9 Spatio-temporal Databases in the Years AheadIntroductionMobile and Wireless ComputingData Warehousing and MiningThe Semantic WebConclusionsReferences9 Spatio-temporalDatabases in the Years AheadManolis Koubarakis1, Yannis Theodoridis2, and Timos Sellis31Technical University of Crete, Greece2University of Piraeus, Greece3National Technical University of Athens, Greece9.1 IntroductionCHOROCHRONOS has been a fruitful and enjoyable project. It contributedmany innovative ideas in the areas of ontology and data modeling, query evalu-ation and prototype systems for spatio-temporal databases. Our ideas have al-ready found uses in various application domains such as moving object databases(see Chapter 4), environmental information systems (see the Dedale applicationin Chapter 5), interactive multimedia applications and virtual worlds (see Chap-ter 8).CHOROCHRONOS has opened many avenues for research in spatio-temporaldatabases, but it also left us with lots of challenging research problems awaitingsolution. Many of these problems have already been emphasized in the conclud-ing sections of each chapter, and there is no reason to repeat them here.1As anepilogue to this book, we would like to challenge the reader by discussing threeimportant application areas and the role spatio-temporal databases can play inthese.9.2 Mobile and Wireless ComputingThe main concept of interest here is the concept of lo cation (location of mo-bile clients, moving application objects and so on) and how it changes overtime (a nice recent survey of this area is [10]). This application area has moti-vated a lot of spatio-temporal research recently (for research carried out outsideCHOROCHRONOS see [13,14,4] and [11,6]) but there are many aspects of theproblem that have not been looked at in detail. In particular, all approaches seemto adopt a centralized database view of the problem while the problem is clearlydistributed [10]. Towards this direction, a recently launched European project[3], where CHOROCHRONOS researchers participate, considers both movingand stationery objects as agents that play the roles of data servers, producersand clients interchangeably. Open issues in all aspects of mobile databases ariseunder this consideration.1We are sure the seasoned database researcher can easily imagine many others!T. Sellis et al. (Eds.): Spatio-temporal Databases, LNCS 2520, pp. 345–347, 2003.c Springer-Verlag Berlin Heidelberg 2003346 Manolis Koubarakis et. al.9.3 Data Warehousing and MiningThere is currently a huge amount of spatio-temporal data that has been collectedover the years. The concept of a data warehouse has naturally been extendedfrom alphanumeric data to temporal, spatial and spatio-temporal data and ef-ficient implementation of OLAP (on-line analytical processing) operations havebeen studied [7,5,12,8,9]. Of particular interest here is the mining of spatio-temporal patterns, since it can lead to important observations in many appli-cations (e.g., environmental monitoring and fleet management). There is verylittleworkinthisareaandmuchremainstobedone[15].9.4 The Semantic WebAccording to the Web’s inventor Tim Berners-Lee, Jim Hendler and Ora Lassila“the Semantic Web will bring structure to the meaningful content of Web pages,creating an environment where software agents roaming from page to page canreadily carry out sophisticated tasks for users.” [2,1]. The main technologiesunderlying this vision are knowledge r epresentation and ontologies for formalizingmeaning, software agents as the most useful abstraction for a computationalentity and XML as the universal language for encoding and sharing information.There is currently a lot of excitement in this area with much research doneunder the auspices of W3C2and DARPA (e.g., see the effort to develop DAML- DARPA’s Agent Markup Language3). Ideas from spatio-temporal ontologies,models, languages and query processing algorithms (as discussed in various chap-ters of this book) cannot really be absent from the Semantic Web vision andthere is much interesting research to be carried out by spatio-temporal databaseresearchers.A related effort is the Geography Markup Language (GML) proposed bythe OpenGIS consortium.4This is an XML-based language for the storage andtransfer of geographic information, including both the spatial and non-spatialproperties of geographic objects. This is a good step towards achieving interop-erability among existing geographical applications.9.5 ConclusionsWe believe that new applications like the ones sketched above will be the ul-timate test for the arsenal of spatio-temporal concepts, models and algorithmsdeveloped in CHOROCHRONOS. We invite the reader to join us in taking upthe challenge!2http://www.w3c.org.3http://www.darpa.mil.4http://www.opengis.org.9 Spatio-temporal Databases in the Years Ahead 347References1. Tim Berners-Lee and Mark Fischetti. Weaving the Web: The original design andultimate destiny of the World Wide Web, by its inventor. Harper, 1999.2. Tim Berners-Lee, James Hendler, and Ora Lassila. The Semantic Web. ScientificAmerican, May 2001.3. DBGlobe: A Data-centric Approach to Global Computing. Seehttp://softsys.cs.uoi.gr/dbglobe/index.htm.4. M. Hadjieleftheriou, G. Kollios, V. Tsotras, and D. Gunopulos. Efficient Indexingof Spatiotemporal Objects. In Proceedings of the 8th International Conference onExtending Database Technology (EDBT 2002), Prague, March 2002.5. J. Han, N. Stefanovic, and K. Koperski. Selective Materialisation: An EfficientMethod for Spatial Datacube Construction. In Proceedings of the Pacific-AsiaConference on Knowledge Discovery and Data Mining (PAKDD’98), pages 144–158, Melbourne, Australia, April 1998.6. I. Lazaridis, K. Porkaew, and S. Mehrotra. Dynamic Queries over Mobile Ob-jects. In Proceedings of the 8th International Conference on Extending DatabaseTechnology (EDBT 2002), Prague, March 2002.7. A.O. Mendelzon and A.A. Vaisman. Temporal Queries in OLAP. In Proceedings ofthe 26th International Conference on Very Large Databases (VLDB 2000), pages242–253, Cairo, Egypt, September 2000.8. D. Papadias, P. Kalnis, J. Zhang, and Y. Tao. Efficient OLAP Operations in SpatialData Warehouses. In Proceedings of the 7th International Symposium on Spatialand Temporal Databases (SSTD 2001), pages 59–78, Redondo Beach, California,USA, July 2001.9. D. Papadias, Y. Tao, P. Kalnis, and J. Zhang. Indexing Spatiotemporal Data Ware-houses. In Proceedings of the 18th International