Distributed stream join under workload variance

Many organizations require real-time analysis of their business data streams to make instantaneous decisions. Data streams are often multidimensional and at the lowest level of abstraction, yet analysts are interested in their multilevel interactive analyses across several dimensions. Online analytical processing (OLAP) is a technique whose utility has been proven for such an analysis. Traditionally, OLAP over streams has been achieved by coupling a stream processing engine (SPE) with an OLAP engine. However, for many organizations, this is not an effective solution as it results in lower performance, resource wastage, and increased complexity and maintenance costs. Hence, we present StreamingCube, which is a unified framework for stream processing and OLAP analysis. The idea of a unified framework is supported by the observation that the incremental computation in stream processing and the maintenance of the materialized view of OLAP have many similarities. To seamlessly integrate the SPE and OLAP, as well as to maintain the OLAP lattice vertices incrementally, a cubify operator is introduced. StreamingCube supports two types of queries, namely, continuous queries (CQs) and OLAP queries. To demonstrate the effectiveness of the proposed framework, a detailed experimental evaluation is presented herein.

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Distributed stream join under workload variance

Cited by 7 publications

References 34 publications

An adaptive non-migrating load-balanced distributed stream window join system

An adaptive non-migrating load-balanced distributed stream window join system

Dynamic redirection of real-time data streams for elastic stream computing

StreamingCube: Seamless Integration of Stream Processing and OLAP Analysis

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