Efficient checking of temporal integrity constraints using bounded history encoding
ACM Transactions on Database Systems (TODS)
Handling infinite temporal data
Selected papers of the 9th annual ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Logical design for temporal databases with multiple granularities
ACM Transactions on Database Systems (TODS)
Temporal FDs on complex objects
ACM Transactions on Database Systems (TODS)
Database System Implementation
Database System Implementation
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
Extending Existing Dependency Theory to Temporal Databases
IEEE Transactions on Knowledge and Data Engineering
Exploiting Uniqueness in Query Optimization
Proceedings of the Tenth International Conference on Data Engineering
Aggregate-Query Processing in Data Warehousing Environments
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Answering Queries with Aggregation Using Views
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Embedded implicational dependencies and their inference problem
STOC '81 Proceedings of the thirteenth annual ACM symposium on Theory of computing
Chain: operator scheduling for memory minimization in data stream systems
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
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)
Change-Point Monitoring for the Detection of DoS Attacks
IEEE Transactions on Dependable and Secure Computing
On scalable attack detection in the network
IEEE/ACM Transactions on Networking (TON)
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
Load shedding in a data stream manager
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
Checks and balances: monitoring data quality problems in network traffic databases
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-Aware Sampling for Data Streams
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Streams, security and scalability
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
Distributed processing of continuous join queries using DHT networks
Proceedings of the 2009 EDBT/ICDT Workshops
Stream schema: providing and exploiting static metadata for data stream processing
Proceedings of the 13th International Conference on Extending Database Technology
An execution environment for C-SPARQL queries
Proceedings of the 13th International Conference on Extending Database Technology
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Monitoring aggregates on network traffic streams is a compelling application of data stream management systems. Often, streaming aggregation queries involve joining multiple inputs (e.g., client requests and server responses) using temporal join conditions (e.g., within 5 seconds), followed by computation of aggregates (e.g., COUNT) over temporal windows (e.g., every 5 minutes). These types of queries help identify malfunctioning servers (missing responses), malicious clients (bursts of requests during a denial-of-service attack), or improperly configured protocols (short timeout intervals causing many retransmissions). However, while such query expression is natural, its evaluation over massive data streams is inefficient. In this paper, we develop rewriting techniques for streaming aggregation queries that join multiple inputs. Our techniques identify conditions under which expensive joins can be optimized away, while providing error bounds for the results of the rewritten queries. The basis of the optimization is a powerful but decidable theory in which constraints over data streams can be formulated. We show the efficiency and accuracy of our solutions via experimental evaluation on real-life IP network data using the Gigascope stream processing engine.