Topics in topic modelling approaches are represented as a collection of weighted words. The labels for the topics, however, are not clearly defined and must be interpreted manually. Topic labelling proposes to automatically label the topics by leveraging a knowledge base or applying data mining and machine learning algorithms. We propose a naive topic labelling approach where we transform the labeling problem into selecting the best label for each word in the topic. The candidate labels are generated by querying a knowledge base using the top-N words of each topic. We construct a heterogeneous graph of topics, words, articles and candidate labels. To rank the candidate labels, we apply truth discovery algorithms on the graph. The performance evaluation using popular topic modelling datasets shows that the approach receives satisfactory accuracy.

To combine and query ordered data from multiple sources, one needs to handle uncertainty about the possible orderings. Examples of such "order-incomplete" data include integrated event sequences such as log entries, lists of properties (e.g., hotels and restaurants) ranked by an unknown function reflecting relevance or customer ratings, and documents edited concurrently with an uncertain order on edits. This paper introduces a query language for order-incomplete data, based on the positive relational algebra with order-aware accumulation. We use partial orders to represent order-incomplete data, and study possible and certain answers for queries in this context. We show that these problems are respectively NP-complete and coNP-complete, but identify many tractable cases depending on the query operators or input partial orders.

Many emerging applications, from domains such as healthcare and oil & gas, require several data processing systems for complex analytics. This demo paper showcases system, a framework that provides multi-platform task execution for such applications. It features a three-layer data processing abstraction and a new query optimization approach for multi-platform settings. We will demonstrate the strengths of system by using real-world scenarios from three different applications, namely, machine learning, data cleaning, and data fusion.

Truth finding is the problem of determining which of the statements made by contradictory sources is correct, in the absence of prior information on the trustworthiness of the sources. A number of approaches to truth finding have been proposed, from simple majority voting to elaborate iterative algorithms that estimate the quality of sources by corroborating their statements. In this paper, we consider the case where there is an inherent structure in the statements made by sources about real-world objects, that imply different quality levels of a given source on different groups of attributes of an object. We do not assume this structuring given, but instead find it automatically, by exploring and weighting the partitions of the sets of attributes of an object, and applying a reference truth finding algorithm on each subset of the optimal partition. Our experimental results on synthetic and real-world datasets show that we obtain better precision at truth finding than baselines in cases where data has an inherent structure.

A number of applications deal with monitoring moving objects: cars, aircrafts, ships, persons, etc. Traditionally, this requires capturing data from sensor networks, image or video analysis, or using other application-specific resources. We show in this demonstration paper how Web content can be exploited instead to gather information (trajectories, metadata) about moving objects. As this content is marred with uncertainty and inconsistency, we develop a methodology for estimating uncertainty and filtering the resulting data. We present as an application a demonstration of a system that constructs trajectories of ships from social networking data, presenting to a user inferred trajectories, meta-information, as well as uncertainty levels on extracted information and trustworthiness of data providers.

In order to ease content enrichment, exchange, and sharing, web-scale collaborative platforms such as Wikipedia or Google Docs enable unbounded interactions between a large number of contributors, without prior knowledge of their level of expertise and reliability. Version control is then essential for keeping track of the evolution of the shared content and its provenance. In such environments, uncertainty is ubiquitous due to the unreliability of the sources, the incompleteness and imprecision of the contributions, the possibility of malicious editing and vandalism acts, etc. To handle this uncertainty, we use a probabilistic XML model as a basic component of our version control framework. Each version of a shared document is represented by an XML tree and the whole document, together with its different versions, is modeled as a probabilistic XML document. Uncertainty is evaluated using the probabilistic model and the reliability measure associated to each source, each contributor, or each editing event, resulting in an uncertainty measure on each version and each part of the document. We show that standard version control operations can be implemented directly as operations on the probabilistic XML model; efficiency with respect to deterministic version control systems is demonstrated on real-world datasets.

This demonstration paper presents a probabilistic XML data merging tool, that represents the outcome of semi-structured document integration as a probabilistic tree. The system is fully automated and integrates methods to evaluate the uncertainty (modeled as probability values) of the result of the merge. It is based on the two-way tree-merge technique and an uncertain data model defined using probabilistic event variables. The resulting probabilistic repository can be queried using a subset of the XPath query language. The demonstration application is based on revisions of the Wikipedia encyclopedia: a Wikipedia article is no longer considered as the latest valid revision but as the merge of all possible revisions, some of which are uncertain.