Crowdsourcing in Enterprise Environments

Size: px
Start display at page:

Download "Crowdsourcing in Enterprise Environments"

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 :

Social Data Science for Intelligent Cities

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 information

The 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. 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 information

Big 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 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 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

Crowdsourcing for Big Data Analytics

Crowdsourcing 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 information

Use Case 4 in Technology Provider A Corporate Knowledge Management Portal Supporting Customer Management Services

Use Case 4 in Technology Provider A Corporate Knowledge Management Portal Supporting Customer Management Services Use Case 4 in Technology Provider A Corporate Knowledge Management Portal Supporting Customer Management Services Challenge An advanced corporate portal supporting sales and technical people in the customer

More information

HOW TO MAKE SENSE OF BIG DATA TO BETTER DRIVE BUSINESS PROCESSES, IMPROVE DECISION-MAKING, AND SUCCESSFULLY COMPETE IN TODAY S MARKETS.

HOW TO MAKE SENSE OF BIG DATA TO BETTER DRIVE BUSINESS PROCESSES, IMPROVE DECISION-MAKING, AND SUCCESSFULLY COMPETE IN TODAY S MARKETS. 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 information

IEEE IoT IoT Scenario & Use Cases: Social Sensors

IEEE 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 information

Internship Opportunities Xerox Research Centre India (XRCI), Bangalore Analytics Research Group

Internship 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 information

The Emergence of AI in Enterprise IT

The Emergence of AI in Enterprise IT THE EMERGENCE OF AI IN ENTERPRISE IT The Emergence of AI in Enterprise IT K R Sanjiv K.R.Sanjiv, Senior Vice President and CTO, Wipro Ramaprasad K R Chief Technologist and Distinguished Member of Technical

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

Proposal 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. 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 information

CSC384 Intro to Artificial Intelligence

CSC384 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 information

CSC384 Intro to Artificial Intelligence

CSC384 Intro to Artificial Intelligence CSC384 Intro to Artificial Intelligence Artificial Intelligence A branch of Computer Science. Examines how we can achieve intelligent behaviour through computation. What is intelligence? Are these Intelligent?

More information

Knowledge Management

Knowledge 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 information

WEB MINING INTRO.

WEB MINING INTRO. 1 WEB MINING INTRO esther@stts.edu Outline 2 Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Conclusion & Exam Questions Four Problems 3 Finding relevant information Low

More information

Social Media Implementations

Social 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 information

Efficient Crowdsourcing for Metadata Generation

Efficient Crowdsourcing for Metadata Generation Efficient Crowdsourcing for Metadata Generation Wolf-Tilo Balke Institute for Information Systems Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de Semantic Metadata Most semantic retrieval

More information

IBM Watson s Next Step: Health. All About the Data January 21 st 2016, Groningen

IBM Watson s Next Step: Health. All About the Data January 21 st 2016, Groningen IBM Watson s Next Step: Health All About the Data January 21 st 2016, Groningen Introduction speaker Dr Nicky S. Hekster Technical Leader Healthcare & LifeSciences IBM Nederland BV Johan Huizingalaan 765

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

A Systemic Artificial Intelligence (AI) Approach to Difficult Text Analytics Tasks

A 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 information

HOW TO DO A SMART DATA PROJECT

HOW 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 information

Msc(ECom&IComp) List of courses offered in

Msc(ECom&IComp) List of courses offered in Msc(ECom&IComp) List of courses offered in 2016-2017 (The below list is NOT finalized) ECOM6004. Legal aspects of I.T. and e-commerce This course provides an introduction to some of the main legal problems

More information

Important dimensions of knowledge. Important dimensions of knowledge (cont.) Introduction to Information Management IIM, NCKU

Important dimensions of knowledge. Important dimensions of knowledge (cont.) Introduction to Information Management IIM, NCKU Introduction to Information Management IIM, NCKU Based on Chapter 11 of Laudon and Laudon (2010). Management Information Systems: Managing the Digital Firm (11th edition), Pearson/PrenticeHall Important

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

EPSRC Cross-SAT Big Data Workshop: Well Sorted Materials

EPSRC 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 information

Bretigny, 21st May 2014

Bretigny, 21st May 2014 1 2013 2014 2014 Bretigny, 21st May 2014 2 2013 2014 Bretigny, 21st May 2014 3 4 wiki.complexworld.eu 5 wiki.complexworld.eu 6 wiki.complexworld.eu ComplexWorld Position Paper Uncertainty in ATM Emergent

More information

Science 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 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 information

WHITE PAPER. What It Takes to Leverage E&P Big Data

WHITE PAPER. What It Takes to Leverage E&P Big Data WHITE PAPER What It Takes to Leverage E&P Big Data What It Takes To Leverage E&P Big Data Author Dr. Satyam Priyadarshy Chief Data Scientist, Halliburton Landmark INTRODUCTION Big Data is one of the most

More information

Business Model Designing the 9 blocks

Business Model Designing the 9 blocks GRUPPO TELECOM ITALIA Catania, 28 Marzo 2014 Business Model Designing the 9 blocks The Open Innovation perspective Telecom Italia Head of Joint Open Lab WAVE @valdamico Agenda Business Model Innovation

More information

1 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 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 information

In this Appendix chapter, we discuss HCI, the interaction between Man and Machine which has been a quite successful approach.

In this Appendix chapter, we discuss HCI, the interaction between Man and Machine which has been a quite successful approach. Human and Computer One, a robot may not injure a human being, or through inaction, allow a human being to come to harm; Two, a robot must obey the orders given it by human beings except where such orders

More information

Computer Science: Principles

Computer Science: Principles The College Board Computer Science: Principles Big Ideas and Key Concepts Learning Objectives and Evidence Statements 2011 The College Board. All rights reserved. Computer Science: Principles is a pilot

More information

Mitel 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 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 information

Master of Science in Marketing Analytics (MSMA)

Master 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 information

Multistep Dynamic Expert Sourcing

Multistep 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 information

IM (Spring 2016) Content

IM (Spring 2016) Content MANAGING KNOWLEDGE Content What is the role of knowledge management and knowledge management programs in business? What types of systems are used for enterprise-wide knowledge management and how do they

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

THE 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 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 information

CONNECTING DATA WITH BUSINESS

CONNECTING DATA WITH BUSINESS CONNECTING DATA WITH BUSINESS Big Data and Data Science consulting Business Value through Data Knowledge Synergic Partners is a specialized Big Data, Data Science and Data Engineering consultancy firm

More information

Data 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 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 information

Veracity of data. New approaches are emerging to account for uncertainty in data at a giant scale. 2013 IBM Corporation

Veracity 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 information

Delft Data Science Seminar January 26, 2015

Delft 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 information

The Future of Data Management Managing Data Beyond Boundaries

The Future of Data Management Managing Data Beyond Boundaries The Future of Data Management Managing Data Beyond Boundaries Ron Agresta Director of Product Management - Data Management Product Line SAS #AnalyticsX The Future of Data Management Managing Data Beyond

More information

Benvenuti! Introduzione: Fabiola Tisbini, Director of IBM Digital Sales,

Benvenuti! Introduzione: Fabiola Tisbini, Director of IBM Digital Sales, Benvenuti! Introduzione: Fabiola Tisbini, Director of IBM Digital Sales, Italy @fabiolatisbini Relatore: Pietro Leo, Executive Architect - IBM Italy CTO for Big Data Analytics & Watson @pieroleo #IBM #AssirmForum15

More information

perspective Responsive enterprise the future of the enterprise

perspective Responsive enterprise the future of the enterprise perspective Responsive enterprise the future of the enterprise Experience is not merely a buzzword today, it is quickly becoming a key differentiator in the digital world. Parameters such as quality, pricing,

More information

Technical Club: New Vision of Computing

Technical 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 information

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours.

01219211 Software Development Training Camp 1 (0-3) Prerequisite : 01204214 Program development skill enhancement camp, at least 48 person-hours. (International Program) 01219141 Object-Oriented Modeling and Programming 3 (3-0) Object concepts, object-oriented design and analysis, object-oriented analysis relating to developing conceptual models

More information

1. Assembly and analysis: locating stakeholders and acquiring background material

1. Assembly and analysis: locating stakeholders and acquiring background material e-rulemaking: Research Problems for IT This document attempts to synthesize some of the discussions of the e-rulemaking workshop at KSG in January 2003. The perspective and organization taken here is explicitly

More information

Department of Design Engineering: Profile and Focus on New Research Areas

Department of Design Engineering: Profile and Focus on New Research Areas Department of Design Engineering: Profile and Focus on New Research Areas The department of Design Engineering will be recruiting new full professors in order to focus its scientific coverage of the rapidly

More information

Introduction to Business Intelligence

Introduction to Business Intelligence Introduction to Business Intelligence Urban Ask Centrum för Affärssystem Gruppen för Ekonomistyrning Agenda I t t i BI Interest in BI Definitions Drivers Vendors and market Some predictions 1 Increasing

More information

context Lightweight Text Analytics using Linked Data

context Lightweight Text Analytics using Linked Data context Lightweight Text Analytics using Linked Data Ali Khalili, Sören Auer, and Axel-Cyrille Ngonga Ngomo University of Leipzig, Institute of Computer Science, AKSW Group, Augustusplatz 10, D-04009 Leipzig,

More information

The Big Data Paradigm Shift. Insight Through Automation

The 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 information

Seminar Intelligent Cities: Sustainability and Big Data

Seminar 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 information

Decision Moments. Diagnostic, predictive and prescriptive analytics accelerate insights that improve over time using your existing investments

Decision Moments. Diagnostic, predictive and prescriptive analytics accelerate insights that improve over time using your existing investments Decision Moments Diagnostic, predictive and prescriptive analytics accelerate insights that improve over time using your existing investments Speed is the big differentiator in business today how quickly

More information

2015 Analyst and Advisor Summit. Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist

2015 Analyst and Advisor Summit. Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist 2015 Analyst and Advisor Summit Advanced Data Analytics Dr. Rod Fontecilla Vice President, Application Services, Chief Data Scientist Agenda Key Facts Offerings and Capabilities Case Studies When to Engage

More information

Food image recognition technology with deep learning. techniques

Food image recognition technology with deep learning. techniques Technology Offer Food image recognition technology with deep learning Summary techniques A Singapore research institution established in Asia and internationally for its research and education in management,

More information

Kelley Executive Partners Social Technology & Business Certification Program

Kelley Executive Partners Social Technology & Business Certification Program Kelley Executive Partners Social Technology & Business Certification Program A unique program designed to maximize the benefits of social technologies across all business units and functions to develop

More information

Knowledge. Managing Knowledge for the Digital Firm. Knowledge Management. Chief Knowledge Officer

Knowledge. Managing Knowledge for the Digital Firm. Knowledge Management. Chief Knowledge Officer Knowledge Managing Knowledge for the Digital Firm In an information economy, knowledge & core competencies are key organizational assets. Knowing how to do things effectively and efficiently in ways that

More information

ICT Perspectives on Big Data: Well Sorted Materials

ICT 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 information

Linked Data for the Enterprise: Opportunities and Challenges

Linked Data for the Enterprise: Opportunities and Challenges Linked Data for the Enterprise: Opportunities and Challenges Marin Dimitrov (Ontotext) Semantic Days 2012, Stavanger About Ontotext Provides software and expertise for creating, managing and exploiting

More information

Managing Knowledge in the Digital Firm by Prentice Hall

Managing Knowledge in the Digital Firm by Prentice Hall Managing Knowledge in the Digital Firm 12.1 2006 by Prentice Hall U.S Enterprise Knowledge Management Software Revenues 2001-2006 Source: Based on the data in emarketer, Portals and Content Management

More information

Workflow Orchestration and Mining for Integrated Asset Management in Smart Oilfileds

Workflow Orchestration and Mining for Integrated Asset Management in Smart Oilfileds Workflow Orchestration and Mining for Integrated Asset Management in Smart Oilfileds Presenter : Fan Sun Tao Zhu Yinglong Xia Muhammad Murtaza 1of 25 Outline Introduction to CiSoft Overview of Integrated

More information

Do Cognitive Styles of Users affect Preference and Performance related to CAPTCHA Challenges?

Do Cognitive Styles of Users affect Preference and Performance related to CAPTCHA Challenges? Do Cognitive Styles of Users affect Preference and Performance related to CAPTCHA Challenges? Marios Belk belk@cs.ucy.ac.cy Christos Fidas christos.fidas@cs.ucy.ac.cy Panagiotis Germanakos Department of

More information

Computer Science: Principles

Computer Science: Principles The College Board Computer Science: Principles Computational Thinking Practices Big Ideas, Key Concepts, and Supporting Concepts 2011 The College Board. All rights reserved. Computer Science: Principles

More information

Zinnya DEL VILLAR & Christophe THOVEX

Zinnya DEL VILLAR & Christophe THOVEX Zinnya DEL VILLAR & Christophe THOVEX Approaching Big Data from a business perspective What is Big Data? What is Big Data? - IoT - Internet - Unstructured - Semi-structured - Structured Volume Variety

More information

IeeeXpert.com. IEEE JAVA DOTNET PROJECTS for M.E/M.TECH/B.E/B.TECH CSE/ IT FINAL YEAR STUDENTS CLOUD COMPUTING

IeeeXpert.com. IEEE JAVA DOTNET PROJECTS for M.E/M.TECH/B.E/B.TECH CSE/ IT FINAL YEAR STUDENTS CLOUD COMPUTING IEEE JAVA DOTNET PROJECTS for M.E/M.TECH/B.E/B.TECH CSE/ IT PROJ ECT CODE 02 03 04 05 06 07 08 09 10 CLOUD COMPUTING EPAS: A Sampling Based Similarity Identification Algorithm for the Cloud Model-Driven

More information

Text Mining - Scope and Applications

Text 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 information

Why big data? Lessons from a Decade+ Experiment in Big Data

Why 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 information

What is Visual Analytics?

What 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 information

I 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. 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 information

GEOGRAPHIC CONTEXT ANALYSIS OF VOLUNTEERED INFORMATION

GEOGRAPHIC 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 information

Structured Knowledge Representation

Structured Knowledge Representation Intelligent Systems: Reasoning and Recognition James L. Crowley ENSIMAG 2 / MoSIG M1 Second Semester 2011/2012 Lesson 11 16 March 2012 Structured Knowledge Representation Structured Knowledge Representation...2

More information

Manjula Ambur NASA Langley Research Center April 2014

Manjula 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 information

Organization. Controlling

Organization. Controlling Management The attainment of organizational goals in an effective and efficient manner through planning, organizing, leading and controlling organizational resources. Planning The management function concerned

More information

Machine Learning and Predictive Analytics Foster Growth [1]

Machine Learning and Predictive Analytics Foster Growth [1] Machine Learning and Predictive Analytics Foster Growth [1] Machine learning technology, which is defined in this ProgrammableWeb article [2], is starting to become a common component in many types of

More information

CRM as a Service. For Customers in the Cloud

CRM 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 information

An interdisciplinary model for analytics education

An 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 information

Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends

Promises 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 information

Speaking in the language of your customer Simran Bagga and Fiona McNeill, SAS

Speaking in the language of your customer Simran Bagga and Fiona McNeill, SAS Speaking in the language of your customer Simran Bagga and Fiona McNeill, SAS #analyticsx Real World Example: Manufacturing Real World Example: Transportation DAYS to manually categorize. Act after that.

More information

AUTONOMOUS REQUIREMENTS SPECIFICATION PROCESSING USING NATURAL LANGUAGE PROCESSING

AUTONOMOUS REQUIREMENTS SPECIFICATION PROCESSING USING NATURAL LANGUAGE PROCESSING AUTONOMOUS REQUIREMENTS SPECIFICATION PROCESSING USING NATURAL LANGUAGE PROCESSING Professor S.G. MacDonell Software Engineering Research Lab stephen.macdonell@aut.ac.nz Dr Kyongho Min School of Computer

More information

Technische Universität Chemnitz Fakultät für Wirtschaftswissenschaften Professur Wirtschaftsinformatik I. The Fifth V

Technische Universität Chemnitz Fakultät für Wirtschaftswissenschaften Professur Wirtschaftsinformatik I. The Fifth V Fakultät für Wirtschaftswissenschaften The Fifth V How Big Data Can Create Value By Data Driven Innovation Prof. Dr. Barbara Dinter Prof. Dr. Barbara Dinter The Fifth V Big Data Driven Innovation Slide

More information

Disusun oleh Tim Dosen STMIK AMIKOM Yogyakarta

Disusun oleh Tim Dosen STMIK AMIKOM Yogyakarta Disusun oleh Tim Dosen STMIK AMIKOM Yogyakarta PANDUAN TEKNIS PEMBUATAN PROPOSAL DAN LAPORAN SKRIPSI - 2014 1 10. Tema/Topik Skripsi STMIK AMIKOM YOGYAKARTA TEMA / TOPIK SKRIPSI STMIK AMIKOM YOGYAKARTA

More information

Foundations of Business Intelligence: Databases and Information Management

Foundations of Business Intelligence: Databases and Information Management Foundations of Business Intelligence: Databases and Information Management Problem: HP s numerous systems unable to deliver the information needed for a complete picture of business operations, lack of

More information

Open Positions in Semantic Interoperability and Cloud Services. PhD, Research Master, Research Assistants (6 Months) Internships (4-6 Months)

Open Positions in Semantic Interoperability and Cloud Services. PhD, Research Master, Research Assistants (6 Months) Internships (4-6 Months) www.openiot.eu Insight www.insight-centre.org Centre for Data Analytics NUI Galway Open Positions in Semantic Interoperability and Cloud Services PhD, Research Master, Research Assistants (6 Months) Internships

More information

Design Exploration, Optimization and Engineering Knowledge Management Simon Pereira Product Manager, ANSYS Inc.

Design Exploration, Optimization and Engineering Knowledge Management Simon Pereira Product Manager, ANSYS Inc. Design Exploration, Optimization and Engineering Knowledge Management Simon Pereira Product Manager, ANSYS Inc. 1 Drive Workbench Workbench Parametric, persistent, and scriptable integration platform ANSYS

More information

What is FuturICT? Why do we need it?

What is FuturICT? Why do we need it? ICT Global computing for our complex world Complexity Science Social Sciences What is FuturICT? FuturICT is a visionary project that will deliver new science and technology to explore, understand and manage

More information

IC05 Introduction on Networks &Visualization Nov. 2009.

IC05 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 information

Cognitive Computer Vision. Fast Customization. Business Ready Solutions Extremely Accurate

Cognitive Computer Vision. Fast Customization. Business Ready Solutions Extremely Accurate Cognitive Computer Vision Fast Customization. Business Ready Solutions Extremely Accurate PROBLEM Each day billions of images are uploaded to the Internet by businesses and consumers worldwide. Unlike

More information

Social Media Analysis and Reccomending Systems

Social Media Analysis and Reccomending Systems Social Media Analysis and Reccomending Systems Roberto Basili (Università di Roma, Tor Vergata) dblp: http://dblp.uni-trier.de/pers/hd/b/basili:roberto.html Google scholar: https://scholar.google.com/citations?user=u1a22fyaaaaj&hl=it&oi=sra

More information

Cloud Thinking. Simplifying Big Data Processing. Rui L. Aguiar, Diogo Gomes Universidade de Aveiro - Portugal

Cloud Thinking. Simplifying Big Data Processing. Rui L. Aguiar, Diogo Gomes Universidade de Aveiro - Portugal Cloud Thinking Simplifying Big Data Processing Rui L. Aguiar, Diogo Gomes Universidade de Aveiro - Portugal Problem A connected world of Information Systems and Electronic Devices produces terabytes of

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

R. Kimball s definition of a DW

R. Kimball s definition of a DW Design of DW R. Kimball s definition of a DW A data warehouse is a copy of transactional data specifically structured for querying and analysis. According to this definition: The form of the stored data

More information

WHAT IS DATA SCIENCE? Grace Tang, Data Scientist, 99.co

WHAT IS DATA SCIENCE? Grace Tang, Data Scientist, 99.co WHAT IS DATA SCIENCE? Grace Tang, Data Scientist, 99.co WHAT IS DATA SCIENCE??? WHAT IS DATA SCIENCE??? What do YOU want to know about Data Science? AGENDA Data Science in the Wild Data Analysis Big Data

More information

Project Number: NML 1

Project Number: NML 1 Project Number: NML 1 Project Title: Dynamic Workload Adjustment in Human- Machine Systems Name of Supervisor: Jianlong Zhou (Jianlong.zhou@nicta.com.au) Name of Co- Supervisor: Dr. Fang Chen (Fang.Chen@nicta.com.au)

More information

Direct-to-Company Feedback Implementations

Direct-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 information

The Database Systems and Information Management Group at Technische Universität Berlin

The 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 information

Research Reports, 2017: Hybrid Cloud, Software Defined Data Center, and Machine Learning

Research Reports, 2017: Hybrid Cloud, Software Defined Data Center, and Machine Learning Research Reports, 2017: Hybrid Cloud, Software Defined Data Center, and Machine Learning IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Q1, 2017 EMA Research: Optimize TCO, Business Agility

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

Course Number Language

Course Number Language Name Automated code analysis for large software systems 20-00-0732-iv English /14 Cloud Security 20-00-0729-se English and /14 Cryptography, Privacy and Security 20-00-0672-se English and /14 Current Topics

More information