WReX: a scalable middleware architecture to enable XML caching for web-services

  • Authors:

  • Junichi Tatemura;Oliver Po;Arsany Sawires;Divyakant Agrawal;K. Selçuk Candan

  • Affiliations:

  • NEC Laboratories America, Cupertino, CA;NEC Laboratories America, Cupertino, CA;University of California Santa Barbara, Santa Barbara, CA;NEC Laboratories America, Cupertino, CA;NEC Laboratories America, Cupertino, CA

  • Venue:
  • Proceedings of the ACM/IFIP/USENIX 2005 International Conference on Middleware
  • Year:
  • 2005

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Abstract

Web service caching, i.e., caching the responses of XML web service requests, is needed for designing scalable web service architectures. Such caching of dynamic content requires maintaining the caches appropriately to reflect dynamic updates to the back-end data source. In the database, especially relational, context, extensive research has addressed the problem of incremental view maintenance. However, only a few attempts have been made to address the cache maintenance problem for XML web service messages. We propose a middleware solution that bridges the gap between the cached web service responses and the backend dynamic data source. We assume, for generality, that the back-end source has a general XML logical data model. Since the RDBMS technology is widely used for storing and querying XML data, we show how our solution can be implemented when the XML data source is implemented on top of an RDBMS. Such implementation exploits the well-known maturity of the RDBMS technology. The middleware solution described in this paper has the following features that distinguish it from the existing technology in this area: (1) It provides declarative description of Web Services based on rich and standards-based view specification language (XQuery/XPath); (2) No knowledge of the source XML schema is assumed, instead the source can be any general well-formed XML data; (3) The solution can be easily deployed on RDBMS, and (4) The size of the auxiliary data needed for the cache maintenance does not depend on the source data size, therefore, the solution is highly scalable. Experimental evaluation is conducted to assess the performance benefits of the proposed approach.