By Topic

Joint Optimization of Index Freshness and Coverage in Real-Time Search Engines

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yongwook Shin ; Dept. of Ind. Eng., Seoul Nat. Univ., Seoul, South Korea ; Junseok Lim ; Jonghun Park

Real-time search engines are increasingly indexing web content using data streams, since a number of web sources including news and social media sites are now delivering up-to-date information via streams. Accordingly, it is a crucial challenge for a real-time search engine using data streams to improve index freshness that primarily depends on the latencies involved during fetching and indexing processes. Retrieval latency is a time lag between document publication and fetching while indexing latency is a delay required for a fetched document to be indexed, which is caused by finiteness of indexing capacity. The problem of retrieval latency can be satisfactorily addressed by use of appropriate fetching scheduling or recent real-time content notification protocols. However, as the entire volume of real-time content rapidly grows, the indexing latency becomes a challenging problem. Furthermore, the need for maximizing index coverage makes it more difficult to reduce the indexing latency under the limited indexing capacity. We consider a problem of jointly optimizing the indexing latency as well as indexindexing latency coverage, in which their relative importance can be adjusted, and propose an optimization model based on inventory control theory. Extensive experiments have been conducted to validate the proposed model, and suggest that the proposed approach outperforms the other alternatives.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:24 ,  Issue: 12 )