Big Data and Semantic Web in Manufacturing

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Big Data and Semantic Web in Manufacturing

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Big Data and Semantic Web in Manufacturing

  1. 1. Big Data and Semantic Web in Manufacturing Nitesh Khilwani, PhD
  2. 2. 2 Outline • Big data in Manufacturing • Big data Analytics • Semantic web technologies • Case study: – 1. ECOS Ontology – 2. Text2Rdf – 3. Semantic Layer for Information management.
  3. 3. 3 Manufacturing • Gone are the days when a manufacturing companies operated autonomously • Using advanced IT systems companies are linking with consumer, manufacturing operations and suppliers to work as single entity. MINING/FARMING SUPPLY CHAIN MANUFACTURING DISTRIBUTION CUSTOMER
  4. 4. 4 Manufacturing • Lots of data is being collected and warehoused – purchases at department/ grocery stores – Bank/Credit Card transactions – Social Network – Web data, e-commerce • Big data: A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. MANUFACT URING Business Technology Workforce
  5. 5. 5 Big Data in Manufacturing • Big Data in manufacturing has the potential to revolutionize how we build, produce and live. • For decades, manufacturers have invested in and developed technologies that leverage “just in time” and “Six Sigma” methodologies. • Next step is data analytics... Big Data: Decisions based on all your data Video and Images Machine-Generated Data Social Data Documents
  6. 6. 6 Big data Use cases Today’s Challenge New Data What’s Possible Manufacturing Inter department Support Product sensors Automated diagnosis, support Healthcare Expensive office visits Remote patient monitoring Preventive care, reduced hospitalization Location-Based Services Based on home zip code Real time location data Geo-advertising, traffic, local search Public Sector Standardized services Citizen surveys Tailored services, cost reductions Retail One size fits all marketing Social media Sentiment analysis segmentation
  7. 7. Big Data Is About… Tapping into diverse data sets Finding and monetizing unknown relationships Data driven business decisions
  8. 8. 8 Big Data Landscape
  9. 9. 9 Tools for Big data Analytics • Visualization: baseline tools for both experienced data scientists and more novice analysts to make sense of data. • Data mining: To make machines more capable to automate the analysis. • Predictive Analysis: Tools to look forward and analyse the problems • Semantic engine: Tools to parse, extract, and analyze data • Data quality and Master Management: To insure quality and management process around data.
  10. 10. 10 Gartner Life Cycle of Emerging Technologies -2013 • Big data and supporting technologies are part of Hype Cycle
  11. 11. 11 Big data in Manufacturing: Benefits and Challenges Benefits Product quality and defect tracking Supply chain management Forecasting of manufacturing output Increase efficiency Simulation and testing of new processes Enabling customization in manufacturing Challenges Building trust between data scientist and managers. Confidence to handle large volume, velocity and variety of data Finding the door for Big data to enter our current system. Determining which technology to use Determining what to do with the analysis from Big data Maintaining the consistency for using big data.
  12. 12. 12 Information floating in Manufacturing Bug Tracking and Management (Bugzilla) Version Control (SVN, Perforce) Knowledge Management System (Wiki) • Issue • Project • Component • Release • Status • Tester • Developer • Component • Code • Version • Issue • Developer • Activity • Project • Requirement • Milestones • Test plan • Process • Author Competency Management (Website) • Hierarchy • Organizational • Structure • Roles • Responsibility • Developer • Tester
  13. 13. 13 Information floating in Manufacturing Bug Tracking and Management (Bugzilla) Version Control (SVN, Perforce) Knowledge Management System (Wiki) • Issue • Project • Component • Release • Status • Tester • Developer • Component • Code • Version • Issue • Developer • Activity • Project • Requirement • Milestones • Test plan • Process • Author Competency Management (Website) • Hierarchy • Organizational • Structure • Roles • Responsibility • Developer • Tester
  14. 14. 14 Information floating in Manufacturing Bug Tracking and Management (Bugzilla) Version Control (SVN, Perforce) Knowledge Management System (Wiki) • Issue • Project • Component • Release • Status • Tester • Developer • Component • Code • Version • Issue • Developer • Activity • Project • Requirement • Milestones • Test plan • Process • Author Competency Management (Website) • Hierarchy • Organizational • Structure • Roles • Responsibility • Developer • Tester • Interval between bug fixing time and change commit time • Mapping between bug owner and change committer • Similarity between bug report and change logs • Alternate contact points for emergency situations.
  15. 15. 15 Architecture for Data Analysis CRM Applications APPLICATION FRAMEWORK Authentication, Authorization, Query transformation, Personalization, display Applications Applications DATA CONNECTION LAYER APPLICATION LAYER USER MANAGEMENT LAYER PROCESSING LAYER SEARCH ENGINE Indexing Crawling Converting TEXT ANALYTICS Thesaurus Clustering Semantic Processing Entity Extraction SEMANTICS Metadata Ontology Tagging
  16. 16. Semantic Web
  17. 17. 17 Semantic Web • Semantic web is an extension of current web technology in which information is annexed with well defined meaning – Provides machine processable semantics of data • Ontology: Basic building block for Semantic Web – Captures semantics and relations of a domain
  18. 18. 18 Semantic Web Technologies • RDF: Language for representing knowledge • RDFS: Vocabulary for defining classes and properties • OWL: Adding relations in RDFS • SPARQL: Query language based on graphs. • Rules: Language to combine different ontology.
  19. 19. 19 Ontology: ECOS • ECOS Ontology: Enterprise Competence Organization Schema – Publishing enterprise competence in an explicit and structured manner
  20. 20. 20 ECOS • Use other ontologies to extend the ECOS. • Standards defined by UN and EU are used in ECOS for representing competences General Specific Enterprise RecordBusiness Company Name Contact PersonAddress Key Person Past Projects Relation Resource Process Unit Skill Product/Service Customer Sector Preference Financial Summary Plan Achievement
  21. 21. 21 TEXT2RDF TEXT2RDF Text mining approach for providing explicit semantic description to the documents.
  22. 22. 22 Semantic Information in Manufacturing Bug Tracking and Management (Bugzilla) Version Control (SVN, Perforce) Knowledge Management System (Wiki) • Issue • Project • Component • Release • Status • Tester • Developer • Component • Code • Version • Issue • Developer • Activity • Project • Requirement • Milestones • Test plan • Process • Author Competency Management (Website) • Hierarchy • Organizational • Structure • Roles • Responsibility • Developer • Tester METADATA METADATA METADATA METADATA SEMANTIC LAYER
  23. 23. 23 Semantics in Enterprise • Supply Chain Management—Biogen Idec • Media Management—BBC • Data Integration in Oil & Gas—Chevron • Web Search and Ecommerce
  24. 24. 24
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