ABSTRACT
Despite recent progresses in methods for processing data about the movement of objects in the geographic space, some fundamental issues remain unresolved. One of them is how to describe movement segments (e.g., semantic trajectories, episodes like stops and moves) and diverse movement patterns (e.g., moving clusters, hotel-restaurant-shop-hotel), with formal semantic descriptions. Another issue is how to arrange descriptive data and measures in a Movement Data Warehouse (MDW) for powerful information analyses and reasonable performance. This paper introduces general definitions for movement segments, movement patterns, their categories and hierarchies. The proposed constructs are semantically enriched with references to concepts (categories) and/or instances of these concepts (objects) arranged in distinct hierarchies. Based on these constructs, we propose a semantic multidimensional model for MDW. A case study illustrates the expressiveness of the proposal for analyzing movement data collected via social media and semantically enriched with Linked Open Data (LOD).
- R. Battle and D. Kolas. Enabling the geospatial Semantic Web with Parliament and GeoSPARQL. Semantic Web, 3(4):355--370, 2012. Google ScholarCross Ref
- V. Bogorny, H. Avancini, B. C. de Paula, C. R. Kuplich, and L. O. Alvares. Weka-STPM: a Software Architecture and Prototype for Semantic Trajectory Data Mining and Visualization. T. GIS, 15(2):227--248, 2011.Google ScholarCross Ref
- V. Bogorny, C. Renso, A. R. de Aquino, F. de Lucca Siqueira, and L. O. Alvares. CONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects. T. GIS, 18(1):66--88, 2014.Google ScholarCross Ref
- S. Dodge, R. Weibel, and A.-K. Lautenschutz. Towards a Taxonomy of Movement Patterns. Information Visualization, 7(3):240--252, June 2008. Google ScholarDigital Library
- R. Fileto, M. Kruger, N. Pelekis, Y. Theodoridis, and C. Renso. Baquara: A Holistic Ontological Framework for Movement Analysis Using Linked Data. In W. Ng, V. C. Storey, and J. Trujillo, editors, ER, volume 8217 of LNCS, pages 342--355. Springer, 2013.Google Scholar
- S. I. V. Filho, R. Fileto, A. S. Furtado, and R. H. Guembarovski. Towards Intelligent Analysis of Complex Networks in Spatial Data Warehouses. In V. Bogorny and L. Vinhas, editors, GeoInfo, pages 134--145. MCT/INPE, 2010.Google Scholar
- L. Gomez, B. Kuijpers, and A. Vaisman. A data model and query language for spatio-temporal decision support. GeoInformatica, 15(3):455--496, 2011. Google ScholarDigital Library
- P. Hitzler, M. Krotzsch, and S. Rudolph. Foundations of Semantic Web Technologies. Chapman & Hall/CRC, 1st edition, 2009. Google ScholarDigital Library
- R. Kimball and M. Ross. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. John Wiley & Sons, Inc., NY, USA, 2nd edition, 2002. Google ScholarDigital Library
- A. Kiryakov, B. Popov, I. Terziev, D. Manov, and D. Ognyanoff. Semantic Annotation, Indexing, and Retrieval. Web Semantics: Science, Services and Agents on the World Wide Web, 2(1):49--79, 2004. Google ScholarDigital Library
- B. Leal, J. A. F. de Macedo, V. C. Times, M. A. Casanova, V. M. P. Vidal, and M. T. M. de Carvalho. From Conceptual Modeling to Logical Representation of Trajectories in DBMS-OR and DW Systems. JIDM, 2(3):463--478, 2011.Google Scholar
- L. Leonardi, S. Orlando, A. Raffaeta, A. Roncato, C. Silvestri, G. L. Andrienko, and N. V. Andrienko. A general framework for trajectory data warehousing and visual OLAP. GeoInformatica, 18(2):273--312, 2014. Google ScholarDigital Library
- G. Marketos, E. Frentzos, I. Ntoutsi, N. Pelekis, A. Raffaeta, and Y. Theodoridis. Building Real World Trajectory Warehouses. In MobiDE, pages 8--15. ACM, 2008. Google ScholarDigital Library
- E. Moreau, F. Yvon, and O. Cappe. Robust Similarity Measures for Named Entities Matching. In 22nd Intl. Conf. on Computational Linguistics - Volume 1, COLING, pages 593--600, Stroudsburg, PA, USA, 2008. Association for Computational Linguistics. Google ScholarDigital Library
- F. Moreno, A. Pineda, R. Fileto, and V. Bogorny. SMoT+: Extending the SMoT Algorithm for Discovering Stops in Nested Sites. Computing and Informatics, 33(2):327--342, 2014.Google Scholar
- R. G. B. Nabo, R. Fileto, C. Renso, and M. Nanni. Annotating Trajectories by Fusing them with Social Media Users' Posts. In Brazilian Symposium on Geoinformatics, GeoInfo, Campos do Jordao, SP, Brazil (submitted), 2014.Google Scholar
- S. Orlando, R. Orsini, A. Raffaeta, A. Roncato, and C. Silvestri. Trajectory Data Warehouses: Design and Implementation Issues. Journal of Computing Science and Engineering, 1(2):240--261, 2007.Google ScholarCross Ref
- C. Parent, S. Spaccapietra, C. Renso, G. L. Andrienko, N. V. Andrienko, V. Bogorny, M. L. Damiani, A. Gkoulalas-Divanis, J. A. F. de Macedo, N. Pelekis, Y. Theodoridis, and Z. Yan. Semantic trajectories modeling and analysis. ACM Comput. Surv., 45(4), 2013. Article 42. Google ScholarDigital Library
- N. Pelekis and Y. Theodoridis. Mobility Data Management and Exploration. Springer, 2014. Google ScholarDigital Library
- A. Raffaeta, L. Leonardi, G. Marketos, G. Andrienko, N. Andrienko, E. Frentzos, N. Giatrakos, S. Orlando, N. Pelekis, A. Roncato, and C. Silvestri. Visual Mobility Analysis using T-Warehouse. J. of Data Warehousing and Mining, 7(1):1--23, 2011. Google ScholarDigital Library
- S. Rinzivillo, F. de Lucca Siqueira, L. Gabrielli, C. Renso, and V. Bogorny. Where Have You Been Today? Annotating Trajectories with DayTag. In SSTD, volume 8098 of LNCS, pages 467--471. Springer, 2013. Google ScholarDigital Library
- J. A. M. R. Rocha, V. C. Times, G. Oliveira, L. O. Alvares, and V. Bogorny. DB-SMoT: A direction-based spatio-temporal clustering method. In IEEE Conf. of Intelligent Systems, pages 114--119. IEEE, 2010.Google ScholarCross Ref
- A. A. Vaisman and E. Zimanyi. What Is Spatio-Temporal Data Warehousing? In DaWaK, volume 5691 of LNCS, pages 9--23. Springer, 2009. Google ScholarDigital Library
- R. Wagner, J. A. F. de Macedo, A. Raffaeta, C. Renso, A. Roncato, and R. Trasarti. Mob-Warehouse: A semantic approach for mobility analysis with a Trajectory Data Warehouse. In SecoGIS, joint to ER 2013, Hong Kong, 2013.Google Scholar
- Z. Yan, D. Chakraborty, C. Parent, S. Spaccapietra, and K. Aberer. Semantic trajectories: Mobility data computation and annotation. ACM TIST, 4(3), 2013. Google ScholarDigital Library
Index Terms
- A Semantic Model for Movement Data Warehouses
Recommendations
Multidimensional models meet the semantic web: defining and reasoning on OWL-DL ontologies for OLAP
DOLAP '12: Proceedings of the fifteenth international workshop on Data warehousing and OLAPData warehouses use a multidimensional model. Based on this model, OLAP cubes enable users to analyze data. For correct OLAP analysis, multidimensional models should be checked. In particular, these models should ensure summarizability. Checking ...
Building data warehouses with semantic data
EDBT '10: Proceedings of the 2010 EDBT/ICDT WorkshopsThe Semantic Web has become a new environment that enables organizations to attach semantic annotations taken from ontologies to the information they generate. As a result, large amounts of complex, semi-structured and heterogeneous semantic data ...
Semantic Integration of Structured Data Powered by Linked Open Data
WIMS '15: Proceedings of the 5th International Conference on Web Intelligence, Mining and SemanticsRecent advances in open data have resulted in vast amounts of tabular datasets containing valuable, actionable information to several stakeholders. However, information pertaining to any given entity is fragmented across several arbitrarily structured ...
Comments