Multiquality Data Replication in Multimedia Databases

• Published in: IEEE transactions on knowledge and data engineering Vol. 19; no. 5; pp. 679 - 694
• Main Authors: Tu, Y.-C., Yan, J., Shen, G., Sunil Prabhakar
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2007; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Compressing; Computer science; control theory; systems; Data processing. List processing. Character string processing; data replication; Exact sciences and technology; Heuristic; heuristic algorithm; Heuristic algorithms; Information systems. Data bases; integer programming; Linear programming; Mathematical models; Memory organisation. Data processing; Multimedia; Multimedia databases; Multimedia systems; Quality; Quality adaptation; Query processing; Runtime; Signal resolution; Software; Spatial databases; Spatial resolution; Stores; Studies; Transcoding; Video compression; Multimedia; Database query; Distributed database; Video signal; Transcoding; Multimedia databases; Quality adaptation; Modeling; Integer programming; heuristic algorithm; Data conversion; data replication; Heuristic method; NP hard problem; Database; Replication; Spatial resolution; User behavior; Multiple database
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2007.1013

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 can 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