Home | Legals | Sitemap | KIT

Publications

Helmholtz Portfolio Theme LSDMA

[Agu15]

Alvaro Aguilera et al. “Towards an Industry Data Gateway: An Integrated Platform for the Analysis of Wind Turbine Data”. In: Science Gateways (IWSG), 2015 7th International Workshop on. June 2015, pp. 62–66. doi: 10.1109/IWSG.2015.8.

[Ame14]

Parinaz Ameri et al. “On the Application and Performance of MongoDB for Climate Satellite Data”. In: 13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2014, Beijing, China, September 24-26, 2014. 2014, pp. 652–659. doi: 10.1109/TrustCom.2014.84. url: http://dx.doi.org/10.1109/TrustCom.2014.84.

[Ame16a]

P. Ameri. “Chapter 6 - Database Techniques for Big Data”. In: Big Data. Ed. by Rajkumar Buyya, Rodrigo N. Calheiros, and Amir Vahid Dastjerdi. Morgan Kaufmann, 2016, pp. 139–159. isbn: 978-0-12-805394-2. doi: 10.1016/B978-0-12-805394-2.00006-4. url: http://www.sciencedirect.com/science/article/pii/B9780128053942000064.

[Ame16b]

P. Ameri. “On a self-tuning index recommendation approach for databases”. In: 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW). May 2016, pp. 201–205. doi: 10.1109/ICDEW.2016.7495648.

[Ame16c]

Parinaz Ameri. “Challenges of Index Recommendation for Databases”. In: Proceedings of the 28th GI-Workshop on Foundations of Databases (Grundlagen von Datenbanken),NÃűrten Hardenberg, Germany, May 24-27, 2016. May 2016, pp. 10–14. url: http://ceur-ws.org/Vol-1594/paper3.pdf.

[Ame16d]

P. Ameri et al. “NoWog: A Workload Generator for Database Performance Benchmarking”. In: 2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). Aug. 2016, pp. 666–673. doi: 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.120.

[AMS15]

P. Ameri, J. Meyer, and A. Streit. “On a new approach to the index selection problem using mining algorithms”. In: 2015 IEEE International Conference on Big Data (Big Data). Oct. 2015, pp. 2801–2810. doi: 10.1109/BigData.2015.7364084.

[Arg13]

Lars Arge et al. “On (Dynamic) Range Minimum Queries in External Memory”. In: Algorithms and Data Structures: 13th International Symposium, WADS 2013, London, ON, Canada, August 12-14, 2013. Proceedings. Ed. by Frank Dehne, Roberto Solis-Oba, and Jörg-Rüdiger Sack. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 37–48. isbn: 978-3-642-40104-6. doi: 10.1007/978-3-642-40104-6_4. url: http://dx.doi.org/10.1007/978-3-642-40104-6_4.

[AS15]

Yaroslav Akhremtsev and Peter Sanders. “Fast Parallel Operations on Search Trees”. In: CoRR abs/1510.05433 (2015). url: http://arxiv.org/abs/1510.05433.

[AS16]

Yaroslav Akhremtsev and Peter Sanders. “Fast Parallel Operations on Search Trees”. In: 2016 IEEE 23rd International Conference on High Performance Computing (HiPC). Dec. 2016, pp. 291–300. doi: 10.1109/HiPC.2016.042.

[ASS15]

Yaroslav Akhremtsev, Peter Sanders, and Christian Schulz. “(Semi-)External Algorithms for Graph Partitioning and Clustering”. In: 17th Workshop on Algorithm Engineering and Experiments (ALENEX). SIAM, 2015, pp. 33–43. doi: 10.1137/1.9781611973754.4.

[Axt15]

Michael Axtmann et al. “Practical Massively Parallel Sorting”. In: 27th ACM Symposium on Parallelism in Algorithms and Architectures, (SPAA). 2015. doi: 10.1145/2755573.2755595.

[Bas16]

Hannah Bast et al. “Route Planning in Transportation Networks”. In: Algorithm Engineering. Ed. by Lasse Kliemann and Peter Sanders. Vol. 9230. LNCS. Springer, 2016.

[Ben16]

Krzysztof Benedyczak et al. “UNICORE 7 - Middleware Services for Distributed and Federated Computing”. In: International Conference on High Performance Computing Simulation (HPCS). 2016. doi: 10.1109/HPCSim.2016.7568392. url: http://dx.doi.org/10.1109/HPCSim.2016.7568392.

[Ber15]

A. A. Bersenev et al. “An approach for integrating kerberized non web-based services with web-based identity federations”. In: Proceedings of the 10th International Conference on Software Paradigm Trends, ICSOFT 2015. doi : 10 . 5220 / 0005509901440150. SCITEPRESS. 2015, pp. 144–150.

[BFO13]

Timo Bingmann, Johannes Fischer, and Vitaly Osipov. “Inducing Suffix and Lcp Arrays in External Memory”. In: 15th Workshop on Algorithm Engineering and Experiments, (ALENEX). SIAM, 2013, pp. 88–102. doi: 10.1137/1.9781611972931.8. url: http://dx.doi.org/10.1137/1.9781611972931.8.

[BKS15]

Timo Bingmann, Thomas Keh, and Peter Sanders. “A bulk-parallel priority queue in external memory with STXXL”. In: 14th Symposium on Experimental Algorithms (SEA). LNCS. Springer, 2015. doi: 10.1007/978-3-319-20086-6\_3.

[Bla15]

Thomas Blank et al. “The Role of Energy Status Data in Solar Power Plants with Li-Ion Batteries”. In: Energy, Science and Technology 2015 (2015), p. 195.

[Ble12]

M. Blessing et al. “Kilovoltage beam model for flat panel imaging system with bow-tie filter for scatter prediction and correction”. In: Physica Medica 28.2 (2012), pp. 134–143. issn: 1120-1797. doi: 10.1016/j.ejmp.2011.04.001. url: //www.sciencedirect.com/science/article/pii/S1120179711000275.

[BS14]

D. Becker and A. Streit. “A neural network based pre–selection of big data in photon science”. In: BDCloud. 2014, pp. 71–76. doi: 10.1109/BDCloud.2014.42.

[BS15a]

D. Becker and A. Streit. Localization of signal peaks in photon science imaging. Tech. rep. UKSim, 2015. doi: 10.1109/uksim.2015.35.

[BS15b]

D. Becker and A. Streit. “Real–time signal identification in big data streams”. In: Bragg–spot localization in photon science. 2015, pp. 611–616. doi: 10.1109/HPCSim.2015.723710.

[BS16a]

D Becker and A Streit. “Real-time Signal identification in Photon Science Imaging”. In: IJSSST (2016).

[BS16b]

D. Becker and A. Streit. “Realtime–Processing of Nanocrystallography Images”. In: UKSim-AMSS. 2016. doi: 10.1109/UKSim.2016.20.

[Bus16]

Hannah Busch et al. “QuantiCod revisited. Neue MÃűglichkeiten zur Analyse mittelalterlicher Handschriften”. In: Book of Abstracts DHd 2015 “ Von Daten zu Erkenntnissen”. Graz, Feb. 2016. url: http://gams.uni-graz.at/o:dhd2015.abstracts-gesamt.

[Cha14b]

Konstantinos Chasapis et al. “Evaluating Power-Performace Benefits of Data Compression in HPC Storage Servers”. In: IARIA Conference. Ed. by Steffen Fries and Petre Dini. Chamonix, France: IARIA XPS Press, Apr. 2014, pp. 29–34. isbn: 978-1-61208-332-2.

[Cha15a]

Swati Chandna et al. “Software workflow for the automatic tagging of medieval manuscript images (SWATI)”. In: SPIE/ IS&T Electronic Imaging. International Society for Optics and Photonics. 2015, pp. 940206–940206.

[Deb12]

M. Debatin et al. “CT reconstruction from few-views by Anisotropic Total Variation minimization”. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC). Oct. 2012, pp. 2295–2296. doi: 10.1109/NSSMIC.2012.6551521.

[Die15]

Stefan Dietrich et al. ASAP3: New Data Taking and Analysis Infrastructure for PETRA III. HEPiX Workshop, Univ. Oxford/UK. Deutsches Elektronen Synchrotron DESY, 2015. url: https://indico.cern.ch/event/346931/contributions/817807/attachments/684652/940445/Dietrich%5C_ASAP3%5C_new%5C_data%5C_taking%5C_infrastructure.pdf.

[Die16]

Stefan Dietrich et al. ASAP3: Status Update and Activities for XFEL. HEPiX Workshop, DESY/Zeuthen. Deutsches Elektronen Synchrotron DESY, 2016. url: https://indico.cern.ch/event/466991/contributions/1143592/attachments/1260614/1862916/Dietrich%5C_ASAP3%5C_Status%5C_Update%5C_and%5C_XFEL%5C_Activities.pdf.

[Emb14]

Michael Embach et al. “eCodicology-Algorithms for the Automatic Tagging of Medieval Manuscripts”. In: The Linked TEI: Text Encoding in the Web (2014), p. 172.

[End14]

Florian Enders et al. Nach der Digitalisierung. Zur computergestÃijtzten Erschlieçung mittelalterlicher Handschriften. 2014. Chap. Nach der Digitalisierung. Zur computergestÃijtzten Erschlieçung mittelalterlicher Handschriften.

[Ert16]

Benjamin Ertl et al. “Identity Harmonization for Federated HPC, Grid and Cloud Services”. In: Proceedings of the 2016 International Conference on High Performance Computing and Simulation. IEEE. 2016, pp. 621–627.

[Gar11a]

A.O. Garcia et al. “Data-intensive analysis for scientific experiments at the Large Scale Data Facility”. In: Large Data Analysis and Visualization (LDAV), 2011 IEEE Symposium on. Oct. 2011, pp. 125–126. doi: 10.1109/LDAV.2011.6092331.

[Ges12a]

Sandra Gesing et al. “A Science Gateway Getting Ready for Serving the International Molecular Simulation Community”. In: Proceedings of Science PoS(EGICF12-EMITC2)050 (Mar. 2012). url: http://pos.sissa.it/archive/conferences/162/050/EGICF12-EMITC2%5C_050.pdf.

[Ges12b]

Sandra Gesing et al. “A Single Sign-On Infrastructure for Science Gateways on a Use Case for Structural Bioinformatics”. In: Journal of Grid Computing 10.4 (2012), pp. 769–790. issn: 1570-7873. doi: 10.1007/s10723-012-9247-y. url: http://link.springer.com/article/10.1007%2Fs10723-012-9247-y.

[Ges12c]

Sandra Gesing et al. “A Single Sign-On Infrastructure for Science Gateways on a Use Case for Structural Bioinformatics”. In: Journal of Grid Computing 10.4 (2012), pp. 769–790. issn: 1572-9184. doi: 10.1007/s10723-012-9247-y.

[Ges12d]

S. Gesing et al. “A Science Gateway Getting Ready for Serving the International Molecular Simulation Community”. In: EGI Community Forum 2012 / EMI Second Technical Conference. Mar. 2012. url: http://pos.sissa.it/archive/conferences/162/050/EGICF12-EMITC2_050.pdf.

[Ges14]

Sandra Gesing et al. “Molecular Simulation Grid (MosGrid): A Science Gateway Tailored to the Molecular Simulation Community”. English. In: Science Gateways for Distributed Computing Infrastructures. Springer International Publishing, 2014, pp. 151–165. isbn: 978-3-319-11267-1. doi: 10.1007/978-3-319-11268-8_11.

[Ges15a]

Sandra Gesing et al. “Challenges and Modifications for Creating a MoSGrid Science Gateway for US and European Infrastructures”. In: Science Gateways (IWSG), 2015 7th International Workshop on. June 2015, pp. 73–79. doi: 10.1109/IWSG.2015.10.

[Ges15b]

Sandra Gesing et al. “Science Gateways - Leveraging Modeling and Simulations in HPC Infrastructures via Increased Usability”. In: International Conference on High Performance Computing Simulation (HPCS). July 2015, pp. 19–26. doi: 10.1109/HPCSim.2015.7237017.

[Gie17]

Andre Giesler et al. “UniProv: A flexible Provenance Tracking System for UNICORE”. In: High-Performance Scientific Computing: First JARA-HPC Symposium, JHPCS 2016, Aachen, Germany, October 4–5, 2016, Revised Selected Papers. Springer International Publishing, 2017, pp. 233–242. isbn: 978-3-319-53862-4. doi: 10.1007/978-3-319-53862-4_20.

[GM14]

Richard Grunzke and Ralph MÃijller-Pfefferkorn. “Certificate-free User-friendly HPC Access with UNICORE”. In: UNICORE Summit 2014 Proceedings. Vol. 26. IAS Series. 2014, pp. 23–30. isbn: 978-3-95806-004-3.

[Gru12]

Richard Grunzke et al. “A Data Driven Science Gateway for Computational Workflows”. In: UNICORE Summit 2012 Proceedings. Vol. 15. IAS Series. 2012, pp. 35–49. isbn: 978–3-89336-829-7. url: http://hdl.handle.net/2128/4705.

[Gru14a]

Richard Grunzke et al. “Best Practices for Metadata Management in LSDMA”. In: Large-Scale Data Management and Analysis (LSDMA) - Big Data in Science. Karlsruhe, 2014, pp. 32–33. doi: 10.5445/IR/1000043270. url: http://dx.doi.org/10.5445/IR/1000043270.

[Gru14b]

Richard Grunzke et al. “Device-driven metadata management solutions for scientific big data use cases”. In: 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. IEEE. 2014, pp. 317–321.

[Gru14c]

Richard Grunzke et al. “Improved Resilience and Usability for Science Gateway Infrastructures via Integrated Virtual Organizations”. In: EGI Community Forum 2014. 2014.

[Gru14d]

Richard Grunzke et al. “Standards-based Metadata Management for Molecular Simulations”. In: Concurrency and Computation: Practice and Experience 26(10) (2014), pp. 1744–1759. issn: 1532-0634. doi: 10.1002/cpe.3116.

[Gru14e]

Richard Grunzke et al. “Towards Generic Metadata Management in Distributed Science Gateway Infrastructures”. In: IEEE/ ACM CCGrid 2014 (14th International Symposium on Cluster, Cloud and Grid Computing). Chicago, IL, US, May 2014, pp. 566–570. doi: 10.1109/CCGrid.2014.98.

[Gru15]

Richard Grunzke et al. “Managing Complexity in Distributed Data Life Cycles Enhancing Scientific Discovery”. In: IEEE 11th International Conference on e-Science. Aug. 2015, pp. 371–380. doi: 10.1109/eScience.2015.72.

[Gru16a]

Richard Grunzke. “Generic Metadata Handling in Scientific Data Life Cycles”. PhD thesis. Doctoral Thesis, Technische UniversitÃďt Dresden, Apr. 2016. url: http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-202070.

[Gru16b]

Richard Grunzke et al. “Metadata Management in the MoSGrid Science Gateway - Evaluation and the Expansion of Quantum Chemistry Support”. In: Journal of Grid Computing (2016), pp. 1–13. issn: 1572-9184. doi: 10.1007/s10723-016-9362-2. url: http://dx.doi.org/10.1007/s10723-016-9362-2.

[Gru16c]

Richard Grunzke et al. “Towards a Metadata-driven Multi-community Research Data Management Service”. In: Proceedings of the 8th International Workshop on Science Gateways (IWSG 2016). Vol. 1871. CEUR-WS, 2016. url: http://ceur-ws.org/Vol-1871/.

[H16]

Heçling H. Monte Carlo pathfinding in radio astronomy. Tech. rep. GLOWSKA, 2016.

[Hag14]

B. Hagemeier et al. “A Workflow for Polarized Light Imaging Using UNICORE Workflow Services”. In: UNICORE Summit. Poznan, Poland, 2014.

[Har14]

M. Hardt et al. “Combining the X.509 and the SAML Federated Identity Management Systems”. In: Proceedings of the 2nd International Conference, SNDS 2014 on Recent Trends in Computer Networks and Distributed Systems Security. doi : 10 . 1007 / 978 - 3 - 642 - 54525 - 2 _ 36. Springer. 2014, pp. 404–415.

[Her12a]

Sonja Herres-Pawlis et al. “Workflow-enhanced Conformational Analysis of Guanidine Zinc Complexes via a Science Gateway”. In: Studies in Health Technology and Informatics, 175:142-151, IOS Press. 2012. doi: 10.3233/978-1-61499-054-3-142.

[Her12b]

Sonja Herres-Pawlis et al. “Workflow-enhanced conformational analysis of guanidine zinc complexes via a science gateway”. In: Volume 175: HealthGrid Applications and Technologies Meet Science Gateways for Life Sciences. Studies in Health Technology and Informatics. 2012, pp. 142–151. doi: 10.3233/978-1-61499-054-3-142.

[Her13a]

Sonja Herres-Pawlis et al. “Orbital Analysis of Oxo and Peroxo Dicopper Complexes via Quantum Chemical Workflows in MoSGrid”. In: Proceedings of the International Workshop on Scientific Gateways 2013 (IWSG). 2013. url: http://ceur-ws.org/Vol-993/paper3.pdf.

[Her13b]

Sonja Herres-Pawlis et al. “User-Friendly Workflows in Quantum Chemistry”. In: Proceedings of the International Workshop on Scientific Gateways 2013 (IWSG). 2013. url: http://ceur-ws.org/Vol-993/paper14.pdf.

[Her13c]

S. Herres-Pawlis et al. “User-friendly metaworkflows in quantum chemistry”. In: 2013 IEEE International Conference on Cluster Computing (CLUSTER). Sept. 2013, pp. 1–3. doi: 10.1109/CLUSTER.2013.6702700.

[Her14a]

Sonja Herres-Pawlis et al. “Expansion of Quantum Chemical Metadata for Workflows in the MoSGrid Science Gateway”. In: Science Gateways (IWSG), 2014 6th International Workshop on. June 2014, pp. 67–72. doi: 10.1109/IWSG.2014.18.

[Her14b]

Sonja Herres-Pawlis et al. “Meta-Metaworkflows for Combining Quantum Chemistry and Molecular Dynamics in the MoSGrid Science Gateway”. In: 6th International Workshop on Science Gateways (IWSG). June 2014, pp. 73–78. doi: 10.1109/IWSG.2014.20.

[Her15a]

Sonja Herres-Pawlis et al. “Multi-layer Meta-metaworkflows for the Evaluation of Solvent and Dispersion Effects in Transition Metal Systems Using the MoSGrid Science Gateways”. In: Science Gateways (IWSG), 2015 7th International Workshop on. June 2015, pp. 47–52. doi: 10.1109/IWSG.2015.13.

[Her15b]

Sonja Herres-Pawlis et al. “Quantum Chemical Meta-Workflows in MoSGrid”. In: Concurrency and Computation: Practice and Experience 27.2 (2015), pp. 344–357. issn: 1532-0634. doi: 10.1002/cpe.3292.

[HGH14]

Alexander Hoffmann, Richard Grunzke, and Sonja Herres-Pawlis. “Insights into the Influence of Dispersion Correction in the Theoretical Treatment of Guanidine-Quinoline Copper(I) Complexes”. In: Journal of Computational Chemistry 35.27 (2014), pp. 1943–1950. issn: 1096-987X. doi: 10.1002/jcc.23706.

[Hof13]

Alexander Hoffmann et al. “User-friendly Metaworkflows in Quantum Chemistry”. In: IEEE International Conference on Cluster Computing (CLUSTER). IEEE. 2013, pp. 1–3. doi: 10.1109/CLUSTER.2013.6702700.

[JÃďk15]

RenÃľ JÃďkel et al. “Architectural Implications for Exascale based on Big Data Workflow Requirements”. English. In: Big Data and High Performance Computing. Vol. 26. Advances in Parallel Computing. IOS Press, 2015, pp. 101–113. isbn: 978-1-61499-582-1. doi: 10.3233/978-1-61499-583-8-101.

[Jej12]

Thomas Jejkal et al. “LAMBDA – The LSDF Execution Framework for Data Intensive Applications”. In: Parallel, Distributed and Network-Based Processing (PDP), 20th Euromicro International Conference on. submitted. 2012, pp. 213–220. doi: 10.1109/PDP.2012.69. url: http://dx.doi.org/10.1109/PDP.2012.69.

[Jej14]

Thomas Jejkal et al. “KIT Data Manager: The Repository Architecture Enabling Cross-Disciplinary Research”. In: Large-Scale Data Management and Analysis (LSDMA) - Big Data in Science. 2014, pp. 9–11. doi: 10.5445/IR/1000043270.

[JS14]

Christopher Jung and Achim Streit. Large-Scale Data Management and Analysis (LSDMA) - Big Data in Science. Springer, 2014. doi: 10.5445/IR/1000043270.

[Jun14]

C Jung et al. “Optimization of data life cycles”. In: Journal of Physics: Conference Series 513.3 (2014), p. 032047. doi: 10.1088/1742-6596/513/3/032047. url: http://stacks.iop.org/1742-6596/513/i=3/a=032047.

[Jun15]

C Jung et al. “Progress in Multi-Disciplinary Data Life Cycle Management”. In: Journal of Physics: Conference Series 664.3 (2015), p. 032018. doi: 10.1088/1742-6596/664/3/032018. url: http://stacks.iop.org/1742-6596/664/i=3/a=032018.

[Kha12]

Andranik Khachatryan et al. “Sensitivity of Self-tuning Histograms: Query Order Affecting Accuracy and Robustness”. In: Scientific and Statistical Database Management: 24th International Conference, SSDBM 2012, Chania, Crete, Greece, June 25-27, 2012. Proceedings. Ed. by Anastasia Ailamaki and Shawn Bowers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 334–342. isbn: 978-3-642-31235-9. doi: 10.1007/978-3-642-31235-9_22.

[KKL14]

Julian Kunkel, Michael Kuhn, and Thomas Ludwig. “Exascale Storage Systems – An Analytical Study of Expenses”. In: Supercomputing Frontiers and Innovations. Volume 1, Number 1 (June 2014). Ed. by Jack Dongarra and Vladimir Voevodin, pp. 116–134. url: http://superfri.org/superfri/article/view/20.

[KMB12]

F. Keller, E. Muller, and K. Bohm. “HiCS: High Contrast Subspaces for Density-Based Outlier Ranking”. In: 2012 IEEE 28th International Conference on Data Engineering. Apr. 2012, pp. 1037–1048. doi: 10.1109/ICDE.2012.88.

[KMe13]

P. Kilpatrick, P. Milligan, and R. Stotzka (editors). “Proceedings of the 2013 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP 2013)”. In: 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. Feb. 2013, pp. i–i. doi: 10.1109/PDP.2013.1.

[Kob15]

Andrei Y Kobitski et al. “An ensemble-averaged, cell density-based digital model of zebrafish embryo development derived from light-sheet microscopy data with single-cell resolution”. In: Scientific reports 5 (2015).

[KrÃij14a]

Jens KrÃijger et al. “Performance Studies on Distributed Virtual Screening”. In: BioMed Research International (2014). doi: 10.1155/2014/624024. url: http://dx.doi.org/10.1155/2014/624024.

[KrÃij14b]

Jens KrÃijger et al. “The MoSGrid Science Gateway - A Complete Solution for Molecular Simulations”. In: Journal of Chemical Theory and Computation 10(6) (2014), pp. 2232–2245. doi: 10.1021/ct500159h.

[KrÃij16]

Jens KrÃijger et al. “Portals and Web-based Resources for Virtual Screening”. In: Current Drug Targets 17 (2016), pp. 1–1. issn: 1389-4501/1873-5592. doi: 10.2174/1389450117666160201105806. url: http://www.eurekaselect.com/node/138922/article.

[KS16]

Eileen Kuehn and Achim Streit. “Online Distance Measurement for Tree Data Event Streams.” In: DASC/PiCom/DataCom/CyberSciTech (2016), pp. 681–688. doi: 10.1109/DASC-PICom-DataCom-CyberSciTec.2016.122. url: http://ieeexplore.ieee.org/document/7588920/.

[Kuh14]

Michael Kuhn et al. “Compression By Default - Reducing Total Cost of Ownership of Storage Systems”. In: Supercomputing. Ed. by Julian Martin Kunkel, Thomas Ludwig, and Hans Werner Meuer. Lecture Notes in Computer Science 8488. Leipzig, Germany: Springer International Publishing, June 2014. isbn: 978-3-319-07517-4. doi: 10.1007/978-3-319-07518-1.

[LAK15]

Michael Lautenschlager, Panagiotis Adamidis, and Michael Kuhn. “Big Data Research at DKRZ âĂŞ Climate Model Data Production Workflow”. In: Big Data and High Performance Computing. 26th ed. Advances in Parallel Computing. IOS Press, 2015, pp. 133–155. isbn: 978-1-61499-582-1. doi: 10.3233/978-1-61499-583-8-133. url: http://ebooks.iospress.nl/volume/big-data-and-high-performance-computing.

[Lut14]

Richard Lutz et al. “Management of Meteorological Mass Data with MongoDB”. In: 28th International Conference on Informatics for Environmental Protection: ICT for Energy Effieciency, EnviroInfo 2014, Oldenburg, Germany, September 10-12, 2014. 2014, pp. 549–556.

[Maa15]

Ahmad Maatouki et al. “A Horizontally-Scalable Multiprocessing Platform Based on Node.js”. In: 2015 IEEE TrustCom/BigDataSE/ISPA, Helsinki, Finland, August 20-22, 2015, Volume 3. 2015, pp. 100–107. doi: 10.1109/Trustcom.2015.618. eprint: arXiv:1507.02798[cs.DC]. url: http://dx.doi.org/10.1109/Trustcom.2015.618.

[McG15]

Gary A. McGilvary et al. “Enhanced Usability of Managing Workflows in an Industrial Data Gateway”. In: Interoperable Infrastructures for Interdisciplinary Big Data Sciences (IT4RIs 15). Aug. 2015, pp. 495–502. doi: 10.1109/eScience.2015.62.

[Mey14]

JÃűrg Meyer et al. “Archival Services and Technologies for Scientific Data”. In: Journal of Physics: Conference Series 513.6 (2014), p. 062033. doi: 10.1088/1742-6596/513/6/062033. url: http://stacks.iop.org/1742-6596/513/i=6/a=062033.

[Mil14]

Paul Millar et al. “Federated AAI: Enabling Collaboration”. In: Big Data in Science, 1st edition. 2014, pp. 22–24.

[Mil15]

Millar et al. “Unlocking data: federated identity with LSDMA and dCache”. In: Journal of Physics, Chep 2015. doi:10.1088/1742-6596/664/ 4/042037. Conference Series by IOP Publishing, 2015.

[OSS12]

Vitaly Osipov, Peter Sanders, and Christian Schulz. “Engineering Graph Partitioning Algorithms”. In: Experimental Algorithms: 11th International Symposium, SEA 2012, Bordeaux, France, June 7-9, 2012. Proceedings. Ed. by Ralf Klasing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 18–26. isbn: 978-3-642-30850-5. doi: 10.1007/978-3-642-30850-5_3.

[Ott13]

JC Otte et al. “realTox: Real-time imaging of toxicant impact in whole organisms at single cell resolution”. In: NAUNYN-SCHMIEDEBERGS ARCHIVES OF PHARMACOLOGY. Vol. 386. SPRINGER 233 SPRING ST, NEW YORK, NY 10013 USA. 2013, S60–S60.

[Pac13]

Lars Packschies et al. “The MoSGrid e-Science Gateway: Molecular Simulations in a Distributed Computing Environment”. In: Journal of Cheminformatics 5(Suppl 1):O3 (2013). doi: 10.1186/1758-2946-5-S1-O3.

[Pet13]

Mariya Petrova et al. “The UNICORE Portal”. In: Proceedings of the 9th UNICORE Summit. Vol. 21. 2013.

[PHZ13]

S. Pyatykh, J. Hesser, and L. Zheng. “Image Noise Level Estimation by Principal Component Analysis”. In: IEEE Transactions on Image Processing 22.2 (Feb. 2013), pp. 687–699. issn: 1057-7149. doi: 10.1109/TIP.2012.2221728.

[Pra15]

Ajinkya Prabhune et al. “An Optimized Generic Client Service API for Managing Large Datasets within a Data Repository”. In: Big Data Computing Service and Applications (BigDataService), 2015 IEEE First International Conference on. Mar. 2015, pp. 44–51. doi: 10.1109/BigDataService.2015.25.

[Pra16a]

Ajinkya Prabhune et al. “MetaStore: A Metadata Framework for Scientific Data Repositories”. In: 2016 IEEE International Conference on Big Data. IEEE. 2016, pp. 3026–3035.

[Pra16b]

Ajinkya Prabhune et al. “Prov2ONE: An Algorithm for Automatically Constructing ProvONE Provenance Graphs”. In: Provenance and Annotation of Data and Processes: 6th International Provenance and Annotation Workshop, IPAW 2016, McLean, VA, USA, June 7-8, 2016, Proceedings. Springer International Publishing, 2016, pp. 204–208. isbn: 978-3-319-40593-3. doi: 10.1007/978-3-319-40593-3_22.

[PZH12]

Stanislav Pyatykh, Lei Zheng, and JÃijrgen Hesser. “Efficient method of pixel neighborhood traversal”. In: Journal of Visual Communication and Image Representation 23.5 (2012), pp. 719–728. issn: 1047-3203. doi: 10.1016/j.jvcir.2012.03.008. url: //www.sciencedirect.com/science/article/pii/S1047320312000582.

[PZH13]

Stanislav Pyatykh, Lei Zheng, and JÃijrgen Hesser. Fast noise variance estimation by principal component analysis. 2013. doi: 10.1117/12.2000276.

[Rig14]

F. Rigoll et al. “A Privacy-Aware Architecture for Energy Management Systems in Smart Grids”. In: Ubiquitous Intelligence and Computing, 2014 IEEE 11th Intl Conf on and IEEE 11th Intl Conf on and Autonomic and Trusted Computing, and IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UTC-ATC-ScalCom). Dec. 2014, pp. 449–455. doi: 10.1109/UIC-ATC-ScalCom.2014.9.

[Rig17]

Fabian Rigoll. “Nutzerorientiertes Energiedatenmanagement”. PhD thesis. Karlsruhe Institute of Technology, 2017. doi: 10.5445/IR/1000068109.

[RS14]

Fabian Rigoll and Hartmut Schmeck. “Konzeption eines Energiedatenmanagementsystems unter Beachtung von Datenschutz und PrivatsphÃďre”. In: VDE-Kongress 2014. Ed. by VDE. VDE. VDE VERLAG GmbH, Oct. 2014.

[Sch16]

Sebastian Schlag et al. “k-way Hypergraph Partitioning via n-Level Recursive Bisection”. In: 18th Workshop on Algorithm Engineering and Experiments (ALENEX). SIAM, 2016, pp. 53–67. doi: 10.1137/1.9781611974317.5.

[SGG13]

Bernd Schuller, Richard Grunzke, and Andre Giesler. “Data Oriented Processing in UNICORE”. In: UNICORE Summit 2013 Proceedings. Vol. 21. IAS Series. 2013, pp. 1–6. isbn: 978-3-89336-910-2.

[SP11]

Bernd Schuller and Tim Pohlmann. “UFTP: High-Performance Data Transfer for UNICORE”. In: Proceedings of the 7th UNICORE Summit. IAS Series 9. Forschungszentrum Jülich GmbH, 2011, pp. 135–142.

[SRB14]

Bernd Schuller, Jedrzej Rybicki, and Krzysztof Benedyczak. “High-Performance Computing on the Web: Extending UNICORE with RESTful Interfaces”. In: Proceedings of the Sixth International Conference on Advances in Future Internet. IARIA XPS Press, 2014, pp. 35–38. isbn: 978-1-61208-377-3. url: http://www.thinkmind.org/%5C-index.php?view=article%5C&articleid=afin%5C_2014%5C_2%5C_10%5C_40020.

[SSM13]

P. Sanders, S. Schlag, and I. MÃijller. “Communication efficient algorithms for fundamental big data problems”. In: 2013 IEEE International Conference on Big Data. Oct. 2013, pp. 15–23. doi: 10.1109/BigData.2013.6691549.

[Ste16a]

Johannes Stegmaier et al. “Automation strategies for large-scale 3D image analysis”. In: at-Automatisierungstechnik 64.7 (2016), pp. 555–566.

[Sto12]

M. Stockhause et al. “Quality assessment concept of the World Data Center for Climate and its application to CMIP5 data”. In: Geoscientific Model Development 5.4 (2012), pp. 1023–1032. doi: 10.5194/gmd-5-1023-2012. url: http://www.geosci-model-dev.net/5/1023/2012/.

[Str15]

M. Strutz et al. “ASAP3 - New Data Taking and Analysis Infrastructure for PETRA III”. In: J. Phys. Conf. Ser. 664.4 (2015), p. 042053. doi: 10.1088/1742-6596/664/4/042053.

[Sts12]

D Stsepankou et al. “Evaluation of robustness of maximum likelihood cone-beam CT reconstruction with total variation regularization”. In: Physics in Medicine and Biology 57.19 (2012), p. 5955. doi: 10.1088/0031-9155/57/19/5955. url: http://stacks.iop.org/0031-9155/57/i=19/a=5955.

[Sut12]

M. Sutter et al. “File Systems and Access Technologies for the Large Scale Data Facility”. In: Remote Instrumentation for eScience and Related Aspects. Ed. by Franco Davoli et al. New York, NY: Springer New York, 2012, pp. 239–256. isbn: 978-1-4614-0508-5. doi: 10.1007/978-1-4614-0508-5_16.

[SZ12]

Nodari Sitchinava and Norbert Zeh. “A Parallel Buffer Tree”. In: Proceedings of the Twenty-fourth Annual ACM Symposium on Parallelism in Algorithms and Architectures. SPAA ’12. Pittsburgh, Pennsylvania, USA: ACM, 2012, pp. 214–223. isbn: 978-1-4503-1213-4. doi: 10.1145/2312005.2312046.

[Szu16]

M. Szuba et al. “A Distributed System for Storing and Processing Data from Earth-Observing Satellites: System Design and Performance Evaluation of the Visualisation Tool”. In: 2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). May 2016, pp. 169–174. doi: 10.1109/CCGrid.2016.19. eprint: arXiv:1511.07693[cs.DC].

[Teu12]

Tanja Teuber et al. “Denoising by second order statistics”. In: Signal Processing 92.12 (2012), pp. 2837–2847. issn: 0165-1684. doi: 10.1016/j.sigpro.2012.04.015. url: http://www.sciencedirect.com/science/article/pii/S0165168412001375.

[Ton12]

Danah Tonne et al. “A federated data zone for the arts and humanities”. In: 2012 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing. IEEE. 2012, pp. 198–205.

[Ton13]

Danah Tonne et al. “Access to the DARIAH bit preservation service for humanities research data”. In: 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing. IEEE. 2013, pp. 9–15.

[VRT12]

Philipp Vanscheidt, Andrea Rapp, and Danah Tonne. “Storage Infrastructure of the Virtual Scriptorium St. Matthias”. In: Digital Humanities 2012 (2012), p. 529.

[WDS13a]

M. Weidner, J. Dees, and P. Sanders. “Fast OLAP query execution in main memory on large data in a cluster”. In: 2013 IEEE International Conference on Big Data. Oct. 2013, pp. 518–524. doi: 10.1109/BigData.2013.6691616.

[WDS13b]

M. Weidner, J. Dees, and P. Sanders. “Fast OLAP query execution in main memory on large data in a cluster”. In: 2013 IEEE International Conference on Big Data. Oct. 2013, pp. 518–524. doi: 10.1109/BigData.2013.6691616.

[Wez15]

J. van Wezel et al. “Towards an Interoperable Data Archive”. In: Proceedings for the PV 2015 Conference. Nov. 2015. url: https://www.eumetsat.int/website/home/News/ConferencesandEvents/DAT_2447480.html.

[Yan13]

Xiaoli Yang et al. “Data Intensive Computing of X-Ray Computed Tomography Reconstruction at the LSDF”. In: Proceedings of the 21st Euromicro Intl. Conf. on Parallel, Distributed and Network-Based Computing (PDP’13). 2013. doi: 10.1109/PDP.2013.21.

[Yan16]

Xiaoli Yang. “Precise and Automated Tomographic Reconstruction with a Limited Number of Projections”. PhD thesis. Karlsruhe, Karlsruher Institut für Technologie (KIT), Diss., 2016, 2016.

[ZGK17]

Lukas Zimmermann, Richard Grunzke, and Jens Krüger. “Maintaining a Science Gateway - Lessons Learned from MoSGrid”. In: Proceedings of the 50th Hawaii International Conference on System Sciences. 2017. url: http://hdl.handle.net/10125/41918.