Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
On the complexity of the view-selection problem
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data mining: concepts and techniques
Data mining: concepts and techniques
The view-selection problem has an exponential-time lower bound for conjunctive queries and views
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
View selection for designing the global data warehouse
Data & Knowledge Engineering - Data warehousing
View selection using randomized search
Data & Knowledge Engineering
Object-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes
IEEE Transactions on Knowledge and Data Engineering
A Formal Perspective on the View Selection Problem
Proceedings of the 27th International Conference on Very Large Data Bases
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
A comparison of two view materialization approaches for disease surveillance system
Proceedings of the 2004 ACM symposium on Applied computing
Mobile agent-based services for view materialization
ACM SIGMOBILE Mobile Computing and Communications Review
Constructing search spaces for materialized view selection
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Selection of Views to Materialize in a Data Warehouse
IEEE Transactions on Knowledge and Data Engineering
Rewriting XPath queries using materialized views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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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 case of a spatial data warehouse, both the view materialization cost and the on-the-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 and expensive than the traditional relational operations. In this paper, we 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. We 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. We demonstrate the cost savings with realistic query scenarios.