Lightweight problem determination in DBMSs using data stream analysis techniques
DOI: 10.1145/1923947.1923969 Conference: Proceedings of the 2010 conference of the Centre for Advanced Studies on Collaborative Research, November 1-4, 2010, Toronto, Ontario, Canada
Problem determination in a database management system can be a difficult task given the complexity of the system and the large amount of data that must be collected and analyzed. Monitoring the system for this data incurs overhead and has a detrimental effect on application performance. As an alternative to the standard practice of storing the performance data and performing offline analysis, we examine an approach where monitoring data is produced as a continuous data stream and data stream mining techniques are applied. We implement this approach as a prototype system called Tempo on IBM DB2®. Tempo implements Top-K analysis, which is a common task performed by database administrators for problem determination. Top-K analysis typically identifies the set of most frequently occurring events, or the highest consumers of system resources. Our experimental evaluation indicates that Tempo is time and space efficient, incurs low overhead, and produces accurate results.
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ABSTRACT: Recently, with the widespread popularity of SNS (Social Network Service), such as Twitter, Facebook, people are increasingly accustomed to sharing feeling, experience and knowledge with each other on Internet. The high accessibility of these web sites has allowed the information to be spread across the social media more quickly and widely, which leads to more and more populations being engaged into this so-called social stream environment. All these make the organization of user relationships become increasingly important and necessary. In this study, we try to discover the potential and dynamical user correlations using those organized social streams in accordance with users’ current interests and needs, in order to assist the collaborative information seeking process. We develop a heuristic approach to build a Dynamically Socialized User Networking (DSUN) model, and define a set of measures (such as interest degree, and popularity degree) and concepts (such as complementary tie, weak tie, and strong tie), to discover and represent users’ current profiling and dynamical correlations. The corresponding algorithms are developed respectively. Finally, the architecture of the functional modules is presented, and the experiment results are demonstrated and discussed based on an application of the proposed model.Multimedia Tools and Applications 07/2014; DOI:10.1007/s11042-014-2153-5 · 1.35 Impact Factor
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