Uncertain data have already widely existed in many practical applications recently, such as
sensor networks, RFID networks, location-based services, and mobile object management …

Abstract Methods for Approximate Query Processing (AQP) are essential for dealing with
massive data. They are often the only means of providing interactive response times when …

We introduce a new approach to characterizing the unobserved portion of a distribution,
which provides sublinear--sample estimators achieving arbitrarily small additive constant …

We give the first optimal algorithm for estimating the number of distinct elements in a data
stream, closing a long line of theoretical research on this problem begun by Flajolet and …

Considerable advances in genomics over the past decade have resulted in vast amounts of
data being generated and deposited in global archives. The growth of these archives …

We present a system that approximates the answer to complex ad-hoc queries in big-data
clusters by injecting samplers on-the-fly and without requiring pre-existing samples …

In emerging Big Data scenarios, obtaining timely, high-quality answers to aggregate queries
is difficult due to the challenges of processing and cleaning large, dirty data sets. To …

Node centrality measures are important in a large number of graph applications, from search
and ranking to social and biological network analysis. In this paper we study node centrality …

Data preparation and data profiling comprise many both basic and complex tasks to analyze
a dataset at hand and extract metadata, such as data distributions, key candidates, and …

For a broad class of practically relevant distribution properties, which includes entropy and
support size, nearly all of the proposed estimators have an especially simple form. Given a …