@book{HagedornSchoebelUflackeretal.2007, author = {Hagedorn, Benjamin and Sch{\"o}bel, Michael and Uflacker, Matthias and Copaciu, Flavius and Milanovic, Nikola}, title = {Proceedings of the fall 2006 workshop of the HPI research school on service-oriented systems engineering}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, isbn = {978-3-939469-58-2}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-33052}, publisher = {Universit{\"a}t Potsdam}, pages = {Getr. Z{\"a}hlung}, year = {2007}, abstract = {1. Design and Composition of 3D Geoinformation Services Benjamin Hagedorn 2. Operating System Abstractions for Service-Based Systems Michael Sch{\"o}bel 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}, language = {en} } @book{AlnemrPolyvyanyyAbuJarouretal.2010, author = {Alnemr, Rehab and Polyvyanyy, Artem and AbuJarour, Mohammed and Appeltauer, Malte and Hildebrandt, Dieter and Thomas, Ivonne and Overdick, Hagen and Sch{\"o}bel, Michael and Uflacker, Matthias and Kluth, Stephan and Menzel, Michael and Schmidt, Alexander and Hagedorn, Benjamin and Pascalau, Emilian and Perscheid, Michael and Vogel, Thomas and Hentschel, Uwe and Feinbube, Frank and Kowark, Thomas and Tr{\"u}mper, Jonas and Vogel, Tobias and Becker, Basil}, title = {Proceedings of the 4th Ph.D. Retreat of the HPI Research School on Service-oriented Systems Engineering}, editor = {Meinel, Christoph and Plattner, Hasso and D{\"o}llner, J{\"u}rgen Roland Friedrich and Weske, Mathias and Polze, Andreas and Hirschfeld, Robert and Naumann, Felix and Giese, Holger}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, isbn = {978-3-86956-036-6}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-40838}, publisher = {Universit{\"a}t Potsdam}, pages = {Getr. Z{\"a}hlung}, year = {2010}, language = {en} } @misc{SerthPodlesnyBornsteinetal.2017, author = {Serth, Sebastian and Podlesny, Nikolai and Bornstein, Marvin and Lindemann, Jan and Latt, Johanna and Selke, Jan and Schlosser, Rainer and Boissier, Martin and Uflacker, Matthias}, title = {An interactive platform to simulate dynamic pricing competition on online marketplaces}, series = {2017 IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC)}, journal = {2017 IEEE 21st International Enterprise Distributed Object Computing Conference (EDOC)}, publisher = {Institute of Electrical and Electronics Engineers}, address = {New York}, isbn = {978-1-5090-3045-3}, issn = {2325-6354}, doi = {10.1109/EDOC.2017.17}, pages = {61 -- 66}, year = {2017}, abstract = {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.}, language = {en} } @misc{HesseMatthiesSinzigetal.2019, author = {Hesse, G{\"u}nter and Matthies, Christoph and Sinzig, Werner and Uflacker, Matthias}, title = {Adding Value by Combining Business and Sensor Data}, series = {Database Systems for Advanced Applications}, volume = {11448}, journal = {Database Systems for Advanced Applications}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-18590-9}, issn = {0302-9743}, doi = {10.1007/978-3-030-18590-9_80}, pages = {528 -- 532}, year = {2019}, abstract = {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.}, language = {en} } @misc{PerscheidUflacker2019, author = {Perscheid, Cindy and Uflacker, Matthias}, title = {Integrating Biological Context into the Analysis of Gene Expression Data}, series = {Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference}, volume = {801}, journal = {Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-99608-0}, issn = {2194-5357}, doi = {10.1007/978-3-319-99608-0_41}, pages = {339 -- 343}, year = {2019}, abstract = {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.}, language = {en} } @article{DreselerBoissierRabletal.2020, author = {Dreseler, Markus and Boissier, Martin and Rabl, Tilmann and Uflacker, Matthias}, title = {Quantifying TPC-H choke points and their optimizations}, series = {Proceedings of the VLDB Endowment}, volume = {13}, journal = {Proceedings of the VLDB Endowment}, number = {8}, publisher = {Association for Computing Machinery}, address = {New York}, issn = {2150-8097}, doi = {10.14778/3389133.3389138}, pages = {1206 -- 1220}, year = {2020}, abstract = {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.}, language = {en} }