|
Poster Session 2
|
Integration of web-based gis
and online analytical processing
Spatial OLAP Server
The main requirements of OLAP server include supporting multiple users, handling huge volume of data efficiently as well as supporting rich OLAP operations. Multiple users access is very common nowadays, especially to network applications. Typical OLAP operations are roll-up, drill-down, slice and dice, pivot. Spatial OLAP server should implement all of these functions. Generally OLAP runs against very large dataset. Hence efficient and effective access methods are critical. Furthermore, when it comes to spatial data, the situation becomes more complicated (Egenhofer, 1994).
To access spatial dataset efficiently, many methods and models have been proposed from the perspective of databases (Egenhofer, 1994; Aref, 1991). In addition to these kinds of effort, materialization of view (Harinarayan, 1996; Stefanovic, 1993) and indexing are very promising fields. In (Harinarayan, 1996), a lattice framework is presented to express dependency among view. With the help of this framework, a greedy algorithm is developed to choose proper views to materialize so that response time is shortened. Stefanovic (1993) improves the algorithm above according to the characteristics of spatial measures. Indexing on summary tables (subcubes of the data cube) would enhance the efficiency of query further, Gupta (1997) propose algorithm for index selection for OLAP which takes efficient use of space into consideration. We would employ these achievements in our system.
Regarding data storage, the spatial data and its attribute are managed by geographical information systems in our system. The non-spatial data, metadata and concept hierarchy are stored in relational DBMS (RDBMS). Spatial OLAP server manages the materialized view which can shorten response time greatly. Data cube, which is constructed and managed by OLAP server, makes it possible for users to observe data from various concept levels. There are two major direction in implementing OLAP Servers, namely Relation OLAP (ROLAP) and Multidimensional OLAP (MOLAP). ROLAP extend traditional relational server to support multidimensional view while MOLAP utilize a direct way, such as, multidimensional array, to manage multidimensional information. ROLAP integrates naturally with existing technology and standards, which is reliable and scalable whereas MOLAP provide efficiency in storage and operations because of its direct representation of multidimensional data. (Zhao 1997; Shoshani, 1997) The former is adopted in our system
Conclusion
Spatial OLAP is a fresh but promising field of research. Its integration with Web-based GIS is an even more interesting area. Web technology makes spatial OLAP more accessible to decision maker. Spatial OLAP equips Web-based GIS important functions for decision support. So far there many questions to them remain open, such as automatic construction of concept hierarchies, data integration, incremental update of spatial data cube.
References
- Aref, Walid G., et al., 1991. Extending a DBMS with Spatial Operations. In: Advances in Spatial Databases, Proceedings of Symposium on Large Spatial Databases, SSD'91.
- Egenhofer, M. J., 1994. Spatial SQL: a query and presentation language. IEEE Transactions on Knowledge and Data Engineering, Volume: 6 No. 1, Feb. 1994 pp.86-95.
- Erik, T., 1997. OLAP Solutions: Building Multidimensional Information Systems, Wiley, US.
- ESRI, May 2000a (Access Date). The ArcIMS 3 Architecture. Available at: http://arconline.esri.com/arconline/
whitepapers.cfm?PID=6
- ESRI, May 2000b(Access Date). ArcIMS 3 Features and Functions. Available at: http://arconline.esri.com/arconline/
whitepapers.cfm?PID=6
- Ester, M., 1997. Spatial Data Mining: A Database Approach. In: Advances in Spatial Database. SSD'97 Berlin.
- Frawley, W. J., et al., 1991. Knowledge Discovery in Databases: An Overview. In: Knowledge Discovery in Database. Edited by Piatetsky-shapiro, G., et al., AAAI/MIT Press, Menlo Park, CA
- Gupta, H., Harinarayan, V., Rajaraman, A., Ullman J. D., 1997. Index Selection for OLAP. In: 13th International Conference on Data Engineering, pp.208-219.
- Harinarayan, V., Rajaraman, A., and Ullaman, J. D., 1996. Implementing data cubes efficiently. In: Proc. 1996 ACM-SIGMOD Int. Conf. Management of Data, Montreal, Canada, June 1996, pp205-216.
- Koperski, K., et al, 1996. Spatial Data Mining: Progress and Challenges. In : Proceedings of SIGMOD'96 Workshop, Data Mining and Knowledge Discovery (DMKD'96), Montreal, Canada, June 1996.
- Lu, W., et al., 1993. Discovery of General Knowledge in Large Spatial Databases. In: Proc. of 1993 Far East Workshop on Geographic Information Systems (FEGIS'93), Singapore, June 1993, pp. 275-289
- Stefanovic, N., 1993. Design and Implementation of On-Line Analytical. In: Processing of Spatial Data. M.Sc. Thesis, Computing Science, Simon Fraser University, Canada. Available at: http://db.cs.sfu.ca/sections/publication/theses/
theses.html
- Shoshani, A., 1997. OLAP and statistical databases: similarities and differences. In: Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems. pp.185 - 196
- Zhao, Y., et al., 1997. An array-based algorithm for simultaneous multidimensional aggregates. In: Proceedings of the ACM SIGMOD international conference on Management of data, pp. 159 - 170.
|
|
|
|
|
