NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Gigascope: high performance network monitoring with an SQL interface
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
What can Hierarchies do for Data Warehouses?
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Querying Multiple Features of Groups in Relational Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
SSDBM '02 Proceedings of the 14th International Conference on Scientific and Statistical Database Management
Evaluating XML-extended OLAP queries based on a physical algebra
Proceedings of the 7th ACM international workshop on Data warehousing and OLAP
Multiple aggregations over data streams
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Warehousing the world: a few remaining challenges
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
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Much effort has been put into building data streams management systems for querying data streams. However, the query languages have mostly been SQL-based and aimed for low-level analysis of base data; therefore, there has been little work on supporting OLAP-like queries that provide real-time multi-dimensional and summarized views of stream data. In this paper, we introduce a multi-dimensional stream query language and its formal semantics. Our approach turns low-level data streams into informative high-level aggregates and enables multi-dimensional and granular OLAP queries against data streams, which supports the requirements of today's real time enterprises much better. A comparison with the STREAM CQL language shows that our approach is more flexible and powerful for high-level OLAP queries, as well as far more compact and concise.