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|Title:||ONLINE DATA PROCESSING AT SCALE||Authors:||LIN QIAN||ORCID iD:||orcid.org/0000-0002-7665-601X||Keywords:||data stream, stream join, distributed database, transaction management, concurrency control, OLTP||Issue Date:||18-Aug-2017||Citation:||LIN QIAN (2017-08-18). ONLINE DATA PROCESSING AT SCALE. ScholarBank@NUS Repository.||Abstract:||The ubiquitous online data bring opportunities of insightful analytics, as well as the challenges of system processing at large scale. Online data are generally processed in two fundamental fashions: stream processing to meet the needs of real-time application, and transaction processing to address the coordination of short atomic computations. In this thesis, we look into the problems of scalable online data processing in distributed database systems, and specifically focus on the design and optimization of the key operators and protocols in real-time OLAP (online analytical processing) and OLTP (online transaction processing). In particular, we investigate the techniques for scalable distributed stream join processing, efficient distributed transaction management, and adaptive and speculative concurrency control protocol.||URI:||http://scholarbank.nus.edu.sg/handle/10635/137731|
|Appears in Collections:||Ph.D Theses (Open)|
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