Publications

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

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. ... 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

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., 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

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.

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.

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

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). Berlin, Heidelberg: 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..

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

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

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

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

Bøgh, K. S., Assent, I. & Magnani, M. (2013). Efficient GPU-based skyline computation. In R. Johnson & A. Kemper (Eds.), Proceedings of the Ninth International Workshop on Data Management on New Hardware , DaMoN '13 [5] Association for Computing Machinery. https://doi.org/10.1145/2485278.2485283

Assent, I. (2011). Efficient algorithms for collaborative decision making for large scale settings. In Proceedings of the 3rd international workshop on Collaborative information retrieval (pp. 15-18). Association for Computing Machinery. https://doi.org/10.1145/2064075.2064081

Mortensen, M. L., Chester, S., Assent, I. & Magnani, M. (2015). Efficient caching for constrained skyline queries. In International Conference on Extending Database Technology (EDBT 2015) (pp. 337-348). openproceedings.org. https://doi.org/10.5441/002/edbt.2015.30

Ma, M., Yang, B. & Jensen, C. S. (2014). Enabling Time-Dependent Uncertain Eco-Weights For Road Networks. In M. A. Nascimento & M. Renz (Eds.), Proceedings of the 1st International ACM Workshop on Managing and Mining Enriched Geo-spatial Data: GeoRich 2014 [1] Association for Computing Machinery. https://doi.org/10.1145/2619112.2619113

Micenková, B., Ng, R. T., Dang, X-H. & Assent, I. (2013). Explaining outliers by subspace separability. In H. Xiong, G. Karypis, B. Thuraisingham, D. Cook & X. Wu (Eds.), Proceedings, IEEE 13th International Conference on Data Mining (ICDM 2013) (pp. 518 - 527 ). IEEE Press. IEEE International Conference on Data Mining (ICDM'13)

Chester, S. & Assent, I. (2015). Explanations for Skyline Query Results. In International Conference on Extending Database Technology (EDBT 2015) (pp. 349-360). openproceedings.org. https://doi.org/10.5441/002/edbt.2015.31

Günnemann, S., Färber, I., Müller, E., Assent, I. & Seidl, T. (2011). External Evaluation Measures for Subspace Clustering. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 1363-1372). Association for Computing Machinery. https://doi.org/10.1145/2063576.2063774

Magnani, M. & Assent, I. (2013). From stars to galaxies: skyline queries on aggregate data. In G. Guerrini & N. W. . Paton (Eds.), International Conference on Extending Database Technology (pp. 477-488 ). Association for Computing Machinery. https://doi.org/10.1145/2452376.2452432

Bøgh, K. S., Chester, S., Sidlauskas, D. & Assent, I. (2014). Hashcube: A Data Structure for Space- and Query-Efficient Skycube Compression. In J. Li & X. S. Wang (Eds.), Proceedings of The 23rd ACM International Conference on Information and Knowledge Management (CIKM 2014) (pp. 1767-1770). Association for Computing Machinery. https://doi.org/10.1145/2661829.2661891

Kristensen, J., Mai, S. T., Assent, I., Jacobsen, J., Vo, B. & Le, A. (2017). Interactive exploration of subspace clusters for high dimensional data. In D. Benslimane, E. Damiani, W. I. Grosky, A. Hameurlain, A. Sheth & R. R. Wagner (Eds.), Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Proceedings (Vol. 10438 LNCS, pp. 327-342). Springer VS. Lecture Notes in Computer Science, Vol.. 10438 https://doi.org/10.1007/978-3-319-64468-4_25

Dang, X-H., Micenková, B., Assent, I. & Ng, R. T. (2013). Local Outlier Detection with Interpretation. In H. Blockeel, K. Kersting , S. Nijssen & F. Železný (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III (pp. 304-320 ). Springer VS. Lecture Notes in Computer Science, Vol.. 8190 https://doi.org/10.1007/978-3-642-40994-3_20

Dang, X-H., Assent, I. & Bailey, J. (2012). Multiple Clustering Views via Constrained Projections. In E. Müller, T. Seidl & A. Zimek (Eds.), 2012 SIAM International Conference on Data Mining: 3rd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings (pp. 23-30). Society for Industrial and Applied Mathematics.

Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenkova, B., Schubert, E. ... Houle, M. E. (2016). On the Evaluation of Outlier Detection: Measures, Datasets, and an Empirical Study Continued. In R. Krestel, D. Mottin & E. Müller (Eds.), Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" (Vol. 1670, pp. 1). ceur workshop proceedings. CEUR Workshop Proceedings, Vol.. 1670

Chester, S., Mortensen, M. L. & Assent, I. (2014). On the Suitability of Skyline Queries for Data Exploration. In K. S. Candan et al. (Ed.), Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference: 1st International Workshop on Exploratory Search in Databases and the Web (ExploreDB 2014) (pp. 161-166). CEUR-WS.org. CEUR Workshop Proceedings

Dang, X-H., Micenková, B., Assent, I. & Ng, R. T. (2013). Outlier Detection with Space Transformation and Spectral Analysis. In C. Kamath, J. Dy, Z. Obradovic, J. Ghosh, S. Parthasarathy & Z-H. Zhou (Eds.), Proceedings of the 2013 SIAM International Conference on Data Mining, SDM (pp. 225-233). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611972832.25

Muller, E., Assent, I., Iglesias, P., Mülle, Y. & Böhm, K. B. (2012). Outlier Ranking via Subspace Analysis in Multiple Views of the Data. In Proceedings of the 12th IEEE International Conference on Data Mining, ICDM (pp. 529 - 538 ). IEEE Communications Society. https://doi.org/10.1109/ICDM.2012.112

Bouros, P., Assent, I., Jeppesen, J. H. & Toftegaard, T. S. (2019). Processing satellite imagery for decision-making in precision agriculture. In P. Papotti (Ed.), Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference (EDBT/ICDT 2019) Sun Site Central Europe.

Müller, E., Assent, I., Günnemann, S. & Seidl, T. (2011). Scalable Density-Based Subspace Clustering. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 1077-1086). Association for Computing Machinery. https://doi.org/10.1145/2063576.2063733

Chester, S., Sidlauskas, D., Assent, I. & Bøgh, K. S. (2015). Scalable Parallelization of Skyline Computation for Multi-core Processors. In 31st IEEE International Conference on Data Engineering (ICDE 2015) (pp. 1083 - 1094). IEEE Computer Society Press. https://doi.org/10.1109/ICDE.2015.7113358

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