The document discusses the Information Workbench developed by Fluid Operations, which integrates linked data and semantic technologies for enterprise applications. It highlights the benefits of using linked data for data integration, collaborative knowledge management, and reporting while providing insights into the platform's features, including visualization, querying, and semantic search. Furthermore, it underscores the application areas of the Information Workbench, such as intelligent data center management and dynamic semantic publishing.

1. THE INFORMATION WORKBENCH
LINKED DATA AND SEMANTIC WIKIS
IN THE ENTERPRISE
Peter Haase SMWCon Fall 2012
fluid Operations AG Cologne

2. fluid Operations (fluidOps)
Linked Data & Semantic Technologies Enterprise Cloud Computing
Software company founded Q1/2008 by team of serial entrepreneurs, privately
held, VC funded
Headquarters in Walldorf / Germany, SAP Partner Port
Currently 45 employees
Named “Cool Vendor” by Gartner March 2010
Global reseller agreement with EMC focus large
enterprise customers Apr 2010
NetApp Advantage Alliance Partner Oct 2010

3. The RDF Data Model
predicate object
subject

4. Who am I and Why am I Here?
A Linked Data Perspective
presenterAt

5. Who am I and Why am I Here?
A Linked Data Perspective
foaf:friend
affiliation
affiliation
develops
foaf:friend
affiliation
develops extends
extends
owl:sameAs
develops
generalChair
affiliation develops

6. Who am I and Why am I Here?
A Linked Data Perspective
foaf:friend
affiliation
develops
foaf:friend

7. Wikis, the Web, Data and Semantics
Collboration (on the Web)
Structured Data

8. The Potential of Linked Data
Linked Data
• Set of standards, principles for publishing, sharing
and interrelating structured knowledge
• From data silos to a Web of Data
• RDF as data model, SPARQL for querying
• Ontologies to describe the semantics
Benefits of Linked Data in the Enterprise
• Enterprise Data Integration: Semantically integrate and
interlink data scattered among different information systems
• Collaborative Knowledge Management and Analytics: Enable cross-organization analysis,
interactive analytics, and reporting, resulting in better business decisions
• Simplified publishing and sharing of data: Increase openness and accessibility of
Enterprise Data
• Enrichment and contextualization through interlinking: Value add by linking to
Linked Open Data

9. Information Workbench
Linked Data and Semantic Wikis in the Enterprise
• Supports the whole process of
interacting with Linked Data
• Data integration
• Visualization & exploration
• Collaborative knowledge management
• Open standards and technologies
• Semantic Wiki based frontend
(Using SMW Syntax)
• Supporting W3C standards (OWL, RDF, SPARQL)
• Community Edition (Open Source) + Enterprise Edition (Commercial)
• Platform for Linked Data Application Development
• Base functionality to build applications without programming
• SDK for easy extensions
• Implementation in Java, very flexible AJAX frontend

10. Information Workbench - Linked Data Platform
 Intelligent Data Access and Analytics
 Flexible self-service UI
 Visualization, exploration,
dashboarding, and reporting
 Semantic search
 Collaboration and knowledge
management
 Curation & authoring
 Collaborative workflows
 Semantics- & Linked Data-based
integration of private and public data
sources based on data providers
 Generic and specific providers for
various data formats and sources
 Supports established mapping
frameworks (e.g. R2RML, SILK, …)
Semantic Web Data  Named graphs for managing
contexts and provenance
10

11. Linked Data Integration Approaches
Centralized Integration Virtualized Integration
• Following a data warehousing approach • Autonomous, distributed data sources
linked through a federation layer
• Data providers periodically gather data
from sources and lift it to semantic data • No central integration required
formats
• Data sources can be added ad hoc,
• Graph-based data format enables pay-as- on demand
you-go integration of legacy data sources
• Federation mediator for query processing
• Information Workbench comes with (routing sub queries to relevant sources)
predefined providers for various formats
and data sources (Spreadsheets, XML, …)
Query
Centralized
Query
Store
Data Provider Federation Mediator

12. Enabling Data Composition & Integration:
Federation of Virtualized Data Sources
Application Layer
Virtualization Layer
Data Layer
SPARQL SPARQL SPARQL SPARQL
Endpoint Endpoint Endpoint Endpoint
Metadata
Registry
Data Source Data Source Data Source Data Source
See also: FedX: Optimization Techniques for Federated Query Processing on Linked Data (ISWC2011)

13. Self-service Linked Data Frontend driven by
Semantic Wiki + Rich Widgets
• Ontology-driven template mechanism
• Declarative specification of the UI
based on available pool of widgets and
declarative wiki-based syntax
• Widgets have direct access to the DB
• Ad hoc data
exploration, visualization, analytics, dashb
oards, ...
Wiki Page in Edit Mode … … and Displayed Result Page

14. Rich Pool of Available Widgets for
Interacting with the Integrated Data
Visualization and Exploration Analytics and Reporting
Authoring and Content Creation
Mashups with Social Media
All widgets can be integrated into the UI
using an intuitive, Wiki-style declarative syntax.

15. Widget-based Visualization and Query
Construction

16. Example Templates

17. Example:
Conference Explorer
• „Linked-Data-a-Thon“: build an
application that makes use of conference
metadata and contextualizes data with
external data sources in two weeks
• Realized with the Information Workbench
http://conference-explorer.fluidops.net/
Data Sources Features
• Conference Metadata (Linked Data) • Conference
• Public bibliographic meta data schedule, timelines, hot topics
• Social Networks: • Statistics and reports
• Twitter • Background information about
• Facebook authors and publications
• LinkedIn • Link to social network profiles and
• LinkedGeoData statistics
17

18. Some Notes on Relationship with SMW
and WikiData
• “People are scared of Wiki markup”
• Semantic links for creating structured data is not something that people use
• Need for form-based approaches
• Wiki editing at most for unstructured documentation
• “We need to support diversity”
• WikiData: Statements that reify claims
• Our approach: Named graphs
• Actually: In enterprise settings, we try to fight diversity (aka inconsistency,
redundancies, mismatches -> c.f. Semantic Master Data Management)

19. Information Workbench
Enterprise Application Areas
Knowledge Management in the
Life Sciences
Digital Libraries, Media and
Content Management
Intelligent Data Center
Management

20. BBC Web Site – Powered by Semantic Technologies

21. Open Sport Ontology

22. Dynamic Semantic Publishing with the BBC
Olympics 2012 requirements
• A lot of output... Page per Athlete [10,000+], Page per country
[200+], Page per Discipline [400-500], Time coded, metadata
annotated, on demand video, 58,000 hours of content
• Almost real time statistics and live event pages with too many
web pages for too few journalists
Dynamic Semantic Publishing (DSP) architecture to automate
content aggregation
Information Workbench for DSP
• Collaborative authoring and linking of
unstructured and structured semantic data
• Ontology and instance data management
• DSP editorial workflows
• Automation of content creation and
enrichment

23. Information Workbench DSP Architecture
Web-Frontend
(Browser)
Journalist, Data Architect, ...
Authoring Collaboration Visualization Search and Analytics
Extensible
Widget Pool Visualization Navigation Collaboratio Social Netw.
Widgets Widgets n Widgets Widgets
Information
Data Management Interlinking
Extraction Publishing Data Querying
Modules and
and Workflows Access and Search
Enrichment Integration
Unpublished Data Published Data
Data Layer SPARQL/RDF HTTP
Staging Live
Database Database

24. User Roles and Editorial Workflow
• Journalist • Media Manager
Edit instance data
View Instance Data Approve/reject instance data edits
• Data Architect
• Subeditor Edit instance data and ontology data
Edit instance data edits
Publish instance data
Edit Approve Publish
Draft Approved Published
Reject
Rejected

25. Demo Dynamic Semantic Publishing

26. Enterprise Clouds Vision
All resources of an adaptive, cloud-enabled IT environment can be set up,
monitored, and maintained from a single, unified, and intuitive
management console:
 Internal and external IT resources accessible across stack without vendor lock-in
 High degree of automation and IT provisioning at click of button on the level of enterprise
landscapes
 Internal portal of private/public IT services with e.g. pay-as-you-go cost models

27. Intelligent Data Center Management
Problem Solution
 Administration silos: compute infrastructure,  Semantic, resource-centric view on data: link
storage, application, … business data with data center resources and
 Business data not interlinked with technical interrogate heterogeneous resources in a
data unified way
 CXOs struggle to have an integrated view on  User-defined dashboards, queries, historical
the resources employed in the data center data management for analytics and reporting
purposes

28. Integrated View On The Data Center
 Integration of different
software and hardware
components, storage
systems, compute
infrastructures, applications,
CRM systems, ticket
systems, project catalogs
 Automatic correlation of
data retrieved from various
systems
 Unified view on data and
metadata across the border
of company units
 Exploration, analysis, and
actions based on the entire
data corpus

29. Data Center Management
• Support collaborative
operations management
in the data center
• Link business data to technical data
• Technical Documentation
• Analytics and Reporting
• Performance and Capacity Monitoring
• Responsibility Management
• Resource Management
• Change Management
• Technical Ticketing System
29

30. Link Business Data To Data Center
Resources
 E.g.: link your customers to their Virtual Landscapes using semantic annotations;
visually explore the connections between the business information and the data
center resources on-demand
 Use Case: Root Cause Analysis and Error Handling
 Identify which customer‘s SAP systems and system landscapes are affected when an error
on the storage level occurs
 Determine where errors on the application level are coming from
 Relate events to each other
 Document and compare solutions for events
  allows fast reaction for error handling and ensures SLA enforcement
Semantic Link in Wiki Page Visual Data Exploration

31. Share Knowledge Within Your Company
Collaborative Acquisition and Augmentation of Knowledge with
Semantic Wiki Technology
 Use Case: Technical Documentation and Responsibility Management
 Use Wiki to collaboratively maintain technical documentation and best practices
 Categorize documentation
 Interlink hardware resources with documentation in a central place
 Assign responsibilities directly to technical resource
Wiki Page in Edit Mode … … and Displayed Result Page

32. Collaboration and Workflows
 At data management level –
collaborative creation of structured
and unstructured documentation
directly through the Wiki page of each
resource (e.g. using edit-form widgets
embedded in the wiki)
 At process/workflow level – formalize
and execute workflows in an
automated way
Ontology-based edit form for adding missing information
Example workflow for a state-based ticketing system

33. Ticketing Support
Integrated tickets and statistics
built on top
Tickets are directly linked to the
personal profile page

34. Analytics and Reporting
Embed dynamic, user-defined charts directly into Semantic Wiki
pages
 E.g.: create tabular summaries of your data and existing connections; specify user-
defined charts and dashboards; generate reports based on historical data
Use case: Performance Monitoring and Capacity Planning
 Monitor the performance & usage of your infrastructure over time using historical data
 Forecast when new infrastructure resources need to be ordered
 Analysis of the impact of new hardware options on utilization rates
Use case: Cost and Demand Forecasting
 Keep track of infrastructure costs for
each customer / project
 Determine what infrastructure resources
will be needed when and for which project
 Compare various infrastructure options
in terms of cost
Example: Employed VMs over Time grouped by Power Status

35. Data Integration with the Information
Workbench
Phase 1 – Integration:
 Data integration in a central
repository via data providers
 Lift existing data sources to RDF
 RDF data integration into a central
repository
 Data alignment using a global
ontology
Phase 2 – Logical Mapping:
 Bring together entities from
different sources
 Generate a logical view and map
data items
 Identify and align equal data across
different data sources
 A logical mapping layer derives IDs
spanning different data sources

36. Benefits of the Semantic Master Data
Management in the Data Center Domain
 Seamless integration
creates
transparency
 Improve data quality
 Reuse data
 Discover
redundancies and
inconsistencies
 Ad hoc analysis
 Reduce time and
effort for
search,
query and report
generation

37. Conclusion
 Semantic technologies offer great potential to overcome the challenges of
today’s enterprise data management challenges
 (Semantic) Wikis to support collaboration in the enterprise:
knowledge management, publishing, collaborative operations
 Semantic Wiki + Rich Widgets for self-service Linked Data frontends
 Plenty of application areas
 Get started with the Information Workbench:
http://www.fluidops.com/information-workbench/

38. CONTACT:
fluid Operations
Altrottstr. 31
Walldorf, Germany
Email: peter.haase@fluidops.com
website: www.fluidops.com
Tel.: +49 6227 3846-527