Time-sensitive computation of aggregate functions over distributed imprecise data

Summary form only given. Many distributed applications in the real world now require real time services in which aggregate queries need to be computed over a set of values. These applications can often tolerate varying degrees of inaccuracy in the results. System designers, on the other hand, would like to provide services with low inaccuracy and minimal management overhead. We focus on addressing the tradeoffs between timeliness, accuracy and cost for data aggregation in distributed environments. Specifically, we address the problem of time-sensitive computation of aggregate queries (count, sum and min) over a set of values represented by intervals with lower and upper bounds. These intervals are approximations based on most recent values about distributed sources. In order to meet the precision constraints from users, a subset of sources needs to be probed for exact values. We first propose algorithms for batch selection of the probing set, where selection is done before probing without the knowledge of the actual values. In addition, we propose an iterative selection approach where the selection of the next probing source depends on the previous returned value.