Skip to main content
SpringerLink
Log in

A data model and query language for spatio-temporal decision support

  • Published:
GeoInformatica Aims and scope Submit manuscript

Abstract

In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of introducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote \(\mathcal{L}_t,\) able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. http://www.opengeospatial.org

  2. http://www.esri.com

  3. See Microstrategy and MapInfo integration in http://www.microstrategy.com/, http://www.mapinfo.com/solutions/capabilities/business-intelligence.

  4. A description and demo of Piet can be found at http://piet.exp.dc.uba.ar/piet

  5. JMAP was developed by the Centre for Research in Geomatics and KHEOPS, http://www.kheops-tech.com/en/jmap/solap.jsp.

  6. MDX is a query language initially proposed by Microsoft as part of the OLEDB for OLAP specification, and later adopted as a standard by most OLAP vendors. See http://msdn2.microsoft.com/en-us/library/ms145506.aspx.

  7. A more complete definition of summable queries can be found in [19].

  8. For simplicity, we do not quantify over layers, although the language could be extended to support this.

  9. Egenhofer and Herring defined the 9-intersection model for binary topological relations [16], where every set of 9-intersections, represented as a 3 × 3 matrix, describes a binary topological relation.

References

  1. Abiteboul S, Hull R, Vianu V (1995) Foundations of databases. Addison-Wesley, Reading, MA

    Google Scholar 

  2. Ahmed TO, Miquel M (2005) Multidimensional structures dedicated to continuous spatiotemporal phenomena. In: 22nd British national conference on databases (BNCOD). Sunderland, UK, pp 29–40

  3. Ahmed TO (2008) Continuous spatial data warehousing. In: International Arab conference on information technology

  4. Allen J (1983) Maintaining knowledge about temporal intervals. Commun ACM 26(11):832–843

    Article  Google Scholar 

  5. Armstrong MP (1988) Temporality in spatial databases. In: GIS/LIS. San Antonino, Texas, USA, pp 880–889

  6. Bédard Y, Merret T, Han J (2001) Fundamentals of spatial data warehousing for geographic knowledge discovery, chap 3. Taylor & Francis, pp 53 – 73

  7. Bédard Y, Rivest S, Proulx MJ (2007) Spatial Online Analytical Processing (SOLAP): concepts, architectures, and solutions from a geomatics engineering perspective, chap 13. IGI Global, pp 298–319

  8. Bettini C, Dyreson C, Evans W, Snodgrass R, Sean Wang X (1998) A glossary of time granularity concepts. Temporal databases: research and practice. Lect Notes Comput Sci 1399:406–411

    Article  Google Scholar 

  9. Borges C, Laender AHF, Davis CA (1999) Piet-QL: spatial data integrity constraints in object oriented geographic data modeling. In: 7th ACM SIGSPATIAL international symposium on advances in geographic information systems (ACM-GIS). Kansas City, USA, pp 1–6

  10. Cabibbo L, Torlone R (1997) Querying multidimensional databases. In: Database programming languages. Lecture notes in computer science, vol 1369. Springer, pp 319–335

  11. Cockcroft S (1997) A taxonomy of spatial data integrity. Geoinformatica 1(4):327–343

    Article  Google Scholar 

  12. Consens M, Mendelzon A (1990) Low complexity aggregation in graphlog and datalog. In: Third international conference on database theory (ICDT). Paris, France, pp 379–394

  13. Dyreson C, Evans W, Lin H, Snodgrass R (2000) Efficiently supporting temporal granularities. IEEE transactions on data and knowledge engineering (TKDE), vol 12(4), pp 568–587

    Article  Google Scholar 

  14. Eder J, Koncilia C, Morzy T (2002) The comet metamodel for temporal data warehouses. In: CAiSE. Toronto, Canada, pp 83–99

  15. Egenhofer M, J. Herring J (1991) Categorizing binary topological relationships between regions, lines, and points in geographic databases. Technical report, Department of Surveying Engineering, University of Maine

  16. Escribano A, Gomez L, Kuijpers B, Vaisman AA (2007) Piet: a gis-olap implementation. In: ACM 10th international workshop on data warehousing and OLAP (DOLAP). ACM, pp 73–80

  17. Gómez L, Kuijpers B, Vaisman AA (2008) Aggregation languages for moving object and places of interest. In: ACM symposium on applied computing SAC. Fortaleza, Ceara, Brazil, pp 857–862

  18. Gómez L, Vaisman A, Zich S (2008) Piet-QL: a query language for GIS-OLAP integration. In: 16th ACM SIGSPATIAL international symposium on advances in geographic information systems (ACM-GIS), 27. Irvine, CA, USA

  19. Gómez L, Haesevoets S, Kuijpers B, Vaisman AA (2009) Spatial aggregation: data model and implementation. Inf Syst 34(6):551–576

    Article  Google Scholar 

  20. Gray J, Bosworth A, Layman A, Pirahesh H (1997) Data cube: a relational operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery (1):29–53

    Article  Google Scholar 

  21. Güting RH, Böhlen M, Jensen C, Lorentzos N, Schneider M, Vazirgiannis M (2000) A foundation for representing and quering moving objects. ACM Trans Database Syst 25(1):1–42

    Article  Google Scholar 

  22. Güting RH, de Almeida VT, Ansorge D, Behr T, Ding Z, Höse T, Hoffmann F, Spiekermann M, Telle U (2005) SECONDO: an extensible dbms platform for research prototyping and teaching. In: 21st international conference on data engineering (ICDE). Tokyo, Japan, pp 1115–1116

  23. Hadzilacos T, Tryfona N (1996) Logical data modelling for geographical applications. Int J Geogr Inf Sci 10(2):179–203

    Article  Google Scholar 

  24. Han J, Stefanovic N, Koperski K (1998) Selective materialization: an efficient method for spatial data cube construction. In: Research and development in knowledge discovery and data mining (PAKDD). Lecture notes in computer science, vol 1394. Springer, pp 144–158

  25. Hurtado C, Mendelzon A, Vaisman A (1999) Maintaining data cubes under dimension updates. In: 15th international conference on data engineering (IEEE/ICDE). Sydney, Australia, pp 346–355

  26. Hurtado CA, Mendelzon AO (2001) Reasoning about summarizability in heterogeneous multidimensional schemas. In: International conference of database theory (ICDT), pp 375–389

  27. Hurtado CA, Mendelzon AO (2002) OLAP dimension constraints. In: 21st ACM SIGACT-SIGMOD-SIGART symposium on principles of database system (PODS). Madison, Wisconsin, USA, pp 169–179

  28. Kemp KK (1997) Fields as a framework for integrating gis and environmental process models. Trans GIS 1(3):219–246

    Article  Google Scholar 

  29. Kimball R (1996) The data warehouse toolkit. Wiley, New York

    Google Scholar 

  30. Kimball R, Ross M (2002) The data warehouse toolkit: the complete guide to dimensional modeling, 2nd edn. Wiley, New York

    Google Scholar 

  31. Klug A (1982) Equivalence of relational algebra and relational calculus query languages having aggregate functions. J ACM 29(3):699–717

    Article  Google Scholar 

  32. Kuijpers B, Vaisman A (2007) A data model for moving objects supporting aggregation. In: Proceedings of the first international workshop on spatio-temporal data mining (STDM’07). Istambul, Turkey

  33. Langran G, Chrisman NR (1988) A framework for temporal geographic information systems. Cartographica 25(3):1–14

    Google Scholar 

  34. López IFV, Snodgrass R, Moon B (2005) Spatiotemporal aggregate computation: a survey. IEEE Trans Knowl Data Eng 17(2):271–286

    Article  Google Scholar 

  35. Malinowski E, Zimányi E (2004) Representing spatiality in a conceptual multidimensional model. In: 12th ACM international workshop on geographic information systems(GIS). Washington, DC, USA, pp 12–22

  36. Mendelzon AO, Vaisman AA (2000) Temporal queries in OLAP. In: 26th international conference on very large data base (VLDB). Cairo, Egypt, pp 242–253

  37. Mendelzon AO, Vaisman AA (2003) Time in multidimensional databases. In: Multidimensional databases. IDEA Group, pp 166–199

  38. Orlando S, Orsini R, Raffaetà A, Roncato A, Silvestri C (2007) Spatio-temporal aggregations in trajectory data warehouses. In: Data warehousing and knowledge discovery (DaWak). Lecture notes in computer science, vol 4654. Springer, Regensburg, Germany, pp 66–77

    Google Scholar 

  39. Paolino L, Tortora G, Sebillo M, Vitiello G, Laurini R (2003) Phenomena: a visual query language for continuous fields. In: 11th ACM international workshop on geographic information systems (GIS). New Orleans, LA, USA, pp 147–153

  40. Papadias D, Kalnis P, Zhang J, Tao Y (2001) Efficient OLAP operations in spatial data warehouses. In: Advances in spatial and temporal databases (SSTD). Lecture notes in computer science, vol 2121. Springer, pp 443–459

  41. Papadias D, Tao Y, Kalnis P, Zhang J (2002) Indexing spatio-temporal data warehouses. In: 18th international conference on data engineering (ICDE). San Jose, CA, USA, pp 166–175

  42. Papadimitriou CH, Suciu D, Vianu V (1996) Topological queries in spatial databases. In: Proceedings 15th ACM SIGACT-SIGMOD-SIGART symposium on principles of database system (PODS). Montreal, Canada, pp 81–92

  43. Paredaens J, den Bussche JV, Gucht DV (1994) Towards a theory of spatial database queries. In: 13th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems, (PODS). Minneapolis, USA, pp 279–288

  44. Paredaens J, Kuper G, Libkin L (eds) (2000) Constraint databases. Springer, Berlin Heidelberg New York

    Google Scholar 

  45. Pedersen TB, Tryfona N (2001) Pre-aggregation in spatial data warehouses. In: Advances in spatial and temporal databases (SSTD), pp 460–480

  46. Pelekis N (2002) Stau: a spatio-temporal extension to ORACLE DBMS. Ph.D thesis, UMIST Department of Computation

  47. Pelekis N, Theodoulidis B, Kopanakis Y, Theodoridis Y (2004) Literature review of spatio-temporal database models. The Knowledge Engineering Review Journal 19(3):235–274

    Google Scholar 

  48. Pelekis N, Theodoridis Y, Vosinakis S, Panayiotopoulos T (2006) Hermes—a framework for location-based data management. In: 10th international conference on extending database technology. Munich, Germany, pp 1130–1134

  49. Peuquet D, Duan N (1995) An event-based spatiotemporal data model (estdm) for temporal analysis of geographical data. Int J Geogr Inf Syst 9(1):7–24

    Article  Google Scholar 

  50. Pourabbas E (2003) Cooperation with geographic databases. In: Multidimensional databases: problems and solutions. Idea group, pp 393–432

  51. Rao F, Zhang L, Yu X, Li Y, Chen Y (2003) Spatial hierarchy and OLAP-favored search in spatial data warehouse. In: ACM 6th international workshop on data warehousing and OLAP (DOLAP). New Orleans, LA, USA, pp 48–55

  52. Rigaux P, Scholl M, Voisard A (2001) Spatial databases: with application to GIS. Data management systems. Morgan Kaufmann, San Mateo, CA

  53. Rivest S, Bédard Y, Marchand P (2001) Towards better support for spatial decision making: defining the characteristics of spatial on-line analytical processing (SOLAP). Geomatica 55(4):539–555

    Google Scholar 

  54. Rodriguez A, Bertossi L, Caniupan M (2008) An inconsistency tolerant approach to querying spatial databases. In: 16th ACM SIGSPATIAL international symposium on advances in geographic information systems (ACM-GIS), 36. Irvine, CA, USA

  55. Shanmugasundaram J, Fayyad UM, Bradley PS (1999) Compressed data cubes for olap aggregate query approximation on continuous dimensions. In: 5th ACM SIGKDD international conference on knowledge discovery and data mining (KDD). San Diego, CA, USA, pp 223–232

  56. Shekhar S, Lu CT, Tan X, Chawla S, Vatsavai RR (2001) Map Cube: a visualization tool for spatial data warehouses, chap 4. Taylor and Francis, pp 73–108

  57. Snodgrass R (ed) (1995) The TSQL2 temporal query language. Kluwer, Boston, MA

    Google Scholar 

  58. Stefanovic N, Han J, Koperski K (2000) Object-based selective materialization for efficient implementation of spatial data cubes. IEEE Trans Knowl Data Eng 12(6):938–958

    Article  Google Scholar 

  59. Tansel A, Clifford J, Gadia S (eds) (1993) Temporal databases: theory, design and implementation. Benjamin Cummings, Redwood City, CA

    Google Scholar 

  60. Toman D (1996) Point vs. interval-based query languages for temporal databases. In: 15th ACM SIGACT-SIGMOD-SIGART symposium on principles of database system (PODS). Montreal, Canada, pp 58–67

  61. Tryfona N, Hadzilacos Th (1998) Logical data modelling of spatio temporal applications: definitions and a model. In: International database engineering and applications symposium(IDEAS). Cardiff, Wales, UK, pp 14–23

  62. Tryfona N, Jensen C (1999) Conceptual data modeling for spatiotemporal applications. Geoinformatica 3(3):245–268

    Article  Google Scholar 

  63. Tryfona N, Price R, Jensen C (2003) Conceptual models for spatiotemporal applications. Spatio-temporal databases: the CHOROCHRONOS approach, pp 79–116

  64. Vaisman AA, Izquierdo A, Ktenas M (2008) A web-based architecture for temporal olap. Int J Web Eng Technol 4(4):465–494

    Article  Google Scholar 

  65. Vaisman A, Zimányi E (2009) What is spatiotemporal data warehousing? Data warehousing and knowledge discovery (DaWak). In: Proceedings of DaWaK, pp 9–23

  66. Vaisman A, Zimányi E (2009) A multidimensional model representing continuous fields in spatial data warehouses. In: Proceedings of ACM-SIGSPATIAL, pp 168–177

  67. Wachowicz M, Healey R (1994) Towards temporality in GIS. In: Innovation in GIS, vol I. Taylor & Francis, pp 105–115

  68. Worboys MF (1992) A model for spatio-temporal information. In: Proceedings of the 5th international symposium on spatial data handling. Charleston, South Carolina, pp 602–611

  69. Worboys MF (1995) GIS: a computing perspective. Taylor & Francis, New York

    Google Scholar 

  70. Zeiler M (1999) Modeling our world: the ESRI guide to geodatabase design. ESRI Press

  71. Zhang L, Li Y, Rao F, Yu X, Chen Y, Liu D (2003) An approach to enabling spatial OLAP by aggregating on spatial hierarchy. In: Data warehousing and knowledge discovery (DaWak). Lecture notes in computer science, vol 2737. Springer, Prague, Czech Republic, pp 35–44

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto Tecnológico de Buenos Aires, Av. Madero 399, Buenos Aires, Argentina

    Leticia Gómez

  2. Hasselt University and Transnational University of Limburg, Gebouw D, 3590, Diepenbeek, Belgium

    Bart Kuijpers

  3. Universidad de Buenos Aires, Ciudad Universitaria, Pabellon I, Buenos Aires, 1428, Argentina

    Alejandro Vaisman

Authors
  1. Leticia Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bart Kuijpers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alejandro Vaisman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alejandro Vaisman.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gómez, L., Kuijpers, B. & Vaisman, A. A data model and query language for spatio-temporal decision support. Geoinformatica 15, 455–496 (2011). https://doi.org/10.1007/s10707-010-0110-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10707-010-0110-7

Keywords

Search

Navigation