Data Pre-Processing: Case Of Sensor Data Consistency Based On Bi-Temporal Concepts

The volume, velocity, variety, veracity and value of data currently produced and consumed by different types of information systems turned big Data into a phenomena of study. For data variety, temporal data commonly represents a source of potential inconsistency. This paper reports on a research endeavor for treating the problem of how to minimize inconsistencies in temporal databases due to unavailability of big data. This problem often occurs in situations where a same query is executed on the same data set at different points in time. To address this issue, we propose query optimization strategies based on query transformation and rewriting rules, to amend data consistency in temporal databases. We validate these strategies proposed via case scenario in sensor data analysis, and via manual data input, both for local and distributed query environments.