Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Approximate join processing over data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Gigascope: a stream database for network applications
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A Data Mining Algorithm for Generalized Web Prefetching
IEEE Transactions on Knowledge and Data Engineering
Stream window join: tracking moving objects in sensor-network databases
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
On In-network Synopsis Join Processing for Sensor Networks
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Maximizing the output rate of multi-way join queries over streaming information sources
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Processing sliding window multi-joins in continuous queries over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Memory-limited execution of windowed stream joins
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Query Processing in Sensor Networks
IEEE Pervasive Computing
Load Shedding for Window Joins on Multiple Data Streams
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
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We address the problem of load shedding for continuous multi-way join queries over multiple data streams. When the arrival rates of tuples from data streams exceed the system capacity, a load shedding algorithm drops some subset of input tuples to avoid system overloads. To decide which tuples to drop among the input tuples, most existing load shedding algorithms determine the priority of each input tuple based on the frequency or some historical statistics of its join attribute value, and then drop tuples with the lowest priority. However, those value-based algorithms cannot determine the priorities of tuples properly in environments where join attribute values are unique and each join attribute value occurs at most once in each data stream. In this paper, we propose a load shedding algorithm specifically designed for such environments. The proposed load shedding algorithm determines the priority of each tuple based on the order of streams in which its join attribute value appears, rather than its join attribute value itself. Consequently, the priorities of tuples can be determined effectively in environments where join attribute values are unique and do not repeat. The experimental results show that the proposed algorithm outperforms the existing algorithms in such environments in terms of effectiveness and efficiency.