TY - BOOK A1 - Hagedorn, Benjamin A1 - Schöbel, Michael A1 - Uflacker, Matthias A1 - Copaciu, Flavius A1 - Milanovic, Nikola T1 - Proceedings of the fall 2006 workshop of the HPI research school on service-oriented systems engineering N2 - 1. Design and Composition of 3D Geoinformation Services Benjamin Hagedorn 2. Operating System Abstractions for Service-Based Systems Michael Schöbel 3. A Task-oriented Approach to User-centered Design of Service-Based Enterprise Applications Matthias Uflacker 4. A Framework for Adaptive Transport in Service- Oriented Systems based on Performance Prediction Flavius Copaciu 5. Asynchronicity and Loose Coupling in Service-Oriented Architectures Nikola Milanovic T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 18 Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-33052 SN - 978-3-939469-58-2 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - BOOK A1 - Alnemr, Rehab A1 - Polyvyanyy, Artem A1 - AbuJarour, Mohammed A1 - Appeltauer, Malte A1 - Hildebrandt, Dieter A1 - Thomas, Ivonne A1 - Overdick, Hagen A1 - Schöbel, Michael A1 - Uflacker, Matthias A1 - Kluth, Stephan A1 - Menzel, Michael A1 - Schmidt, Alexander A1 - Hagedorn, Benjamin A1 - Pascalau, Emilian A1 - Perscheid, Michael A1 - Vogel, Thomas A1 - Hentschel, Uwe A1 - Feinbube, Frank A1 - Kowark, Thomas A1 - Trümper, Jonas A1 - Vogel, Tobias A1 - Becker, Basil ED - Meinel, Christoph ED - Plattner, Hasso ED - Döllner, Jürgen Roland Friedrich ED - Weske, Mathias ED - Polze, Andreas ED - Hirschfeld, Robert ED - Naumann, Felix ED - Giese, Holger T1 - Proceedings of the 4th Ph.D. Retreat of the HPI Research School on Service-oriented Systems Engineering T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 31 KW - Hasso-Plattner-Institut KW - Forschungskolleg KW - Klausurtagung KW - Service-oriented Systems Engineering KW - Hasso Plattner Institute KW - Research School KW - Ph.D. Retreat KW - Service-oriented Systems Engineering Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-40838 SN - 978-3-86956-036-6 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - GEN A1 - Serth, Sebastian A1 - Podlesny, Nikolai A1 - Bornstein, Marvin A1 - Lindemann, Jan A1 - Latt, Johanna A1 - Selke, Jan A1 - Schlosser, Rainer A1 - Boissier, Martin A1 - Uflacker, Matthias T1 - An interactive platform to simulate dynamic pricing competition on online marketplaces T2 - 2017 IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC) N2 - E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies. Y1 - 2017 SN - 978-1-5090-3045-3 U6 - https://doi.org/10.1109/EDOC.2017.17 SN - 2325-6354 SP - 61 EP - 66 PB - Institute of Electrical and Electronics Engineers CY - New York ER - TY - GEN A1 - Perscheid, Cindy A1 - Uflacker, Matthias T1 - Integrating Biological Context into the Analysis of Gene Expression Data T2 - Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference N2 - High-throughput RNA sequencing produces large gene expression datasets whose analysis leads to a better understanding of diseases like cancer. The nature of RNA-Seq data poses challenges to its analysis in terms of its high dimensionality, noise, and complexity of the underlying biological processes. Researchers apply traditional machine learning approaches, e. g. hierarchical clustering, to analyze this data. Until it comes to validation of the results, the analysis is based on the provided data only and completely misses the biological context. However, gene expression data follows particular patterns - the underlying biological processes. In our research, we aim to integrate the available biological knowledge earlier in the analysis process. We want to adapt state-of-the-art data mining algorithms to consider the biological context in their computations and deliver meaningful results for researchers. KW - Gene expression KW - Machine learning KW - Feature selection KW - Association rule mining KW - Biclustering KW - Knowledge bases Y1 - 2019 SN - 978-3-319-99608-0 SN - 978-3-319-99607-3 U6 - https://doi.org/10.1007/978-3-319-99608-0_41 SN - 2194-5357 SN - 2194-5365 VL - 801 SP - 339 EP - 343 PB - Springer CY - Cham ER - TY - GEN A1 - Hesse, Günter A1 - Matthies, Christoph A1 - Sinzig, Werner A1 - Uflacker, Matthias T1 - Adding Value by Combining Business and Sensor Data BT - an Industry 4.0 Use Case T2 - Database Systems for Advanced Applications N2 - Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data’s full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs. KW - Industry 4.0 KW - Internet of Things KW - Data integration Y1 - 2019 SN - 978-3-030-18590-9 SN - 978-3-030-18589-3 U6 - https://doi.org/10.1007/978-3-030-18590-9_80 SN - 0302-9743 SN - 1611-3349 VL - 11448 SP - 528 EP - 532 PB - Springer CY - Cham ER - TY - JOUR A1 - Dreseler, Markus A1 - Boissier, Martin A1 - Rabl, Tilmann A1 - Uflacker, Matthias T1 - Quantifying TPC-H choke points and their optimizations JF - Proceedings of the VLDB Endowment N2 - TPC-H continues to be the most widely used benchmark for relational OLAP systems. It poses a number of challenges, also known as "choke points", which database systems have to solve in order to achieve good benchmark results. Examples include joins across multiple tables, correlated subqueries, and correlations within the TPC-H data set. Knowing the impact of such optimizations helps in developing optimizers as well as in interpreting TPC-H results across database systems. This paper provides a systematic analysis of choke points and their optimizations. It complements previous work on TPC-H choke points by providing a quantitative discussion of their relevance. It focuses on eleven choke points where the optimizations are beneficial independently of the database system. Of these, the flattening of subqueries and the placement of predicates have the biggest impact. Three queries (Q2, Q17, and Q21) are strongly ifluenced by the choice of an efficient query plan; three others (Q1, Q13, and Q18) are less influenced by plan optimizations and more dependent on an efficient execution engine. Y1 - 2020 U6 - https://doi.org/10.14778/3389133.3389138 SN - 2150-8097 VL - 13 IS - 8 SP - 1206 EP - 1220 PB - Association for Computing Machinery CY - New York ER -