Abstract

Recent advances in large scale data streaming technologies enabled the deploy- ment of a huge number of streaming sources in the surrounding environment, e.g., sensor fields. Streaming sources do not live in isolation. Instead, close-by stream- ing sources experience similar environmental conditions. Hence, close-by streaming sources may indulge in a correlated behavior and generate a “phenomenon”. A phenomenon is characterized by a group of streaming sources that show “similar be- havior” over a period of time. Examples of detectable phenomena include pollution clouds in the air, oil spills at the ocean surface, fire zones in a building, water floods of a river, migration of birds, and epidemic spread of diseases. This dissertation pro- poses a framework to detect, track, and query various forms of phenomena in data streaming environments. This framework empowers data stream management sys- tems (DSMSs) with phenomenon-awareness capabilities. Phenomenon-aware data stream systems use high-level knowledge about phenomena in the data streaming environment to optimize the execution of subsequent user queries. To approach the above goal, this dissertation proposes the principle that “phe- nomenon detection guides query processing” and explores this principle’s implica- tions on DSMSs. Hence, user queries have the option to view the streaming envi- ronment at a higher level, i.e., the phenomenon level. In such a phenomenon-aware query processing paradigm, streams are prioritized and are processed based on a mechanism that tunes query processing towards data streams that contribute to detected phenomena. This dissertation provides a formal definition for a phenomenon, models the phe- nomenon behavior, and proposes an extended syntax that enables the users to reg- ister their interesting phenomenon patterns with the system. Also, this dissertation adopts the concept of phenomenon-aware query processing by adding two major com- ponents to DSMSs: the Phenomenon Detection and Tracking module (PDT-module) and the phenomenon-aware optimizer. The PDT-module encompasses scalable tech- niques to detect the appearance of new phenomena and to track the propagation of already-detected phenomena. The phenomenon-aware optimizer is an adaptive optimizer that optimizes user queries continuously based on the feedback it receives from the PDT-module. Finally, this dissertation considers phenomenon awareness at the distributed setup of sensor networks by providing a phenomenon-aware data ac- quisition protocol and by extending the phenomenon detection process to the sensor- network platform. As a vehicle for this research, the Nile-PhenomenaBase system is prototyped as a framework for phenomenon-aware query processing inside Nile, a data stream management system developed at Purdue University.

Citations