N. Ashish, C. A. Knoblock and C. Shahabi, Selectively materializing data in mediators by analyzing user queries, Fourth Int. Conf. Coop. Inf. Syst. (CoopIS), Edinburgh, Scotland, September 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... management system [1, 2, 4, 10, 16] Web data may be extracted, integrated, represented and materialized in a data warehouse for OLAP and DSS [6, 17, 24] and also may be managed by using a hybrid approach for taking advantage of the data warehousing and the virtual integration approach [3, 21, 23, 26]. External data is important for carrying out meaningful OLAP in the context of e commerce. For example, an online book shop manager may make use of integrating discount book information or information about new publications from other online book shops in his data warehouse, in order to analyze ....

Ashish, N.; Knoblock, C. A.; Shahabi, C.: Selectively Materializing Data in Mediators by Analyzing User Queries, Proc. of the International Conference on Cooperative Information Systems, Edinburgh, Scotland, 1999

....systems these include Internet Softbot [Etzioni and Weld, 1994] Retzina [Decker et al. 1997] Infosleuth [Bayardo et al. 1997] SIMS [Arens et al. 1993] and many others. There have been important efforts to optimize the performance of such agents. For instance, Adali et al. 1996; Ashish et al. 1999] show how to use intelligentcaching methods to improve the performance of systems that access different data sources. In both planning and databases, researchers have noticed that when a server receives numerous requests, then it may make sense to leverage commonalities across those requests ....

Naveen Ashish, Craig A. Knoblock, and Cyrus Shahabi. Selectively materializing data in mediators by analyzing user queries. In Proceedings of the 4th IFCIS International Conference on Cooperative Information Systems, (CoopIS), pages 256--266, Edinburgh, Scotland, September 1999.

....44 Delta F. Ozcan, V.S. Subrahmanian and J. Dix et al. 1997] and ARIADNE [Ambite et al. 1998] Although all these systems provide mechanisms to optimize individual requests, the only one which addresses the problem of optimizing overall agent performance is ARIADNE. In [Ashish 1998; Ashish et al. 1999], the authors propose techniques to selectively materialize data to improve the performance of subsequent requests. They use the LOOM [MacGregor 1990] knowledge representation language for modeling data and maintain an ontology of classes of information sources in LOOM. They determine what to ....

....what portion of the data requested in the query is present in the cache. A query is then created to retrieve the rest of the data from disk. The problem with semantic caching is that containment checking is hard and having a large number of semantic regions creates performance problems. Both [Ashish et al. 1999] and [Dar et al. 1996] process one query at a time, and try to reduce the execution time by using caches, whereas we examine a set of requests (in our framework, agents can be built on top of legacy software code bases such as PowerPoint, Excel, route planners, etc. which may not support a ....

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Ashish, N., Knoblock, C. A., and Shahabi, C. 1999. Selectively materializing data in mediators by analyzing user queries. In Proceedings of the 4th IFCIS International Conference on Cooperative Information Systems, (CoopIS) (Edinburgh, Scotland, September 1999), pp. 256--266.

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N. Ashish, C. A. Knoblock and C. Shahabi, Selectively materializing data in mediators by analyzing user queries, Fourth Int. Conf. Coop. Inf. Syst. (CoopIS), Edinburgh, Scotland, September 1999.

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N. Ashish, C. A. Knoblock and C. Shahabi, Selectively materializing data in mediators by analyzing user queries, Fourth Int. Conf. Coop. Inf. Syst. (CoopIS), Edinburgh, Scotland, September 1999.

....this data and define it as an additional information source as shown in Figure 15. Given a query the planner would use the materialized data instead of the original Web source(s) to answer the query when it reduced the cost of a query. The reader is referred to [ Ashish et al. 1998 ] and [ Ashish et al. 1999 ] for details. The system selects the data to materialize by considering a combination of several factors which are described below. Head of State Person ISA ISA Country country nm map World Governments page wrapper country nm Factbook index wrapper Map database Factbook country page ....

....Data as Another Source The Distribution of User Queries From the user query distribution we determine what classes of data are queried most frequently by users since it is often useful to materialize such classes. We have developed an algorithm known as the CM (Cluster and Merge) algorithm [ Ashish et al. 1999 ] which identifies useful classes of information to materialize by extracting patterns in user queries. A key feature of this algorithm is that it finds compact patterns to be extracted. A compact patterns are important from a query planning perspective since we define a new information source ....

Naveen Ashish, Craig A. Knoblock, and Cyrus Shahabi. Selectively materializing data in mediators by analyzing user queries. In Fourth International Conference on Cooperative Information Systems (CoopIS), Edinburgh, Scotland, September 1999.

....They are: ffl Designing the overall materialization framework. This addresses the issue of how we represent and use the materialized data. We describe our approach in [ Ashish, Knoblock, Shahabi, 1998 ] and will briefly review it in the following section. ffl Selecting data to materialize. In [ Ashish, Knoblock, Shahabi, 1999 ] we also stress the need to materialize data in a selective fashion. This leaves us with the issue of how to select the portion of data that is most useful to materialize. Our approach to the issue of selecting what data to materialize is the primary focus of this paper. 2 Overall Approach We ....

....National product Education Population National product EUROPEAN COUNTRY COUNTRY INFONATION COUNTRY FACTBOOK COUNTRY EUROPEAN COUNTRY CACHE (a) b) Figure 1: Information modeling in SIMS to answer the query. Further details of this approach are provided in [ Ashish, Knoblock, Shahabi, 1998 ] and [ Ashish, Knoblock, Shahabi, 1999 ] 3 Selecting Data to Materialize We mentioned in [ Ashish, Knoblock, Shahabi, 1999 ] the need for materializing data in a selective manner. The brute force approach of simply materializing all the data in all the Web sources being integrated is impractical for two main reasons. First, the ....

[Article contains additional citation context not shown here]

Ashish, N.; Knoblock, C. A.; and Shahabi, C. 1999. Selectively materializing data in mediators by analyzing user queries. In Fourth International Conference on Cooperative Information Systems (CoopIS).

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