SPUD—Semantic Processing of Urban Data

• Published in: Journal of web semantics Vol. 24; pp. 11 - 17
• Main Authors: Kotoulas, Spyros, Lopez, Vanessa, Lloyd, Raymond, Sbodio, Marco Luca, Lecue, Freddy, Stephenson, Martin, Daly, Elizabeth, Bicer, Veli, Gkoulalas-Divanis, Aris, Di Lorenzo, Giusy, Schumann, Anika, Mac Aonghusa, Pol
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Amsterdam Elsevier B.V 01.01.2014; Elsevier
• Subjects: Applications; City data; Diagnosis; Exact sciences and technology; General aspects; Information and communication sciences; Information science. Documentation; Linked data; Reasoning; Sciences and techniques of general use; Semantic integration; Semantic integration; Diagnosis; Linked data; Applications; City data; Reasoning; Word; Diversity; Updating; Cataloging; Information; Heterogeneity; Input; Semantics; Operating system; Integration; Urban environment; Data processing; Data; Requirement; Volume; Information processing; Environment; Frequency; Models; Syntax
• ISSN: 1570-8268; 1873-7749
• DOI: 10.1016/j.websem.2013.12.003

We present SPUD, a semantic environment for cataloging, exploring, integrating, understanding, processing and transforming urban information. A series of challenges are identified: namely, the heterogeneity of the domain and the impracticality of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), the complex data dependencies and the sensitivity of the information. We propose an approach for the incremental and continuous integration of static and streaming data, based on Semantic Web technologies and apply our technology to a traffic diagnosis scenario. We demonstrate our approach through a system operating on real data in Dublin and we show that semantic technologies can be used to obtain business results in an environment with hundreds of heterogeneous datasets coming from distributed data sources and spanning multiple domains.