Log based method for faster IoT queries

Abstract

With increase in the size of Internet of Things IoT device networks and applications, tremendous increase is witnessed in the size of IoT data. To build smart applications using IoT data, its efficient storage is important to facilitate faster queries. IoT data is represented in Resource Description Framework RDF and stored in relational format. This thesis addresses the issue of faster query processing of this data. It resents a Log Based Method LBM to partition IoT data. IoT systems exhibit skewedness in data access patterns as some records are accessed more frequently than the other. LBM exploits this skewedness in access patterns of records. It incorporates Forward Algorithm FA and Backward Algorithm BA to analyse the query workload and partition the basic triple table into hot and cold data tables. For our experiment, 8% of hot data table is found to solve 78.6% queries. The query execution is found to be 67.5% faster on partitioned data than triple table. To further accelerate FA and BA, we have executed them in parallel as well which is found to be 42% faster than its serial execution.