A Model and Framework for Matching Complementary Spatio-Temporal Needs

Currently, systems that let people search for opportunities to fulfill their spatio-temporal needs are built according to the conceptual model of service provider and consumer: After the providers make their needs publicly available, consumers use a specifically tailored query engine to find fitting offers. E.g., in carpooling, someone wants to fill an empty seat and to share costs (and publishes this offer), while another person wants to travel the same route. This model prevents the consuming side from making their needs available to the service providers and makes it hard to generalize, as query engines require rigid (often domain-specific) properties. Addressing this problem, we propose a generic model for publishing and processing complementary spatio-temporal needs. Our model uses a simulator to assess how well the collaboration between different entities would approximate their goals. To reuse existing concepts and embed the model into the emerging Semantic Web, everything is modeled in accordance with Linked Data principles.

[1]  M. Sanderson,et al.  Analyzing geographic queries , 2004 .

[2]  Mark Fischetti,et al.  Weaving the web - the original design and ultimate destiny of the World Wide Web by its inventor , 1999 .

[3]  Ioannis Vlahavas,et al.  Intelligent techniques for planning , 2004 .

[4]  Simon Scheider,et al.  Matching Complementary Spatio-Temporal Needs of People , 2015 .

[5]  Yan Dong,et al.  A Similarity-Oriented RDF Graph Matching Algorithm for Ranking Linked Data , 2012, 2012 IEEE 12th International Conference on Computer and Information Technology.

[6]  C. Caulfield,et al.  A survey of a personalised location-based service architecture for property hunting , 2014 .

[7]  Andreas Henrich,et al.  Characteristics of geographic information needs , 2007, GIR '07.

[8]  Reynold Cheng,et al.  On querying historical evolving graph sequences , 2011, Proc. VLDB Endow..

[9]  Harvey J. Miller,et al.  Beyond sharing: cultivating cooperative transportation systems through geographic information science , 2013 .

[10]  Shih-Chia Huang,et al.  A cloud computing framework for real-time carpooling services , 2012, 2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM2012).

[11]  Bernhard Seeger,et al.  Geographic Information Retrieval , 2004, WebDyn@WWW.

[12]  Simon Scheider,et al.  Towards sustainable mobility behavior: research challenges for location-aware information and communication technology , 2016, GeoInformatica.

[13]  Stephan Winter,et al.  Time geography for ad-hoc shared-ride trip planning in mobile geosensor networks , 2007 .

[14]  Filomena Ferrucci,et al.  A Matching-Algorithm based on the Cloud and Positioning Systems to Improve Carpooling , 2011, DMS.

[15]  Stephan Winter,et al.  A Spatio-Temporal Model Towards Ad-Hoc Collaborative Decision-Making , 2010, AGILE Conf..

[16]  Daniel Graziotin An Analysis of issues against the adoption of Dynamic Carpooling , 2013, ArXiv.

[17]  Grant Donald McKenzie,et al.  A Temporal Approach to Defining Place Types based on User-Contributed Geosocial Content , 2015 .

[18]  Amit P. Sheth,et al.  Semantic Services, Interoperability and Web Applications - Emerging Concepts , 2011, Semantic Services, Interoperability and Web Applications.

[19]  Lei Chen,et al.  Spatial Crowdsourcing: Challenges and Opportunities , 2016, IEEE Data Eng. Bull..

[20]  Peter Grindrod,et al.  Evolving graphs: dynamical models, inverse problems and propagation , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  Krzysztof Janowicz,et al.  Semantic Referencing - Determining Context Weights for Similarity Measurement , 2010, GIScience.

[22]  Simon Scheider,et al.  A civilized cyberspace for geoprivacy , 2014, GeoPrivacy '14.