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Preview: Optimizing View Materialization Cost in Spatial Data Warehouses

Preview: Optimizing View Materialization Cost in Spatial Data Warehouses

Songmei Yu, Vijayalakshmi Atluri, Nabil Adam
Copyright: © 2010 |Pages: 17
ISBN13: 9781605667485|ISBN10: 160566748X|ISBN13 Softcover: 9781616924522|EISBN13: 9781605667492
DOI: 10.4018/978-1-60566-748-5.ch003
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MLA

Yu, Songmei, et al. "Preview: Optimizing View Materialization Cost in Spatial Data Warehouses." Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, IGI Global, 2010, pp. 47-63. https://doi.org/10.4018/978-1-60566-748-5.ch003

APA

Yu, S., Atluri, V., & Adam, N. (2010). Preview: Optimizing View Materialization Cost in Spatial Data Warehouses. In T. Nguyen (Ed.), Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications (pp. 47-63). IGI Global. https://doi.org/10.4018/978-1-60566-748-5.ch003

Chicago

Yu, Songmei, Vijayalakshmi Atluri, and Nabil Adam. "Preview: Optimizing View Materialization Cost in Spatial Data Warehouses." In Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, 47-63. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-748-5.ch003

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Abstract

One of the major challenges facing a data warehouse is to improve the query response time while keeping the maintenance cost to a minimum. Recent solutions to tackle this problem suggest to selectively materialize certain views and compute the remaining views on-the-fly, so that the cost is optimized. Unfortunately, in the case of a spatial data warehouse, both the view materialization cost and the onthe- fly computation cost are often extremely high. This is due to the fact that spatial data are larger in size and spatial operations are more complex than the traditional relational operations. In this chapter, the authors propose a new notion, called preview, for which both the materialization and on-the-fly costs are significantly smaller than those of the traditional views. Essentially, to achieve these cost savings, a preview pre-processes the non-spatial part of the query, and maintains pointers to the spatial data. In addition, it exploits the hierarchical relationships among the different views by maintaining a universal composite lattice, and mapping each view onto it. The authors present a cost model to optimally decompose a spatial query into three components, the preview part, the materialized view part and the on-the-fly computation part, so that the total cost is minimized. They demonstrate the cost savings with realistic query scenarios, and implement their method to show the optimal cost savings.

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