Font Size: a A A

Research On Predictive Query Over Data Streams

Posted on:2008-06-12Degree:MasterType:Thesis
Country:ChinaCandidate:P FuFull Text:PDF
GTID:2178360272969248Subject:Computer software and theory
Abstract/Summary:PDF Full Text Request
Recently a new class of data-intensive applications has become widely recognized: applications in which the data is modeled best not as persistent relations but rather as transient data streams. Examples of such applications include financial applications, network monitoring, security, telecommunications data management, web applications, manufacturing, sensor networks, and others.After the characteristics of the data stream in the application field is listed, the demand of the models and algorithms is proposed. On this basis, a new data stream management method called complex window model is proposed, which satisfies the demand of manipulations in the data stream and adapts the change of the speed of coming data. A continuous aggregate query algorithm based complex window is proposed. The algorithm fit the characteristics of the data stream. As a result, it can give a approximate result and fit the real time application.There are many traditional methods in the field of forecasting, including curve simulation, linear regression etc, which are applied only to solve the simple functions, especially polynomial functions. Adopting gene expression programming (GEP), a predictive mathematical model for forecasting the aggregate value over data streams is first proposed. The algorithm is simple and easy to operate which search functions in the great space. As a result, this forecasting model can be used in many kinds of the data stream. Besides, when the frequency of forecast failing is greater than a predefined threshold, an adaptive strategy for the predictive mathematical model is proposed.
Keywords/Search Tags:data stream, complex window, predictive query, gene expression programming (GEP), function models stream, great mutation strategy
PDF Full Text Request
Related items