Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 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
Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Performing Group-By before Join
Proceedings of the Tenth International Conference on Data Engineering
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Including Group-By in Query Optimization
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Eager Aggregation and Lazy Aggregation
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Tribeca: A Stream Database Manager for Network Traffic Analysis
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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
TelegraphCQ: continuous dataflow processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Approximating a Data Stream for Querying and Estimation: Algorithms and Performance Evaluation
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Nile: A Query Processing Engine for Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Approximate Aggregation Techniques for Sensor Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Load Shedding for Aggregation Queries over Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Static optimization of conjunctive queries with sliding windows over infinite streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Evaluating window joins over punctuated streams
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Multiple aggregations over data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Semantics and evaluation techniques for window aggregates in data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Stream window join: tracking moving objects in sensor-network databases
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Window-aware load shedding for aggregation queries over data streams
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Window join approximation over data streams with importance semantics
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A data stream language and system designed for power and extensibility
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Incremental Evaluation of Sliding-Window Queries over Data Streams
IEEE Transactions on Knowledge and Data Engineering
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
Resource sharing in continuous sliding-window aggregates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Estimating aggregate join queries over data streams using discrete cosine transform
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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We address continuously processing an aggregation join query over data streams. Queries of this type involve both join and aggregation operations, with windows specified on join input streams. To our knowledge, the existing researches address join query optimization and aggregation query optimization as separate problems. Our observation, however, is that by putting them within the same scope of query optimization we can generate more efficient query execution plans. This is through more versatile query transformations, the key idea of which is to perform aggregation before join so join execution time may be reduced. This idea itself is not new (already proposed in the database area), but developing the query transformation rules faces a completely new set of challenges. In this paper, we first propose a query processing model of an aggregation join query with two key stream operators: (1) aggregation set update, which produces an aggregation set of tuples (one tuple per group) and updates it incrementally as new tuples arrive, and (2) aggregation set join, i.e., join between a stream and an aggregation set of tuples. Then, we introduce the concrete query transformation rules specialized to work with streams. The rules are far more compact and yet more general than the rules proposed in the database area. Then, we present a query processing algorithm generic to all alternative query execution plans that can be generated through the transformations, and study the performances of alternative query execution plans through extensive experiments.