• Jennifer .P, Dr.A. Muthukumaravel


In the information filtering system, the clients publish to a server with repeated queries that explicit their information needs and get disclosed every time relevant information is published. To do this work in an efficient way, servers employ indexing schemes that holds quick meet of the incoming information with the query database. Such indexing schemes involve (i) main-memory trie-based data structures that cluster similar queries by capturing common elements between them and (ii) efficient filtering mechanisms that exploit this clustering to achieve high throughput and low filtering times. However, indexing schemes are sensitive to the query insertion order and cannot adapt to an evolving query workload, humiliating the filtering work over time. Here, we present an adaptive trie-based algorithm that gives better current techniques by relying on query statistics to rearrange the query database. Contradictory to previous methods used, we show that the nature of the constructed tries, rather than their compactness, is the determining factor for efficient filtering performance. Our algorithm does not depend on the order of insertion of queries in the database, manages to cluster queries even when clustering possibilities are limited, and achieves its filtering time improvement over its competitors. Finally, we will demonstrate that our solution is easily extensible to multi-core machines.


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How to Cite

Jennifer .P, Dr.A. Muthukumaravel. (2020). EFFICIENT INFORMATION FILTERING MECHANISM. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(6), 10170-10175. Retrieved from http://mail.palarch.nl/index.php/jae/article/view/2580