Synopses for probabilistic data over large domains

Many real world applications produce data with uncertainties drawn from measurements over a continuous domain space. Recent research in the area of probabilistic databases has mainly focused on managing and querying discrete data in which the domain is limited to a small number of values (i.e. on the order of 10). When the size of the domain increases, current methods fail due to their nature of explicitly storing each value/probability pair. Such methods are not capable of extending their use to continuous-valued attributes. In this paper, we provide a scalable, accurate, space efficient probabilistic data synopsis for uncertain attributes defined over a continuous domain. Our synopsis construction methods are all error-aware to ensure that our synopsis provides an accurate representation of the underlying data given a limited space budget. Additionally, we are able to provide approximate query results over the synopsis with error bounds. We provide an extensive experimental evaluation to show that our proposed methods improve upon the current state of the art in terms of construction time and query accuracy. In particular, our synopsis can be constructed in O(N2) time (where N is the number of tuples in the database). We also demonstrate the ability of our synopsis to answer a variety of interesting queries on a real data set and show that our query error is reduced by up to an order of magnitude over the previous state-of-the-art method.