@phdthesis{Heinze2015,
  author    = {Heinze, Theodor},
  title     = {Analyse von Patientendaten und Entscheidungsunterst{\"u}tzung in der Telemedizin},
  school      = {Universit{\"a}t Potsdam},
  pages     = {173},
  year      = {2015},
  language  = {de}
}
@article{LindauerHoosHutteretal.2015,
  author    = {Lindauer, Marius and Hoos, Holger H. and Hutter, Frank and Schaub, Torsten},
  title     = {An automatically configured algorithm selector},
  series = {The journal of artificial intelligence research},
  volume    = {53},
  journal   = {The journal of artificial intelligence research},
  publisher = {AI Access Foundation},
  address   = {Marina del Rey},
  issn      = {1076-9757},
  pages     = {745 -- 778},
  year      = {2015},
  abstract  = {Algorithm selection (AS) techniques - which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently - have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. This holds specifically for the machine learning techniques that form the core of current AS procedures, and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single, highly-parameterized algorithm framework. Our approach, dubbed AutoFolio, allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods. We demonstrate AutoFolio can significantly improve the performance of claspfolio 2 on 8 out of the 13 scenarios from the Algorithm Selection Library, leads to new state-of-the-art algorithm selectors for 7 of these scenarios, and matches state-of-the-art performance (statistically) on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieves average speedup factors between 1.3 and 15.4.},
  language  = {en}
}
@article{PabloAlarconArroyoBordihnetal.2015,
  author    = {Pablo Alarcon, Pedro and Arroyo, Fernando and Bordihn, Henning and Mitrana, Victor and Mueller, Mike},
  title     = {Ambiguity of the multiple interpretations on regular languages},
  series = {Fundamenta informaticae},
  volume    = {138},
  journal   = {Fundamenta informaticae},
  number    = {1-2},
  publisher = {IOS Press},
  address   = {Amsterdam},
  issn      = {0169-2968},
  doi       = {10.3233/FI-2015-1200},
  pages     = {85 -- 95},
  year      = {2015},
  abstract  = {A multiple interpretation scheme is an ordered sequence of morphisms. The ordered multiple interpretation of a word is obtained by concatenating the images of that word in the given order of morphisms. The arbitrary multiple interpretation of a word is the semigroup generated by the images of that word. These interpretations are naturally extended to languages. Four types of ambiguity of multiple interpretation schemata on a language are defined: o-ambiguity, internal ambiguity, weakly external ambiguity and strongly external ambiguity. We investigate the problem of deciding whether a multiple interpretation scheme is ambiguous on regular languages.},
  language  = {en}
}
@article{LiangLiuLiuetal.2015,
  author    = {Liang, Feng and Liu, Yunzhen and Liu, Hai and Ma, Shilong and Schnor, Bettina},
  title     = {A Parallel Job Execution Time Estimation Approach Based on User Submission Patterns within Computational Grids},
  series = {International journal of parallel programming},
  volume    = {43},
  journal   = {International journal of parallel programming},
  number    = {3},
  publisher = {Springer},
  address   = {New York},
  issn      = {0885-7458},
  doi       = {10.1007/s10766-013-0294-1},
  pages     = {440 -- 454},
  year      = {2015},
  abstract  = {Scheduling performance in computational grid can potentially benefit a lot from accurate execution time estimation for parallel jobs. Most existing approaches for the parallel job execution time estimation, however, require ample past job traces and the explicit correlations between the job execution time and the outer layout parameters such as the consumed processor numbers, the user-estimated execution time and the job ID, which are hard to obtain or reveal. This paper presents and evaluates a novel execution time estimation approach for parallel jobs, the user-behavior clustering for execution time estimation, which can give more accurate execution time estimation for parallel jobs through exploring the job similarity and revealing the user submission patterns. Experiment results show that compared to the state-of-art algorithms, our approach can improve the accuracy of the job execution time estimation up to 5.6 \%, meanwhile the time that our approach spends on calculation can be reduced up to 3.8 \%.},
  language  = {en}
}
@incollection{KiyGessnerLuckeetal.2015,
  author    = {Kiy, Alexander and Geßner, Hendrik and Lucke, Ulrike and Gr{\"u}newald, Franka},
  title     = {A Hybrid and Modular Framework for Mobile Campus Applications},
  series = {i-com},
  volume    = {2015},
  booktitle = {i-com},
  number    = {14},
  publisher = {de Gruyter},
  address   = {Berlin},
  issn      = {2196-6826},
  doi       = {10.1515/icom-2015-0016},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {63 -- 73},
  year      = {2015},
  abstract  = {Mobile devices and associated applications (apps) are an indispensable part of daily life and provide access to important information anytime and anywhere. However, the availability of university-wide services in the mobile sector is still poor. If they exist they usually result from individual activities of students and teachers. Mobile applications can have an essential impact on the improvement of students' self-organization as well as on the design and enhancement of specific learning scenarios, though. This article introduces a mobile campus app framework, which integrates central campus services and decentralized learning applications. An analysis of strengths and weaknesses of different approaches is presented to summarize and evaluate them in terms of requirements, development, maintenance and operation. The article discusses the underlying service-oriented architecture that allows transferring the campus app to other universities or institutions at reasonable cost. It concludes with a presentation of the results as well as ongoing discussions and future work},
  language  = {en}
}