SPMgr: Dynamic workflow manager for sampling and filtering data streams over Apache Storm

Youngkuk Kim, Siwoon Son and Yang-Sae Moon

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 7, 1550147719862206

Abstract: In this article, we address dynamic workflow management for sampling and filtering data streams in Apache Storm. As many sensors generate data streams continuously, we often use sampling to choose some representative data or filtering to remove unnecessary data. Apache Storm is a real-time distributed processing platform suitable for handling large data streams. Storm, however, must stop the entire work when it changes the input data structure or processing algorithm as it needs to modify, redistribute, and restart the programs. In addition, for effective data processing, we often use Storm with Kafka and databases, but it is difficult to use these platforms in an integrated manner. In this article, we derive the problems when applying sampling and filtering algorithms to Storm and propose a dynamic workflow management model that solves these problems. First, we present the concept of a plan consisting of input, processing, and output modules of a data stream. Second, we propose Storm Plan Manager, which can operate Storm, Kafka, and database as a single integrated system. Storm Plan Manager is an integrated workflow manager that dynamically controls sampling and filtering of data streams through plans. Third, as a key feature, Storm Plan Manager provides a Web client interface to visually create, execute, and monitor plans. In this article, we show the usefulness of the proposed Storm Plan Manager by presenting its design, implementation, and experimental results in order.

Keywords: Data stream; Apache Storm; data sampling; data filtering; distributed processing; workflow management (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:7:p:1550147719862206

DOI: 10.1177/1550147719862206

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