How to Efficiently and Predictably use Resources in Safety-critical Systems

Reliable Data Mining in Uncertain Data

Abstract - Our ability to extract knowledge from data is often impaired by unreliable, erroneous, obsolete, imprecise, sparse, and noisy data. Existing solutions for data mining often assume that all data are uniformly reliable and representative. Oblivious to sample size and sample variance, it is clear that mined patterns may be spurious, that is, caused by random variations rather than a causal signal. This is particularly problematic if latent features and deep learning methods are used to mine patterns, as their lack of interpretability prevents domain experts and decision makers from explaining spurious conclusions. This presentation will survey data mining algorithms that can exploit reliability information of data to enrich mined patterns with significance information. In detail, we will discuss the use of Monte Carlo and agent-based simulation to gain insights on the reliability of data mining results and we will look at applications for handling.

CV - Andreas is a tenure-track assistant professor at the Department of Geography and Geoinformation Science at George Mason University (GMU), USA. He received his Ph.D. in Computer Science, summa cum laude, under supervision of Dr. Hans-Peter Kriegel at LMU Munich in 2013. Since joining GMU in 2016, Andreas' research has received more than $2,000,000 in research grants by the National Science Foundation (NSF) and the Defense Advanced Research Projects Agency (DARPA). Andreas' research focuses on big spatial data, spatial data mining, social network mining, and uncertain database management. His research quest is to work interdisciplinary and bridge the gap between data-science and geo-science. Since 2011, Andreas has published more than 90 papers in refereed conferences and journals leading to an h-index of 18. For the work presented in this talk, Andreas has received the SSTD 2019 best vision paper award (runner-up), the SSTD 2019 best paper award (runner-up), and the ACM SIGSPATIAL 2019 GIS Cup 1st Place award.