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Aurora

Applications that deal with continuous data streams are becoming increasingly important primarily due to the emergence of sensors and similar small-scale embedded computing devices that continuously produce large volumes of data they obtain from their environment. Many stream-based applications, such as environmental monitoring, surveillance, tracking, plant maintenance, and telecommunications data management, require the ability to handle huge volumes of continuous data streams arriving in real-time; to be able to function efficiently under uncertainty in data, and to be able to deliver results in a timely fashion. Existing database management systems are inherently ill-equipped for supporting such tasks, mainly because they are designed based on the implicit assumption that the system is a passive data repository storing a large but finite collection of data elements, which are processed in response to human-initiated queries. Moreover, in many of these applications, there is a need to ask questions that require comparing and combining stored, historical data with real-time streaming data. The primary goal of the Auroraproject is to build a single infrastructure that can efficiently and seamlessly meet the requirements of such demanding applications. To this end, we are currently critically rethinking many existing data management and processing issues, as well as developing new proactive data processing concepts and techniques.

Project status: Complete

Project Home Page: http://www.cs.brown.edu/research/aurora/

Publications

Stonebraker, M., and Cetintemel, U. One Size Fits All: An Idea Whose Time has Come and Gone. In Proceedings of the 21th IEEE International Conference on Data Engineering (ICDE'05) (2005). [ pdf ]

Stonebraker, M., Cetintemel, U., and Zdonik, S. The 8 Requirements of Real-Time Stream Processing. ACM SIGMOD Record 34, 4 (Dec 2005), 42-47. [ pdf ]

Balakrishnan, H., Balazinska, M., Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Galvez, E., Salz, J., Stonebraker, M., Tatbul, N., Tibbets, R., and Zdonik, S. Retrospective on Aurora. Very Large Databases Journal 13, 4 (2004). [ pdf ]

Abadi, D., Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Erwin, C., Galvez, E., Hatoun, M., Hwang, J., Maskey, A., Rasin, A., Singer, A., Stonebraker, M., Tatbul, N., Xing, Y., R.Yan, and Zdonik, S. Aurora: A Data Stream Management System (demo description). In Proceedings of the ACM SIGMOD International Conference on Management of Data (San Diego, CA, Jun 2003). [ pdf ]

Abadi, D. J., Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., Stonebraker, M., Tatbul, N., and Zdonik, S. Aurora: A New Model and Architecture for Data Stream Management. The VLDB Journal 12 (2003), 120-139. [ pdf ]

Carney, D., Cetintemel, U., Rasin, A., Zdonik, S., Cherniack, M., and Stonebraker, M. Operator Scheduling in a Data Stream Manager. In Proceedings of the 29th International Conference on Very Large Data Bases (VLDB'03) (Berlin, Germany, 2003). [ pdf ]

Carney, D., Cetintemel, U., Rasin, A., Zdonik, S., Cherniack, M., and Stonebraker, M. Reducing Execution Overhead in a Data Stream Manager. In Proceedings of the 1st Workshop on Management and Processing of Data Streams (MPDS'03) (2003). [ pdf ]

Cherniack, M., Balakrishnan, H., Balazinska, M., Carney, D., Cetintemel, U., Xing, Y., and Zdonik, S. Scalable Distributed Stream Processing. In Proceedings of the 1st Biennial Conference on Innovative Data Systems Research, Classless Inter-Domain Routing (CIDR '03) (2003). [ pdf ]

Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., Stonebraker, M., Tatbul, N., and Zdonik, S. Monitoring Streams: A New Class of Data Management Applications. In Proceedings of the 28th Conference on Very Large Databases (VLDB) (Aug 2002), pp. 215-226. [ pdf ]