Logo des Repositoriums

Auflistung nach Autor:in "Lange, Matthias"

1 - 6 von 6
  • Konferenzbeitrag
    Datenaustausch und Datenintegration zur Modellierung und Analyse metabolischer Netzwerke am Beispiel von Kulturpflanzen
    (Informatik 2009 – Im Focus das Leben, 2009) Weise, Stephan; Colmsee, Christian; Grafahrend-Belau, Eva; Junker, Björn; Klukas, Christian; Lange, Matthias; Scholz, Uwe; Schreiber, Falk
  • Konferenzbeitrag
    Improving search results in life science by recommendations based on semantic information
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2015) Colmsee, Christian; Chen, Jinbo; Schneider, Kerstin; Scholz, Uwe; Lange, Matthias
    The management and handling of big data is a major challenge in the area of life science. Beside the data storage, information retrieval methods have to be adapted to huge data amounts as well. Therefore we present an approach to improve search results in life science by recommendations based on semantic information. In detail we determine relationships between documents by searching for shared database IDs as well as ontology identifiers. We have established a pipeline based on Hadoop allowing a distributed computation of large amounts of textual data. A comparison with the widely used cosine similarity has been performed. Its results are presented in this work as well.
  • Konferenzbeitrag
    Information retrieval in life sciences: The LAILAPS search engine
    (INFORMATIK 2012, 2012) Lange, Matthias; Chen, Jinbo; Scholz, Uwe
    Retrieval and citation of primary data is the important factor in the approaching e-science age. Solving the challenge of building a flexible but homogeneous bioinformatics information retrieval infrastructure to access and query the world life science databases is a crucial factor for an efficient building bioinformatics infrastructure. In this contribution, we demonstrate the use of nine features, which are determined per database entry, in combination with a neural networks as relevance approximator, a novel approach to increase the quality of information retrieval in life science. The implementation of this concept is the LAILAPS search portal. It was designed to support scientist to extract relevant records in a set of millions entries come from private or public databases. In order to consider the fact that data relevance is highly subjective, we support use specific training of several relevance predicting neural networks. In order to make the neural networks working, a continuously training of the networks is performed in background. Here, the system use the user feedback, eighter by conclusions from the user interaction with the query result browser or by manual rating the data quality. Featured by an intuitive web frontend, the user may search over millions of integrated life science data records. The web frontend comprise a browser for relevance ordered query result, a keyword based query system supporting auto completion, spelling suggestions and synonyms. A data browser is provided to inspect and rate matching data records, and finally a recommender system to suggest closely related records. The system is available at http://lailaps.ipk-gatersleben.de
  • Konferenzbeitrag
    Joint workshop on data management for science (DMS)
    (Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2015) Dorok, Sebastian; König-Ries, Birgitta; Lange, Matthias; Rahm, Erhard; Saake, Gunter; Seeger, Bernhard
  • Konferenzbeitrag
    OPTIMAS-DW, MetaCrop and VANTED: A case study for data integration, curation and visualisation in life sciences
    (INFORMATIK 2013 – Informatik angepasst an Mensch, Organisation und Umwelt, 2013) Colmsee, Christian; Czauderna, Tobias; Grafahrend-Belau, Eva; Hartmann, Anja; Lange, Matthias; Mascher, Martin; Weise, Stephan; Scholz, Uwe; Schreiber, Falk
    Since the data volume in life sciences has been growing exponentially in recent years, it is indispensable to develop databases and tools for efficient data integration, curation and visualisation. Focusing on data handling in crop plant research, this paper presents an approach, which combines (i) a data warehouse (OPTIMAS-DW) for integrating experimental data, (ii) an information system (MetaCrop) for manually curated biochemical pathways, and (iii) a visualisation software (VANTED) for integrated data visualisation. The functionality and usability of the concept will be illustrated by a use case.
  • Textdokument
    Supporting Security in Industrial Automation and Control Systems using Domain-Specific Modelling
    (INFORMATIK 2021, 2021) Altschaffel, Robert; Hempel, Ivo; Keil, Oliver; Schindler, Josef; Szemkus, Martin; Dittmann, Jana; Lange, Matthias; Waedt, Karl; Ding, Yongjian
    This paper explores how domain specific modelling can be used to support the identification of potential vulnerabilities and risks in Industrial Automation and Control Systems (IACS) to enhance security by enabling a mitigation of these vulnerabilities. This approach can be used to support already deployed IACS or to include Security-by-Design and Security Defence-in-Depth principles in the planning of future facilities. This paper explores the requirements for such a modelling approach including domain and security specific aspects. Three interlinked aspects of IACS which require different modelling approaches are identified leading to three distinct types of models: Infrastructure, cyber-process, and physical process. These three types are relevant for different attack vectors and to judge the potential impact of any attack. This paper shows examples for these three models and how these models can be used to identify vulnerabilities with the aim to close them.