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Treffer A.,Enterprise Platform and Integration Concepts
Technische Berichte des Hasso-Plattner-Instituts fur Softwaresystemtechnik an der Universitat Potsdam | Year: 2013

This report outlines research ideas that were formed over the last few months. We propose to use an in-memory database to store and access execution traces, especially of unit tests. Based on this, IDEs can be extended to search and visualize the data in the context of its source. This might help to increase developer productivity when understanding code or searching for bugs. Source


Treffer A.,Enterprise Platform and Integration Concepts
Technische Berichte des Hasso-Plattner-Instituts fur Softwaresystemtechnik an der Universitat Potsdam | Year: 2014

This paper presents how Omniscient Debuggers allow the advanced navigation of a program execution, which greatly improves the time required to find the source of a failure. Source


Schapranow M.-P.,Enterprise Platform and Integration Concepts | Plattner H.,Enterprise Platform and Integration Concepts
Proceedings - 2013 IEEE International Conference on Big Data, Big Data 2013 | Year: 2013

Costs and time required for sequencing of DNA and RNA declined through use of next generation sequencing technology, e.g. up to 30-times coverage reads are generated in less than two days. However, its interpretation and analysis is still a time-consuming process potentially taking weeks. In this work, we present a completely new architecture for processing and analyzing genome data. It builds on the in-memory database technology to eliminate time-consuming file-based data operations and to enable real-time data analysis. We found out that the use of in-memory technology as an integral component for genome data processing and its analysis significantly reduces time and costs to obtain relevant results, e.g. in the course of personalized medicine. © 2013 IEEE. Source


Schapranow M.-P.,Enterprise Platform and Integration Concepts | Hager F.,Enterprise Platform and Integration Concepts | Fahnrich C.,Enterprise Platform and Integration Concepts | Ziegler E.,SAP | Plattner H.,Enterprise Platform and Integration Concepts
International Journal on Advances in Life Sciences | Year: 2014

Latest medical diagnostics, such as genome sequencing, generate increasing amounts of "big medical data". Healthcare providers and medical experts are facing challenges outside of their original field of expertise, such as data processing, data analysis, or data interpretation. Specific software tools optimized for the use by the target audience as well as systematic processes for data processing and analysis in clinical and research environments are still missing. Our work focuses on the integration of data acquired from latest next-generation sequencing technology, its systematical processing, and instant analysis for researchers and clinicians in the course of precision medicine.We focus on the medical field of oncology to optimize the time spent on acquiring, combining, and analyzing relevant data to make well-informed treat-ment decisions based on latest international knowledge. We share our research results on building a distributed in-memory computing platform for genome data processing, which enables instantaneous analysis of genome data for the first time. For that, we present our technical foundation and building blocks of in-memory technology as well as business processes to integrate genome data analysis in the clinical routine. © 2014 by authors. Source


Schapranow M.-P.,Enterprise Platform and Integration Concepts | Klinghammer K.,Charite - Medical University of Berlin | Fahnrich C.,Enterprise Platform and Integration Concepts | Plattner H.,Enterprise Platform and Integration Concepts
2014 IEEE 16th International Conference on e-Health Networking, Applications and Services, Healthcom 2014 | Year: 2015

Latest medical diagnostics generate increasing amounts of big medical data. Specific software tools optimized for the use by healthcare experts and researchers as well as systematic processes for data processing and analysis in clinical and research environments are still missing. Our work focuses on the integration of high-throughput next-generation sequencing data and its systematic processing and its instantaneous analysis to use them in the course of precision medicine. We share our research results on designing a generic research process for drug response analysis including specific software tools built on top of our distributed in-memory computing platform for processing of big medical data. Furthermore, we present our technical foundations as well as process aspects of integrating and combining heterogeneous data sources, such as genome, patient, and experimental data. © 2014 IEEE. Source

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