Proceedings of the Eighth International Conference on Data Engineering
Exploiting Punctuation Semantics in Continuous Data Streams
IEEE Transactions on Knowledge and Data Engineering
Nile: A Query Processing Engine for Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Flexible time management in data stream systems
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A heartbeat mechanism and its application in gigascope
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Revision Processing in a Stream Processing Engine: A High-Level Design
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Design, implementation, and evaluation of the linear road bnchmark on the stream processing core
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Out-of-order processing: a new architecture for high-performance stream systems
Proceedings of the VLDB Endowment
Towards a streaming SQL standard
Proceedings of the VLDB Endowment
Declarative Testing: A Paradigm for Testing Software Applications
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
Microsoft CEP server and online behavioral targeting
Proceedings of the VLDB Endowment
On-the-fly progress detection in iterative stream queries
Proceedings of the VLDB Endowment
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Microsoft StreamInsight (StreamInsight, for brevity) is a platform for developing and deploying streaming applications. StreamInsight adopts a deterministic stream model that leverages a temporal algebra as the underlying basis for processing long-running continuous queries. In most streaming applications, continuous query processing demands the ability to cope with high input rates that are characterized by imperfections in event delivery (i.e., incomplete or out-of-order data). StreamInsight is architected to handle imperfections in event delivery, to generate real-time low-latency output, and to provide correctness guarantees on the resultant output. On one hand, streaming operators are similar to their well-understood relational counterparts - with a precise algebra as the basis of their behavior. On the other hand, streaming operators are unique in their non-blocking nature, which guarantees low-latency and incremental result delivery. While our deterministic temporal algebra paves the way towards easier testing of the streaming system, one unique challenge is that as the field evolves with more customers adopting streaming solutions, the semantics, behavior, and variety of operators is constantly under churn. This paper overviews the test framework for the StreamInsight query processor and highlights the challenges in verifying the functional correctness of its operators. The paper discusses the extensibility and the reusability of the proposed streaming test infrastructure, as the research and industrial communities address new and constantly evolving challenges in stream query processing.