Stochastic data acquisition for answering queries as time goes by

Data and actions are tightly coupled. On one hand, data analysis results trigger decision making and actions. On the other hand, the action of acquiring data is the very first step in the whole data processing pipeline. Data acquisition almost always has some costs, which could be either monetary costs or computing resource costs such as sensor battery power, network transfers, or I/O costs. Using out-dated data to answer queries can avoid the data acquisition costs, but there is a penalty of potentially inaccurate results. Given a sequence of incoming queries over time, we study the problem of sequential decision making on when to acquire data and when to use existing versions to answer each query. We propose two approaches to solve this problem using reinforcement learning and tailored locality-sensitive hashing. A systematic empirical study using two real-world datasets shows that our approaches are effective and efficient.