Publish Subscribe

The rapid growth of technology has considerably changed the manner and scale of information management, a new class of data-intensive application has become widely: applications in which the data is modeled best not as persistent relations but rather as transient data streams. Examples of such applications include sensor network, stock tickers, network monitoring, Web logs, ect., there, data items arrive continuously in multiple, rapid, time-varying, possibly unpredictable and unbounded streams.

In all these applications, it is not feasible to simply load the arriving data into a traditional database management system and operate on it there. Traditional database systems are not designed for rapid and continuous loading of individual data items, and they do not directly support the publish/subscribe systems which are typical of data stream applications. So many aspects should be reconsidered of data management and processing in presence of data streams in the fields of applications, languages, engines, and benchmarks.

Our research focuses on engine issues of data stream processing, which filters stream data according to users’ interests and deliveries the data to the corresponding users with high performance, powerful expressiveness, self-adaptive capability.

(Botao Wang)