2 11.1 Definitions and
More informationBIG 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 informationMasters in Information Technology
Computer - Information Technology MSc & MPhil - 2015/6 - July 2015 Masters in Information Technology Programme Requirements Taught Element, and PG Diploma in Information Technology: 120 credits: IS5101
More informationData Analytics in Health Care
Data Analytics in Health Care ONUP 2016 April 4, 2016 Presented by: Dennis Giokas, CTO, Innovation Ecosystem Group A lot of data, but limited information 2 Data collection might be the single greatest
More informationInnovations in Big Data Analytics (Technical Insights)
Brochure More information from http://www.researchandmarkets.com/reports/2725522/ Innovations in Big Data Analytics (Technical Insights) Description: The exponential growth of digital data has been well
More informationThe Database Systems and Information Management Group at Technische Universität Berlin
Group at Technische Universität Berlin 1 Introduction Group, in German known by the acronym DIMA, is part of the Department of Software Engineering and Theoretical Computer Science at the TU Berlin. It
More informationWhat is Visual Analytics?
What is Visual Analytics? Methods@Manchester Oscar de Bruijn Decision and Cognitive Sciences Manchester Business School 1 Overview What is the problem? How does Visual Analytics offer a solution What is
More informationKnowledge Management
Knowledge Management Management Information Code: 164292-02 Course: Management Information Period: Autumn 2013 Professor: Sync Sangwon Lee, Ph. D D. of Information & Electronic Commerce 1 00. Contents
More informationBIG DATA TECHNOLOGY. Hadoop Ecosystem
BIG DATA TECHNOLOGY Hadoop Ecosystem Agenda Background What is Big Data Solution Objective Introduction to Hadoop Hadoop Ecosystem Hybrid EDW Model Predictive Analysis using Hadoop Conclusion What is Big
More informationCollaborations 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 informationTechnical Club: New Vision of Computing
1 Technical Club: New Vision of Computing Core Discipline : Mentor : Computer Science Engineering Dr. Shripal Vijayvergia, Associate Professor, CSE Co-Mentor : 1. Mr. Subhash Gupta, Assistant Professor,
More informationICT Perspectives on Big Data: Well Sorted Materials
ICT Perspectives on Big Data: Well Sorted Materials 3 March 2015 Contents Introduction 1 Dendrogram 2 Tree Map 3 Heat Map 4 Raw Group Data 5 For an online, interactive version of the visualisations in
More informationGEOGRAPHIC CONTEXT ANALYSIS OF VOLUNTEERED INFORMATION
GEOGRAPHIC CONTEXT ANALYSIS OF VOLUNTEERED INFORMATION (GEOCONAVI) Frank O. Ostermann COST Energic Meeting 26.05.2014, Zürich GEOGRAPHIC CONTEXT ANALYSIS OF VOLUNTEERED INFORMATION PRESENTATION OVERVIEW
More informationBig Data in Pictures: Data Visualization
Big Data in Pictures: Data Visualization Huamin Qu Hong Kong University of Science and Technology What is data visualization? Data visualization is the creation and study of the visual representation of
More information3rd 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 information14:30 Watson applicaties bouwen met IBM Bluemix
A New Era of Thinking IBM BusinessConnect A New Era of Thinking 14:30 Watson applicaties bouwen met IBM Bluemix Rob Pennock pennock@nl.ibm.com Software Architect - IBM Cloud 1 2016 IBM Corporation What
More informationUsing Provenance to Improve Workflow Design
Using Provenance to Improve Workflow Design Frederico T. de Oliveira, Leonardo Murta, Claudia Werner, Marta Mattoso COPPE/ Computer Science Department Federal University of Rio de Janeiro (UFRJ) {ftoliveira,
More informationMultichannel Customer Listening and Social Media Analytics
( Multichannel Customer Listening and Social Media Analytics KANA Experience Analytics Lite is a multichannel customer listening and social media analytics solution that delivers sentiment, meaning and
More informationText Analytics for Competitive Analysis and Market Intelligence Aiaioo Labs - 2011
Text Analytics for Competitive Analysis and Market Intelligence Aiaioo Labs - 2011 Bangalore, India Title Text Analytics Introduction Entity Person Comparative Analysis Entity or Event Text Analytics Text
More informationResearch and Innovation Strategy: delivering a flexible workforce receptive to research and innovation
Research and Innovation Strategy: delivering a flexible workforce receptive to research and innovation Contents List of Abbreviations 3 Executive Summary 4 Introduction 5 Aims of the Strategy 8 Objectives
More informationCognitive z. Mathew Thoennes IBM Research System z Research June 13, 2016
Cognitive z Mathew Thoennes IBM Research System z Research June 13, 2016 Agenda What is Cognitive? Watson Explorer Overview Demo What is cognitive? Cognitive analytics - A set of technologies and processes
More informationDoctor 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 informationCRM as a Service. For Customers in the Cloud
CRM as a Service For Customers in the Cloud Customer Relationship Management Our mission: to help our customer identify, define, design and deliver the best CRM strategy, in terms of For our Customer with
More informationAn interdisciplinary model for analytics education
An interdisciplinary model for analytics education Raffaella Settimi, PhD School of Computing, DePaul University Drew Conway s Data Science Venn Diagram http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
More informationScience 2.0 & Big Data Science 2.0 Conference, Hamburg, March 25, 2015
Science 2.0 & Big Data Science 2.0 Conference, Hamburg, March 25, 2015 b Prof. Dr. Stefanie Lindstaedt b www.know-center.at Know-Center GmbH Know-Center Austria s Research Center for Data-driven Business
More informationConclusions and Further Work
Conclusions and Further Work Page 245 CHAPTER EIGHT Conclusions and Further Work This final chapter brings the thesis to a close by returning to the agenda which was established in chapter 1. It summarises
More informationHealth Informatics and Artificial Intelligence: the next big thing in health/aged care
Health Informatics and Artificial Intelligence: the next big thing in health/aged care Professor Michael Blumenstein Griffith University ACSA National Conference, Adelaide Tuesday, September 9 th 2014
More informationBig Data Research in the AMPLab: BDAS and Beyond
Big Data Research in the AMPLab: BDAS and Beyond Michael Franklin UC Berkeley 1 st Spark Summit December 2, 2013 UC BERKELEY AMPLab: Collaborative Big Data Research Launched: January 2011, 6 year planned
More informationDirect-to-Company Feedback Implementations
SEM Experience Analytics Direct-to-Company Feedback Implementations SEM Experience Analytics Listening System for Direct-to-Company Feedback Implementations SEM Experience Analytics delivers real sentiment,
More informationAppendices master s degree programme Artificial Intelligence 2014-2015
Appendices master s degree programme Artificial Intelligence 2014-2015 Appendix I Teaching outcomes of the degree programme (art. 1.3) 1. The master demonstrates knowledge, understanding and the ability
More informationIDC US UPCOMING EVENT CALENDAR
IDC US UPCOMING EVENT CALENDAR Software as a Service (SaaS) Summit Contact: Patty Caron, Program Director, pcaron@idc.com Lead Analysts: Michael Fauscette and Robert P. Mahowald September 17, 2008 San
More informationSocial Semantic Emotion Analysis for Innovative Multilingual Big Data Analytics Markets
Social Semantic Emotion Analysis for Innovative Multilingual Big Data Analytics Markets D7.11 Detailed Training Activities Plan Project ref. no H2020 141111 Project acronym Start date of project (dur.)
More informationWhite Paper. Data Mining for Business
White Paper Data Mining for Business January 2010 Contents 1. INTRODUCTION... 3 2. WHY IS DATA MINING IMPORTANT?... 3 FUNDAMENTALS... 3 Example 1...3 Example 2...3 3. OPERATIONAL CONSIDERATIONS... 4 ORGANISATIONAL
More informationScalable End-User Access to Big Data http://www.optique-project.eu/ HELLENIC REPUBLIC National and Kapodistrian University of Athens
Scalable End-User Access to Big Data http://www.optique-project.eu/ HELLENIC REPUBLIC National and Kapodistrian University of Athens 1 Optique: Improving the competitiveness of European industry For many
More informationSearch and Data Mining: Techniques. Introduction Anna Yarygina Boris Novikov
Search and Data Mining: Techniques Introduction Anna Yarygina Boris Novikov Data Analytics: Conference Sections Fundamentals for data analytics Mechanisms and features Big Data Huge data Target analytics
More informationReflection Report International Semester
Reflection Report International Semester Studying abroad at KTH Royal Institute of Technology Stockholm 18-01-2011 Chapter 1: Personal Information Name and surname: Arts, Rick G. B. E-mail address: Department:
More informationRoadmapping Discussion Summary. Social Media and Linked Data for Emergency Response
Roadmapping Discussion Summary Social Media and Linked Data for Emergency Response V. Lanfranchi 1, S. Mazumdar 1, E. Blomqvist 2, C. Brewster 3 1 OAK Group, Department of Computer Science University of
More informationCRITEO INTERNSHIP PROGRAM 2015/2016
CRITEO INTERNSHIP PROGRAM 2015/2016 A. List of topics PLATFORM Topic 1: Build an API and a web interface on top of it to manage the back-end of our third party demand component. Challenge(s): Working with
More information1 st Symposium on Colossal Data and Networking (CDAN-2016) March 18-19, 2016 Medicaps Group of Institutions, Indore, India
1 st Symposium on Colossal Data and Networking (CDAN-2016) March 18-19, 2016 Medicaps Group of Institutions, Indore, India Call for Papers Colossal Data Analysis and Networking has emerged as a de facto
More informationBUSINESS-TO-BUSINESS MARKETING 2016-2017
BUSINESS-TO-BUSINESS MARKETING 2016-2017 September 2015 335 pages ISBN# 9781577832300 Published by Richard K. Miller & Associates (RKMA) PART I: MARKET OVERVIEW 1 BUSINESS-TO-BUSINESS MARKETING 1.1 B2B
More informationHOW TO DO A SMART DATA PROJECT
April 2014 Smart Data Strategies HOW TO DO A SMART DATA PROJECT Guideline www.altiliagroup.com Summary ALTILIA s approach to Smart Data PROJECTS 3 1. BUSINESS USE CASE DEFINITION 4 2. PROJECT PLANNING
More informationMLg. Big Data and Its Implication to Research Methodologies and Funding. Cornelia Caragea TARDIS 2014. November 7, 2014. Machine Learning Group
Big Data and Its Implication to Research Methodologies and Funding Cornelia Caragea TARDIS 2014 November 7, 2014 UNT Computer Science and Engineering Data Everywhere Lots of data is being collected and
More informationStatistical 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 informationBIG 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 informationThe 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 informationWhy Semantic Analysis is Better than Sentiment Analysis. A White Paper by T.R. Fitz-Gibbon, Chief Scientist, Networked Insights
Why Semantic Analysis is Better than Sentiment Analysis A White Paper by T.R. Fitz-Gibbon, Chief Scientist, Networked Insights Why semantic analysis is better than sentiment analysis I like it, I don t
More informationPromises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends
Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends Spring 2015 Thomas Hill, Ph.D. VP Analytic Solutions Dell Statistica Overview and Agenda Dell Software overview Dell in
More informationBig Data. What is Big Data? Over the past years. Big Data. Big Data: Introduction and Applications
Big Data Big Data: Introduction and Applications August 20, 2015 HKU-HKJC ExCEL3 Seminar Michael Chau, Associate Professor School of Business, The University of Hong Kong Ample opportunities for business
More informationDATA WAREHOUSE AND DATA MINING NECCESSITY OR USELESS INVESTMENT
Scientific Bulletin Economic Sciences, Vol. 9 (15) - Information technology - DATA WAREHOUSE AND DATA MINING NECCESSITY OR USELESS INVESTMENT Associate Professor, Ph.D. Emil BURTESCU University of Pitesti,
More informationMaster s Program in Information Systems
The University of Jordan King Abdullah II School for Information Technology Department of Information Systems Master s Program in Information Systems 2006/2007 Study Plan Master Degree in Information Systems
More informationMitel Professional Services Catalog for Contact Center JULY 2015 SWEDEN, DENMARK, FINLAND AND BALTICS RELEASE 1.0
Mitel Professional Services Catalog for Contact Center JULY 2015 SWEDEN, DENMARK, FINLAND AND BALTICS RELEASE 1.0 Contents MITEL PROFESSIONAL SERVICES DELIVERY METHODOLOGY... 2 CUSTOMER NEEDS... 2 ENGAGING
More informationBig Data Analytics for Every Organization
Big Data Analytics for Every Organization Cloud-based services give you the analytic power to reach greater heights 2014 Fair Isaac Corporation. All rights reserved. 1 In every industry, a handful of competitors
More informationStandards for Big Data in the Cloud
Standards for Big Data in the Cloud International Cloud Symposium 15/10/2013 Carola Carstens (Project Officer) DG CONNECT, Unit G3 Data Value Chain European Commission Outline 1) Data Value Chain Unit
More informationRequirements Analysis Concepts & Principles. Instructor: Dr. Jerry Gao
Requirements Analysis Concepts & Principles Instructor: Dr. Jerry Gao Requirements Analysis Concepts and Principles - Requirements Analysis - Communication Techniques - Initiating the Process - Facilitated
More informationSchool of Computer Science
School of Computer Science Head of School Professor S Linton Taught Programmes M.Sc. Advanced Computer Science Artificial Intelligence Computing and Information Technology Information Technology Human
More informationSystems for Fun and Profit
Department of Computing Building Internet-Scale Distributed Systems for Fun and Profit Peter Pietzuch prp@doc.ic.ac.uk Large-Scale Distributed Systems Group http://platypus.doc.ic.ac.uk Peter R. Pietzuch
More informationBig Data Management Assessed Coursework Two Big Data vs Semantic Web F21BD
Big Data Management Assessed Coursework Two Big Data vs Semantic Web F21BD Boris Mocialov (H00180016) MSc Software Engineering Heriot-Watt University, Edinburgh April 5, 2015 1 1 Introduction The purpose
More informationBig 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 informationFUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM
International Journal of Innovative Computing, Information and Control ICIC International c 0 ISSN 34-48 Volume 8, Number 8, August 0 pp. 4 FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT
More information