A New Technique for Enhancing Linked-List Data Retrieval: Reorganize Data Using Artifically Synthesized Queries | Zendy

B. John Oommen | Zendy

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A New Technique for Enhancing Linked-List Data Retrieval: Reorganize Data Using Artifically Synthesized Queries

Author(s) -

B. John Oommen

Publication year - 1994

Publication title -

the computer journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.319

H-Index - 64

eISSN - 1460-2067

pISSN - 0010-4620

DOI - 10.1093/comjnl/37.7.598

Subject(s) - computer science , filter (signal processing) , transformation (genetics) , data stream , distribution (mathematics) , discrete cosine transform , set (abstract data type) , generality , algorithm , data mining , theoretical computer science , information retrieval , mathematics , artificial intelligence , programming language , computer vision , image (mathematics) , telecommunications , mathematical analysis , psychology , biochemistry , chemistry , psychotherapist , gene

Let R = {R 1 , R 2 ,..., R N } be a set of data elements. The elements of R are accessed by the users of the system according to a fixed but unknown distribution S = {s 1 , s 2 ,..., s N }, referred to as the users' query distribution. In this paper we consider the problem of organizing data so as to optimize its retrieval. However, rather than organize the data according to Q, the stream of queries presented by the user, we suggest a scheme by which the data is organized based on a synthesized query stream Q'. This synthesized stream possesses an underlying distribution, S'. Thus, in effect, the data organization is achieved according to the distribution S' and so, in one sense, the user's query distribution is modified without his knowing it

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