Predictive maintenance strives to anticipate equipment failures to allow for advance scheduling of corrective maintenance, thereby preventing unexpected equipment downtime and improving service quality for the customers. We present a data-driven approach based on multiple-instance learning for predicting equipment failures by mining equipment event logs which, while usually not designed for predicting failures, contain rich operational information. We discuss problem domain and formulation, evaluation metrics and predictive maintenance work flow. We experimentally compare our approach to competing methods. For evaluation, we use real life datasets with billions of log messages from two large fleets of medical equipment. We share insights gained from mining such data. Our predictive maintenance approach, deployed by a major medical device provider over the past several months, learns and evaluates predictive models from terabytes of log data, and actively monitors thousands of medical scanners.

In many areas of life, we now have almost complete electronic archives reaching back for well over two decades. This includes, for example, the body of research papers in computer science, all news articles written in the US, and most people's personal email. However, we have only rather limited methods for analyzing and understanding these collections. While keyword-based retrieval systems allow efficient access to individual documents in archives, we still lack methods for understanding a corpus as a whole. In this paper, we explore methods that provide a temporal summary of such corpora in terms of landmark documents, authors, and topics. In particular, we explicitly model the temporal nature of influence between documents and re-interpret summarization as a coverage problem over words anchored in time. The resulting models provide monotone sub-modular objectives for computing informative and non-redundant summaries over time, which can be efficiently optimized with greedy algorithms. Our empirical study shows the effectiveness of our approach over several baselines.

To facilitate direct comparisons between different products, we present an approach to constructing short and comparative summaries based on product reviews. In particular, the user can view automatically aligned pairs of snippets describing reviewers' opinions on different features (also selected automatically by our approach) for two selected products. We propose a submodular objective function that avoids redundancy, that is efficient to optimize, and that aligns the snippets into pairs. Snippets are chosen from product reviews and thus easy to obtain. In our experiments, we show that the method constructs qualitatively good summaries, and that it can be tuned via supervised learning.

HistoryViz provides a new perspective on a certain kind of textual data, in particular the data available in the Wikipedia, where different entities are described and put in historical perspective. Instead of browsing through pages each describing a certain topic, we can look at the relations between entities and events connected with the selected entities. The presented solution implemented in HistoryViz provides user with a graphical interface allowing viewing events concerning the selected person on a timeline and viewing relations to other entities as a graph that can be dynamically expanded.

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