Uncertain OLAP over Multidimensional Data Streams: State-of-the-Art Analysis and Research Perspectives

Abstract

Multidimensional data streams are playing a leading role in next-generation Data Stream Management Systems (DSMS). This essentially because real-life data streams are inherently multidimensional, multi-level and multi-granular in nature, hence opening the door to a wide spectrum of applications ranging from environmental sensor networks to monitoring and tracking systems, and so forth. As a consequence, there is a need for innovative models and algorithms for representing and processing such streams. Moreover, supporting OLAP analysis and mining tasks is a “first-class” issue in the major context of knowledge discovery from streams, for which above-mentioned models and algorithms are baseline components. This issue becomes more problematic when uncertain and imprecise multidimensional data streams are considered. Inspired by these critical research challenges, in this paper we present a state-of-the-art technique for supporting OLAP over uncertain multidimensional data streams, and provide research perspectives for future efforts in this scientific field.