Stochastic models in queueing theory
Stochastic models in queueing theory
On the relevance of long-range dependence in network traffic
IEEE/ACM Transactions on Networking (TON)
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Characterizing memory requirements for queries over continuous data streams
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Continuous queries over data streams
ACM SIGMOD Record
Theory, Volume 1, Queueing Systems
Theory, Volume 1, Queueing Systems
Streaming queries over streaming data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Monitoring streams: a new class of data management applications
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Data stream management system for MavHome
Proceedings of the 2004 ACM symposium on Applied computing
NFMi: An Inter-domain Network Fault Management System
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
ETL queues for active data warehousing
Proceedings of the 2nd international workshop on Information quality in information systems
Event-based lossy compression for effective and efficient OLAP over data streams
Data & Knowledge Engineering
A model for continuous query latencies in data streams
Proceedings of the First International Workshop on Algorithms and Models for Distributed Event Processing
Utility-maximizing event stream suppression
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Currently, stream data processing is an active area of research, which includes everything from algorithms and architectures for stream processing to modelling, and analysis of various components of a stream processing system. In this paper, we present an analysis of relational operators used for stream processing using queueing theory and study behaviors of streaming data in a query processing system. Our approach enables us to compute the fundamental performance metrics of relational operators ---select, project, and join over data streams. Furthermore, this approach establishes a way to find the probability distribution functions of both the number of tuples and the waiting time of tuples in the system. Finally, we designed and implemented a number of experiments to validate the accuracy and effectiveness of our analysis.