Y. Arens and C. A. Knoblock. Intelligent Caching: Selecting, Representing, and Reusing Data in an Information Server. In Proc. CIKM'94 Conference, Gaithersburg, Maryland, pp. 433-438, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....the systems described here is planned. 6 Related Work There has been significant work on general algorithms for query planning, selective materialization, and the optimization of these from the AI perspective, for example TSIMMIS [5] Information Manifold [23] Infosleuth [27] HERMES [1] SIMS [2], etc. and of course on applying agents as the way to embody these algorithms [24, 32, 12, 22] In Biology, compared to the work being done to create the raw data, all the work on how to organize and retrieve it is relatively small. Most of the work in computer science directed to biological ....

Y. Arens and C.A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proc. 3rd Intl. Conf. on Information and Knowledge Management, 1994.

....be reused by a new query. A satellite data analysis application is used to experimentally show the performance benefits achieved using the techniques presented in the paper. 1 Introduction Exploiting reuse is a powerful mechanism for improving the performance of computational systems in general [10]. For relational database systems, it has been shown that identifying common subexpressions [16, 19, 36] and applying view materialization strategies [18, 22, 29, 40] can yield sizable decreases in query execution time when processing multiple query batches. When applications that do not conform ....

Y. Arens and C. A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proceedings of 1994.

....and so are easily mapped to gene names specific to the organism of interest. 5 Related Work There has been significant work on general algorithms for query planning, selective materialization, and the optimization of these from the AI perspective, for example TSIMMIS [4] Infosleuth [19] SIMS [1], etc. and of course on applying agents as the way to embody these algorithms [16, 21, 10] In Biology, compared to the work being done to create the raw data, all the work on how to organize and retrieve it is relatively small. Most of the work in computer science directed to biological data ....

Y. Arens and C.A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proc. 3rd Intl. Conf. on Info. and Know. Mgmt., 1994.

....caching which is widely used in operative and database management systems is improper on Web retrieval systems and tuple caching has certain limitations. Thus much effort has been spent to cache user queries with the corresponding answers (instead of pages or tuples) to allow their future reuse [5, 14, 18]. Query caching takes a particular advantage when the user often refines a query, for example, by adding or removing a query term. In this case, many of the answers may already be cached and can be delivered to the user right away. Importantly, when accessing the payment sites, the query caching ....

....[14] considers semantic cache mainly for data stored in relational databases. Query caching in heterogeneous systems was discussed in [18] where it is reduced to a Datalog query evaluation, which, however, may by computationally hard. Intelligent query caching is also used in the SIMS project [5], where some important principles for any intelligent caching mechanism were developed. These principles are the following : 1) a query 10 http: www.yahoo.com Science Computer Science 15 cache should process both containment and intersection cases; 2) a cache item should not be large; 3) a ....

Y. Arens and C. A. Knoblock. Intelligent Caching: Selecting, Representing, and Reusing Data in an Information Server. In Proc. CIKM'94 Conference, Gaithersburg, Maryland, pp. 433-438, 1994.

....the systems described here is planned. 6 Related Work There has been significant work on general algorithms for query planning, selective materialization, and the optimization of these from the AI perspective, for example TSIMMIS [5] Information Manifold [20] Infosleuth [23] HERMES [1] SIMS [2], Fig. 6. Overview of gene expression processing organization etc. and of course on applying agents as the way to embody these algorithms [21, 26, 11, 19] In Biology, compared to the work being done to create the raw data, all the work on how to organize and retrieve it is relatively small. ....

Y. Arens and C.A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proc. 3rd Intl. Conf. on Information and Knowledge Management, 1994.

....type of caching is not without tradeoffs; it uses more memory, and one must be careful to not introduce inconsistencies between the external source and the cache. A recent discussion of some of these tradeoffs in the context of higher level multi source information agent caching can be found in [1]. 3 Agent Architecture: Building Blocks for Agent Behaviors An information agent s reusable behaviors are facilitated by its reusable agent architecture, i.e. the domainindependent abstraction of the local infobase schema, and a set of generic software components for knowledge representation, ....

Y. Arens and C.A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proc. 3rd Intl. Conf. on Information and Knowledge Management, 1994.

....the domain model and the query planner in the mediator generates plans to retrieve the requested information from one or more sources. Please refer to [3] for a more detailed description of SIMS. 2. 2 Materializing Data in Mediators Our approach to optimization is based on an idea described in [2] where we identify useful classes of information to materialize, materialize the data in these classes in a database local to the mediator and define these classes as auxiliary information sources that the mediator can access. For instance in the countries application suppose we determined that ....

Y. Arens and C. A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proceedings of the Third International Conference on Information and Knowledge Management, Gaithersburg, MD, 1994.

....information. All these forms of learning can improve the efficiency of the system, and the last one can also improve its accuracy. 1.6. 1 Caching Retrieved Data Data that is required frequently or is very expensive to retrieve can be cached in the local agent and then retrieved more efficiently [Arens and Knoblock, 1994]. An elegant feature of using Loom to model the domain is that cached information can easily be represented and stored in Loom. The data is currently brought into the local agent for processing, so caching is simply a matter of retaining the data and recording what data has been retrieved. To ....

Yigal Arens and Craig A. Knoblock. Intelligent caching: Selecting, rep- resenting, and reusing data in an information server. In Proceedings of the Third International Conference on Information and Knowledge Management, Gaithersburg, MD, 1994.

....Education Population National product EUROPEAN COUNTRY COUNTRY INFONATION COUNTRY FACTBOOK COUNTRY EUROPEAN COUNTRY CACHE (a) b) Figure 1: Information modeling in SIMS 2.2. Materializing Data in Mediators Our approach to optimization is based on an idea described in [4] where we identify useful classes of information to materialize, materialize the data in these classes in 4 a database local to the mediator and define these classes as auxiliary information sources that the mediator can access. For instance in the countries application 4 suppose we determined ....

Y. Arens and C. A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proceedings of the Third International Conference on Information and Knowledge Management, Gaithersburg, MD, 1994.

....Learning. An intelligent information agent should be able to improve both its accuracy and performance over time, and deal with the changing envronment. In SIMS, we have explored three forms of learning. First, the agents can cache frequently retrieved or difficult to retrieve information [3]. Second, an agent can learn about the contents of the information sources in order to minimize the costs of retrieval. In particular, an agent can perform semantic query optimization, based on its declarative models and rules learned from the sources, to reformulate a query plan into a cheaper, ....

Yigal Arens and Craig A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proceedings of the Third International Conference on Information and Knowledge Management, Gaithersburg, MD, 1994.

....information. All these forms of learning can improve the efficiency of the system, and the last one can also improve its accuracy. 1.6. 1 Caching Retrieved Data Data that is required frequently or is very expensive to retrieve can be cached in the local agent and then retrieved more efficiently [ Arens and Knoblock, 1994 ] An elegant feature of using Loom to model the domain is that cached information can easily be represented and stored in Loom. The data is currently brought into the local agent for processing, 20 Chapter 1 so caching is simply a matter of retaining the data and recording what data has been ....

Yigal Arens and Craig A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proceedings of the Third International Conference on Information and Knowledge Management, Gaithersburg, MD, 1994.

....the operations and their order for processing the data, and then performs semantic query optimization to minimize the overall execution time. This paper describes these three basic components of the query processing in SIMS. 1 Introduction SIMS [ Arens et al. 1993; Knoblock et al. 1994; Arens and Knoblock, 1994; Ambite et al. 1995; Arens et al. 1996 ] is an information mediator that provides access and integration of multiple sources of information. Queries are expressed in a uniform language, independent of the distribution of information over sources, of the various query languages, the location of ....

....to intelligently retrieve and process data. Information sources are constantly changing, new information becomes available, old information may be eliminated or temporarily unavailable, and so on. Thus, SIMS dynamically selects This is an updated version of the paper that originally appeared as [Arens et al. 1994]. The research reported here was supported in part by Rome Laboratory of the Air Force Systems Command and the Defense Advanced Research Projects Agency under contract no. F30602 91 C 0081, and in part by the National Science Foundation under grant number IRI9313993. Views and conclusions ....

Yigal Arens and Craig A. Knoblock. Intelligent caching: Selecting, representing, and reusing data in an information server. In Proceedings of the Third International Conference on Information and Knowledge Management, Gaithersburg, MD, 1994.

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Y. Arens and C. A. Knoblock. Intelligent Caching: Selecting, Representing, and Reusing Data in an Information Server. In Proc. CIKM'94 Conference, Gaithersburg, Maryland, pp. 433-438, 1994.

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Y. Arens and C. A. Knoblock. Intelligent Caching: Selecting, Representing, and Reusing Data in an Information Server. In Proc. CIKM '94 Conf., Gaithersburg, MD, pp. 433--438, 1994.

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Y. Arens and C.A. Knoblock, Intelligent caching: selecting, representing, and reusing data in an information server, in: Proc. CIKM'94 Conference, Gaithersburg, MA, 1994, pp. 433--438.

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