An architecture for stream OLAP exploiting SPE and OLAP engine

Nakabasami, Kosuke; Amagasa, Toshiyuki; Shaikh, Salman Ahmed; Gass, Franck; Kitagawa, Hiroyuki

doi:10.1109/bigdata.2015.7363771

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…In addition to the above-mentioned solutions, which may be used for stream OLAP by either integrating them with an SPE or an OLAP engine, there exists a few solutions that couples multiple engines to achieve stream OLAP over evolving data streams. For instance, [12] coupled an SPE with an OLAP engine to perform OLAP analysis over data streams. Similarly, SnappyData [14] integrated Apache Spark [15] as a computational engine and Apache GemFire as an inmemory transactional store to support transaction processing and approximate analytical queries on high-speed data streams.…”

Section: A Stream Olapmentioning

confidence: 99%

“…While some are interested in minimizing the OLAP-querying cost, others focus on materializing the maximum number of vertices within a given memory. In [12], we presented an optimization algorithm to select the best set of vertices to materialize, which can minimize the OLAP-querying cost within a given memory. StreamingCube supports the optimization algorithm proposed in [12].…”

Section: B Physical Representationmentioning

confidence: 99%

“…In [12], we presented an optimization algorithm to select the best set of vertices to materialize, which can minimize the OLAP-querying cost within a given memory. StreamingCube supports the optimization algorithm proposed in [12]. In addition, the user can explicitly specify which vertices to materialize through the configuration file (Appendix A).…”

Section: B Physical Representationmentioning

confidence: 99%

“…Traditionally, OLAP analysis over data streams is achieved by coupling a stream processing engine (SPE) with an OLAP engine. For instance, [12] coupled µCosminexus SPE [13] with a locally developed OLAP engine to process and perform OLAP analysis over data streams. Similarly, SnappyData [14] integrated Apache Spark [15] as a computational engine and Apache GemFire as an in-memory transactional store to support transaction processing and approximate analytical queries on high speed data streams.…”

Section: Introductionmentioning

confidence: 99%

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StreamingCube: Seamless Integration of Stream Processing and OLAP Analysis

Shaikh

Kitagawa

2020

IEEE Access

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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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Section: A Stream Olapmentioning

confidence: 99%

Section: B Physical Representationmentioning

confidence: 99%