Towards Automatic Generation of Smartphone Database Benchmarks through Workload characterization of smartphone databases
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In this dissertation, we introduce the first steps of a framework to create a benchmarking tool which aims to emulate the workloads of Android applications to compare different mobile database management system implementations. First, we describe a clustering scheme where we analyze the query logs to group the SQL queries by semantic similarity. Then, we introduce a session identification technique for mobile query workloads where we identify bursts of activities created by the users' actions. Finally, we elaborate on using these clusters and session information to model common behaviors and unusual patterns. We demonstrate that these common patterns can be used to realistically emulate synthetic workloads created by Android applications, allowing to test the performance of different mobile database management systems. We present results over real world data traces gathered in the wild as well as data traces gathered where users are asked to note their activities.