2 Search Results for "Koch, Uwe"

Document
DOI: 10.4230/OASIcs.KiVS.2011.201
Automated generic integration of flight logbook data into aircraft maintenance systems

Authors: Oliver Hunte, Carsten Kleiner, Uwe Koch, Arne Koschel, and Björn Koschel

Published in: OASIcs, Volume 17, 17th GI/ITG Conference on Communication in Distributed Systems (KiVS 2011)

Abstract
The automated transfer of flight logbook information from aircrafts into aircraft maintenance systems leads to reduced ground and maintenance time and is thus desirable from an economical point of view. Until recently, flight logbooks have not been managed electronically in aircrafts or at least the data transfer from aircraft to ground maintenance system has been executed manually. Latest aircraft types such as the Airbus A380 or the Boeing 787 do support an electronic logbook and thus make an automated transfer possible. A generic flight logbook transfer system must deal with different data formats on the input side -- due to different aircraft makes and models -- as well as different, distributed aircraft maintenance systems for different airlines as aircraft operators. This article contributes the concept and top level distributed system architecture of such a generic system for automated flight log data transfer. It has been developed within a joint industry and applied research project. The architecture has already been successfully evaluated in a prototypical implementation.
Cite as

Oliver Hunte, Carsten Kleiner, Uwe Koch, Arne Koschel, and Björn Koschel. Automated generic integration of flight logbook data into aircraft maintenance systems. In 17th GI/ITG Conference on Communication in Distributed Systems (KiVS 2011). Open Access Series in Informatics (OASIcs), Volume 17, pp. 201-204, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{hunte_et_al:OASIcs.KiVS.2011.201,
  author =	{Hunte, Oliver and Kleiner, Carsten and Koch, Uwe and Koschel, Arne and Koschel, Bj\"{o}rn},
  title =	{{Automated generic integration of flight logbook data into aircraft maintenance systems}},
  booktitle =	{17th GI/ITG Conference on Communication in Distributed Systems (KiVS 2011)},
  pages =	{201--204},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-27-9},
  ISSN =	{2190-6807},
  year =	{2011},
  volume =	{17},
  editor =	{Luttenberger, Norbert and Peters, Hagen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.KiVS.2011.201},
  URN =		{urn:nbn:de:0030-drops-29730},
  doi =		{10.4230/OASIcs.KiVS.2011.201},
  annote =	{Keywords: system integration, data mapping, XML, aerospace engineering, generic interface, configurable mapping}
}
Document
DOI: 10.4230/DagSemProc.08421.10
Outlier detection and ranking based on subspace clustering

Authors: Thomas Seidl, Emmanuel Müller, Ira Assent, and Uwe Steinhausen

Published in: Dagstuhl Seminar Proceedings, Volume 8421, Uncertainty Management in Information Systems (2009)

Abstract
Detecting outliers is an important task for many applications including fraud detection or consistency validation in real world data. Particularly in the presence of uncertain data or imprecise data, similar objects regularly deviate in their attribute values. The notion of outliers has thus to be defined carefully. When considering outlier detection as a task which is complementary to clustering, binary decisions whether an object is regarded to be an outlier or not seem to be near at hand. For high-dimensional data, however, objects may belong to different clusters in different subspaces. More fine-grained concepts to define outliers are therefore demanded. By our new OutRank approach, we address outlier detection in heterogeneous high dimensional data and propose a novel scoring function that provides a consistent model for ranking outliers in the presence of different attribute types. Preliminary experiments demonstrate the potential for successful detection and reasonable ranking of outliers in high dimensional data sets.
Cite as

Thomas Seidl, Emmanuel Müller, Ira Assent, and Uwe Steinhausen. Outlier detection and ranking based on subspace clustering. In Uncertainty Management in Information Systems. Dagstuhl Seminar Proceedings, Volume 8421, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{seidl_et_al:DagSemProc.08421.10,
  author =	{Seidl, Thomas and M\"{u}ller, Emmanuel and Assent, Ira and Steinhausen, Uwe},
  title =	{{Outlier detection and ranking based on subspace clustering}},
  booktitle =	{Uncertainty Management in Information Systems},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8421},
  editor =	{Christoph Koch and Birgitta K\"{o}nig-Ries and Volker Markl and Maurice van Keulen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagSemProc.08421.10},
  URN =		{urn:nbn:de:0030-drops-19344},
  doi =		{10.4230/DagSemProc.08421.10},
  annote =	{Keywords: Outlier detection, outlier ranking, subspace clustering, data mining}
}
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