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ORCIDThomas Seidl 0001
Person information
- affiliation (since 2016): Ludwig Maximilians University of Munich, Institute for Computer Science, Germany
- affiliation (2002-2016): RWTH Aachen University, Germany
Other persons with the same name
- Thomas Seidl 0002 — Fraunhofer Institute for Factory Operation and Automation (IFF)
- Thomas Seidl 0003 — VfB Stuttgart, Stuttgart, Germany
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2020 – today
- 2023
[j47]Theresa Ullmann
, Anna Beer, Maximilian Hünemörder, Thomas Seidl, Anne-Laure Boulesteix:
Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study. Adv. Data Anal. Classif. 17(1): 211-238 (2023)- [c299]Rajat Koner, Tanveer Hannan, Suprosanna Shit, Sahand Sharifzadeh, Matthias Schubert, Thomas Seidl, Volker Tresp:
InstanceFormer: An Online Video Instance Segmentation Framework. AAAI 2023: 1188-1195 - [c298]Michael Fromm, Max Berrendorf, Evgeniy Faerman, Thomas Seidl:
Cross-Domain Argument Quality Estimation. ACL (Findings) 2023: 13435-13448 - [c297]Yao Zhang, Yunpu Ma, Thomas Seidl, Volker Tresp:
Adaptive Multi-Resolution Attention with Linear Complexity. IJCNN 2023: 1-8 - [c296]David Winkel, Niklas Strauß, Matthias Schubert, Yunpu Ma, Thomas Seidl:
Constrained Portfolio Management Using Action Space Decomposition for Reinforcement Learning. PAKDD (2) 2023: 373-385 - [c295]Sandra Gilhuber, Julian Busch, Daniel Rotthues, Christian M. M. Frey, Thomas Seidl:
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. ECML/PKDD (1) 2023: 75-91 - [c294]Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok, Thomas Seidl:
How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning. ECML/PKDD (2) 2023: 330-347 - [i20]Tanveer Hannan, Rajat Koner, Maximilian Bernhard, Suprosanna Shit, Bjoern H. Menze, Volker Tresp, Matthias Schubert, Thomas Seidl:
GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation. CoRR abs/2305.17096 (2023) - [i19]Sandra Gilhuber, Julian Busch, Daniel Rotthues, Christian M. M. Frey, Thomas Seidl:
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. CoRR abs/2308.00146 (2023) - [i18]Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok, Thomas Seidl:
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning. CoRR abs/2308.08224 (2023) - 2022
- [j46]Ellen Hohma, Christian M. M. Frey, Anna Beer, Thomas Seidl:
SCAR - Spectral Clustering Accelerated and Robustified. Proc. VLDB Endow. 15(11): 3031-3044 (2022) - [c293]Sandra Gilhuber, Philipp Jahn, Yunpu Ma, Thomas Seidl:
VERIPS: Verified Pseudo-label Selection for Deep Active Learning. ICDM 2022: 951-956 - [c292]Sandra Gilhuber, Max Berrendorf, Yunpu Ma, Thomas Seidl:
Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations. IAL@PKDD/ECML 2022: 43-48 - [c291]David Winkel, Niklas Strauß, Matthias Schubert, Thomas Seidl:
Risk-Aware Reinforcement Learning for Multi-Period Portfolio Selection. ECML/PKDD (6) 2022: 185-200 - [i17]Michael Fromm, Max Berrendorf, Johanna Reiml, Isabelle Mayerhofer, Siddharth Bhargava, Evgeniy Faerman, Thomas Seidl:
Towards a Holistic View on Argument Quality Prediction. CoRR abs/2205.09803 (2022) - [i16]Rajat Koner, Tanveer Hannan, Suprosanna Shit, Sahand Sharifzadeh, Matthias Schubert, Thomas Seidl, Volker Tresp:
InstanceFormer: An Online Video Instance Segmentation Framework. CoRR abs/2208.10547 (2022) - 2021
- [j45]Daniyal Kazempour, Johannes Winter, Peer Kröger, Thomas Seidl:
On Methods and Measures for the Inspection of Arbitrarily Oriented Subspace Clusters. Datenbank-Spektrum 21(3): 213-223 (2021) - [c290]Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl:
Argument Mining Driven Analysis of Peer-Reviews. AAAI 2021: 4758-4766 - [c289]Sandra Obermeier, Anna Beer, Florian Wahl, Thomas Seidl:
Cluster Flow - an Advanced Concept for Ensemble-Enabling, Interactive Clustering. BTW 2021: 175-194 - [c288]Daniyal Kazempour, Anna Beer, Melanie Oelker, Peer Kröger, Thomas Seidl:
Compound Segmentation via Clustering on Mol2Vec-based Embeddings. e-Science 2021: 60-69 - [c287]Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman:
Diversity Aware Relevance Learning for Argument Search. ECIR (2) 2021: 264-271 - [c286]Anna Beer, Ekaterina Allerborn, Valentin Hartmann, Thomas Seidl:
KISS - A fast kNN-based Importance Score for Subspaces. EDBT 2021: 391-396 - [c285]Anna Wimbauer, Florian Richter, Thomas Seidl:
PErrCas: Process Error Cascade Mining in Trace Streams. ICPM Workshops 2021: 224-236 - [c284]Anna Beer, Lisa Stephan, Thomas Seidl:
LUCKe - Connecting Clustering and Correlation Clustering. ICDM (Workshops) 2021: 431-440 - [c283]Julian Busch, Maximilian Hünemörder, Janis Held, Peer Kröger, Thomas Seidl:
Implicit Hough Transform Neural Networks for Subspace Clustering. ICDM (Workshops) 2021: 441-448 - [c282]Ludwig Zellner, Janina Sontheim, Florian Richter, Gabriel Lindner, Thomas Seidl:
SCORER-Gap: Sequentially Correlated Rules for Event Recommendation Considering Gap Size. ICDM (Workshops) 2021: 925-934 - [c281]Andreas Lohrer, Anna Beer, Maximilian Archimedes Xaver Hünemörder, Jenny Lauterbach, Thomas Seidl, Peer Kröger:
AnyCORE - An Anytime Algorithm for Cluster Outlier REmoval. LWDA 2021: 145-156 - [c280]Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl:
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification. SSDBM 2021: 121-132 - [e9]Thomas Seidl, Michael Fromm, Sandra Obermeier:
Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021. CEUR Workshop Proceedings 2993, CEUR-WS.org 2021 [contents] - [i15]Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl:
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification. CoRR abs/2103.03939 (2021) - [i14]Yao Zhang, Yunpu Ma, Thomas Seidl, Volker Tresp:
Adaptive Multi-Resolution Attention with Linear Complexity. CoRR abs/2108.04962 (2021) - [i13]Nataliia Kees, Michael Fromm, Evgeniy Faerman, Thomas Seidl:
Active Learning for Argument Strength Estimation. CoRR abs/2109.11319 (2021) - 2020
- [j44]Florian Richter, Yifeng Lu, Daniyal Kazempour, Thomas Seidl:
"Show Me the Crowds!" Revealing Cluster Structures Through AMTICS. Data Sci. Eng. 5(4): 360-374 (2020) - [j43]Dietrich Trautmann, Michael Fromm, Volker Tresp, Thomas Seidl, Hinrich Schütze:
Relational and Fine-Grained Argument Mining. Datenbank-Spektrum 20(2): 99-105 (2020) - [c279]Florian Richter, Janina Sontheim, Ludwig Zellner, Thomas Seidl:
TADE: Stochastic Conformance Checking Using Temporal Activity Density Estimation. BPM 2020: 220-236 - [c278]Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl:
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels. CIKM (Workshops) 2020 - [c277]Florian Richter, Yifeng Lu, Daniyal Kazempour, Thomas Seidl:
AMTICS: Aligning Micro-clusters to Identify Cluster Structures. DASFAA (1) 2020: 752-768 - [c276]Julian Busch, Jiaxing Pi, Thomas Seidl:
PushNet: Efficient and Adaptive Neural Message Passing. ECAI 2020: 1039-1046 - [c275]Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl:
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned. ECIR (2) 2020: 3-11 - [c274]Daniyal Kazempour, Peer Kröger, Thomas Seidl:
Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering. ICDM (Workshops) 2020: 300-307 - [c273]Daniyal Kazempour, Long Mathias Yan, Peer Kröger, Thomas Seidl:
You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters. ICDM (Workshops) 2020: 308-315 - [c272]Daniyal Kazempour, Anna Beer, Peer Kröger, Thomas Seidl:
I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering. ICDM (Workshops) 2020: 316-323 - [c271]Thomas Seidl:
Keynote Data Mining on Process Data. ICPM 2020: 1 - [c270]Florian Richter, Yifeng Lu, Ludwig Zellner, Janina Sontheim, Thomas Seidl:
TOAD: Trace Ordering for Anomaly Detection. ICPM 2020: 169-176 - [c269]Ludwig Zellner, Florian Richter, Janina Sontheim, Andrea Maldonado, Thomas Seidl:
Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation. ICPM Workshops 2020: 206-217 - [c268]Florian Richter, Andrea Maldonado, Ludwig Zellner, Thomas Seidl:
OTOSO: Online Trace Ordering for Structural Overviews. ICPM Workshops 2020: 218-229 - [c267]Andrea Maldonado, Janina Sontheim, Florian Richter, Thomas Seidl:
Performance Skyline: Inferring Process Performance Models from Interval Events. ICPM Workshops 2020: 230-242 - [c266]Yao Zhang, Yifeng Lu, Thomas Seidl:
KNNAC: An Efficient k Nearest Neighbor Based Clustering with Active Core Detection. iiWAS 2020: 62-71 - [c265]Yifeng Lu, Yao Zhang, Florian Richter, Thomas Seidl:
k-Nearest Neighbor based Clustering with Shape Alternation Adaptivity. IJCNN 2020: 1-8 - [c264]Anna Beer, Daniyal Kazempour, Julian Busch, Alexander Tekles, Thomas Seidl:
Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data. LWDA 2020: 11-22 - [c263]Felix Borutta, Daniyal Kazempour, Felix Mathy, Peer Kröger, Thomas Seidl:
Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform. PAKDD (1) 2020: 356-368 - [c262]Anna Beer, Dominik Seeholzer, Nadine Sarah Schüler, Thomas Seidl:
Angle-Based Clustering. SISAP 2020: 312-320 - [c261]Anna Beer, Valentin Hartmann, Thomas Seidl:
Orderings of Data - More Than a Tripping Hazard: Visionary. SSDBM 2020: 17:1-17:4 - [p5]Yifeng Lu, Florian Richter, Thomas Seidl:
Efficient Infrequent Pattern Mining Using Negative Itemset Tree. Complex Pattern Mining 2020: 1-16 - [i12]Julian Busch, Jiaxing Pi, Thomas Seidl:
PushNet: Efficient and Adaptive Neural Message Passing. CoRR abs/2003.02228 (2020) - [i11]Julian Busch, Evgeniy Faerman, Matthias Schubert, Thomas Seidl:
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering. CoRR abs/2009.12875 (2020) - [i10]Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl:
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels. CoRR abs/2010.12316 (2020) - [i9]Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman:
Diversity Aware Relevance Learning for Argument Search. CoRR abs/2011.02177 (2020) - [i8]Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl:
Argument Mining Driven Analysis of Peer-Reviews. CoRR abs/2012.07743 (2020)
2010 – 2019
- 2019
- [j42]Janis Held, Anna Beer, Thomas Seidl:
Chain-detection Between Clusters. Datenbank-Spektrum 19(3): 219-230 (2019) - [j41]Daniyal Kazempour, Markus Mauder, Peer Kröger, Thomas Seidl:
Detecting global hyperparaboloid correlated clusters: a Hough-transform based multicore algorithm. Distributed Parallel Databases 37(1): 39-72 (2019) - [j40]Marwan Hassani, Daniel Töws, Alfredo Cuzzocrea, Thomas Seidl:
BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams. Int. J. Data Sci. Anal. 8(3): 223-239 (2019) - [j39]Florian Richter, Thomas Seidl:
Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times. Inf. Syst. 84: 265-282 (2019) - [c260]Florian Richter, Ludwig Zellner, Imen Azaiz, David Winkel, Thomas Seidl:
LIProMa: Label-Independent Process Matching. Business Process Management Workshops 2019: 186-198 - [c259]Janis Held, Anna Beer, Thomas Seidl:
Chain-detection for DBSCAN. BTW (Workshops) 2019: 173-183 - [c258]Daniyal Kazempour, Maksim Kazakov, Peer Kröger, Thomas Seidl:
DICE: Density-based Interactive Clustering and Exploration. BTW 2019: 547-550 - [c257]Anna Beer, Daniyal Kazempour, Thomas Seidl:
Rock - Let the points roam to their clusters themselves. EDBT 2019: 630-633 - [c256]Daniyal Kazempour, Lisa Krombholz, Peer Kröger, Thomas Seidl:
A Galaxy of Correlations. EDBT 2019: 702-705 - [c255]Daniyal Kazempour, Thomas Seidl:
Insights into a running clockwork: On interactive process-aware clustering. EDBT 2019: 706-709 - [c254]Anna Beer, Daniyal Kazempour, Marcel Baur, Thomas Seidl:
Human Learning in Data Science. HCI (34) 2019: 170-176 - [c253]Daniyal Kazempour, Anna Beer, Thomas Seidl:
Data on RAILs: On Interactive Generation of Artificial Linear Correlated Data. HCI (34) 2019: 184-189 - [c252]Anna Beer, Nadine Sarah Schüler, Thomas Seidl:
A Generator for Subspace Clusters. LWDA 2019: 69-73 - [c251]Maximilian Archimedes Xaver Hünemörder, Anna Beer, Daniyal Kazempour, Thomas Seidl:
CODEC - Detecting Linear Correlations in Dense Clusters using coMAD-based PCA. LWDA 2019: 111-114 - [c250]Daniyal Kazempour, Anna Beer, Oliver Schrüfer, Thomas Seidl:
Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents. LWDA 2019: 127-138 - [c249]Daniyal Kazempour, Long Mathias Yan, Thomas Seidl:
From Covariance to Comode in context of Principal Component Analysis. LWDA 2019: 139-143 - [c248]Florian Richter, Florian Wahl, Alona Sydorova, Thomas Seidl:
k-process: Model-Conformance-based Clustering of Process Instances. LWDA 2019: 161-172 - [c247]Janina Sontheim, Florian Richter, Thomas Seidl:
Temporal Deviations on Event Sequences. LWDA 2019: 173-177 - [c246]Florian Richter, Ludwig Zellner, Janina Sontheim, Thomas Seidl:
Model-Aware Clustering of Non-conforming Traces. OTM Conferences 2019: 193-200 - [c245]Anna Beer, Jennifer Lauterbach, Thomas Seidl:
MORe++: k-Means Based Outlier Removal on High-Dimensional Data. SISAP 2019: 188-202 - [c244]Maximilian Archimedes Xaver Hünemörder, Daniyal Kazempour, Peer Kröger, Thomas Seidl:
SIDEKICK: Linear Correlation Clustering with Supervised Background Knowledge. SISAP 2019: 221-230 - [c243]Daniyal Kazempour, M. A. X. Hünemörder, Thomas Seidl:
On coMADs and Principal Component Analysis. SISAP 2019: 273-280 - [c242]Anna Beer, Daniyal Kazempour, Lisa Stephan, Thomas Seidl:
LUCK- Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function. SSDBM 2019: 181-184 - [c241]Anna Beer, Thomas Seidl:
Graph Ordering and Clustering: A Circular Approach. SSDBM 2019: 185-188 - [c240]Daniyal Kazempour, Kilian Emmerig, Peer Kröger, Thomas Seidl:
Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform. SSDBM 2019: 222-225 - [c239]Daniyal Kazempour, Thomas Seidl:
On systematic hyperparameter analysis through the example of subspace clustering. SSDBM 2019: 226-229 - [c238]Michael Fromm, Evgeniy Faerman, Thomas Seidl:
TACAM: Topic And Context Aware Argument Mining. WI 2019: 99-106 - [c237]Yifeng Lu, Florian Richter, Thomas Seidl:
LSCMiner: Efficient Low Support Closed Itemsets Mining. WISE 2019: 293-309 - [i7]Michael Fromm, Evgeniy Faerman, Thomas Seidl:
TACAM: Topic And Context Aware Argument Mining. CoRR abs/1906.00923 (2019) - [i6]Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl:
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned. CoRR abs/1911.08342 (2019) - 2018
- [j38]Göran Kauermann, Thomas Seidl:
Data Science: a proposal for a curriculum. Int. J. Data Sci. Anal. 6(3): 195-199 (2018) - [c236]Yifeng Lu, Florian Richter, Thomas Seidl:
Efficient Infrequent Itemset Mining Using Depth-First and Top-Down Lattice Traversal. DASFAA (1) 2018: 908-915 - [c235]Yifeng Lu, Thomas Seidl:
Towards Efficient Closed Infrequent Itemset Mining Using Bi-Directional Traversing. DSAA 2018: 140-149 - [c234]Daniyal Kazempour, Anna Beer, Friederike Herzog, Daniel Kaltenthaler, Johannes-Y. Lohrer, Thomas Seidl:
FATBIRD: A Tool for Flight and Trajectories Analyses of Birds. eScience 2018: 75-82 - [c233]Daniyal Kazempour, Andrian Mörtlbauer, Peer Kröger, Thomas Seidl:
Mirror Mirror on the Wall, What is the Fairest Linear Parameter Space Representation of All? On Representations of Linear Parameter Space in Context of Clustering. LWDA 2018: 169-173 - [c232]Daniyal Kazempour, Thomas Seidl:
Identifying Entangled Data Points on Iteration Trajectories of Clusterings. LWDA 2018: 174-178 - [c231]Daniyal Kazempour, Kevin Bein, Peer Kröger, Thomas Seidl:
D-MASC: A Novel Search Strategy for Detecting Regions of Interest in Linear Parameter Space. SISAP 2018: 163-176 - [c230]Daniyal Kazempour, Anna Beer, Johannes-Y. Lohrer, Daniel Kaltenthaler, Thomas Seidl:
PARADISO: an interactive approach of parameter selection for the mean shift algorithm. SSDBM 2018: 26:1-26:4 - [r2]Thomas Seidl:
Nearest Neighbor Classification. Encyclopedia of Database Systems (2nd ed.) 2018 - 2017
- [j37]Daniel Schüller, Christian Beecks, Marwan Hassani, Jennifer Hinnell, Bela Brenger, Thomas Seidl, Irene Mittelberg:
Automated Pattern Analysis in Gesture Research: Similarity Measuring in 3D Motion Capture Models of Communicative Action. Digit. Humanit. Q. 11(2) (2017) - [j36]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowl. Inf. Syst. 50(2): 417-446 (2017) - [j35]Marwan Hassani, Thomas Seidl:
Using internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam. J. Comput. Sci. 4(3): 171-183 (2017) - [c229]Florian Richter, Thomas Seidl:
TESSERACT: Time-Drifts in Event Streams Using Series of Evolving Rolling Averages of Completion Times. BPM 2017: 289-305 - [c228]Thomas Seidl:
Multimedia Similarity Search. BTW (Workshops) 2017: 397 - [c227]Marwan Hassani, Daniel Töws, Thomas Seidl:
Understanding the bigger picture: batch-free exploration of streaming sequential patterns with accurate prediction. SAC 2017: 866-869 - [c226]Merih Seran Uysal, Kai Driessen, Tobias Brockhoff, Thomas Seidl:
Fast Similarity Search with the Earth Mover's Distance via Feasible Initialization and Pruning. SISAP 2017: 141-155 - [c225]Yifeng Lu, Marwan Hassani, Thomas Seidl:
Incremental Temporal Pattern Mining Using Efficient Batch-Free Stream Clustering. SSDBM 2017: 7:1-7:12 - [c224]Daniyal Kazempour, Markus Mauder, Peer Kröger, Thomas Seidl:
Detecting Global Hyperparaboloid Correlated Clusters Based on Hough Transform. SSDBM 2017: 31:1-31:6 - [e8]Christian Beecks, Felix Borutta, Peer Kröger, Thomas Seidl:
Similarity Search and Applications - 10th International Conference, SISAP 2017, Munich, Germany, October 4-6, 2017, Proceedings. Lecture Notes in Computer Science 10609, Springer 2017, ISBN 978-3-319-68473-4 [contents] - 2016
- [j34]Christian Beecks, Marwan Hassani, Bela Brenger, Jennifer Hinnell, Daniel Schüller, Irene Mittelberg, Thomas Seidl:
Efficient Query Processing in 3D Motion Capture Gesture Databases. Int. J. Semantic Comput. 10(1): 5-26 (2016) - [j33]Marwan Hassani, Thomas Seidl:
Clustering Big Data streams: recent challenges and contributions. it Inf. Technol. 58(4): 206-213 (2016) - [c223]Marwan Hassani, Pascal Spaus, Alfredo Cuzzocrea, Thomas Seidl:
I-HASTREAM: Density-Based Hierarchical Clustering of Big Data Streams and Its Application to Big Graph Analytics Tools. CCGrid 2016: 656-665 - [c222]Merih Seran Uysal, Daniel Sabinasz, Thomas Seidl:
Approximation-Based Efficient Query Processing with the Earth Mover's Distance. DASFAA (2) 2016: 165-180 - [c221]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Jochen Schmücking, Thomas Seidl:
Efficient Query Processing using the Earth's Mover Distance in Video Databases. EDBT 2016: 389-400 - [c220]Marwan Hassani, Yifeng Lu, Thomas Seidl:
Towards an Efficient Ranking of Interval-Based Patterns. EDBT 2016: 688-689 - [c219]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Distance-based Multimedia Indexing. EDBT 2016: 722-723 - [c218]Tom De Nies, Christian Beecks, Fréderic Godin, Wesley De Neve, Grzegorz Stepien, Dörthe Arndt, Laurens De Vocht, Ruben Verborgh, Thomas Seidl, Erik Mannens, Rik Van de Walle:
Normalized Semantic Web Distance. ESWC 2016: 69-84 - [c217]Marwan Hassani, Yifeng Lu, Jens Wischnewsky, Thomas Seidl:
A geometric approach for mining sequential patterns in interval-based data streams. FUZZ-IEEE 2016: 2128-2135 - [c216]Laurens De Vocht, Christian Beecks, Ruben Verborgh, Erik Mannens, Thomas Seidl, Rik Van de Walle:
Effect of Heuristics on Serendipity in Path-Based Storytelling with Linked Data. HCI (4) 2016: 238-251 - [c215]Erik Scharwächter, Emmanuel Müller, Jonathan F. Donges, Marwan Hassani, Thomas Seidl:
Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining. ICDM 2016: 1191-1196 - [c214]Klaus Schoeffmann, Christian Beecks, Mathias Lux, Merih Seran Uysal, Thomas Seidl:
Content-based retrieval in videos from laparoscopic surgery. Medical Imaging: Image-Guided Procedures 2016: 97861V - [c213]Roland Assam, Subramanyam Sathyanarayana, Thomas Seidl:
Infusing Geo-Recency Mixture Models for Effective Location Prediction in LBSN. SDM 2016: 855-863 - [c212]