Big Data and Semantic Web in Manufacturing

Big Data and Semantic Web in Manufacturing
Nitesh Khilwani, PhD

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
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
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
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
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

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

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
Gartner Life Cycle of Emerging Technologies -2013
• Big data and supporting technologies are part of Hype Cycle

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
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
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
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
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

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

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

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

Big Data and Semantic Web in Manufacturing

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