OLAPing Uncertain Multidimensional Data Streams
University:ICAR-CNR and University of Calabria, Italy
Multidimensional data streams are playing a leading role in next-generation 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 talk we will present an overview of major research issues in this context and an innovative technique for supporting OLAP over uncertain multidimensional data streams.