@inproceedings{BenderGrum2016,
  author    = {Bender, Benedict and Grum, Marcus},
  title     = {Entwicklung eines Architekturkonzepts zum flexiblen Einsatz von Analytics},
  series = {Proceedings INFORMATIK - Jahrestagung der Gesellschaft f{\"u}r Informatik e.V. ; Lecture Notes in Informatics (LNI)},
  booktitle = {Proceedings INFORMATIK - Jahrestagung der Gesellschaft f{\"u}r Informatik e.V. ; Lecture Notes in Informatics (LNI)},
  number    = {P259},
  publisher = {Gesellschaft f{\"u}r Informatik e.V.},
  address   = {Bonn},
  pages     = {815 -- 824},
  year      = {2016},
  abstract  = {Die optimale Dimensionierung von IT-Hardware stellt Entscheider aufgrund der stetigen Weiterentwicklung zunehmend vor Herausforderungen. Dies gilt im Speziellen auch f{\"u}r Analytics-Infrastrukturen, die zunehmend auch neue Software zur Analyse von Daten einsetzen, welche in den Ressourcenanforderungen stark variieren. Damit eine flexible und gleichzeitig effiziente Gestaltung von Analytics-Infrastrukturen erreicht werden kann, wird ein dynamisch arbeitendes Architekturkonzept vorgeschlagen, das Aufgaben auf Basis einer systemspezifischen Entscheidungsmaxime mit Hilfe einer Eskalationsmatrix verteilt und hierf{\"u}r Aufgabencharakteristiken sowie verf{\"u}gbare Hardwareausstattungen entsprechend ihrer Auslastung ber{\"u}cksichtigt.},
  language  = {de}
}
@article{GrumBenderAlfaetal.2018,
  author    = {Grum, Marcus and Bender, Benedict and Alfa, A. S. and Gronau, Norbert},
  title     = {A decision maxim for efficient task realization within analytical network infrastructures},
  series = {Decision support systems : DSS ; the international journal},
  volume    = {112},
  journal   = {Decision support systems : DSS ; the international journal},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0167-9236},
  doi       = {10.1016/j.dss.2018.06.005},
  pages     = {48 -- 59},
  year      = {2018},
  abstract  = {Faced with the increasing needs of companies, optimal dimensioning of IT hardware is becoming challenging for decision makers. In terms of analytical infrastructures, a highly evolutionary environment causes volatile, time dependent workloads in its components, and intelligent, flexible task distribution between local systems and cloud services is attractive. With the aim of developing a flexible and efficient design for analytical infrastructures, this paper proposes a flexible architecture model, which allocates tasks following a machine-specific decision heuristic. A simulation benchmarks this system with existing strategies and identifies the new decision maxim as superior in a first scenario-based simulation.},
  language  = {en}
}
@article{HauptBenderFabianetal.2018,
  author    = {Haupt, Johannes and Bender, Benedict and Fabian, Benjamin and Lessmann, Stefan},
  title     = {Robust identification of email tracking},
  series = {European Journal of Operational Research},
  volume    = {271},
  journal   = {European Journal of Operational Research},
  number    = {1},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0377-2217},
  doi       = {10.1016/j.ejor.2018.05.018},
  pages     = {341 -- 356},
  year      = {2018},
  abstract  = {Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking invades user privacy in that email tracking techniques gather data without user consent or awareness. Striving to increase privacy in email communication, this paper develops a detection engine to be the core of a selective tracking blocking mechanism in the form of three contributions. First, a large collection of email newsletters is analyzed to show the wide usage of tracking over different countries, industries and time. Second, we propose a set of features geared towards the identification of tracking images under real-world conditions. Novel features are devised to be computationally feasible and efficient, generalizable and resilient towards changes in tracking infrastructure. Third, we test the predictive power of these features in a benchmarking experiment using a selection of state-of-the-art classifiers to clarify the effectiveness of model-based tracking identification. We evaluate the expected accuracy of the approach on out-of-sample data, over increasing periods of time, and when faced with unknown senders. (C) 2018 Elsevier B.V. All rights reserved.},
  language  = {en}
}