Efficiently updating materialized views
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Continuous queries over append-only databases
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Incremental maintenance of views with duplicates
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Cost-based query scrambling for initial delays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An adaptive query execution system for data integration
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
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
Issues in data stream management
ACM SIGMOD Record
Chain: operator scheduling for memory minimization in data stream systems
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Aurora: a data stream management system
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
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
PSoup: a system for streaming queries over streaming data
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
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
Exploiting k-constraints to reduce memory overhead in continuous queries over data streams
ACM Transactions on Database Systems (TODS)
Flexible time management in data stream systems
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Update-pattern-aware modeling and processing of continuous queries
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Streaming queries over streaming data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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
Operator scheduling in a data stream manager
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
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Semantics and implementation of continuous sliding window queries over data streams
ACM Transactions on Database Systems (TODS)
Mode Aware Stream Query Processing
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Window Update Patterns in Stream Operators
ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
Detecting Moving Objects in Noisy Radar Data Using a Relational Database
ADBIS '09 Proceedings of the 13th East European Conference on Advances in Databases and Information Systems
A magic approach to optimizing incremental relational expressions
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Supporting views in data stream management systems
ACM Transactions on Database Systems (TODS)
Distributed stream join query processing with semijoins
Distributed and Parallel Databases
Transformation of continuous aggregation join queries over data streams
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
Adaptive query processing in data stream management systems under limited memory resources
PIKM '10 Proceedings of the 3rd workshop on Ph.D. students in information and knowledge management
How soccer players would do stream joins
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Optimized processing of multiple aggregate continuous queries
Proceedings of the 20th ACM international conference on Information and knowledge management
Performance Modeling of Spatio-Temporal Algorithms Over GEDS Framework
International Journal of Grid and High Performance Computing
Enhanced stream processing in a DBMS kernel
Proceedings of the 16th International Conference on Extending Database Technology
Database support for processing complex aggregate queries over data streams
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Efficient tracking of moving objects using a relational database
Information Systems
GEDS: GPU execution of spatio-temporal queries over spatio-temporal data streams
Journal of Embedded Computing
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Two research efforts have been conducted to realize sliding-window queries in data stream management systems, namely, query reevaluation and incremental evaluation. In the query reevaluation method, two consecutive windows are processed independently of each other. On the other hand, in the incremental evaluation method, the query answer for a window is obtained incrementally from the answer of the preceding window. In this paper, we focus on the incremental evaluation method. Two approaches have been adopted for the incremental evaluation of sliding-window queries, namely, the input-triggered approach and the negative tuples approach. In the input-triggered approach, only the newly inserted tuples flow in the query pipeline and tuple expiration is based on the timestamps of the newly inserted tuples. On the other hand, in the negative tuples approach, tuple expiration is separated from tuple insertion where a tuple flows in the pipeline for every inserted or expired tuple. The negative tuples approach avoids the unpredictable output delays that result from the input-triggered approach. However, negative tuples double the number of tuples through the query pipeline, thus reducing the pipeline bandwidth. Based on a detailed study of the incremental evaluation pipeline, we classify the incremental query operators into two classes according to whether an operator can avoid the processing of negative tuples or not. Based on this classification, we present several optimization techniques over the negative tuples approach that aim to reduce the overhead of processing negative tuples while avoiding the output delay of the query answer. A detailed experimental study, based on a prototype system implementation, shows the performance gains over the input-triggered approach of the negative tuples approach when accompanied with the proposed optimizations.