Auflistung nach Autor:in "Renz, Matthias"
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- ZeitschriftenartikelCross domain fusion for spatiotemporal applications: taking interdisciplinary, holistic research to the next level(Informatik Spektrum: Vol. 45, No. 5, 2022) Renz, Matthias; Kröger, Peer; Koschmider, Agnes; Landsiedel, Olaf; Tavares de Sousa, NelsonExploiting the power of collective use of complementing data sources for the discovery of new correlations and findings offers enormous additional value compared to the summed values of isolated analysis of the individual information sources. In this article, we will introduce the concept of “cross domain fusion” (CDF) as a machine learning and pattern mining driven and multi-disciplinary research approach for fusing data and knowledge from a variety of sources enabling the discovery of answers of the question to be examined from a more complete picture. The article will give a basic introduction in this emerging field and will highlight examples of basic CDF tasks in the field of marine science.
- ZeitschriftenartikelCross Domain Fusion in der Archäologie – Interview mit Dr. Michael Kempf und Prof. Dr. Oliver Nakoinz(Informatik Spektrum: Vol. 45, No. 5, 2022) Renz, Matthias; Strohm, Steffen; Kempf, Michael; Nakoinz, Oliver
- KonferenzbeitragEfficient Reverse k-Nearest Neighbor Estimation(Datenbanksysteme in Business, Technologie und Web (BTW 2007) – 12. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 2007) Achtert, Elke; Böhm, Christian; Kröger, Peer; Kunath, Peter; Pryakhin, Alexey; Renz, MatthiasThe reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. In addition, many applications require a fast approximate answer of RkNN-queries. For these scenarios, it is important to generate a fast answer with high recall. In this paper, we propose the first approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use an approximation of the nearest-neighbor-distances in order to prune the search space. We show that our method scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.
- ZeitschriftenartikelEfficient Traffic Density Prediction in Road Networks Using Suffix Trees(KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Kriegel, Hans-Peter; Renz, Matthias; Schubert, Matthias; Züfle, AndreasRecently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. In this paper, we propose a statistical approach to predict the density on any edge in such a network at a future point of time. Our method combines long-term and short-term observations of a traffic network in order to predict traffic density for the near future. In our experiments, we show the capability of our approach to make useful predictions about the traffic density and illustrate the efficiency of our new algorithm when calculating these predictions.
- ZeitschriftenartikelGetting the big picture in cross-domain fusion(Informatik Spektrum: Vol. 45, No. 4, 2022) von Hanxleden, Reinhard; Biastoch, Arne; Fohrer, Nicola; Renz, Matthias; Vafeidis, AthanasiosA central promise of cross-domain fusion (CDF) is the provision of a “bigger picture” that integrates different disciplines and may span very different levels of detail. We present a number of settings that call for this bigger picture, with a particular focus on how information from several domains can be made easily accessible and visualizable for different stakeholders. We propose harnessing an approach that is now well established in interactive maps, which we refer to as the “Google maps approach” (Google LLC, Mountain View, CA, USA), which combines effective filtering with intuitive user interaction. We expect this approach to be applicable to a range of CDF settings.
- ZeitschriftenartikelInterview mit Ulf Leser zu Cross-Domain Fusion(Informatik Spektrum: Vol. 45, No. 4, 2022) Hasselbring, Wilhelm; Renz, Matthias; Kröger, Peer
- KonferenzbeitragA mutual pruning approach for RkNN join processing(Datenbanksysteme für Business, Technologie und Web (BTW) 2016, 2013) Emrich, Tobias; Kröger, Peer; Niedermayer, Johannes; Renz, Matthias; Züfle, AndreasA reverse k-nearest neighbour (RkNN) query determines the objects from a database that have the query as one of their k-nearest neighbors. Processing such a query has received plenty of attention in research. However, the effect of running multiple RkNN queries at once (join) or within a short time interval (bulk/group query) has, to the best of our knowledge, not been addressed so far. In this paper, we analyze RkNN joins and discuss possible solutions for solving this problem. During our performance analysis we provide evaluation results showing the IO and CPU performance of the compared algorithms for a variety of different setups.
- ZeitschriftenartikelVorwort zum Sonderheft „Cross-Domain Fusion“(Informatik Spektrum: Vol. 45, No. 4, 2022) Renz, Matthias; Koschmider, Agnes; Kröger, Peer
- ZeitschriftenartikelVorwort zum Sonderheft „Cross-Domain Fusion“ – Heft 2(Informatik Spektrum: Vol. 45, No. 5, 2022) Renz, Matthias; Koschmider, Agnes; Kröger, Peer