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In contrast to other database applications, multimedia data can have a wide range of quality parameters, such as spatial and temporal resolution and compression format. Users can request data with specific quality requirements due to the needs of their application or the limitations of their resources. The database can support multiple qualities by converting data from the original (high) quality to another (lower) quality to support a user's query or precompute and store multiple quality replicas of data items. On-the-fly conversion of multimedia data (such as video transcoding) is very CPU intensive and can limit the level of concurrent access supported by the database. Storing all possible replicas, on the other hand, requires unacceptable increases in storage requirements. In this paper, we address the problem of multiple-quality replica selection subject to an overall storage constraint. We establish that the problem is NP-hard and provide heuristic solutions under two different system models: Hard-Quality and Soft-Quality. Under the soft-quality model, users are willing to negotiate their quality needs, as opposed to the hard-quality system wherein users will only accept the exact quality requested. Extensive simulations show that our algorithm performs significantly better than other heuristics. Our algorithms are flexible in that they can be extended to deal with changes in query pattern.
Quality adaptation, integer programming, data replication, heuristic algorithm.
Jingfeng Yan, Gang Shen, Sunil Prabhakar, Yi-Cheng Tu, "Multiquality Data Replication in Multimedia Databases", IEEE Transactions on Knowledge & Data Engineering, vol. 19, no. , pp. 679-694, May 2007, doi:10.1109/TKDE.2007.1013
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