Scalable Joins over Big Data Streams: Actual and Future Research Trends

Abstract

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.