@book{4336,
  abstract     = {{Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems.

The very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning.

The papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.}},
  editor       = {{Holzinger, Andreas and Röcker, Carsten and Ziefle, Martina}},
  isbn         = {{978-3-319-16225-6}},
  issn         = {{1611-3349}},
  keywords     = {{HCI, ambient assisted living, big data, computational intelligence, context awareness, data centric medicine, decision support, interactive data mining, keyword detection, knoweldge bases, knoweldge discovery, machine learning, medical decision support, medical informatics, natural language processing, pervasive health, smart home, ubiquitous computing, visualization, wearable sensors}},
  pages        = {{275}},
  publisher    = {{Springer}},
  title        = {{{Smart Health: Open Problems and Future Challenges}}},
  doi          = {{10.1007/978-3-319-16226-3}},
  volume       = {{8700}},
  year         = {{2015}},
}

@inproceedings{597,
  abstract     = {{This paper is aimed to discuss current research using data mining techniques and industry statistics in production environments. The general research approach is based on the idea of using data mining processes and techniques of industry statistics to find rare and hidden patterns behind failures of complex components. A case study will be applied to illustrate how the technique is carried out and where the limits of this approach occur. The case study deals with a component supplier of printing machines, which received an increasing number of client complaints, all related to one distinct problem. The observed failures seem to occur only among clients with very high quality standards. The affected component undergoes a very complex production process with several steps in different departments. Every single production unit records data information from multiple process variables and at different points in time. In the beginning there was no understanding of the failure causes in production at all. Therefore a huge amount of production data had to be analyzed to find the pattern that discloses the failure.
The data mining process starts with a first step in which the given data sets are prepared and then cleaned. Followed up by building a prediction model. The aim is to detect the root causes for failures and to predict potential failures in affected components. This paper shows how to use data mining to get the answer on pressing production failures.
}},
  author       = {{Scheideler, Eva and Ahlemeyer-Stubbe, Andrea}},
  booktitle    = {{Production engineering and management : proceedings, 5th international conference, October 1 and 2, 2015, Trieste, Italy}},
  editor       = {{Padoano, Elio and Villmer, Franz-Josef}},
  isbn         = {{978-3-941645-11-0}},
  keywords     = {{Data mining, production failure, multi-variant analysis, multivariate process control, predictive modelling, case study}},
  location     = {{Trieste, Italy}},
  number       = {{1}},
  pages        = {{163--174}},
  publisher    = {{Hochschule Ostwestfalen-Lippe}},
  title        = {{{Data Mining: A Potential Detector to Find Failure in Complex Components}}},
  year         = {{2015}},
}

@inproceedings{2113,
  abstract     = {{In this paper, we sketch an idea for the integration of singleclass support vector machines (SVM) into fuzzy class learning. As result,we  obtain  robust  and  transparent  rule-based  fuzzy  classification  models suitable for online-classification tasks. In particular, the singleclass SVM is employed to extend the applicability of convex fuzzy classifica-tion models to nonconvex datainherent structures. The key point of thisextension  is  the  preservation  of  the  interpretability  for  both,  the  classlearning and the classification process. The feasibility of the approach isdemonstrated in the context of a banknote authentication application.}},
  author       = {{Hempel, Arne-Jens and Hähnel, Holger and Mönks, Uwe and Lohweg, Volker}},
  booktitle    = {{BVAu 2012 - 3. Jahresolloquium "Bildverarbeitung in der Automation" Centrum Industrial IT, Lemgo,}},
  keywords     = {{fuzzy  classification, pattern  recognition, single-class  support vector machine, data mining}},
  publisher    = {{inIT-Institut für industrielle Informationstechnik}},
  title        = {{{SVM-integrated Fuzzy Pattern Classification for Nonconvex Data-Inherent Structures Applied to Banknote Authentication}}},
  year         = {{2012}},
}

@inproceedings{4823,
  abstract     = {{The purpose of this paper is to explore alternative approaches and strategies for email filtering and notification with the rationale of developing an unobtrusive notification interface that can adapt to the user's context.}},
  author       = {{Röcker, Carsten and Bayon, Victor and Memisoglu, Maral and Streitz, Norbert}},
  booktitle    = {{ Proceedings of the 2005 International Conference on Active Media Technology}},
  editor       = {{Tarumi, H. and Li, Y. and Yoshida, T.}},
  isbn         = {{0-7803-9035-0}},
  keywords     = {{Displays, Calendars, Resumes, Filtering, Electronic mail, Personal digital assistants, Filters, Books, Data mining}},
  location     = {{Takamatsu, Kagawa, Japan}},
  pages        = {{137--138}},
  publisher    = {{IEEE}},
  title        = {{{Context-Dependent Email Notification Using Ambient Displays and Mobile Devices}}},
  doi          = {{10.1109/AMT.2005.1505288}},
  year         = {{2005}},
}