In-memory preprocessing of streaming sensory data - a partitioned relational database approach

Abstract

In this paper we present a database architecture and an application area and method in detail. As sensory data stream in the database it is efficient to preprocess them in-memory before integrating in the repository in order to save storage I/O cost. After data are integrated, it is important to allow efficient quering based on data retrieval profile. This can also be supported by the presented database architecture by partitioning the database upon different criteria. It is mandatory to hide the internal partitioned architectural details from higher layers, so options to allow transparent quering options are also presented. We have implemented a test system and experimental results are also given.