@article{NevesLeser2015,
  author    = {Neves, Mariana and Leser, Ulf},
  title     = {Question answering for Biology},
  series = {Methods : focusing on rapidly developing techniques},
  volume    = {74},
  journal   = {Methods : focusing on rapidly developing techniques},
  publisher = {Elsevier},
  address   = {San Diego},
  issn      = {1046-2023},
  doi       = {10.1016/j.ymeth.2014.10.023},
  pages     = {36 -- 46},
  year      = {2015},
  abstract  = {Biologists often pose queries to search engines and biological databases to obtain answers related to ongoing experiments. This is known to be a time consuming, and sometimes frustrating, task in which more than one query is posed and many databases are consulted to come to possible answers for a single fact. Question answering comes as an alternative to this process by allowing queries to be posed as questions, by integrating various resources of different nature and by returning an exact answer to the user. We have surveyed the current solutions on question answering for Biology, present an overview on the methods which are usually employed and give insights on how to boost performance of systems in this domain. (C) 2014 Elsevier Inc. All rights reserved.},
  language  = {en}
}
@misc{HesseMatthiesSinzigetal.2019,
  author    = {Hesse, G{\"u}nter and Matthies, Christoph and Sinzig, Werner and Uflacker, Matthias},
  title     = {Adding Value by Combining Business and Sensor Data},
  series = {Database Systems for Advanced Applications},
  volume    = {11448},
  journal   = {Database Systems for Advanced Applications},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-18590-9},
  issn      = {0302-9743},
  doi       = {10.1007/978-3-030-18590-9_80},
  pages     = {528 -- 532},
  year      = {2019},
  abstract  = {Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data's full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.},
  language  = {en}
}