THE INFORMATION WORKBENCHLINKED DATA AND SEMANTIC WIKIS IN THE ENTERPRISE

Peter Haasefluid Operations AG

SMWCon Fall 2012 Cologne

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

The RDF Data Model

predicatesubject object

Who am I and Why am I Here?A Linked Data Perspective

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Who am I and Why am I Here?A Linked Data Perspective

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Who am I and Why am I Here?A Linked Data Perspective

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tion

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Wikis, the Web, Data and SemanticsCo

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ation

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Structured Data

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

Information WorkbenchLinked 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

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Information Workbench - Linked Data Platform

Semantic Web Data

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, …)

Named graphs for managing contexts and provenance

Linked Data Integration Approaches

Centralized Integration•Following a data warehousing approach

•Data providers periodically gather data from sources and lift it to semantic data formats

•Graph-based data format enables pay-as-you-go integration of legacy data sources

•Information Workbench comes with predefined providers for various formats and data sources (Spreadsheets, XML, …)

Virtualized Integration• Autonomous, distributed data sources

linked through a federation layer

• No central integration required

• Data sources can be added ad hoc, on demand

• Federation mediator for query processing (routing sub queries to relevant sources)

Centralized Store

Data Provider

Query

Federation Mediator

Query

Enabling Data Composition & Integration:Federation of Virtualized Data Sources

Application Layer

Virtualization Layer

Data Layer

Data Source Data Source Data Source Data Source

SPARQLEndpoint

SPARQLEndpoint

SPARQLEndpoint

SPARQLEndpoint Metadata

Registry

See also: FedX: Optimization Techniques for Federated Query Processing on Linked Data (ISWC2011)

Self-service Linked Data Frontend driven bySemantic 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, dashboards, ...

Wiki Page in Edit Mode … … and Displayed Result Page

Rich Pool of Available Widgets for Interacting with the Integrated Data

Analytics and ReportingVisualization and Exploration

Mashups with Social MediaAuthoring and Content Creation

All widgets can be integrated into the UIusing an intuitive, Wiki-style declarative syntax.

Widget-based Visualization and Query Construction

Example Templates

Example:Conference Explorer

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• „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

Data Sources• Conference Metadata (Linked Data)• Public bibliographic meta data• Social Networks:

• Twitter• Facebook• LinkedIn

• LinkedGeoData

Features• Conference schedule, timelines,

hot topics• Statistics and reports• Background information about

authors and publications• Link to social network profiles and

statistics

http://conference-explorer.fluidops.net/

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)

Information WorkbenchEnterprise Application Areas

Knowledge Management in the Life Sciences

Digital Libraries, Media and Content Management

Intelligent Data Center Management

BBC Web Site – Powered by Semantic Technologies

Open Sport Ontology

Dynamic Semantic Publishing with the BBC

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

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 DSP Architecture

Staging Database

LiveDatabase

Data Layer SPARQL/RDF HTTP

Journalist, Data Architect, ...

Web-Frontend(Browser)

Unpublished Data Published Data

Social Netw. Widgets

Collaboration Widgets

Navigation Widgets

Extensible Widget Pool Visualization

Widgets

Interlinking and

Integration

Information Extraction

andEnrichment

Querying and Search

CollaborationAuthoring Visualization Search and Analytics

Publishing Workflows

Data Management Modules Data

Access

User Roles and Editorial Workflow

• JournalistView Instance Data

• SubeditorEdit instance data

Draft Approved

Rejected

PublishedApprove Publish

Reject

Edit

• Media ManagerEdit instance dataApprove/reject instance data edits

• Data ArchitectEdit instance data and ontology data editsPublish instance data

Demo Dynamic Semantic Publishing

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

Intelligent Data Center Management

Problem Administration silos: compute infrastructure,

storage, application, … Business data not interlinked with technical

data CXOs struggle to have an integrated view on

the resources employed in the data center

Solution Semantic, resource-centric view on data: link

business data with data center resources and interrogate heterogeneous resources in a unified way

User-defined dashboards, queries, historical data management for analytics and reporting purposes

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

Data Center Management

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• 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

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 occursDetermine where errors on the application level are coming fromRelate 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

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 practicesCategorize documentation Interlink hardware resources with documentation in a central placeAssign responsibilities directly to technical resource

Wiki Page in Edit Mode … … and Displayed Result Page

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

Ticketing Support

Tickets are directly linked to the personal profile page

Integrated tickets and statistics built on top

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

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

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

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/

CONTACT:fluid OperationsAltrottstr. 31Walldorf, Germany

Email: peter.haase@fluidops.comwebsite: www.fluidops.comTel.: +49 6227 3846-527