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ORCIDHendrik Blockeel
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- affiliation: Catholic University of Leuven, Belgium
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2020 – today
- 2024
[j51]Daan Van Wesenbeeck
, Aras Yurtman, Wannes Meert, Hendrik Blockeel:
LoCoMotif: discovering time-warped motifs in time series. Data Min. Knowl. Discov. 38(4): 2276-2305 (2024)- [c120]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
DeepSaDe: Learning Neural Networks That Guarantee Domain Constraint Satisfaction. AAAI 2024: 12199-12207 - 2023
- [j50]Hendrik Blockeel, Laurens Devos, Benoît Frénay, Géraldin Nanfack, Siegfried Nijssen:
Decision trees: from efficient prediction to responsible AI. Frontiers Artif. Intell. 6 (2023) - [j49]Lola Botman, Jonas Soenen, Konstantinos Theodorakos, Aras Yurtman, Jessa Bekker, Koen Vanthournout, Hendrik Blockeel, Bart De Moor, Jesus Lago:
A Scalable Ensemble Approach to Forecast the Electricity Consumption of Households. IEEE Trans. Smart Grid 14(1): 757-768 (2023) - [c119]Aras Yurtman, Jonas Soenen, Wannes Meert, Hendrik Blockeel:
Estimating Dynamic Time Warping Distance Between Time Series with Missing Data. ECML/PKDD (5) 2023: 221-237 - [c118]Youmna Ismaeil, Daria Stepanova, Trung-Kien Tran, Hendrik Blockeel:
FeaBI: A Feature Selection-Based Framework for Interpreting KG Embeddings. ISWC 2023: 599-617 - [c117]Huu Tan Mai, Youmna Ismaeil, Daria Stepanova, Trung-Kien Tran, Hendrik Blockeel:
Look beyond the Surface: A Demo for Explaining Knowledge Graph Embeddings and Entity Similarity. ISWC (Posters/Demos/Industry) 2023 - [i28]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
DeepSaDe: Learning Neural Networks that Guarantee Domain Constraint Satisfaction. CoRR abs/2303.01141 (2023) - [i27]Jonas Soenen, Elia Van Wolputte, Vincent Vercruyssen, Wannes Meert, Hendrik Blockeel:
AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection. CoRR abs/2305.12958 (2023) - [i26]Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert, Hendrik Blockeel:
LoCoMotif: Discovering time-warped motifs in time series. CoRR abs/2311.17582 (2023) - 2022
- [c116]Jonas Schouterden, Jessa Bekker, Jesse Davis, Hendrik Blockeel:
Unifying Knowledge Base Completion with PU Learning to Mitigate the Observation Bias. AAAI 2022: 4137-4145 - [c115]Kshitij Goyal, Wannes Meert, Hendrik Blockeel, Elia Van Wolputte, Koen Vanderstraeten, Wouter Pijpops, Kurt Jaspers:
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming. BNAIC/BENELEARN 2022: 47-64 - [c114]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
SaDe: Learning Models that Provably Satisfy Domain Constraints. ECML/PKDD (5) 2022: 410-425 - [c113]Youmna Ismaeil, Daria Stepanova, Trung-Kien Tran, Piyapat Saranrittichai, Csaba Domokos, Hendrik Blockeel:
Towards Neural Network Interpretability Using Commonsense Knowledge Graphs. ISWC 2022: 74-90 - [i25]Florian Busch, Moritz Kulessa, Eneldo Loza Mencía, Hendrik Blockeel:
Combining Predictions under Uncertainty: The Case of Random Decision Trees. CoRR abs/2208.07403 (2022) - [i24]Kshitij Goyal, Wannes Meert, Hendrik Blockeel, Elia Van Wolputte, Koen Vanderstraeten, Wouter Pijpops, Kurt Jaspers:
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming. CoRR abs/2210.01515 (2022) - 2021
- [c112]Florian Busch, Moritz Kulessa, Eneldo Loza Mencía, Hendrik Blockeel:
Combining Predictions Under Uncertainty: The Case of Random Decision Trees. DS 2021: 78-93 - [i23]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
SaDe: Learning Models that Provably Satisfy Domain Constraints. CoRR abs/2112.00552 (2021) - 2020
- [c111]Elia Van Wolputte, Hendrik Blockeel:
Missing Value Imputation with MERCS: A Faster Alternative to MissForest. DS 2020: 502-516 - [c110]Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
Multi-directional Rule Set Learning. DS 2020: 517-532 - [c109]Jonas Soenen, Sebastijan Dumancic, Toon van Craenendonck, Hendrik Blockeel:
Tackling Noise in Active Semi-supervised Clustering. ECML/PKDD (2) 2020: 121-136 - [c108]Evgeniya Korneva, Hendrik Blockeel:
Towards Better Evaluation of Multi-target Regression Models. PKDD/ECML Workshops 2020: 353-362 - [i22]Kshitij Goyal, Sebastijan Dumancic, Hendrik Blockeel:
Feature Interactions in XGBoost. CoRR abs/2007.05758 (2020)
2010 – 2019
- 2019
- [c107]Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel:
Learning Relational Representations with Auto-encoding Logic Programs. IJCAI 2019: 6081-6087 - [c106]Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
LazyBum: Decision Tree Learning Using Lazy Propositionalization. ILP 2019: 98-113 - [i21]Sebastijan Dumancic, Tias Guns, Wannes Meert, Hendrik Blockeel:
Learning Relational Representations with Auto-encoding Logic Programs. CoRR abs/1903.12577 (2019) - [i20]Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
LazyBum: Decision tree learning using lazy propositionalization. CoRR abs/1909.05044 (2019) - 2018
- [j48]Hendrik Blockeel:
Declarative data analysis. Int. J. Data Sci. Anal. 6(3): 217-223 (2018) - [j47]Leander Schietgat, Celine Vens, Ricardo Cerri, Carlos N. Fischer, Eduardo P. Costa, Jan Ramon, Claudia M. A. Carareto, Hendrik Blockeel:
A machine learning based framework to identify and classify long terminal repeat retrotransposons. PLoS Comput. Biol. 14(4) (2018) - [c105]Elia Van Wolputte, Evgeniya Korneva, Hendrik Blockeel:
MERCS: Multi-Directional Ensembles of Regression and Classification Trees. AAAI 2018: 4276-4283 - [c104]Evgeniya Korneva, Hendrik Blockeel:
Model Selection for Multi-directional Ensemble of Regression and Classification Trees. BNCAI 2018: 52-64 - [c103]Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel:
COBRASTS: A New Approach to Semi-supervised Clustering of Time Series. DS 2018: 179-193 - [c102]Luc De Raedt, Hendrik Blockeel, Samuel Kolb, Stefano Teso, Gust Verbruggen:
Elements of an Automatic Data Scientist. IDA 2018: 3-14 - [c101]Toon van Craenendonck, Sebastijan Dumancic, Elia Van Wolputte, Hendrik Blockeel:
COBRAS: Interactive Clustering with Pairwise Queries. IDA 2018: 353-366 - [c100]Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel:
Interactive Time Series Clustering with COBRASTS. ECML/PKDD (3) 2018: 654-657 - [i19]Toon van Craenendonck, Sebastijan Dumancic, Hendrik Blockeel:
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints. CoRR abs/1801.09955 (2018) - [i18]Toon van Craenendonck, Sebastijan Dumancic, Elia Van Wolputte, Hendrik Blockeel:
COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints. CoRR abs/1803.11060 (2018) - [i17]Toon van Craenendonck, Wannes Meert, Sebastijan Dumancic, Hendrik Blockeel:
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series. CoRR abs/1805.00779 (2018) - 2017
- [j46]Toon van Craenendonck, Hendrik Blockeel:
Constraint-based clustering selection. Mach. Learn. 106(9-10): 1497-1521 (2017) - [j45]Sebastijan Dumancic, Hendrik Blockeel:
An expressive dissimilarity measure for relational clustering using neighbourhood trees. Mach. Learn. 106(9-10): 1523-1545 (2017) - [c99]Sebastijan Dumancic, Hendrik Blockeel:
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation. IJCAI 2017: 1631-1637 - [c98]Toon van Craenendonck, Sebastijan Dumancic, Hendrik Blockeel:
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints. IJCAI 2017: 2871-2877 - [c97]Hendrik Blockeel:
PU-learning Disjunctive Concepts in ILP. ILP (Late Breaking Papers) 2017: 6-10 - [c96]Sebastijan Dumancic, Hendrik Blockeel:
Demystifying Relational Latent Representations. ILP 2017: 63-77 - [r13]Hendrik Blockeel:
Bias Specification Language. Encyclopedia of Machine Learning and Data Mining 2017: 125-128 - [r12]Hendrik Blockeel:
Hypothesis Language. Encyclopedia of Machine Learning and Data Mining 2017: 625-629 - [r11]Hendrik Blockeel:
Hypothesis Space. Encyclopedia of Machine Learning and Data Mining 2017: 629-632 - [r10]Soumya Ray, Stephen Scott, Hendrik Blockeel:
Multi-Instance Learning. Encyclopedia of Machine Learning and Data Mining 2017: 864-875 - [r9]Soumya Ray, Stephen Scott, Hendrik Blockeel:
Multiple-Instance Learning. Encyclopedia of Machine Learning and Data Mining 2017: 882-892 - [r8]Hendrik Blockeel:
Observation Language. Encyclopedia of Machine Learning and Data Mining 2017: 917-920 - [r7]Jan Struyf, Hendrik Blockeel:
Relational Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1090-1096 - [i16]Sebastijan Dumancic, Hendrik Blockeel:
Demystifying Relational Latent Representations. CoRR abs/1705.05785 (2017) - 2016
- [j44]Gitte Vanwinckelen, Vinicius Tragante do Ó, Daan Fierens, Hendrik Blockeel:
Instance-level accuracy versus bag-level accuracy in multi-instance learning. Data Min. Knowl. Discov. 30(2): 313-341 (2016) - [j43]Hossein Rahmani, Hendrik Blockeel, Andreas Bender:
Using a Human Drug Network for generating novel hypotheses about drugs. Intell. Data Anal. 20(1): 183-197 (2016) - [c95]Sebastijan Dumancic, Hendrik Blockeel:
An Efficient and Expressive Similarity Measure for Relational Clustering Using Neighbourhood Trees. ECAI 2016: 1674-1675 - [c94]Leonor Becerra-Bonache, Hendrik Blockeel, María Galván, François Jacquenet:
Relational Grounded Language Learning. ECAI 2016: 1764-1765 - [c93]Aäron Verachtert, Hendrik Blockeel, Jesse Davis:
Dynamic Early Stopping for Naive Bayes. IJCAI 2016: 2082-2088 - [c92]Hendrik Blockeel:
Identifying Non-Redundant Literals in Clauses with Uniqueness Propagation. ILP (Short Papers) 2016: 8-13 - [c91]Hendrik Blockeel, Svetlana Valevich:
A Simple Framework for Theta-Subsumption Testing in Prolog. ILP (Short Papers) 2016: 14-19 - [c90]Leonor Becerra-Bonache, Hendrik Blockeel, María Galván, François Jacquenet:
Learning Language Models from Images with ReGLL. ECML/PKDD (3) 2016: 55-58 - [i15]Sebastijan Dumancic, Hendrik Blockeel:
An expressive dissimilarity measure for relational clustering using neighbourhood trees. CoRR abs/1604.08934 (2016) - [i14]Sebastijan Dumancic, Hendrik Blockeel:
Unsupervised Relational Representation Learning via Clustering: Preliminary Results. CoRR abs/1606.08658 (2016) - [i13]Sebastijan Dumancic, Wannes Meert, Hendrik Blockeel:
Theory reconstruction: a representation learning view on predicate invention. CoRR abs/1606.08660 (2016) - [i12]Toon van Craenendonck, Hendrik Blockeel:
Constraint-Based Clustering Selection. CoRR abs/1609.07272 (2016) - 2015
- [j42]Hossein Rahmani, Hendrik Blockeel, Andreas Bender:
Using a Human Disease Network for augmenting prior knowledge about diseases. Intell. Data Anal. 19(4): 897-916 (2015) - [j41]Hendrik Blockeel:
Data Mining: From Procedural to Declarative Approaches. New Gener. Comput. 33(2): 115-135 (2015) - [j40]Maurice Bruynooghe, Hendrik Blockeel, Bart Bogaerts, Broes De Cat, Stef De Pooter, Joachim Jansen, Anthony Labarre, Jan Ramon, Marc Denecker, Sicco Verwer:
Predicate logic as a modeling language: modeling and solving some machine learning and data mining problems with IDP3. Theory Pract. Log. Program. 15(6): 783-817 (2015) - [c89]Leonor Becerra-Bonache, Hendrik Blockeel, María Galván, François Jacquenet:
A First-Order-Logic Based Model for Grounded Language Learning. IDA 2015: 49-60 - [c88]Denny Verbeeck, Hendrik Blockeel:
Slower Can Be Faster: The iRetis Incremental Model Tree Learner. IDA 2015: 322-333 - [c87]Toon van Craenendonck, Hendrik Blockeel:
Limitations of Using Constraint Set Utility in Semi-Supervised Clustering. MetaSel@PKDD/ECML 2015: 27-42 - [c86]Antoine Adam, Hendrik Blockeel:
Dealing with Overlapping Clustering: A Constraint-based Approach to Algorithm Selection. MetaSel@PKDD/ECML 2015: 43-54 - 2014
- [c85]Gitte Vanwinckelen, Hendrik Blockeel:
Look before you leap: Some insights into learner evaluation with cross-validation. SSDM@ECML/PKDD 2014: 3-20 - [e7]Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti:
Advances in Intelligent Data Analysis XIII - 13th International Symposium, IDA 2014, Leuven, Belgium, October 30 - November 1, 2014. Proceedings. Lecture Notes in Computer Science 8819, Springer 2014, ISBN 978-3-319-12570-1 [contents] - [i11]Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language. CoRR abs/1402.0565 (2014) - 2013
- [j39]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Guest editor's introduction: special issue of the ECML PKDD 2013 journal track. Data Min. Knowl. Discov. 27(3): 291-293 (2013) - [j38]Pirooz Shamsinejadbabaki, Mohamad Saraee, Hendrik Blockeel:
Causality-based cost-effective action mining. Intell. Data Anal. 17(6): 1075-1091 (2013) - [j37]Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination: Decoupling the Operators from the Constraint Language. J. Artif. Intell. Res. 47: 393-439 (2013) - [j36]Robert Brijder, Hendrik Blockeel:
On the inference of non-confluent NLC graph grammars. J. Log. Comput. 23(4): 799-814 (2013) - [j35]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Guest editor's introduction: special issue of the ECML PKDD 2013 journal track. Mach. Learn. 93(1): 1-3 (2013) - [c84]Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
On the Completeness of Lifted Variable Elimination. StarAI@AAAI 2013 - [c83]Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
Completeness Results for Lifted Variable Elimination. AISTATS 2013: 572-580 - [c82]Pan Hu, Celine Vens, Bart Verstrynge, Hendrik Blockeel:
Generalizing from Example Clusters. Discovery Science 2013: 64-78 - [c81]Denny Verbeeck, Francis Maes, Kurt De Grave, Hendrik Blockeel:
Multi-objective optimization with surrogate trees. GECCO 2013: 679-686 - [c80]Celine Vens, Bart Verstrynge, Hendrik Blockeel:
Semi-supervised Clustering with Example Clusters. KDIR/KMIS 2013: 45-51 - [c79]Eduardo P. Costa, Sicco Verwer, Hendrik Blockeel:
Estimating Prediction Certainty in Decision Trees. IDA 2013: 138-149 - [c78]Nima Taghipour, Jesse Davis, Hendrik Blockeel:
Generalized Counting for Lifted Variable Elimination. ILP 2013: 107-122 - [c77]Nima Taghipour, Jesse Davis, Hendrik Blockeel:
First-order Decomposition Trees. NIPS 2013: 1052-1060 - [c76]Antoine Adam, Hendrik Blockeel, Sander Govers, Abram Aertsen:
SCCQL : A Constraint-Based Clustering System. ECML/PKDD (3) 2013: 681-684 - [p5]Hendrik Blockeel:
Statistical Relational Learning. Handbook on Neural Information Processing 2013: 241-281 - [e6]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I. Lecture Notes in Computer Science 8188, Springer 2013, ISBN 978-3-642-40987-5 [contents] - [e5]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II. Lecture Notes in Computer Science 8189, Springer 2013, ISBN 978-3-642-40990-5 [contents] - [e4]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III. Lecture Notes in Computer Science 8190, Springer 2013, ISBN 978-3-642-40993-6 [contents] - [i10]Nima Taghipour, Jesse Davis, Hendrik Blockeel:
First-Order Decomposition Trees. CoRR abs/1306.0751 (2013) - [i9]Maurice Bruynooghe, Hendrik Blockeel, Bart Bogaerts, Broes De Cat, Stef De Pooter, Joachim Jansen, Anthony Labarre, Jan Ramon, Marc Denecker, Sicco Verwer:
Predicate Logic as a Modeling Language: Modeling and Solving some Machine Learning and Data Mining Problems with IDP3. CoRR abs/1309.6883 (2013) - 2012
- [j34]Hendrik Blockeel, Toon Calders, Élisa Fromont, Bart Goethals, Adriana Prado, Céline Robardet:
An inductive database system based on virtual mining views. Data Min. Knowl. Discov. 24(1): 247-287 (2012) - [j33]Joris Maervoet, Celine Vens, Greet Vanden Berghe, Hendrik Blockeel, Patrick De Causmaecker:
Outlier detection in relational data: A case study in geographical information systems. Expert Syst. Appl. 39(5): 4718-4728 (2012) - [j32]Hossein Rahmani, Hendrik Blockeel, Andreas Bender:
Predicting Genes Involved in Human Cancer Using Network Contextual Information. J. Integr. Bioinform. 9(1) (2012) - [j31]Joaquin Vanschoren, Hendrik Blockeel, Bernhard Pfahringer, Geoffrey Holmes:
Experiment databases - A new way to share, organize and learn from experiments. Mach. Learn. 87(2): 127-158 (2012) - [c75]Hendrik Blockeel, Bart Bogaerts, Maurice Bruynooghe, Broes De Cat, Stef De Pooter, Marc Denecker, Anthony Labarre, Jan Ramon, Sicco Verwer:
Modeling Machine Learning and Data Mining Problems with FO(·). ICLP (Technical Communications) 2012: 14-25 - [c74]Nima Taghipour, Daan Fierens, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination with Arbitrary Constraints. AISTATS 2012: 1194-1202 - [i8]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
A Revised Publication Model for ECML PKDD. CoRR abs/1207.6324 (2012) - [i7]Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel:
Lifted Variable Elimination: A Novel Operator and Completeness Results. CoRR abs/1208.3809 (2012) - 2011
- [j30]Werner Uwents, Gabriele Monfardini, Hendrik Blockeel, Marco Gori, Franco Scarselli:
Neural networks for relational learning: an experimental comparison. Mach. Learn. 82(3): 315-349 (2011) - [j29]Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro M. Domingos, Kristian Kersting, Xifeng Yan:
Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning. Mach. Learn. 83(2): 133-135 (2011) - [c73]Hossein Rahmani, Hendrik Blockeel, Andreas Bender:
Collaboration-Based Function Prediction in Protein-Protein Interaction Networks. IDA 2011: 318-327 - [c72]Beau Piccart, Andy Georges, Hendrik Blockeel, Lieven Eeckhout:
Ranking commercial machines through data transposition. IISWC 2011: 3-14 - [c71]Beau Piccart, Hendrik Blockeel, Andy Georges, Lieven Eeckhout:
Predictive Learning in Two-Way Datasets. ILP (Late Breaking Papers) 2011: 61-68 - [c70]Tijn Witsenburg, Hendrik Blockeel:
K-Means Based Approaches to Clustering Nodes in Annotated Graphs. ISMIS 2011: 346-357 - [c69]Tijn Witsenburg, Hendrik Blockeel:
Improving the Accuracy of Similarity Measures by Using Link Information. ISMIS 2011: 501-512 - [c68]Robert Brijder, Hendrik Blockeel:
Characterizing Compressibility of Disjoint Subgraphs with NLC Grammars. LATA 2011: 167-178 - [i6]Hendrik Blockeel, Luc Dehaspe, Bart Demoen, Gerda Janssens, Jan Ramon, Henk Vandecasteele:
Improving the Efficiency of Inductive Logic Programming Through the Use of Query Packs. CoRR abs/1106.1803 (2011) - 2010
- [j28]Leander Schietgat, Celine Vens, Jan Struyf, Hendrik Blockeel, Dragi Kocev, Saso Dzeroski:
Predicting gene function using hierarchical multi-label decision tree ensembles. BMC Bioinform. 11: 2 (2010) - [j27]Kristien Van Loon, Fabian Güiza Grandas, Geert Meyfroidt, Jean-Marie Aerts, Jan Ramon, Hendrik Blockeel, Maurice Bruynooghe, Greta Van den Berghe, Daniel Berckmans:
Prediction of Clinical Conditions after Coronary Bypass Surgery using Dynamic Data Analysis. J. Medical Syst. 34(3): 229-239 (2010) - [j26]Daan Fierens, Jan Ramon, Hendrik Blockeel, Maurice Bruynooghe:
A comparison of pruning criteria for probability trees. Mach. Learn. 78(1-2): 251-285 (2010) - [c67]Celine Vens, Eduardo P. Costa, Hendrik Blockeel:
Top-Down Induction of Phylogenetic Trees. EvoBIO 2010: 62-73 - [c66]Arno J. Knobbe, Hendrik Blockeel, Arne Koopman, Toon Calders, Bas Obladen, Carlos Bosma, Hessel Galenkamp, Eddy Koenders, Joost N. Kok:
InfraWatch: Data Management of Large Systems for Monitoring Infrastructural Performance. IDA 2010: 91-102 - [c65]