A framework for utility data integration in the UK

In this paper we investigate various factors which prevent utility knowledge from being fully exploited and suggest that integration techniques can be applied to improve the quality of utility records. The paper suggests a framework which supports knowledge and data integration. The framework supports utility integration at two levels: the schema and data level. Schema level integration ensures that a single, integrated geospatial data set is available for utility enquiries. Data level integration improves utility data quality by reducing inconsistency, duplication and conflicts. Moreover, the framework is designed to preserve autonomy and distribution of utility data. The ultimate aim of the research is to produce an integrated representation of underground utility infrastructure in order to gain more accurate knowledge of the buried services. It is hoped that this approach will enable us to understand various problems associated with utility data, and to suggest some potential techniques for resolving them.

[1]  N. Boukhelifa,et al.  THE UNCERTAIN REALITY OF UNDERGROUND ASSETS , 2007 .

[2]  Simon Marvin,et al.  Urban Infrastructure: The Contemporary Conflict Between Roads and Utilities , 1997 .

[3]  Alon Y. Halevy,et al.  Answering queries using views: A survey , 2001, The VLDB Journal.

[4]  Mahmoud R. Halfawy,et al.  Municipal Infrastructure Asset Management Systems: State-of-the-Art Review , 2005 .

[5]  Alexandra Poulovassilis,et al.  Data integration by bi-directional schema transformation rules , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[6]  Anthony G. Cohn,et al.  Knowledge-Based Recognition of Utility Map Sub-Diagrams , 2007 .

[7]  Dan Suciu,et al.  Schema mediation in peer data management systems , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[8]  Yaser A. Bishr,et al.  Overcoming the Semantic and Other Barriers to GIS Interoperability , 1998, Int. J. Geogr. Inf. Sci..

[9]  Erhard Rahm,et al.  Generic Schema Matching with Cupid , 2001, VLDB.

[10]  Ken Barker,et al.  Integrating relational database schemas using a standardized dictionary , 2001, SAC.

[11]  Stefano Spaccapietra,et al.  On Spatial Database Integration , 1998, Int. J. Geogr. Inf. Sci..

[12]  Amihai Motro,et al.  Superviews: Virtual Integration of Multiple Databases , 1987, IEEE Transactions on Software Engineering.

[13]  Yannis Manolopoulos,et al.  Spatial Databases , 2004 .

[14]  Todd D. Millstein,et al.  Navigational Plans For Data Integration , 1999, AAAI/IAAI.

[15]  Ashok Samal,et al.  A feature-based approach to conflation of geospatial sources , 2004, Int. J. Geogr. Inf. Sci..

[16]  Chris Clifton,et al.  SEMINT: A tool for identifying attribute correspondences in heterogeneous databases using neural networks , 2000, Data Knowl. Eng..

[17]  Amihai Motro,et al.  Multiplex, Fusionplex and Autoplex: three generations of information integration , 2004, SGMD.

[18]  Pedro M. Domingos,et al.  Reconciling schemas of disparate data sources: a machine-learning approach , 2001, SIGMOD '01.

[19]  Agnès Voisard,et al.  Spatial Databases: With Application to GIS , 2001 .

[20]  F. J. MONKHOUSE,et al.  The Ordnance Survey , 1965, Nature.