Publications

Assent, I. (2009). Efficient adaptive retrieval and mining in large multimedia databases. Datenbank-Spektrum, 9(29), 57.

Assent, I., Krieger, R. & Seidl, T. (2008). Evolutionäre Clustering-Algorithmen: Bionik-Methoden für Knowledge Discovery in Datenbanken. R W T H - Themen, (1). http://www.rwth-aachen.de/global/show_document.asp?id=aaaaaaaaaabfhbu

Kranen, P., Assent, I. & Seidl, T. (2012). An Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams. Datenbank-Spektrum, 12(1), 43-50 . https://doi.org/10.1007/s13222-012-0083-9

Mai, S. T., Assent, I., Jacobsen, J. & Dieu, M. S. (2018). Anytime parallel density-based clustering. Data Mining and Knowledge Discovery, 32(4), 1121-1176. https://doi.org/10.1007/s10618-018-0562-1

Assent, I., Krieger, R., Glavic, B. & Seidl, T. (2008). Clustering Multidimensional Sequences in Spatial and Temporal Databases. Knowledge and Information Systems, 16(1), 29-51. https://doi.org/10.1007/s10115-007-0121-3

Assent, I. (2012). Clustering high dimensional data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(4), 340-350. https://doi.org/10.1002/widm.1062

Assent, I. (2010). Effiziente ähnlichkeitssuche und Data Mining in großen Multimediadatenbanken. IT - Information Technology, 52(1), 45-47. https://doi.org/10.1524/itit.2010.0570

Chung, Y., Mortensen, M. L., Binnig, C. & Kraska, T. (2018). Estimating the impact of unknown unknowns on aggregate qery results. ACM Transactions on Database Systems, 43(1), [3]. https://doi.org/10.1145/3167970

Müller, E., Assent, I., Günnemann, S., Seidl, T. & Dy, J. (2015). MultiClust special issue on discovering, summarizing and using multiple clusterings. Machine Learning, 98(1-2), 1-5. https://doi.org/10.1007/s10994-014-5445-0

Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., Assent, I. & Houle, M. E. (2016). On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30(4), 891-927. https://doi.org/10.1007/s10618-015-0444-8

Yiu, M. L., Assent, I. & Jensen, C. S. (2012). Outsourced Similarity Search on Metric Data Assets. I E E E Transactions on Knowledge & Data Engineering, 24(2), 338-352. https://doi.org/10.1109/TKDE.2010.222

Son, M. T., Amer-Yahia, S., Assent, I., Birk, M., Storgaard Dieu, M., Jacobsen, J. & Kristensen, J. (2019). Scalable Interactive Dynamic Graph Clustering on Multicore CPUs. IEEE Transactions on Knowledge and Data Engineering, 31(7), 1239-1252. [8340880]. https://doi.org/10.1109/TKDE.2018.2828086

Bøgh, K. S., Chester, S. & Assent, I. (2016). SkyAlign: a portable, work-efficient skyline algorithm for multicore and GPU architectures. The VLDB Journal, 25(6), 817-841. https://doi.org/10.1007/s00778-016-0438-1

Shao, J., Tan, Y., Gao, L., Yang, Q., Plant, C. & Assent, I. (2019). Synchronization-based clustering on evolving data stream. Information Sciences, 501, 573-587. https://doi.org/10.1016/j.ins.2018.09.035

Magnani, M., Assent, I. & Mortensen, M. L. (2014). Taking the Big Picture: representative skylines based on significance and diversity. V L D B Journal, 23(5), 795-815. https://doi.org/10.1007/s00778-014-0352-3

Kranen, P., Assent, I., Baldauf, C. & Seidl, T. (2011). The ClusTree : indexing micro-clusters for anytime stream mining. Knowledge and Information Systems, 29(2), 249-272. https://doi.org/10.1007/s10115-010-0342-8

Yang, B., Guo, C., Ma, Y. & Jensen, C. S. (2015). Toward personalized, context-aware routing. V L D B Journal, 24(2), 297-318. https://doi.org/10.1007/s00778-015-0378-1

Bøgh, K. S., Chester, S. & Assent, I. (2015). Work-Efficient Parallel Skyline Computation for the GPU. Proceedings of the VLDB Endowment, 8(9), 962-973. https://doi.org/10.14778/2777598.2777605

Assent, I. (2009). Efficient adaptive retrieval and mining in large multimedia databases. Lecture Notes in Informatics, 144. http://btw2009.uni-muenster.de/oe20/cms/website.php?id=/de/index/programm.htm

Nguyen, H. V., Gopalkrishnan, V. & Assent, I. (2011). An Unbiased Distance-based Outlier Detection Approach for High-dimensional Data. Lecture Notes in Computer Science, 6587, 138-152. https://doi.org/10.1007/978-3-642-20149-3_12

Assent, I., Wichterich, M., Krieger, R., Kremer, H. & Seidl, T. (2009). Anticipatory DTW for Efficient Similarity Search in Time Series Databases. International Conference on Very Large Data Bases. Proceedings, 826-837. http://www.informatik.uni-trier.de/~ley/db/journals/pvldb/pvldb2.html

Assent, I., Kranen, P., Baldauf, C. & Seidl, T. (2012). AnyOut : Anytime Outlier Detection Approach for High-dimensional Data. Lecture Notes in Computer Science, 7238, 228-242. https://doi.org/10.1007/978-3-642-29038-1_18

Beecks, C., Assent, I. & Seidl, T. (2011). Content-Based Multimedia Retrieval in the Presence of Unknown User Preferences. Lecture Notes in Computer Science, 6523, 140-150. https://doi.org/10.1007/978-3-642-17832-0_14

Kremer, H., Günnemann, S., Ivanescu, A-M., Assent, I. & Seidl, T. (2011). Efficient Processing of Multiple DTW Queries in Time Series Databases. Lecture Notes in Computer Science, 6809, 150-167. https://doi.org/10.1007/978-3-642-22351-8_9

Müller, E., Günnemann, S., Assent, I. & Seidl, T. (2009). Evaluating Clustering in Subspace Projections of High Dimensional Data. V L D B Journal, (1), 1270-1281. http://www.informatik.uni-trier.de/~ley/db/journals/pvldb/pvldb2.html

Assent, I., Günnemann, S., Kremer, H. & Seidl, T. (2009). High-dimensional Indexing for Multimedia Features. Lecture Notes in Informatics, 187-206. http://btw2009.uni-muenster.de/oe20/cms/website.php?id=/de/index/programm.htm

Seidl, T., Müller, E., Assent, I. & Steinhausen, U. (2008). Outlier detection and ranking based on subspace clustering. Dagstuhl Seminar Proceedings. http://www.dagstuhl.de/Materials/index.en.phtml?08421

Assent, I., Domeniconi, C., Gullo, F., Tagarelli, A. & Zimek, A. (Eds.) (2013). Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering. Association for Computing Machinery. http://dl.acm.org.ez.statsbiblioteket.dk:2048/citation.cfm?id=2501006&coll=DL&dl=ACM

Assent, I. (2008). Efficient adaptive retrieval and mining in large multimedia databases. http://darwin.bth.rwth-aachen.de/opus3/volltexte/2008/2259/

Ma, M. Y. (2015). Enabling Time-Dependent Uncertain Edge Weights and Stochastic Routing in Road Networks. Department of Computer Science, Aarhus University.

Mortensen, M. L. (2016). Multi-Criteria Decision Support Queries in Exploratory & Open World Settings.

Assent, I., Krieger, R., Welter, P., Herbers, J. & Seidl, T. (2009). Data Mining For Robust Flight Scheduling. In Data Mining for Business Applications (pp. 267-282) http://www.springer.com/computer/database+management+&+information+retrieval/book/978-0-387-79419-8

Wichterich, M., Kranen, P., Assent, I. & Seidl, T. (2010). Efficient EMD-based Similarity Search in Medical Image Databases. In C. Plant & C. Böhm (Eds.), Database Technology for Life Sciences and Medicine (pp. 175-202). World Scientific. Science, Engineering and Biology Informatics , Vol.. 6

Zimek, A., Assent, I. & Vreeken , J. (2014). Frequent Pattern Mining Algorithms for Data Clustering. In C. C. Aggarwal & J. Han (Eds.), Frequent Pattern Mining (pp. 403-423). Springer. https://doi.org/10.1007/978-3-319-07821-2_16

Kranen, P., Müller, E., Assent, I., Krieger, R. & Seidl, T. (2010). Incremental Learning of Medical Data for Multi-Step Patient Health Classification. In C. Plant & C. Böhm (Eds.), Database Technology for Life Sciences and Medicine (pp. 321-344). World Scientific. Science, Engineering and Biology Informatics , Vol.. 6

Pham, T. H., Kristensen, J., Mai, S. T., Assent, I., Jacobsen, J., Vo, B. & Le, A. (2018). Interactive Exploration of Subspace Clusters on Multicore Processors. In A. Hameurlain, R. Wagner, D. Benslimane, E. Damiani & W. I. Grosky (Eds.), Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX: Special Issue on Database- and Expert-Systems Applications (Vol. 11310, pp. 169-199). Springer VS. Lecture Notes in Computer Science (LNCS), Vol.. 11310 https://doi.org/10.1007/978-3-662-58415-6_6

Müller, E., Assent, I., Günnemann, S., Gerwert, P., Hannen, M., Jansen, T. & Seidl, T. (2011). A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. In T. Härder, W. Lehner, B. Mitschang, H. Schöning & H. Schwarz (Eds.), Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011) (pp. 347-366). Gesellschaft für Informatik e.V..

Ciosici, M. R. & Assent, I. (2019). Abbreviation Explorer - an interactive system for pre-evaluation of Unsupervised Abbreviation Disambiguation. In W. Ammar, A. Louis & N. Mostafazadeh (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations) Association for Computational Linguistics. https://aclweb.org/anthology/papers/N/N19/N19-4001/

Assent, I. (2009). Actively building private recommender networks for evolving reliable relationships. In Proc. International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2009) (pp. 1611-1614) https://doi.org/10.1109/ICDE.2009.145

Thai Son, M., Assent, I., Birk, M. S., Dieu, M. S., Jacobsen, J., Kristensen, J., Rahman, H., Thirumuruganathan, S., Das, G., Omidvar-Tehrani, B., Borromeo, R. M., Chen, L., Miller, R., Benouaret, I., Amer-Yahia, S. & Roy, S. B. (2019). An Efficient Greedy Algorithm for Sequence Recommendation. In DEXA 2019: International Conference on Database and Expert Systems Applications, Aug 2019, Linz, Austria (pp. 314-326) https://doi.org/10.1007/978-3-030-27615-7_24

Thai Son, M., Assent, I. & Storgaard, M. (2016). AnyDBC: An efficient anytime density-based clustering algorithm for very large complex datasets. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1025-1034). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939750

Thai Son, M., Assent, I. & Le, A. T. (2016). Anytime OPTICS: An efficient approach for hierarchical density-based clustering. In S. B. Navathe, W. Wu, S. Shekhar, X. Du, X. Sean Wang & H. Xiong (Eds.), Database Systems for Advanced Applications - 21st International Conference, DASFAA 2016, Proceedings (Vol. 9642, pp. 164-179). Springer VS. Lecture Notes in Computer Science (LNCS), Vol.. 9642 https://doi.org/10.1007/978-3-319-32025-0_11

Müller, E., Assent, I., Krieger, R., Günnemann, S. & Seidl, T. (2009). DensEst: Density Estimation for Data Mining in High Dimensional Spaces. In Proceedings of the Ninth SIAM International Conference on Data Mining (pp. 175-186) http://www.siam.org/proceedings/datamining/2009/dm09.php

Neerbek, J., Assent, I. & Dolog, P. (2018). Detecting Complex Sensitive Information via Phrase Structure in Recursive Neural Networks. In D. Phung, V. S. Tseng, G. I. Webb, B. Ho, M. Ganji & L. Rashidi (Eds.), Advances in Knowledge Discovery and Data Mining: PAKDD '18 (Vol. 10939, pp. 373-385). Springer VS. Lecture Notes in Computer Science (LNCS), No. 10939 https://doi.org/10.1007/978-3-319-93040-4_30

Assent, I., Kranen, P., Baldauf, C. & Seidl, T. (2010). Detecting Outliers on Arbitrary Data Streams using Anytime Approaches. In International Workshop on Novel Data Stream Pattern Mining Techniques (StreamKDD 2010) in conjunction with 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA (2010) https://doi.org/10.1145/1833280.1833282

Dang, X-H., Assent, I., Ng, R. T., Zimek, A. & Schubert, E. (2014). Discriminative features for identifying and interpreting outliers. In Proceedings, 2014 IEEE 30th International Conference on Data Engineering (ICDE) (pp. 88-99). [6816642] IEEE Computer Society Press. https://doi.org/10.1109/ICDE.2014.6816642

Assent, I., Krieger, R., Müller, E. & Seidl, T. (2008). EDSC: Efficient Density-Based Subspace Clustering. In Proceeding of the 17th ACM conference on Information and knowledge management 2008, Napa Valley, California, USA (pp. 1093-1102) https://doi.org/10.1145/1458082.1458227

Guo, C., Ma, M., Yang, B., Jensen, C. S. & Kaul, M. (2012). EcoMark: Evaluating Models of Vehicular Environmental Impact. In I. Cruz & C. Knoblock (Eds.), Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL GIS 2012 (pp. 269-278). Association for Computing Machinery. https://doi.org/10.1145/2424321.2424356

Vicente, C. R., Assent, I. & Jensen, C. S. (2011). Effective Privacy-Preserving Online Route Planning. In 2011 12th IEEE International Conference on Mobile Data Management (MDM) (pp. 119-128). IEEE Computer Society Press. https://doi.org/10.1109/MDM.2011.20

Wichterich, M., Assent, I., Philipp, K. & Seidl, T. (2008). Efficient EMD-based Similarity Search in Multimedia Databases via Flexible Dimensionality Reduction. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data (pp. 199-212) https://doi.org/10.1145/1376616.1376639

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