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document database
Quality-based I/O resource managementfor multimedia document database servers
      
The algorithm has been tested on the UW English Document Database I of the University of Washington and its performance has been evaluated by a suitable measure of segmentation accuracy.
      
Search operations and browsing facilities over an XML document database require special support at the physical level.
      
In this paper, which treats Swedish full text retrieval, the problem of morphological variation of query terms in the document database is studied.
      
These six combinations were compared to each other and to a baseline combination, where no attempt was made to counteract the problem of morphological variation of query terms in the document database.
      
In addition, although the system still gives users the option of dynamically querying the web, the incorporation of a document database has improved the response time involved in the search process.
      
All this work was accomplished using as example an enterprise document database with various documents like contracts, business notes and bug reports.
      
As an example, take a client who wants to access a document database.
      
Furthermore, it may be difficult to obtain their complete document database from these autonomous search engines.
      
Here we have chosen the invariant clusters obtained on the whole document database as described in section 2.2.1.
      
It focuses on the distribution of words used in the documents assigned to the considered grid cell compared to the whole document database.
      
It can always be completely reconstructed for a changed document database.
      
In a distributed document database system, a query is processed by passing it to a set of individual collections and collating the responses.
      
In meta-search, a distributed document database consists of a set of document collections, where each collection may be held at a different location.
      
In this article, we present the application Wishbone, an innovative search engine to explore complex document database.
      
In this paper we have shown how to implement a distributed document database using Java technology and web services.
      
In this study we have extended this principle to the whole document database.
      
LSI is applied to each document database to filter term frequencies.
      
Moreover, the broker is responsible for exploiting the partitioning policy of the document database.
      
Section 3 describes the experimental environment in terms of document database and queries.
      
 

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