PROJECT VISTA: Integrating Heterogeneous Utility Data A very brief overview
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School of somethingFACULTY OF OTHER
School of ComputingFACULTY OF ENGINEERING
PROJECT VISTA:Integrating Heterogeneous Utility Data
A very brief overview
Anthony Beck
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Visualising integrated information on buried assets to reduce streetworks
(VISTA)
The Project
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VISTA
4 Year project funded by the DTI
now the Department for Business, Enterprise and Regulatory Reform
Joint project:
Academic partners Leeds and Nottingham universities.
In collaboration with 20+ utility and other partners.
Goal: to reduce the direct and indirect costs of utility maintenance associated with the highways. Aim: Swift, safe, cost-effective streetworks.
Catalyst: The Traffic Management Act 2008. Legislation will expect utility companies to share their asset data digitally.
Leeds Research Challenge: More effective integration, representation and use of existing digital assets.
Problem: Each utility organisation maintains their data in different database environments with different logical and physical models.
Solution: To find mechanisms to integrate heterogeneous data to generate a seamless and consistent integrated virtual data model.
Utility users should have timely and appropriate access to the data they need.
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VISTA
The Problem
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The Problem
Streetworks - Small operational improvement translates into large economic saving
• c. 4M street openings p.a.
• Direct costs of £1B p.a.
• Indirect costs of £3B-£5B p.a.
• Pollution, congestion etc.
Need to know asset location and attributes:
• Planning
• Management
• Maintenance
• Can take months to generate composite maps
Many databases: varying accuracy and provenance
Safety!
• Recent example – 3 near misses on high voltage electric in one city in one week.
• Data not collected
• Data ignored!
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The Problem
Abstraction of reality
Reality
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Water:
• Omissions
• Inconsistencies
• Spatial inaccuracies
Which representation is correct?
Uncertainty
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Problem Domain
Different ways of storing asset data• Paper – CAD – GIS
• Raster to Vector conversion
Different ways of structuring digital asset data• Different syntactic and schematic models
• Global Schema based integration
Different ways of describing asset data• Semantic inconsistency
• Thesauri/ontology reconciliation
Different ways of sharing and representing asset data• Paper – CAD – GIS
• Different symbols and conventions
• Uncertainty
• User/domain tailored visualisations
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Utility Asset Management
For Street Works this rich data model can be reduced to a single print out.
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Integration work in MTU and VISTA
Aims: provide a framework which will integrate information from multiple utility asset stores.
• Syntactic (format) integration
• Schematic (design/model) integration
• Semantic integration
Objectives:
• identify and agree a core set of location and attribute data
• to provide a common framework within which to represent these multiple information sources
• to construct and evaluate a prototype system
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VISTA
Integration Framework
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Why VISTA integration?
In principal
Integration can occur if each utility dataset were made available as a WFS
In practice this would lead to:
• Knowledge articulation issues: semantic inconsistency
• Limited query, analysis and visualisation: schematic inconsistency
VISTA aims to fully integrated source data models to increase domain knowledge
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Virtual Integration Framework
The framework supports utility integration at two levels:
the schema level
• Integration based on a common utility data model (global schema)
• Resolves schematic heterogeneity
the data level
• Ontology/Global Thesauri employed at the data level
• Resolves semantic heterogeneity
• When the same asset type is given different names by different companies
• When different asset types are given the same name by different companies
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Resolving syntactic heterogeneity
OGC interoperable snapshot• Only access necessary
attributes (increased security)
• Proprietary GIS can be altered with minimal impact on system (increased flexibility)
• Reduced impact on server side processing overhead (increased operational activity)
• Potential to materialise results using change only updates (reduced processing overhead – improved access times)
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Resolving Schematic Heterogeneity
Global Schema used to mediate integration
• Mappings and transforms generated between source tables and the Global Schema
• During prototyping metadata managed using Radius Studio
• Allows rapid validation by utility partners
Data held within Oracle Spatial repository
• Generic PL/SQL code defined to integrate source data and materialise the result set
• Mapping and transformation metadata held in Radius Studio will be directly integrated as a service.
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Resolving Schematic Heterogeneity
Currently 29 Global Schema fields grouped into 11 types
Distinguished between core and non-core data
• Asset x 10 fields (5 core)
• Condition x 1 field (0 core)
• Confidence x 1 field (0 core)
• Date x 1 field (0 core)
• Detection System x 1 field (0 core)
• Dimension x 5 fields (5 core)
• Domain x 4 fields (2 core)
• GIS x 2 fields (1 core)
• Location x 3 fields (1 core)
• Rehabilitation work x 1 field (0 core)
• Risk x 1 field (0 core)
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Resolving Semantic Heterogeneity
Generation of a cross domain global thesaurus
Thesaurus: a list of terms linked together by hierarchical, and associative or equivalence relationships.
• Can be converted into an OWL ontology
• Developed within MultiTes Pro
Reconciling semantic heterogeneities
Focus on asset types and subtypes in water and sewer domains
Subject Category
Scope Note
Used For
Broader Term
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Virtual Integration Framework
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Virtual Integration Framework
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Virtual Integration Framework
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Virtual Integration Framework
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Virtual Integration Framework
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Generic Integration
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Thank you