Professor Dr. Christian Beecks

Professorship of practical computer science (Prof. Beecks)
Professor Dr. Christian Beecks

Einsteinstraße 62, Room 203
48149 Münster
T: +49 251 83-33804
F: +49 251 83-33755
christian.beecks@uni-muenster.de

Christian Beecks is head of the Data Management and Analytics Group in the Computer Science Department at the University of Münster in Germany. In addition, he is a senior researcher in the Data Science and Artificial Intelligence Department at the Fraunhofer Institute for Applied Information Technology FIT, where he leads the Intelligent Data Analytics Research Group. His research interests include Machine Learning, Data Mining, Data Engineering, and Knowledge Discovery, where he specialized in efficient algorithms and machine learning methods for scalable data analytics in complex data spaces. He has authored more than 100 conference and journal papers and won several awards. In addition, he is a reviewer for various international conferences and journals.

More information can be found at DBLP and Google Scholar.

 

  • Research Areas

    • Big Data
    • Multimedia Databases
    • Data Science
    • Data Engineering
    • Machine Learning

  • CV

    Education

    -
    Ph.D. in Computer Science, RWTH Aachen University, Germany. Ph.D. Thesis: Distance-based Similarity Models for Content-based Multimedia Retrieval.
    -
    Diploma in Computer Science, RWTH Aaachen University, Germany. Diploma Thesis: Relevance Feedback für EMD-basierte Ähnlichkeitssuche.

    Positions

    since
    Research Group Leader, Data Science and Artificial Intelligence, Fraunhofer Institute for Applied Information Technology FIT, Deutschland.
    since
    Professor of Computer Science, University of Münster, Germany.
    -
    Senior Researcher, User-Centered Ubiquitous Computing, Fraunhofer Institute for Applied Information Technology FIT, Germany.
    -
    Akademischer Rat, RWTH Aachen University, Germany.
    -
    Post-doctoral researcher, RWTH Aachen University, Germany.
    -
    Ph.D. student and research assistant, RWTH Aachen University, Germany.

    Honors

    Best Research Paper Award: "Complexity-Adaptive Gaussian Process Model Inference for Large-Scale Data" - SIAM International Conference on Data Mining
    Best Poster Award: "Towards digitized gesture analytics. Gaining new insights into gesture and gesture space use through motion-capture technology" - Warwick Workshop on Gesture & Technology
    Best Paper Award: "Automated Pattern Analysis in Gesture Research: Similarity Measuring in 3D Motion Capture Models of Communicative Action" - Göttingen Dialog in Digital Humanities
    Best Paper Award: "Ptolemaic Indexing of the Signature Quadratic Form Distance" - International Conference on Similarity Search and Applications

  • Publications

    • Berns F, Beecks C. . ‘Stochastic Time Series Representation for Interval Pattern Mining via Gaussian Processes.’ In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), 10-18. doi: 10.1137/1.9781611976700.2.
    • Berns F, Beecks C. . ‘Complexity-Adaptive Gaussian Process Model Inference for Large-Scale Data.’ In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), 360-368. doi: 10.1137/1.9781611976700.41.
    • Berns F, Ramsdorf T, Beecks C. . ‘Machine Learning for Storage Location Prediction in Industrial High Bay Warehouses.’ In 1st International Workshop on Industrial Machine Learning at 25th International Conference on Pattern Recognition 2020, 650-661.: Springer. doi: 10.1007/978-3-030-68799-1_47.
    • Berns F, Schmidt K, Bracht I, Beecks C. . ‘3CS Algorithm for Efficient Gaussian Process Model Retrieval.’ In Proceedings 25th of the International Conference on Pattern Recognition 2020, 1773-1780.: IEEE Computer Society. doi: 10.1109/ICPR48806.2021.9412805.
    • Berns F, Strueber JH, Beecks C. . ‘Local Gaussian Process Model Inference Classification for Time Series Data.’ Contributed to the 33rd International Conference on Scientific and Statistical Database Management, Tampa, Florida, USA. doi: 10.1145/3468791.3468839.

    • Berns F, Beecks C. . ‘Automatic Gaussian Process Model Retrieval for Big Data.’ Contributed to the ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland. doi: 10.1145/3340531.3412182.
    • Berns F, Beecks C. . ‘Towards Large-scale Gaussian Process Models for Efficient Bayesian Machine Learning.’ In Proceedings of the 9th International Conference on Data Science, Technology and Applications. doi: 10.5220/0009874702750282.
    • Berns F, Beecks C. . ‘Large-scale Retrieval of Bayesian Machine Learning Models for Time Series Data via Gaussian Processes.’ In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR,, 71-80.: SciTePress. doi: 10.5220/0010109700710080.
    • Berns F, Lange-Hegermann M, Beecks C. . ‘Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0.’ In Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics - Volume 1: IN4PL,, 87-92.: SciTePress. doi: 10.5220/0010130300870092.

    • Berns F, Schmidt K, Grass A, Beecks C. . ‘A New Approach for Efficient Structure Discovery in IoT.’ In 2019 IEEE International Conference on Big Data (Big Data), 4152-4156.: IEEE. doi: 10.1109/BigData47090.2019.9006082.
    • Beecks C, Berns F, Schmidt KW. . ‘Ptolemaic Indexing for Managing and Querying Internet of Things (IoT) Data.’ In 2019 IEEE International Conference on Big Data (Big Data), 4148-4151.: IEEE. doi: 10.1109/BigData47090.2019.9005725.
    • Sandfort F, Strieth-Kalthoff F, Kühnemund M, Beecks C, Glorius F. . ‘A Structure-Based Platform for Predicting Chemical Reactivity.’ ChemRxiv 2019. doi: 10.26434/chemrxiv.9981488.v1.
    • Berns F, Rossetto L, Schoeffmann K, Beecks C, Awad G. . ‘V3C1 Dataset: An Evaluation of Content Characteristics.’ In Proceedings of the 2019 on International Conference on Multimedia Retrieval, ICMR 2019, Ottawa, ON, Canada, June 10-13, 2019, edited by El-Saddik A, Bimbo AD, Zhang Z, Hauptmann AG, Candan KS, Bertini M, Xie L, Wei X, 334-338.: ACM. doi: 10.1145/3323873.3325051.
    • Leibetseder A, Münzer B, Primus MJ, Kletz S, Schoeffmann K, Berns F, Beecks C. . ‘lifeXplore at the Lifelog Search Challenge 2019.’ In Proceedings of the ACM Workshop on Lifelog Search Challenge, LSC@ICMR 2019, Ottawa, ON, Canada, 10 June 2019, edited by Gurrin C, Schöffmann K, Joho H, Dang-Nguyen D, Riegler M, Piras L, 13-17.: ACM. doi: 10.1145/3326460.3329157.
    • Beecks C, Schmidt KW, Berns F, Grass A. . ‘Gaussian Processes for Anomaly Description in Production Environments.’ In Proceedings of the Workshops of the Joint Conference on Extending Database Technology and Database Theory, EDBT/ICDT 2019, Lisbon, Portugal, March 26, 2019.: CEUR-WS.org.

    • Grass A, Beecks C, Soto JAC. . ‘Unsupervised Anomaly Detection in Production Lines.’ In Selected papers from the International Conference on Machine Learning for Cyber Physical Systems, ML4CPS 2018, Karlsruhe, Germany, October 23-24, 2018, 18-25.: Springer. doi: 10.1007/978-3-662-58485-9\_3.
    • Beecks C, Devasya S, Schlutter R. . ‘Machine Learning for Enhanced Waste Quantity Reduction: Insights from the MONSOON Industry 4.0 Project.’ In Selected papers from the International Conference on Machine Learning for Cyber Physical Systems, ML4CPS 2018, Karlsruhe, Germany, October 23-24, 2018, 1-6.: Springer. doi: 10.1007/978-3-662-58485-9\_1.
    • Beecks C, Grass A, Devasya S. . ‘Metric Indexing for Efficient Data Access in the Internet of Things.’ In IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA, December 10-13, 2018, 5132-5136. doi: 10.1109/BigData.2018.8622387.
    • Beecks C, Berrendorf M. . ‘Optimal k-Nearest-Neighbor Query Processing via Multiple Lower Bound Approximations.’ In IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA, December 10-13, 2018, 614-623. doi: 10.1109/BigData.2018.8622493.
    • Beecks C, Grass A. . ‘Efficient Point-Based Pattern Search in 3D Motion Capture Databases.’ In 6th IEEE International Conference on Future Internet of Things and Cloud, FiCloud 2018, Barcelona, Spain, August 6-8, 2018, edited by Younas M, Disso JP, 230-235.: IEEE Computer Society. doi: 10.1109/FiCloud.2018.00041.
    • Schoeffmann K, Husslein H, Kletz S, Petscharnig S, Münzer B, Beecks C. . ‘Video retrieval in laparoscopic video recordings with dynamic content descriptors.’ Multimedia Tools Appl. 77, No. 13: 16813-16832. doi: 10.1007/s11042-017-5252-2.

    • Beecks C, Borutta F, Kröger P, Seidl T (Eds.): . Similarity Search and Applications - 10th International Conference, SISAP 2017, Munich, Germany, October 4-6, 2017, Proceedings. : Springer Verlag. doi: 10.1007/978-3-319-68474-1.
    • Münzer B, Primus MJ, Hudelist MA, Beecks C, Hürst W, Schoeffmann K. . ‘When content-based video retrieval and human computation unite: Towards effective collaborative video search.’ In 2017 IEEE International Conference on Multimedia & Expo Workshops, ICME Workshops, Hong Kong, China, July 10-14, 2017, 214-219.: IEEE Computer Society. doi: 10.1109/ICMEW.2017.8026262.
    • Beecks C, Kletz S, Schoeffmann K. . ‘Large-Scale Endoscopic Image and Video Linking with Gradient-Based Signatures.’ In Third IEEE International Conference on Multimedia Big Data, BigMM 2017, Laguna Hills, CA, USA, April 19-21, 2017, 17-21.: IEEE Computer Society. doi: 10.1109/BigMM.2017.44.
    • Schüller D, Beecks C, Hassani M, Hinnell J, Brenger B, Seidl T, Mittelberg I. . ‘Automated Pattern Analysis in Gesture Research: Similarity Measuring in 3D Motion Capture Models of Communicative Action.’ Digital Humanities Quarterly 11, No. 2.

    • Nies TD, Beecks C, Godin F, Neve WD, Stepien G, Arndt D, Vocht LD, Verborgh R, Seidl T, Mannens E, Walle RV. . ‘A Distance-Based Approach for Semantic Dissimilarity in Knowledge Graphs.’ In Tenth IEEE International Conference on Semantic Computing, ICSC 2016, Laguna Hills, CA, USA, February 4-6, 2016, 254-257.: IEEE Computer Society. doi: 10.1109/ICSC.2016.55.
    • Hudelist MA, Beecks C, Schoeffmann K. . ‘Finding the chameleon in your video collection.’ In Proceedings of the 7th International Conference on Multimedia Systems, MMSys 2016, Klagenfurt, Austria, May 10-13, 2016, edited by Timmerer C, 31:1-31:4.: ACM. doi: 10.1145/2910017.2910631.
    • Hudelist MA, Cobarzan C, Beecks C, Werken R, Kletz S, Hürst W, Schoeffmann K. . ‘Collaborative Video Search Combining Video Retrieval with Human-Based Visual Inspection.’ In MultiMedia Modeling - 22nd International Conference, MMM 2016, Miami, FL, USA, January 4-6, 2016, Proceedings, Part II, edited by Tian Q, Sebe N, Qi G, Huet B, Hong R, Liu X, 400-405.: Springer. doi: 10.1007/978-3-319-27674-8_40.
    • Hürst W, Ching AIV, Hudelist MA, Primus MJ, Schoeffmann K, Beecks C. . ‘A New Tool for Collaborative Video Search via Content-based Retrieval and Visual Inspection.’ In Proceedings of the 2016 ACM Conference on Multimedia Conference, MM 2016, Amsterdam, The Netherlands, October 15-19, 2016, edited by Hanjalic A, Snoek C, Worring M, Bulterman DCA, Huet B, Kelliher A, Kompatsiaris Y, Li J, 731-732.: ACM. doi: 10.1145/2964284.2973824.
    • Schoeffmann K, Beecks C, Lux M, Uysal MS, Seidl T. . ‘Content-based retrieval in videos from laparoscopic surgery.’ In Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, California, United States, 27 February - 3 March 2016, edited by Webster RJ, Yaniv ZR, 97861V.: SPIE. doi: 10.1117/12.2216864.
    • Vocht LD, Beecks C, Verborgh R, Mannens E, Seidl T, Walle RV. . ‘Effect of Heuristics on Serendipity in Path-Based Storytelling with Linked Data.’ In Human Interface and the Management of Information: Information, Design and Interaction - 18th International Conference, HCI International 2016 Toronto, Canada, July 17-22, 2016, Proceedings, Part I, edited by Yamamoto S, 238-251.: Springer. doi: 10.1007/978-3-319-40349-6_23.
    • Nies TD, Beecks C, Godin F, Neve WD, Stepien G, Arndt D, Vocht LD, Verborgh R, Seidl T, Mannens E, Walle RV. . ‘Normalized Semantic Web Distance.’ In The Semantic Web. Latest Advances and New Domains - 13th International Conference, ESWC 2016, Heraklion, Crete, Greece, May 29 - June 2, 2016, Proceedings, edited by Sack H, Blomqvist E, d'Aquin M, Ghidini C, Ponzetto SP, Lange C, 69-84.: Springer. doi: 10.1007/978-3-319-34129-3_5.
    • Beecks C, Uysal MS, Seidl T. . ‘Distance-based Multimedia Indexing.’ In Proceedings of the 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, March 15-16, 2016, Bordeaux, France, March 15-16, 2016., edited by Pitoura E, Maabout S, Koutrika G, Marian A, Tanca L, Manolescu I, Stefanidis K, 722-723.: OpenProceedings.org. doi: 10.5441/002/edbt.2016.105.
    • Uysal MS, Beecks C, Sabinasz D, Schmücking J, Seidl T. . ‘Efficient Query Processing using the Earth's Mover Distance in Video Databases.’ In Proceedings of the 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, March 15-16, 2016, Bordeaux, France, March 15-16, 2016., edited by Pitoura E, Maabout S, Koutrika G, Marian A, Tanca L, Manolescu I, Stefanidis K, 389-400.: OpenProceedings.org. doi: 10.5441/002/edbt.2016.36.
    • Beecks C, Grass A. . ‘Multi-step threshold algorithm for efficient feature-based query processing in large-scale multimedia databases.’ In 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, December 5-8, 2016, edited by Joshi J, Karypis G, Liu L, Hu X, Ak R, Xia Y, Xu W, Sato A, Rachuri S, Ungar LH, Yu PS, Govindaraju R, Suzumura T, 596-605.: IEEE. doi: 10.1109/BigData.2016.7840652.
    • Beecks C, Hassani M, Brenger B, Hinnell J, Schüller D, Mittelberg I, Seidl T. . ‘Efficient Query Processing in 3D Motion Capture Gesture Databases.’ Int. J. Semantic Computing 10, No. 1: 5-26. doi: 10.1142/S1793351X16400018.

    • Uysal MS, Beecks C, Schmücking J, Seidl T. . ‘Efficient similarity search in scientific databases with feature signatures.’ In Proceedings of the 27th International Conference on Scientific and Statistical Database Management, SSDBM '15, La Jolla, CA, USA, June 29 - July 1, 2015, edited by Gupta A, Rathbun SL, 30:1-30:12.: ACM. doi: 10.1145/2791347.2791384.
    • Beecks C, Hassani M, Hinnell J, Schüller D, Brenger B, Mittelberg I, Seidl T. . ‘Spatiotemporal Similarity Search in 3D Motion Capture Gesture Streams.’ In Advances in Spatial and Temporal Databases - 14th International Symposium, SSTD 2015, Hong Kong, China, August 26-28, 2015. Proceedings, edited by Claramunt C, Schneider M, Wong RC, Xiong L, Loh W, Shahabi C, Li K, 355-372.: Springer. doi: 10.1007/978-3-319-22363-6_19.
    • Uysal MS, Beecks C, Sabinasz D, Seidl T. . ‘FELICITY: A Flexible Video Similarity Search Framework Using the Earth Mover's Distance.’ In Similarity Search and Applications - 8th International Conference, SISAP 2015, Glasgow, UK, October 12-14, 2015, Proceedings, edited by Amato G, Connor RCH, Falchi F, Gennaro C, 347-350.: Springer. doi: 10.1007/978-3-319-25087-8_34.
    • Beecks C, Uysal MS, Seidl T. . ‘Content-Based Image Retrieval with Gaussian Mixture Models.’ In MultiMedia Modeling - 21st International Conference, MMM 2015, Sydney, NSW, Australia, January 5-7, 2015, Proceedings, Part I, edited by He X, Luo S, Tao D, Xu C, Yang J, Hasan MA, 294-305.: Springer. doi: 10.1007/978-3-319-14445-0_26.
    • Hassani M, Beecks C, Töws D, Seidl T. . ‘Mining Sequential Patterns of Event Streams in a Smart Home Application.’ In Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB, Trier, Germany, October 7-9, 2015., edited by Bergmann R, Görg S, Müller G, 159-170.: CEUR-WS.org.
    • Uysal MS, Beecks C, Sabinasz D, Seidl T. . ‘Effective Content-Based Near-Duplicate Video Detection.’ In 2015 IEEE International Symposium on Multimedia, ISM 2015, Miami, FL, USA, December 14-16, 2015, 254-257.: IEEE Computer Society. doi: 10.1109/ISM.2015.60.
    • Beecks C, Uysal MS, Seidl T. . ‘Earth Mover's Distance vs. Quadratic form Distance: An Analytical and Empirical Comparison.’ In 2015 IEEE International Symposium on Multimedia, ISM 2015, Miami, FL, USA, December 14-16, 2015, 233-236.: IEEE Computer Society. doi: 10.1109/ISM.2015.76.
    • Beecks C, Hassani M, Obeloer F, Seidl T. . ‘Efficient Query Processing in 3D Motion Capture Databases via Lower Bound Approximation of the Gesture Matching Distance.’ In 2015 IEEE International Symposium on Multimedia, ISM 2015, Miami, FL, USA, December 14-16, 2015, 148-153.: IEEE Computer Society. doi: 10.1109/ISM.2015.86.
    • Beecks C, Schoeffmann K, Lux M, Uysal MS, Seidl T. . ‘Endoscopic Video Retrieval: A Signature-Based Approach for Linking Endoscopic Images with Video Segments.’ In 2015 IEEE International Symposium on Multimedia, ISM 2015, Miami, FL, USA, December 14-16, 2015, 33-38.: IEEE Computer Society. doi: 10.1109/ISM.2015.21.
    • Beecks C, Hassani M, Obeloer F, Seidl T. . ‘Efficient Distance-Based Gestural Pattern Mining in Spatiotemporal 3D Motion Capture Databases.’ In IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, NJ, USA, November 14-17, 2015, 1425-1432.: IEEE Computer Society. doi: 10.1109/ICDMW.2015.194.
    • Vocht LD, Beecks C, Verborgh R, Seidl T, Mannens E, Walle RV. . ‘Improving Semantic Relatedness in Paths for Storytelling with Linked Data on the Web.’ In The Semantic Web: ESWC 2015 Satellite Events - ESWC 2015 Satellite Events Portorož, Slovenia, May 31 - June 4, 2015, Revised Selected Papers, edited by Gandon F, Guéret C, Villata S, Breslin JG, Faron-Zucker C, Zimmermann A, 31-35.: Springer. doi: 10.1007/978-3-319-25639-9_6.
    • Beecks C, Uysal MS, Hermanns J, Seidl T. . ‘Gradient-based Signatures for Efficient Similarity Search in Large-scale Multimedia Databases.’ In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015, edited by Bailey J, Moffat A, Aggarwal CC, Rijke M, Kumar R, Murdock V, Sellis TK, Yu JX, 1241-1250.: ACM. doi: 10.1145/2806416.2806459.
    • Uysal MS, Beecks C, Sabinasz D, Seidl T. . ‘Large-scale Efficient and Effective Video Similarity Search.’ In Proceedings of the 2015 Workshop on Large-Scale and Distributed System for Information Retrieval, LSDS-IR 2015, Melbourne, Australia, October 23, 2015, edited by Altingovde IS, Cambazoglu BB, Tonellotto N, 3-8.: ACM. doi: 10.1145/2809948.2809950.
    • Uysal MS, Beecks C, Seidl T. . ‘On efficient content-based near-duplicate video detection.’ In 13th International Workshop on Content-Based Multimedia Indexing, CBMI 2015, Prague, Czech Republic, June 10-12, 2015, 1-6.: IEEE. doi: 10.1109/CBMI.2015.7153633.
    • Hassani M, Beecks C, Töws D, Serbina T, Haberstroh M, Niemietz P, Jeschke S, Neumann S, Seidl T. . ‘Sequential Pattern Mining of Multimodal Streams in the Humanities.’ In Datenbanksysteme für Business, Technologie und Web (BTW), 16. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 4.-6.3.2015 in Hamburg, Germany. Proceedings, edited by Seidl T, Ritter N, Schöning H, Sattler K, Härder T, Friedrich S, Wingerath W, 683-686.: GI.
    • Beecks C, Uysal MS, Seidl T. . ‘Distance-based Multimedia Indexing.’ In Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2.-3. März 2015, Hamburg, Germany, edited by Ritter N, Henrich A, Lehner W, Thor A, Friedrich S, Wingerath W, 265-268.: GI.
    • Töws D, Hassani M, Beecks C, Seidl T. . ‘Optimizing Sequential Pattern Mining Within Multiple Streams.’ In Datenbanksysteme für Business, Technologie und Web (BTW 2015) - Workshopband, 2.-3. März 2015, Hamburg, Germany, edited by Ritter N, Henrich A, Lehner W, Thor A, Friedrich S, Wingerath W, 223-232.: GI.
    • Beecks C, Uysal MS, Seidl T. . ‘Gradient-based signatures for big multimedia data.’ In 2015 IEEE International Conference on Big Data, Big Data 2015, Santa Clara, CA, USA, October 29 - November 1, 2015, 2834-2835.: IEEE. doi: 10.1109/BigData.2015.7364093.

    • Seidl T, Hassani M, Beecks C (Eds.): . Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, Aachen, Germany, September 8-10, 2014. : CEUR-WS.org.
    • Beecks C, Kirchhoff S, Seidl T. . ‘On the Stability of Signature-Based Distance Functions for Content-Based Image Retrieval.’ In Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, Aachen, Germany, September 8-10, 2014., edited by Seidl T, Hassani M, Beecks C, 226.: CEUR-WS.org.
    • Godin F, Nies TD, Beecks C, Vocht LD, Neve WD, Mannens E, Seidl T, Walle RV. . ‘The Normalized Freebase Distance.’ In The Semantic Web: ESWC 2014 Satellite Events - ESWC 2014 Satellite Events, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers, edited by Presutti V, Blomqvist E, Troncy R, Sack H, Papadakis I, Tordai A, 218-221.: Springer. doi: 10.1007/978-3-319-11955-7_22.
    • Uysal MS, Beecks C, Schmücking J, Seidl T. . ‘Efficient Filter Approximation Using the Earth Mover's Distance in Very Large Multimedia Databases with Feature Signatures.’ In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, November 3-7, 2014, edited by Li J, Wang XS, Garofalakis MN, Soboroff I, Suel T, Wang M, 979-988.: ACM. doi: 10.1145/2661829.2661877.
    • Uysal MS, Beecks C, Seidl T. . ‘On Efficient Query Processing with the Earth Mover's Distance.’ In Proceedings of the 7th Workshop on Ph.D Students, PIKM@CIKM 2014, Shanghai, China, November 3, 2014, edited by Melo G, Kacimi M, Varde AS, 25-32.: ACM. doi: 10.1145/2663714.2668047.
    • Nies TD, Beecks C, Neve WD, Seidl T, Mannens E, Walle RV. . ‘Towards Named-Entity-based Similarity Measures: Challenges and Opportunities.’ In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval, ESAIR '14, Shanghai, China, November 7, 2014, edited by Alonso O, Kamps J, Karlgren J, 9-11.: ACM. doi: 10.1145/2663712.2666194.
    • Beecks C, Kirchhoff S, Seidl T. . ‘On stability of signature-based similarity measures for content-based image retrieval.’ Multimedia Tools Appl. 71, No. 1: 349-362. doi: 10.1007/s11042-012-1334-3.

    • Beecks C, Kirchhoff S, Seidl T. . ‘Signature matching distance for content-based image retrieval.’ In International Conference on Multimedia Retrieval, ICMR'13, Dallas, TX, USA, April 16-19, 2013, edited by Jain R, Prabhakaran B, Worring M, Smith JR, Chua T, 41-48.: ACM. doi: 10.1145/2461466.2461474.
    • Beecks C, Uysal MS, Driessen P, Seidl T. . ‘Content-based exploration of multimedia databases.’ In 11th International Workshop on Content-Based Multimedia Indexing, CBMI 2013, Veszprém, Hungary, June 17-19, 2013, 59-64.: IEEE. doi: 10.1109/CBMI.2013.6576553.
    • Hetland ML, Skopal T, Lokoc J, Beecks C. . ‘Ptolemaic access methods: Challenging the reign of the metric space model.’ Inf. Syst. 38, No. 7: 989-1006. doi: 10.1016/j.is.2012.05.011.
    • Beecks C. . Distance based similarity models for content based multimedia retrieval Dissertation thesis, RWTH Aachen University.

    • Beecks C, Seidl T. . ‘On Stability of Adaptive Similarity Measures for Content-Based Image Retrieval.’ In Advances in Multimedia Modeling - 18th International Conference, MMM 2012, Klagenfurt, Austria, January 4-6, 2012. Proceedings, edited by Schoeffmann K, Mérialdo B, Hauptmann AG, Ngo C, Andreopoulos Y, Breiteneder C, 346-357.: Springer. doi: 10.1007/978-3-642-27355-1_33.
    • Ivanescu AM, Wichterich M, Beecks C, Seidl T. . ‘The ClasSi coefficient for the evaluation of ranking quality in the presence of class similarities.’ Frontiers Comput. Sci. 6, No. 5: 568-580. doi: 10.1007/s11704-012-1175-2.
    • Krulis M, Skopal T, Lokoc J, Beecks C. . ‘Combining CPU and GPU architectures for fast similarity search.’ Distributed and Parallel Databases 30, No. 3-4: 179-207. doi: 10.1007/s10619-012-7092-4.

    • Lokoc J, Beecks C, Seidl T, Skopal T. . ‘Parameterized earth mover's distance for efficient metric space indexing.’ In Fourth International Conference on Similarity Search and Applications, SISAP 2011, Lipari Island, Italy, June 30 - July 01, 2011, edited by Ferro A, 121-122.: ACM. doi: 10.1145/1995412.1995438.
    • Beecks C, Ivanescu AM, Seidl T, Martin D, Pischke P, Kneer R. . ‘Applying similarity search for the investigation of the fuel injection process.’ In Fourth International Conference on Similarity Search and Applications, SISAP 2011, Lipari Island, Italy, June 30 - July 01, 2011, edited by Ferro A, 117-118.: ACM. doi: 10.1145/1995412.1995436.
    • Lokoc J, Hetland ML, Skopal T, Beecks C. . ‘Ptolemaic indexing of the signature quadratic form distance.’ In Fourth International Conference on Similarity Search and Applications, SISAP 2011, Lipari Island, Italy, June 30 - July 01, 2011, edited by Ferro A, 9-16.: ACM. doi: 10.1145/1995412.1995417.
    • Beecks C, Uysal MS, Seidl T. . ‘L2-Signature Quadratic Form Distance for Efficient Query Processing in Very Large Multimedia Databases.’ In Advances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Taipei, Taiwan, January 5-7, 2011, Proceedings, Part I, edited by Lee K, Tsai W, Liao HM, Chen T, Hsieh J, Tseng C, 381-391.: Springer. doi: 10.1007/978-3-642-17832-0_36.
    • Beecks C, Assent I, Seidl T. . ‘Content-Based Multimedia Retrieval in the Presence of Unknown User Preferences.’ In Advances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Taipei, Taiwan, January 5-7, 2011, Proceedings, Part I, edited by Lee K, Tsai W, Liao HM, Chen T, Hsieh J, Tseng C, 140-150.: Springer. doi: 10.1007/978-3-642-17832-0_14.
    • Beecks C, Ivanescu AM, Kirchhoff S, Seidl T. . ‘Modeling multimedia contents through probabilistic feature signatures.’ In Proceedings of the 19th International Conference on Multimedia 2011, Scottsdale, AZ, USA, November 28 - December 1, 2011, edited by Candan KS, Panchanathan S, Prabhakaran B, Sundaram H, Feng W, Sebe N, 1433-1436.: ACM. doi: 10.1145/2072298.2072033.
    • Beecks C, Lokoc J, Seidl T, Skopal T. . ‘Indexing the signature quadratic form distance for efficient content-based multimedia retrieval.’ In Proceedings of the 1st International Conference on Multimedia Retrieval, ICMR 2011, Trento, Italy, April 18 - 20, 2011, edited by Natale FGBD, Bimbo AD, Hanjalic A, Manjunath BS, Satoh S, 24.: ACM. doi: 10.1145/1991996.1992020.
    • Schoeffmann K, Ahlströ}m D, Beecks C. . ‘3D Image Browsing on Mobile Devices.’ In 2011 IEEE International Symposium on Multimedia, ISM 2011, Dana Point, CA, USA, December 5-7, 2011, 335-336.: IEEE Computer Society. doi: 10.1109/ISM.2011.60.
    • Beecks C, Seidl T. . ‘Analyzing the inner workings of the Signature Quadratic Form Distance.’ In Proceedings of the 2011 IEEE International Conference on Multimedia and Expo, ICME 2011, 11-15 July, 2011, Barcelona, Catalonia, Spain, 1-6.: IEEE Computer Society. doi: 10.1109/ICME.2011.6012243.
    • Beecks C, Ivanescu AM, Kirchhoff S, Seidl T. . ‘Modeling image similarity by Gaussian mixture models and the Signature Quadratic Form Distance.’ In IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, November 6-13, 2011, edited by Metaxas DN, Quan L, Sanfeliu A, Gool LJV, 1754-1761.: IEEE Computer Society. doi: 10.1109/ICCV.2011.6126440.
    • Krulis M, Lokoc J, Beecks C, Skopal T, Seidl T. . ‘Processing the signature quadratic form distance on many-core GPU architectures.’ In Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, United Kingdom, October 24-28, 2011, edited by Macdonald C, Ounis I, Ruthven I, 2373-2376.: ACM. doi: 10.1145/2063576.2063970.

    • Beecks C, Uysal MS, Seidl T. . ‘Similarity matrix compression for efficient signature quadratic form distance computation.’ In Third International Workshop on Similarity Search and Applications, SISAP 2010, 18-19 September 2010, Istanbul, Turkey, edited by Ciaccia P, Patella M, 109-114.: ACM. doi: 10.1145/1862344.1862361.
    • Beecks C, Driessen P, Seidl T. . ‘Index support for content-based multimedia exploration.’ In Proceedings of the 18th International Conference on Multimedia 2010, Firenze, Italy, October 25-29, 2010, edited by Bimbo AD, Chang S, Smeulders AWM, 999-1002.: ACM. doi: 10.1145/1873951.1874134.
    • Beecks C, Wiedenfeld S, Seidl T. . ‘Improving the Efficiency of Content-Based Multimedia Exploration.’ In 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey, 23-26 August 2010, 3163-3166.: IEEE Computer Society. doi: 10.1109/ICPR.2010.774.
    • Beecks C, Uysal MS, Seidl T. . ‘A comparative study of similarity measures for content-based multimedia retrieval.’ In Proceedings of the 2010 IEEE International Conference on Multimedia and Expo, ICME 2010, 19-23 July 2010, Singapore, 1552-1557.: IEEE Computer Society. doi: 10.1109/ICME.2010.5582949.
    • Beecks C, Stadelmann T, Freisleben B, Seidl T. . ‘Visual speaker model exploration.’ In Proceedings of the 2010 IEEE International Conference on Multimedia and Expo, ICME 2010, 19-23 July 2010, Singapore, 727-728.: IEEE Computer Society. doi: 10.1109/ICME.2010.5583176.
    • Beecks C, Uysal MS, Seidl T. . ‘Efficient k-nearest neighbor queries with the Signature Quadratic Form Distance.’ In Workshops Proceedings of the 26th International Conference on Data Engineering, ICDE 2010, March 1-6, 2010, Long Beach, California, USA, 10-15.: IEEE Computer Society. doi: 10.1109/ICDEW.2010.5452772.
    • Beecks C, Uysal MS, Seidl T. . ‘Signature Quadratic Form Distance.’ In Proceedings of the 9th ACM International Conference on Image and Video Retrieval, CIVR 2010, Xi'an, China, July 5-7, 2010, edited by Li S, Gao X, Sebe N, 438-445.: ACM. doi: 10.1145/1816041.1816105.

    • Beecks C, Uysal MS, Seidl T. . ‘Signature quadratic form distances for content-based similarity.’ In Proceedings of the 17th International Conference on Multimedia 2009, Vancouver, British Columbia, Canada, October 19-24, 2009, edited by Gao W, Rui Y, Hanjalic A, Xu C, Steinbach EG, El-Saddik A, Zhou MX, 697-700.: ACM. doi: 10.1145/1631272.1631391.
    • Wichterich M, Beecks C, Sundermeyer M, Seidl T. . ‘Exploring multimedia databases via optimization-based relevance feedback and the earth mover's distance.’ In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, China, November 2-6, 2009, edited by Cheung DW, Song I, Chu WW, Hu X, Lin JJ, 1621-1624.: ACM. doi: 10.1145/1645953.1646187.
    • Beecks C, Wichterich M, Seidl T. . „Metrische Anpassung der Earth Mover's Distanz zur Ähnlichkeitssuche in Multimedia-Datenbanken.“ In Datenbanksysteme in Business, Technologie und Web (BTW 2009), 13. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), Proceedings, 2.-6. März 2009, Münster, Germany, herausgegeben von Freytag JC, Ruf T, Lehner W, Vossen G, 207-216.: GI.
    • Wichterich M, Beecks C, Sundermeyer M, Seidl T. . ‘Relevance Feedback for the Earth Mover's Distance.’ In Adaptive Multimedia Retrieval. Understanding Media and Adapting to the User - 7th International Workshop, AMR 2009, Madrid, Spain, September 24-25, 2009, Revised Selected Papers, edited by Detyniecki M, García-Serrano A, Nürnberger A, 72-86.: Springer. doi: 10.1007/978-3-642-18449-9_7.

    • Wichterich M, Beecks C, Seidl T. . ‘Ranking multimedia databases via relevance feedback with history and foresight support.’ In Proceedings of the 24th International Conference on Data Engineering Workshops, ICDE 2008, April 7-12, 2008, Cancún, México, 596-599.: IEEE Computer Society. doi: 10.1109/ICDEW.2008.4498386.

  • Funded Projects

    knowlEdge

    • Towards AI powered manufacturing services, processes, and products in an edge-to-cloud-knowlEdge continuum for humans [in-the-loop]

    • Website: www.knowledge-project.eu

    • Duration: January 2021 to December 2023

    • Funding: EC H2020 - Research and innovation actions

    • Description: Artificial intelligence (AI) is the software engine for the fourth industrial revolution that is changing the way we live and work. However, the complex technologies and the lack of skilled talent are barriers to progressing AI and thus increasing product quality and business sustainability. The EU-funded knowlEdge project will address the need for new AI solutions that are agile, reusable, distributed, scalable, accountable, secure, standardised and collaborative. The proposed new framework will ensure the secure management of distributed data and facilitate knowledge exchange. To achieve its goal, the project will combine innovative technologies from data management, data analytics and knowledge management.

    GAIA

    • Gaussian processes for automatic and interpretable anomaly detection

    • Website: www.dataninja.nrw

    • Duration: April 2021 to December 2024

    • Funding: Ministry for Culture and Science of North Rhine-Westphalia

    • Description: This research project aims to explore Gaussian processes for efficient detection and interpretation of anomalies in multivariate time series data. In particular, unsupervised Gaussian processes will be investigated and further developed in order to identify, understand and resolve underlying correlations and anomalies. In order to learn Gaussian process models in a scalable and real-time manner, we intend to develop new streaming algorithms, which will be implemented in an open source manner and with reference to industrial standards, and tested in application-oriented scenarios, together with industry partners.

    EPIX

    • Efficient Ptolemaic Indexing

    • Duration: April 2021 to March 2023

    • Funding: German Research Fundation

    • Description: Concomitant with the rapid growth of heterogeneous data, the demand for efficient and scalable data access increases. Ptolemaic Access Methods provide a domain-agnostic approach for indexing and accessing complex data spaces based on metric similarity models. While initial studies have already demonstrated the efficiency of this comparatively young indexing method in various data-intensive domains, the fundamentals of this approach are largely unexplored. Questions concerning the approximation of distances in metric and ptolemaic data spaces, the geometry of ptolemaic queries, as well as the interaction of different lower bounding methods are currently considered to be not sufficiently answered. The aim of this research project is to investigate the fundamentals of ptolemaic and metric access methods and to methodically advance the findings obtained in order to demonstrate the performance of this class of access methods for indexing large, complex data spaces. This research project thus pursues the overall goal of advancing the development of efficient data technologies for exploring digital data resources.