NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The TSQL2 Temporal Query Language
The TSQL2 Temporal Query Language
Two Approaches to Event Definition
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
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
SARI-SQL: Event Query Language for Event Analysis
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
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
Sequenced event set pattern matching
Proceedings of the 14th International Conference on Extending Database Technology
AIMS: a tool for the view-based analysis of streams of flight data
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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In modern streaming applications a tremendous amount of data is harvested, processed, and stored. Data stream or Complex Event Processing (CEP) engines are used to identify interesting, abnormal, or even dangerous patterns within these streams. In monitoring scenarios, however, often the temporal phases of the observed objects are interesting for the user together with the detection of abnormal patterns. In this paper, we argue that technical support for phase analysis allows for considerably reducing the complexity of continuous queries as provided, e.g., by the Continuous Query Language (CQL). Such phases provide an advanced layer of abstraction allowing for easily formulating phase-related queries in an intuitive way.