We present a decision-theoretic approach for sampling information sources in resource-constrained environments, where there is uncertainty regarding source trustworthiness. We exploit diversity among sources to stratify the population into homogeneous subgroups to both minimise redundant sampling and mitigate the effect of source collusion. We show through empirical evaluation that our model is...

Truth Discovery is an important problem arising in data analytics related fields such as data mining, database, and big data. It concerns about finding the most trustworthy information from a dataset acquired from a number of unreliable sources. Due to its importance, the problem has been extensively studied in recent years and a number techniques have already been proposed. However, all of the...

Modern information management applications often require integrating data from a variety of data sources, some of which may copy or buy data from other sources. When these data sources model a dynamically changing world (e.g., people’s contact information changes over time, restaurants open and go out of business), sources often provide out-of-date data. Errors can also creep into data when sou...

Although William Harvey is usually remembered for his discovery of the circulation of the blood, this is not the only reason for choosing him as the pivot of this lecture: it is also his attitude to truth and knowledge which well exemplifies the spirit of his age, and this spirit is more important than any single discovery, however significant. Harvey was born towards the end of the 16th centur...

[1] The emergence of digital evidence and the widespread implementation of e-discovery has brought both benefit and repercussion. In many respects, digital evidence has proven to be a better truth detector than its paper counterpart. At the same time, the volumes in which digital evidence exists make time-tested discovery techniques impractical. In fact, so significant are the technological dif...

Although Semantic Web service discovery has been extensively studied in the literature ([7], [12], [15] and [10]), we are far from achieving an effective, complete and automated discovery process. Using the incidence calculus [4], a truth-functional probabilistic calculus, and a lightweight brokering mechanism [17], the article explores the suitability of integrating probabilistic reasoning in ...

Knowledge Management is a discipline with many faces – among very provocative ones is the research area dealing with automatic discovery of the hidden truth within the data describing the world around us. The basic idea of knowledge discovery is to let the computer search for the knowledge whereas the humans give just broad directions about where and how to search. Surprisingly, it is often the...

Patterns are an essential part of music and there are many different algorithms that aim to discover them. Based on the improvements brought by using data fusion methods to find the consensus of algorithms on other MIR tasks, we hypothesize that fusing the output from musical pattern discovery algorithms will improve the pattern discovery results. In this paper, we explore two methods to combin...

Causal discovery algorithms based on probabilistic graphical models have recently emerged in geoscience applications for the identification and visualization of dynamical processes. The key idea is to learn the structure of a graphical model from observed spatio-temporal data, thus finding pathways of interactions in the observed physical system. Studying those pathways allows geoscientists to ...