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Prof. Dr.
Wrobel, Stefan
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PublicationConstructing Spaces and Times for Tactical Analysis in Football( 2021)
;Andrienko, Gennady ;Andrienko, Natalia ;Anzer, Gabriel ;Bauer, Pascal ;Budziak, Guido ;Weber, HendrikA possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query techniques for flexible selection of episodes of situation development, a method for dynamic aggregation of data from selected groups of episodes, and a data structure for representing the aggregates that enables their exploration and use in further analysis. The aggregation, which is meant to abstract general movement patterns, involves construction of new timehomomorphic reference systems owing to iterative application of aggregation operators to a sequence of data selections. As similar patterns may occur at different spatial locations, we also propose constructing new spatial reference systems for aligning and matching movements irrespective of their absolute locations. The approach was tested in application to tracking data from two Bundesliga games of the 2018/2019 season. It enabled detection of interesting and meaningful general patterns of team behaviors in three classes of situations defined by football experts. The experts found the approach and the underlying concepts worth implementing in tools for football analysts. 
PublicationEffective approximation of parametrized closure systems over transactional data streams( 2020)Strongly closed itemsets, defined by a parameterized closure operator, are a generalization of ordinary closed itemsets. Depending on the strength of closedness, the family of strongly closed itemsets typically forms a tiny subfamily of ordinary closed itemsets that is stable against changes in the input. In this paper we consider the problem of mining strongly closed itemsets from transactional data streams. Utilizing their algebraic and algorithmic properties, we propose an algorithm based on reservoir sampling for approximating this type of itemsets in the landmark streaming setting, prove its correctness, and show empirically that it yields a considerable speedup over a straightforward naive algorithm without any significant loss in precision and recall. We motivate the problem setting considered by two practical applications. In particular, we first experimentally demonstrate that the above properties, i.e., compactness and stability, make strongly closed itemsets an excellent indicator of certain types of concept drifts in transactional data streams. As a second application we consider computeraided product configuration, a realworld problem raised by an industrial project. For this problem, which is essentially exact concept identification, we propose a learning algorithm based on a certain type of subset queries formed by strongly closed itemsets and show on realworld datasets that it requires significantly less query evaluations than a naive algorithm based on membership queries.

PublicationA review of machine learning for the optimization of production processes( 2019)
;Stoll, AnkeDue to the advances in the digitalization process of the manufacturing industry and the resulting available data, there is tremendous progress and large interest in integrating machine learning and optimization methods on the shop floor in order to improve production processes. Additionally, a shortage of resources leads to increasing acceptance of new approaches, such as machine learning to save energy, time, and resources, and avoid waste. After describing possible occurring data types in the manufacturing world, this study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry. The review shows that there is hardly any correlation between the used data, the amount of data, the machine learning algorithms, the used optimizers, and the respective problem from the production. The detailed correlations between these criteria and the recent progress made in this area as well as the issues that are still unsolved are discussed in this paper. 
PublicationProbabilistic and exact frequent subtree mining in graphs beyond forests( 2019)
;Welke, PascalMotivated by the impressive predictive power of simple patterns, we consider the problem of mining frequent subtrees in arbitrary graphs. Although the restriction of the pattern language to trees does not resolve the computational complexity of frequent subgraph mining, in a recent work we have shown that it gives rise to an algorithm generating probabilistic frequent subtrees, a random subset of all frequent subtrees, from arbitrary graphs with polynomial delay. It is based on replacing each transaction graph in the input database with a forest formed by a random subset of its spanning trees. This simple technique turned out to be quite powerful on molecule classification tasks. It has, however, the drawback that the number of sampled spanning trees must be bounded by a polynomial of the size of the transaction graphs, resulting in less impressive recall even for slightly more complex structures beyond molecular graphs. To overcome this limitation, in this work we propose an algorithm mining probabilistic frequent subtrees also with polynomial delay, but by replacing each graph with a forest formed by an exponentially large implicit subset of its spanning trees. We demonstrate the superiority of our algorithm over the simple one on threshold graphs used e.g. in spectral clustering. In addition, providing sufficient conditions for the completeness and efficiency of our algorithm, we obtain a positive complexity result on exact frequent subtree mining for a novel, practically and theoretically relevant graph class that is orthogonal to all graph classes defined by some constant bound on monotone graph properties. 
PublicationProbabilistic frequent subtrees for efficient graph classification and retrieval( 2018)
;Welke, PascalFrequent subgraphs proved to be powerful features for graph classification and prediction tasks. Their practical use is, however, limited by the computational intractability of pattern enumeration and that of graph embedding into frequent subgraph feature spaces. We propose a simple probabilistic technique that resolves both limitations. In particular, we restrict the pattern language to trees and relax the demand on the completeness of the mining algorithm, as well as on the correctness of the pattern matching operator by replacing transaction and query graphs with small random samples of their spanning trees. In this way we consider only a random subset of frequent subtrees, called probabilistic frequent subtrees, that can be enumerated efficiently. Our extensive empirical evaluation on artificial and benchmark molecular graph datasets shows that probabilistic frequent subtrees can be listed in practically feasible time and that their predictive and retrieval performance is very close even to those of complete sets of frequent subgraphs. We also present different fast techniques for computing the embedding of unseen graphs into (probabilistic frequent) subtree feature spaces. These algorithms utilize the partial order on tree patterns induced by subgraph isomorphism and, as we show empirically, require much less evaluations of subtree isomorphism than the standard bruteforce algorithm. We also consider partial embeddings, i.e., when only a part of the feature vector has to be calculated. In particular, we propose a highly effective practical algorithm that significantly reduces the number of pattern matching evaluations required by the classical minhashing algorithm approximating Jaccardsimilarities. 
PublicationBig Data, Big Opportunities( 2015)
;Beyer, UweAngetrieben von den technischen Innovationen in der Informatik stehen in allen Bereichen von Wirtschaft, Gesellschaft und Privatleben heute immer mehr Daten zur VerfÃ¼gung, die potenziell Ã¼bertragen, gespeichert und analysiert werden kÃ¶nnten, um daraus nÃ¼tzliche Informationen als Grundlage fÃ¼r neue Dienste zu gewinnen. Technische Neuerungen wie die verteilte oder speicherresidente Verarbeitung von Daten haben dazu gefÃ¼hrt, dass unsere AnalysefÃ¤higkeiten so stark gewachsen sind, dass eine neue Klasse von Anwendungen mÃ¶glich erscheint. Unter dem Schlagwort ,,Big Data"" scheint sich daher zurzeit eine Revolution bei der Nutzung von Daten in allen Bereichen anzukÃ¼ndigen. Der vorliegende Artikel versucht angesichts aktueller Studien zur Nutzung von Big DataAnsÃ¤tzen zu beleuchten, inwieweit die groÃŸen Ã¶ffentlichen Erwartungen sich tatsÃ¤chlich schon im praktischen Ansatz insbesondere in Unternehmen niederschlagen. Er identifiziert darÃ¼ber hinaus auf Basis allgemeiner und in den Studien zu beobachtender Trends die wichtigsten Herausforderungen, denen sich das Thema Big Data in den nÃ¤chsten Jahren stellen muss, wenn es die hohen aktuellen Erwartungen auch lÃ¤ngerfristig einlÃ¶sen will. 
PublicationSpatiotemporal modeling and analysis. Introduction and overview( 2012)Over the past ve to seven years the analysis of trajectory data has established itself as an independent research discipline within the area of data mining. In this article we provide an overview on data characteristics, stateoftheart preprocessing and analysis methods of trajectory data. We conclude the article with a collection of challenges that arise due to the increasing variety of spatiotemporal data sources and which have to be solved for the application of spatiotemporal analysis methods in practice.

PublicationMovement data anonymity through generalization( 2010)
;Monreale, Anna ;Andrienko, Gennady ;Andrienko, Natalia ;Giannotti, Fosca ;Pedreschi, Dino ;Rinzivillo, SalvatoreWireless networks and mobile devices, such as mobile phones and GPS receivers, sense and track the movements of people and vehicles, producing societywide mobility databases. This is a challenging scenario for data analysis and mining. On the one hand, exciting opportunities arise out of discovering new knowledge about human mobile behavior, and thus fuel intelligent infomobility applications. On other hand, new privacy concerns arise when mobility data are published. The risk is particularly high for GPS trajectories, which represent movement of a very high precision and spatiotemporal resolution: the deidentification of such trajectories (i.e., forgetting the ID of their associated owners) is only a weak protection, as generally it is possible to reidentify a person by observing her routine movements. In this paper we propose a method for achieving true anonymity in a dataset of published trajectories, by defining a transformation of the original GPS trajectories based on spatial generalization and kanonymity. The proposed method offers a formal data protection safeguard, quantified as a theoretical upper bound to the probability of reidentification. We conduct a thorough study on a reallife GPS trajectory dataset, and provide strong empirical evidence that the proposed anonymity techniques achieve the conflicting goals of data utility and data privacy. In practice, the achieved anonymity protection is much stronger than the theoretical worst case, while the quality of the cluster analysis on the trajectory data is preserved. 
PublicationFrequent subgraph mining in outerplanar graphs( 2010)
;Ramon, J.In recent years there has been an increased interest in frequent pattern discovery in large databases of graph structured objects. While the frequent connected subgraph mining problem for tree datasets can be solved in incremental polynomial time, it becomes intractable for arbitrary graph databases. Existing approaches have therefore resorted to various heuristic strategies and restrictions of the search space, but have not identified a practically relevant tractable graph class beyond trees. In this paper, we consider the class of outerplanar graphs, a strict generalization of trees, develop a frequent subgraph mining algorithm for outerplanar graphs, and show that it works in incremental polynomial time for the practically relevant subclass of wellbehaved outerplanar graphs, i.e., which have only polynomially many simple cycles. We evaluate the algorithm empirically on chemo and bioinformatics applications. 
PublicationEfficient discovery of interesting patterns based on strong closedness( 2009)Finding patterns that are interesting to a user in a certain application context is one of the central goals of data mining research. Regarding all patterns above a certain frequency threshold as interesting is one way of defining interestingness. In this paper, however, we argue that in many applications, a different notion of interestingness is required in order to be able to capture "long", and thus particularly informative, patterns that are correspondingly of low frequency. To identify such patterns, our proposed measure of interestingness is based on the degree or strength of closedness of the patterns. We show that (i) indeed this definition selects long interesting patterns that are difficult to identify with frequencybased approaches, and (ii) that it selects patterns that are robust against noise and/or dynamic changes. We prove that the family of interesting patterns proposed here forms a closure system and use the corresponding closure operator to design a mining algorithm listing these patterns in amortized quadratic time. In particular, for nonsparse datasets its time complexity is O(nm) per pattern, where n denotes the number of items and m the size of the database. This is equal to the best known time bound for listing ordinary closed frequent sets, which is a special case of our problem. We also report empirical results with realworld datasets.