Eddies: continuously adaptive query processing
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
Tribeca: A Stream Database Manager for Network Traffic Analysis
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Issues in data stream management
ACM SIGMOD Record
STREAM: the stanford stream data manager (demonstration description)
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
PIPES: a public infrastructure for processing and exploring streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Distributed operation in the Borealis stream processing engine
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
AQuery: query language for ordered data, optimization techniques, and experiments
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Linear road: a stream data management benchmark
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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For many applications, data is collected at very large rates from various sources. Applications that produce results from this data have a requirement for very efficient processing in order to achieve timely decisions. An example of such a demanding applications is one that takes decisions on stock acquisition based on the price updates that happen constantly while the market is open for transactions. Our proposed technique is a simple yet effective way to reduce the access time to the streaming data.In this paper we propose an efficient indexing technique that improves the access time to data elements in sliding windows of streamed database systems. This technique, called Categorized Sliding Window, is based on splitting the data into categories and using bit vectors to avoid accesses to non-relevant data.Our experimental results show large improvements compared with simpler techniques. For the standard Linear Road benchmark we observe a performance improvement of 3.3x for a complex continuous query. Also relevant is the fact that 90% of the performance improvement is achieved with only 65% of the maximum number of categories, which represents a memory overhead of only 13.5%.