31 Aug

Scientific Publications

Refereed Scientific Publications

2017

  • Brasoveanu, A.M.P., Sabou, M., Scharl, A., Hubmann-Haidvogel, A. and Fischl, D. (2017). “Visualizing Statistical Linked Knowledge Sources for Decision Support”, Semantic Web Journal, (8)1, pp. 113-137.
  • Filatov, M. and Kantere, V. (2017). “Multi-workflow optimization in PAW”, in EDBT, March 19-23, 2017, (accepted).
  • Filatov, M. and Kantere, V. (2017). “Tutorial on Data Analytics in Multi-Engine Environments”, in DASFAA, March 27-30, 2017, Suzhou, China (accepted).
  • Weichselbraun, A., Gindl, S., Fischer, F., Vakulenko, S. and Scharl, A. (2017). “Aspect-Based Extraction and Analysis of Affective Knowledge from Social Media Stream”, IEEE Intelligent Systems: Forthcoming.

2016

  •  Arif, M., Vandierendonck, H., Nikolopoulos, D.S. and de Supinski, B.R. (2016). “A Scalable and Composable Map-Reduce System”, in Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH) in conjunction with IEEE International Conference on Big Data.
  • Doka, K., Filatov, M., Giannakouris, V., Kantere, V., Koziris, N., Mantas, C., Papailiou, N., Papaioannou, V. and Tsoumakos, D. (2016). “Optimizing, Planning and Executing Analytics Workflows over Multiple Engines”, International Workshop on Multi-Engine Data AnaLytics (MEDAL).
  • Doka, K., Papailiou, N., Giannakouris, V., Tsoumakos, D. and Koziris, N. (2016). “Mix ‘n’ Match Multi-Engine Analytics”, in Proceedings of the 2016 IEEE International Conference on Big Data (BigData 2016), December 5-8, 2016, Washington DC, USA.
  • Filatov, M. and Kantere, V. (2016). “PAW: A Platform for Analytics Workflows”, in International Conference on Extending Database Technology, March 15-18, 2016, Bordeaux, France.
  • Giannakouris, V, Papailiou, N., Tsoumakos, D. and Koziris, N. (2016). “MuSQLE: Distributed SQL Query Execution Over Multiple Engine Environments”, in Proceedings of the 2016 IEEE International
  • Conference on Big Data (BigData 2016), December 5-8, 2016, Washington DC, USA.
  • Mytilinis, I., Τsoumakos, D. and N. Koziris, N. (2016). “Distributed Wavelet Thresholding for Maximum Error Metrics”, in Proceedings of the 2016 ACM SIGMOD/PODS International Conference on Management of Data, June 26 – July 1, 2016, San Francisco, USA.
  • Papailiou, N., Doka, K., Giannakouris, V., Papaioannou, V., Tsoumakos, D. and Koziris, N. (2016). “Robust and Adaptive Multi-Engine Analytics using IReS (demo paper)”, in Proceedings of the 10th International Workshop on Enabling Real-Time Business Intelligence (BIRTE 2016), in conjunction with the VLDB 2016 Conference, 2016, New Delhi, India.
  • Papakonstantinou, N., Zakkak, F. and Pratikakis, P. (2016). “Hierarchical Parallel Dynamic Dependence Analysis for Recursively Task-Parallel Programs”, IEEE International Parallel and Distributed Processing Symposium 2016 (IPDPS).
  • Sabou, M., Onder, I., Brasoveanu, A. and Scharl, A. (2016). “Towards cross-domain data analytics in tourism: a linked data based approach”, Information Technology & Tourism, Vol. 16, Issue 1, pp 71–101.
  • Scharl, A., Lalicic, L. and Önder, I. (2016). “Tourism Intelligence and Visual Media Analytics for Destination Management Organizations”, Analytics in Smart Tourism Design – Concepts and Methods. Eds. Z. Xiang and D.R. Fesenmaier. Cham: Springer. 165-178.
  • Scharl, A., Weichselbraun, A., Göbel, M., Rafelsberger, W. and Kamolov, R. (2016). “Scalable Knowledge Extraction and Visualization for Web Intelligence”, 49th Hawaii International Conference on System Sciences (HICSS-2016). Kauai, USA: IEEE Press, pp. 3749-3757. [Best Paper Award]
  • Sun, J. (2016). “Student Research Poster: A Scalable General-Purpose System for Large-Scale Graph Analytics.”, in PACT ’16: Proceedings of the 25th international conference on Parallel architectures and compilation, pp. 456-456.
  • Vandierendonck, H., Murphy, K., Arif, M., Sun, J. and Nikolopoulos, D.S. (2016). “Operator and Workflow Optimization for High-Performance Analytics”, International Workshop on Multi-Engine Data AnaLytics (MEDAL).
  • Vandierendonck, H., Murphy, K., Arif, M. and Nikolopoulos, D.S (2016). “HPTA: High-Performance Text Analytics”, in Proceedings of the 2016 IEEE International Conference on Big Data (BigData 2016), December 5-8, 2016, Washington DC, USA.
  • Weichselbraun, A., Scharl, A. and Gindl, S. (2016). “Extracting Opinion Targets from Environmental Web Coverage and Social Media Streams”, 49th Hawaii International Conference on System Sciences (HICSS-2016). Kauai, USA: IEEE Press, pp. 1040-1048.

2015

  • Arif, M. and Vandierendonck, H. (2015). “A Case Study of OpenMP Applied to Map/Reduce-Style Applications”, 11th International Workshop on OpenMP, IWOMP 2015, Aachen, Germany.
  • Doka, K., Tsoumakos, D. and Koziris, N. (2015). “Intelligent, Multi-Engine Resource Scheduler for Big Data Analytics Workflows”, European Data Forum 2015 (EDF).
  • Doka, K., Papailiou, N., Tsoumakos, D., Mantas, C. and Koziris, N. (2015). “IReS: Intelligent, Multi-Engine Resource Scheduler for Big Data Analytics Workflows”, in Proceedings of the 2015 ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD’15, Demo track), pp. 1451-1456.
  • Kantere, V. (2015). “Approximate Querying in Big Heterogeneous Data Sources” IEEE International Congress on Big Data (IEEE BigData), pp. 712-715.
  • Kantere, V. and Filatov, M. (2015). “A Workflow Model for Adaptive Analytics on Big Data”, IEEE International Congress on Big Data (IEEE BigData), pp. 673-676.
  • Kantere, V. and Filatov, M. (2015). “A Framework for Big Data Analytics”, in Proceedings of the International C* Conference on Computer Science & Software Engineering (C3S2E), pp. 125-132.
  • Kantere, V. and Filatov, M. (2015). “Towards Adaptive Analytics on Big Data Sources”, International Conference on Data Mining, Internet Computing, and Big Data (BigData2015), pp. 84-94
  • Kantere, V. (2015). “Datom: Towards Modular Data Management”, IEEE International Conference on Information Reuse and Integration (IEEE IRI), pp. 443-450.
  • Kantere, V., Kementsietsidis, A., Orfanoudakis, G. and Sellis., T. (2015). “Query Relaxation across Heterogeneous Data Sources”, in ACM International Conference on Information and Knowledge Management (CIKM), pp. 473-482.
  • Kantere, V. and Filatov, M. (2015). “Modelling Processes of Big Data Analytics”, International Conference on Web Information System Engineering (WISE), pp. 309-322.
  • Mytilinis, I., Tsoumakos, D., Kantere, V., Nanos, A. and Koziris, N. (2015). “I/O Performance Modeling for Big Data Applications over Cloud Infrastructures”, in IEEE International Conference on Cloud Engineering (IEEE IC2E 2015), pp. 201-206.
  • Papailiou, N., Tsoumakos, D., Karras, P. and Koziris, N. (2015). “Graph-Aware, Workload-Adaptive SPARQL Query Caching”, in Proceedings of Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp.1777-1792.
  • Scharl, A., Herring, D., Rafelsberger, W., Hubmann-Haidvogel, A., Kamolov, R., Fischl, D., Föls, M. and Weichselbraun, A. (2015). “Semantic Systems and Visual Tools to Support Environmental Communication”, IEEE Systems Journal: 99, pp. 1-10.
  • Vandierendonck, H.  (2015). “Efficiently Scheduling Task Dataflow Parallelism: A Comparison Between Swan and QUARK”, in Proceedings of the Exascale Applications and Software Conference. pp. 36-41.
  • Vandierendonck, H. (2015). “Compiler and Runtime Support for Hybrid Static/Dynamic Scheduling”, Compilers for Parallel Computing Workshop, CPC-15.

2014

  • Kantere, V. (2014). “A Holistic Framework for Big Scientific Data Management”, International Congress on Big Data (IEEE BigData 2014).
  • Kantere, V. (2014). “Mapping Construction Compliant with Schema Semantics”, International Conference on Database and Expert Systems Applications (DEXA 2014).
  • Katsogridakis, P. and Pratikakis, P. (2014). “Hama and Hadoop: A Performance Comparison”, Summer School on Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES).

Trade Publications

  • Scharl, A. and Weichselbraun, A. (2014). “Die Meinungs-Erforscher”, Swiss CIO Magazine, Computerworld, 20 June 2014, 32-35.

Project Summary

The ASAP FP7 Research Project develops a dynamic open-source execution framework for scalable data analytics, and applies this framework to two use cases focusing on Web content and telecommunications data. ASAP assumes that no single execution model is suitable for all types of tasks, and no single data model (and store) is suitable for all types of data.

Acknowledgement

This project receives funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement number 619706.


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