Phenomenon-aware data stream management systems
Abstract
Recent advances in large scale data streaming technologies enabled the deployment 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 streaming 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 behavior” 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, floods of a river, migration of birds, and epidemic spread of diseases. This dissertation proposes a framework to detect, track, and query various forms of phenomena in data streaming environments. This framework empowers data stream management systems (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. This dissertation provides a formal definition of a phenomenon, models the phenomenon behavior, and proposes an extended syntax that enables users to register their interesting phenomenon patterns with the system. Also, this dissertation adopts the concept of phenomenon-aware query processing by adding two major components to DSMSs: the Phenomenon Detection and Tracking module (PDT-module) and the phenomenon-aware optimizer. The PDT-module encompasses scalable techniques 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 acquisition 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.
Degree
Ph.D.
Advisors
Aref, Purdue University.
Subject Area
Computer science
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