Linked Enterprise Data in F/HE organisations

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Overview

Intro to the session Dave Flanders, JISC Damian Steer, Bristol University Paul Miller, Cloud of Data

Break

Hugh Davis and Yvonne Howard, Southampton University

Georgi Kobilarov, uberblic labs Sean O'Riain, DERI Galway Panel

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Why Linked Data in the Enterprise?

More data in more formats from more sources, going to more destinations

Data syntax is pretty much solved, transport nearly, semantics is still a major problem (and we need to start thinking about pragmatics)

Reconciling local concerns with cross institutional overview

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Reconciling the local with the global: traditional silos

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Reconciling the local with the global: centralised system

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Reconciling the local with the global: Linked Data Service system

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Drivers: Business Intelligence areas of interest

Finance/Costing Student DataInformation man-agement

Performance mea-surement

Staff data BenchmarkingStrategic planning MarketingResearch data BCEEstates

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Drivers: national data flows

The Higher Education Statistics Agency (HESA) student records, staff, finance and destination of graduates

Universities & Colleges Admissions Service (UCAS) applicant data and course data

Higher Education Funding Council for England (HEFCE)

National Student Survey, estates, finance, demographics Universities and Colleges Information Systems

Association (UCISA) expenditure on IT, staff and student spend, IT spend,

workstations and training

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Drivers: national data flows

Higher Education Academy PostGrad experience surveys

Learning Records Service (LRS, formerly MIAP) Unique Learner Number, Personal Learning Record, Learning

Provider Register The Administrative Data Liaison Service (ADLS)

School data (attendance, destinations etc) Office of National Statistics (ONS)

School data

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BI system requirements

Accessible when needed

Concise, pictorial or graphical

Easy to understand Easy to export to a

presentation or document

Up to date, current Known update times

and intervals Can select data for time

period

Good, reliable quality and integrity of data items

[All, major] internal information sources are included

Drill-down and roll-up capabilities

Easy to add new information sources (internal or external)

Allows the user to ask “What if... ?” questions

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Questions for the panel

What are the pros and cons of adopting linked data technology for typical intra-institutional tasks such as BI and system integration?

Pro: Starts the “how can we make the data clean?” question Data disambiguation Can handle very disparate data sets quickly Facilitates mashups Converting data to RDF can be done in the pub ;-)

Con: You have to clean the data (but you can shame owners

into cleaning it up themselves)

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Questions for the panel Which architectures or patterns are best suited for

enterprise use? Separate data store

ILRT ResearchRevealed Siri.com Tripit.com Powerset

Federated search Native RDF data store

RKB explorer Southampton ECS content platform

Social models: Internal corporate Social semantic enterprise Semantic enterprise

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Questions for the panel What practical experiences can we learn from, and

what tools are available? Experiences

ILRT ResearchRevealed; linked five different datasites RDFa – fits existing web publishing processes Integrating very disparate datasets and inferencing useful

extra information: Tripit.com, Siri.com, Powerset, Freebase

SemTech report http://www.semhe.org MIT CIO 2010 symposium http://3roundstones.com/led_book/led-contents.html LATC linked data application support http://latc-project.eu

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Questions for the panel

What practical experiences can we learn from, and what tools are available?

Tools Jena RKB explorer Oracle 10 OpenLink Virtuoso http://sig.ma (http://sindice.com) Liferay portal KOPF user interface D2R extract RDF from Dbs Silk framework- link discovery Collabra – vocabulary alignment