%0 Conference Paper %1 noauthororeditor %A Grunzke, Richard %A Hesser, Jürgen %A Starek, Jürgen %A Kepper, Nick %A Gesing, Sandra %A Hardt, Marcus %A Hartmann, Volker %A Kindermann, Stephan %A Potthoff, Jan %A Hausmann, Michael %A Müller-Pfefferkorn, Ralph %A Jäkel, René %B 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing %D 2014 %K bigdata forschungsdaten metadata %P 317-321 %T Device-Driven Metadata Management Solutions for Scientific Big Data Use Cases %X Big Data applications in science are producing huge amounts of data, which require advanced processing, handling, and analysis capabilities. For the organization of large scale data sets it is essential to annotate these with metadata, index them, and make them easily findable. In this paper we investigate two scientific use cases from biology and photon science, which entail complex situations in regard to data volume, data rates and analysis requirements. The LSDMA project provides an ideal context for this research, combining both innovative R&D on the processing, handling, and analysis level and a wide range of research communities in need of scalable solutions. To facilitate the advancement of data life cycles we present preferred metadata management strategies. In biology the Open Microscopy Environment (OME) and in photon science NeXus/ICAT are presented. We show that these are well suited for the respective data life cycles. To facilitate searching across communities we discuss solutions involving the Open Archive Initiative - Protocol for Metadata Harvesting (OAI-PMH) and Apache Lucene/Solr.

@inproceedings{noauthororeditor, abstract = {Big Data applications in science are producing huge amounts of data, which require advanced processing, handling, and analysis capabilities. For the organization of large scale data sets it is essential to annotate these with metadata, index them, and make them easily findable. In this paper we investigate two scientific use cases from biology and photon science, which entail complex situations in regard to data volume, data rates and analysis requirements. The LSDMA project provides an ideal context for this research, combining both innovative R&D on the processing, handling, and analysis level and a wide range of research communities in need of scalable solutions. To facilitate the advancement of data life cycles we present preferred metadata management strategies. In biology the Open Microscopy Environment (OME) and in photon science NeXus/ICAT are presented. We show that these are well suited for the respective data life cycles. To facilitate searching across communities we discuss solutions involving the Open Archive Initiative - Protocol for Metadata Harvesting (OAI-PMH) and Apache Lucene/Solr.}, added-at = {2017-08-21T13:26:42.000+0200}, author = {Grunzke, Richard and Hesser, Jürgen and Starek, Jürgen and Kepper, Nick and Gesing, Sandra and Hardt, Marcus and Hartmann, Volker and Kindermann, Stephan and Potthoff, Jan and Hausmann, Michael and Müller-Pfefferkorn, Ralph and Jäkel, René}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/23bfc5faff91370571e13df24f7ac713a/diglezakis}, booktitle = {22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing}, description = {Device-Driven Metadata Management Solutions for Scientific Big Data Use Cases - IEEE Xplore Document}, eventtitle = {22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing}, interhash = {3e3f23e73b92925210028a38badf2643}, intrahash = {3bfc5faff91370571e13df24f7ac713a}, keywords = {bigdata forschungsdaten metadata}, pages = {317-321}, timestamp = {2017-08-21T11:26:42.000+0200}, title = {Device-Driven Metadata Management Solutions for Scientific Big Data Use Cases}, year = 2014 }