大数据流上的可扩展连接:实际和未来的研究趋势

A. Cuzzocrea
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引用次数: 0

摘要

连接是海量大数据流上大量大数据分析工具的基础。连接是在静态数据集的背景下发展起来的,由于其在广泛的应用环境中的多功能性,从环境网络到物流系统,从智慧城市应用到医疗保健系统,从能源管理系统到预测工具等,因此在流数据集的背景下引起了极大的兴趣。传统上,大数据流上的join吸引了越来越多的数据库和数据挖掘社区的关注,然后在更广泛的大数据社区落地。根据这些考虑,本文对大数据流上可扩展连接的实际和未来趋势进行了批判性的回顾。
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Scalable Joins over Big Data Streams: Actual and Future Research Trends
Joins are at the basis of a plethora of big data analytics tools over massive big data streams. Developed in the context of static data sets, joins have emerged as of tremendous interest in the context of streaming data sets, due to their versatility in a wide range of applicative settings, ranging from environmental networks to logistics systems, from smart city applications to healthcare systems, from energy management systems to prognostic tools, and so forth. Joins over big data streams has traditionally attracted the attention of a growing part of the database and data mining community, then landing in the wider big data community. Following these considerations, this paper proposes a critical review of actual and future trends in the context of scalable joins over big data streams.
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