Efficient query processing for rich and diverse real - time data

Nehme, Rimma Ph.D., Purdue University, August 2009. Efficient Query Processing for Rich and Diverse Real-Time Data. Major Professors: Elisa Bertino and Elke A. Rundensteiner. In recent years, data streams have become ubiquitous as technology is improving and the prices of sensor, location-tracking and portable devices are falling. Examples of streaming data include observations from sensor networks, location updates from GPS devices, measurements from health monitoring devices, status updates, com­ ments and self-expressions from users on the web, e.g., by “twittering”. The current state-of-the-art Data Stream Management Systems (or short DSMSs) typically con­ sider a very simple streaming environment, where data streams transmit only data tuples, and based on these arriving data tuples, continuous queries are evaluated on the server. For execution of a continuous query, typically, a single execution plan (based on the latest overall statistics) is employed for processing all arriving data. We believe that such “first-generation” DSMSs (or as we also refer to them “Streams 1.0” systems), while enabling users and applications to pose queries over data streams, are, however, ill-equipped to support many of the functionalities crucial to the newly-emerging streaming applications, e.g., ubiquitous healthcare or geo-social networking. Motivated by the growing trend of such new stream-based applications, in this thesis, we propose to equip DSMSs with several important functionalities, namely: 1. the access control enforcement for security of streaming data 2. the tagging of streaming data for producing “richer” and more meaningful re­ sults, and