Many methods have been proposed to make machine learning more interpretable, but these have mainly been evaluated with simple use cases and well-curated datasets. In contrast, real-world data presents issues that can compromise the proper interpretation of explanations by end users. In this work, we investigate the impact of missing data and imputation on how users would understand, and use exp...

This paper lays out provocative assertions about major challenges for the modeling and simulation community. One relates to building M&S for the purpose of assisting the search for strategies that are flexible, adaptive, and robust despite uncertainty. A key aspect of this search is exploratory analysis, coupled with selective zoom. These, in turn, require multiresolution modeling with sound mo...

Probabilistic range query is an important type of query in the area of uncertain data management. A probabilistic range query returns all the data objects within a specific range from the query object with a probability no less than a given threshold. In this paper, we assume that each uncertain object stored in the database is associated with a multi-dimensional Gaussian distribution, which de...

Managing and mining uncertain data is becoming important with the increase in the use of devices responsible for generating uncertain data, for example sensors, RFIDs, etc. In this paper, we extend the notion of distance-based outliers for uncertain data. To the best of our knowledge, this is the first work on distance-based outlier detection on uncertain data of Gaussian distribution. Since th...

With the advance in technology, sensor networks have been widely used in many application areas such as environmental surveillance. Sensors distributed in these networks serve as good sources for data. This calls for distributed data mining, which searches for implicit, previously unknown, and potentially useful patterns that might be embedded in the distributed data. Many existing distributed ...

We consider problems on data sets where each data point has uncertainty described by an individual probability distribution. We develop several frameworks and algorithms for calculating statistics on these uncertain data sets. Our examples focus on geometric shape fitting problems. We prove approximation guarantees for the algorithms with respect to the full probability distributions. We then e...

Many existing distributed data mining algorithms do not allow users to express the patterns to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous patterns that are not interesting to users. Moreover, due to inherited measurement inaccuracies and/or network latencies, data are often riddled with uncertainty. Th...

Data uncertainty inherently exists in a large number of applications due to factors such as limitations of measuring equipments, update delay, and network bandwidth. Recently, modeling and querying uncertain data have attracted considerable attention from the database community. However, how to perform advanced analysis on uncertain data remains an interesting question. In this paper, we focus ...

With increasing complexity of manufacturing processes, the volume of data that has to be evaluated rises accordingly. The complexity and data volume make any kind of manual data analysis infeasable. At this point, data mining techniques become interesting. The application of current techniques is of complex nature because most of the data is captured by sensor measurement tools. Therefore, ever...

Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores, such as probabilities. A concrete example of such data is weather data. We will demo a game-based setup for exploring the effectiveness of different approaches (graphics vs NLG) to communicating uncertainty in rainfall and temperature predictions (www.macs.hw.ac.uk/ InteractionLab/weathergame/ ). ...