@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}
}
@article{SchlosserWaltherBoissieretal.2019,
  author    = {Schlosser, Rainer and Walther, Carsten and Boissier, Martin and Uflacker, Matthias},
  title     = {Automated repricing and ordering strategies in competitive markets},
  series = {AI communications : AICOM ; the European journal on artificial intelligence},
  volume    = {32},
  journal   = {AI communications : AICOM ; the European journal on artificial intelligence},
  number    = {1},
  publisher = {IOS Press},
  address   = {Amsterdam},
  issn      = {0921-7126},
  doi       = {10.3233/AIC-180603},
  pages     = {15 -- 29},
  year      = {2019},
  abstract  = {Merchants on modern e-commerce platforms face a highly competitive environment. They compete against each other using automated dynamic pricing and ordering strategies. Successfully managing both inventory levels as well as offer prices is a challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. We show how to derive optimized data-driven pricing and ordering strategies which are based on demand learning techniques and efficient dynamic optimization models. We verify the superior performance of our self-adaptive strategies by comparing them to different rule-based as well as data-driven strategies in duopoly and oligopoly settings. Further, to study and to optimize joint dynamic ordering and pricing strategies on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams of consumers with configurable characteristics.},
  language  = {en}
}
@article{PerscheidGrasnickUflacker2019,
  author    = {Perscheid, Cindy and Grasnick, Bastien and Uflacker, Matthias},
  title     = {Integrative Gene Selection on Gene Expression Data},
  series = {Journal of Integrative Bioinformatics},
  volume    = {16},
  journal   = {Journal of Integrative Bioinformatics},
  number    = {1},
  publisher = {De Gruyter},
  address   = {Berlin},
  issn      = {1613-4516},
  doi       = {10.1515/jib-2018-0064},
  pages     = {17},
  year      = {2019},
  abstract  = {The advance of high-throughput RNA-Sequencing techniques enables researchers to analyze the complete gene activity in particular cells. From the insights of such analyses, researchers can identify disease-specific expression profiles, thus understand complex diseases like cancer, and eventually develop effective measures for diagnosis and treatment. The high dimensionality of gene expression data poses challenges to its computational analysis, which is addressed with measures of gene selection. Traditional gene selection approaches base their findings on statistical analyses of the actual expression levels, which implies several drawbacks when it comes to accurately identifying the underlying biological processes. In turn, integrative approaches include curated information on biological processes from external knowledge bases during gene selection, which promises to lead to better interpretability and improved predictive performance. Our work compares the performance of traditional and integrative gene selection approaches. Moreover, we propose a straightforward approach to integrate external knowledge with traditional gene selection approaches. We introduce a framework enabling the automatic external knowledge integration, gene selection, and evaluation. Evaluation results prove our framework to be a useful tool for evaluation and show that integration of external knowledge improves overall analysis results.},
  language  = {en}
}
@article{KowarkUflackerZeier2012,
  author    = {Kowark, Thomas and Uflacker, Matthias and Zeier, Alexander},
  title     = {Towards a shared platform for virtual collaboration monotoring in design research},
  year      = {2012},
  language  = {en}
}
@article{UflackerKowarkZeier2011,
  author    = {Uflacker, Matthias and Kowark, Thomas and Zeier, Alexander},
  title     = {An instrument for real-time design interaction capture},
  isbn      = {978-3-642-13756-3},
  year      = {2011},
  language  = {en}
}
@article{Uflacker2010,
  author    = {Uflacker, Matthias},
  title     = {Computational analysis of virtual team collaboration in teh early stages of engineering design},
  isbn      = {978-3-86956-036-6},
  year      = {2010},
  language  = {en}
}