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

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

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. 2

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 3

4 Manufacturing Technology Business MANUFACT URING Workforce 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. 4

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... Video and Images Documents Big Data: Decisions based on all your data Social Data Machine-Generated Data 5

6 Big data Use cases Today s Challenge New Data What s Possible Manufacturing Inter department Support Healthcare Expensive office visits Location-Based Services Based on home zip code Public Sector Standardized services Retail One size fits all marketing Product sensors Remote patient monitoring Real time location data Citizen surveys Social media Automated diagnosis, support Preventive care, reduced hospitalization Geo-advertising, traffic, local search Tailored services, cost reductions Sentiment analysis segmentation 6

7 Big Data Is About Tapping into diverse data sets Finding and monetizing unknown relationships Data driven business decisions

8 Big Data Landscape 8

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. 9

10 Gartner Life Cycle of Emerging Technologies Big data and supporting technologies are part of Hype Cycle 10

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. 11

12 Information floating in Manufacturing Bug Tracking and Management (Bugzilla) Version Control (SVN, Perforce) Knowledge Management System (Wiki) Competency Management (Website) Issue Project Component Release Status Tester Developer Component Code Version Issue Developer Activity Project Requirement Milestones Test plan Process Author Hierarchy Organizational Structure Roles Responsibility Developer Tester 12

13 Information floating in Manufacturing Bug Tracking and Management (Bugzilla) Version Control (SVN, Perforce) Knowledge Management System (Wiki) Competency Management (Website) Issue Project Component Release Status Tester Developer Component Code Version Issue Developer Activity Project Requirement Milestones Test plan Process Author Hierarchy Organizational Structure Roles Responsibility Developer Tester 13

14 Information floating in Manufacturing Bug Tracking and Management (Bugzilla) Issue Project Component Release Status Tester Developer Version Control (SVN, Perforce) Component Code Version Issue Developer Activity Knowledge Management System (Wiki) 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. Project Requirement Milestones Test plan Process Author Competency Management (Website) Hierarchy Organizational Structure Roles Responsibility Developer Tester 14

15 Architecture for Data Analysis APPLICATION LAYER Applications Applications Applications APPLICATION FRAMEWORK USER MANAGEMENT LAYER Authentication, Authorization, Query transformation, Personalization, display TEXT ANALYTICS SEARCH ENGINE SEMANTICS PROCESSING LAYER Thesaurus Clustering Semantic Processing Entity Extraction Indexing Crawling Converting Metadata Ontology Tagging DATA CONNECTION LAYER CRM 15

16 Semantic Web

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 17

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. 18

19 Ontology: ECOS ECOS Ontology: Enterprise Competence Organization Schema Publishing enterprise competence in an explicit and structured manner 19

20 ECOS Company Name Address Summary Contact Person Key Person Sector Product/Service General Past Projects Customer Business Enterprise Record Relation Preference Achievement Financial Specific Resource Process Unit Skill Plan Use other ontologies to extend the ECOS. Standardsdefined by UN and EU are used in ECOS for representing competences 20

21 TEXT2RDF TEXT2RDF Text mining approach for providing explicit semantic description to the documents. 21

22 Semantic Information in Manufacturing METADATA METADATA METADATA METADATA Bug Tracking and Management (Bugzilla) SEMANTIC LAYER Version Control (SVN, Perforce) Knowledge Management System (Wiki) Competency Management (Website) Issue Component Project Project Code Requirement Component Version Milestones Release Issue Test plan Status Developer Process Tester Activity Author Developer Hierarchy Organizational Structure Roles Responsibility Developer Tester 22

23 Semantics in Enterprise Supply Chain Management Biogen Idec Media Management BBC Data Integration in Oil& Gas Chevron Web Search and Ecommerce 23

24 24

25 What Makes it Big Data? VOLUME VELOCITY VARIETY VALUE SOCIAL BLOG SMART METER Data at rest Data in motion Data in many forms Data with sense BIG DATA = Transactions + Interactions + Observations

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