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21 results on '"Feremans, Len"'

1. Efficient pattern-based anomaly detection in a network of multivariate devices

Author

Feremans, Len, Cule, Boris, and Goethals, Bart

Subjects
Computer Science - Social and Information Networks, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Networking and Internet Architecture
Abstract

Many organisations manage service quality and monitor a large set devices and servers where each entity is associated with telemetry or physical sensor data series. Recently, various methods have been proposed to detect behavioural anomalies, however existing approaches focus on multivariate time series and ignore communication between entities. Moreover, we aim to support end-users in not only in locating entities and sensors causing an anomaly at a certain period, but also explain this decision. We propose a scalable approach to detect anomalies using a two-step approach. First, we recover relations between entities in the network, since relations are often dynamic in nature and caused by an unknown underlying process. Next, we report anomalies based on an embedding of sequential patterns. Pattern mining is efficient and supports interpretation, i.e. patterns represent frequent occurring behaviour in time series. We extend pattern mining to filter sequential patterns based on frequency, temporal constraints and minimum description length. We collect and release two public datasets for international broadcasting and X from an Internet company. \textit{BAD} achieves an overall F1-Score of 0.78 on 9 benchmark datasets, significantly outperforming the best baseline by 3\%. Additionally, \textit{BAD} is also an order-of-magnitude faster than state-of-the-art anomaly detection methods.

Published
2023

4. A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event Logs

Author

Feremans, Len, Vercruyssen, Vincent, Meert, Wannes, Cule, Boris, Goethals, Bart, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ceci, Michelangelo, editor, Loglisci, Corrado, editor, Manco, Giuseppe, editor, Masciari, Elio, editor, and Ras, Zbigniew, editor

Published
2020
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5. Pattern-Based Anomaly Detection in Mixed-Type Time Series

Author

Feremans, Len, Vercruyssen, Vincent, Cule, Boris, Meert, Wannes, Goethals, Bart, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Brefeld, Ulf, editor, Fromont, Elisa, editor, Hotho, Andreas, editor, Knobbe, Arno, editor, Maathuis, Marloes, editor, and Robardet, Céline, editor

Published
2020
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11. Efficient Discovery of Sets of Co-occurring Items in Event Sequences

Author

Cule, Boris, Feremans, Len, Goethals, Bart, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Frasconi, Paolo, editor, Landwehr, Niels, editor, Manco, Giuseppe, editor, and Vreeken, Jilles, editor

Published
2016
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13. Efficiently Mining Frequent Representative Motifs in Large Collections of Time Series

Author

Rotman, Stijn, Čule, Boris, Feremans, Len, Rotman, Stijn, Čule, Boris, and Feremans, Len

Abstract

The discovery of repeated structures in time series, known as motifs, is an important data mining task. Various techniques exist to mine motifs in either a database of time series or within one or two individual time series, either for a user-defined motif length or a range of lengths. However, mining frequent motifs of variable length in large time series databases remains an unsolved task that is computationally expensive. We propose FRM-Miner, an efficient algorithm for discovering more informative patterns in time series data, i.e., motifs of different length that are non-overlapping, occur frequently and where the euclidean distance between the motif and its various occurrences is minimal. Unlike current state-of-the-art approaches, FRM-Miner can efficiently find variable motif lengths in large time series databases. Through extensive experimentation, we show desirable properties of FRM-Miner, including robustness to noise, which facilitates the discovery of motifs that remain undetected using state-of-the-art methods. Additionally, our method is highly scalable, taking only 2.95 hours to discover informative sets of motifs on all 128 time series data sets of the UCR Time Series Archive, where related state-of-the-art algorithms such as Ostinato require several days.

Published
2023

14. Combining instance and feature neighbours for extreme multi-label classification

Author

Feremans, Len, Cule, Boris, Vens, Celine, and Goethals, Bart

Abstract

Extreme multi-label classification problems occur in different applications such as prediction of tags or advertisements. We propose a new algorithm that predicts labels using a linear ensemble of labels from instance- and feature-based nearest neighbours. In the feature-based nearest neighbours method, we precompute a matrix containing the similarities between each feature and label. For the instance-based nearest neighbourhood, we create an algorithm that uses an inverted index to compute cosine similarity on sparse datasets efficiently. We extend this baseline with a new top-kquery algorithm that combines term-at-a-time and document-at-a-time traversal with tighter pruning based on a partition of the dataset. On ten real-world datasets, we find that our method outperforms state-of-the-art methods such as multi-label k-nearest neighbours, instance-based logistic regression, binary relevance with support vector machines and FastXml on different evaluation metrics. We also find that our algorithm is orders of magnitude faster than these baseline algorithms on sparse datasets and requires less than 20 ms per instance to predict labels for extreme datasets without the need for expensive hardware.

Published
2024
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16. Fleet-Oriented Pattern Mining Combined with Time Series Signature Extraction for Understanding of Wind Farm Response to Storm Conditions

Author

Daems, Pieter Jan, Feremans, Len, Verstraeten, Timothy, Cule, Boris, Goethals, Bart, Helsen, Jan, Daems, Pieter Jan, Feremans, Len, Verstraeten, Timothy, Cule, Boris, Goethals, Bart, and Helsen, Jan

Abstract

Offshore wind turbine installations are rapidly spreading around Europe and all over the world. These turbines are typically installed in large wind farms combining turbines of the same type. Farm owners target maximal performance of the farm in general and particularly predictability of behaviour. The latter is getting increasingly important since offshore wind farms are being managed more and more as conventional power plants driven by the electricity market supply and demand considerations. The context of zero subsidy farms exposes farm operators to fluctuations in electricity market prices. As such, deep understanding of farm behaviour is essential to come up with a good strategy to deal with these fluctuations. This paper focusses on the automated extraction of farm-wide response to storm conditions. The input data for the analysis are status logs and SCADA 1-second data. The status logs record the important turbine controller events. Typically, they consist of a number, a time of occurrence, and a time of de-activation. The number is linked to a detailed description. The SCADA data consists of time series of the most important sensors in the turbine: power produced, RPM, wind speed,… The advantage of the 1-sec data over the traditional 10-minute averages is that the dynamic event content is much more preserved. Data of several offshore wind farms is used in the analysis to have a solid dataset. In total, 5 years of data of more than 50 turbines is used. We show a novel farm-wide pattern mining approach that extracts events occurring for multiple turbines in the same time period. This allows us to identify those events that are predominantly driven by global wind excitations (e.g. gusts) or grid events (e.g. low voltage ride through). From the extracted events, we lift out the storm conditions. For these conditions, a further investigation of the time series data is done. Using event detection algorithms, we extract the signatures of the stop events that each t, SCOPUS: cp.k, DecretOANoAutActif, info:eu-repo/semantics/published

Published
2021

17. Mining cohesive patterns in sequences and extreme multi-label classification

Author

Feremans, Len and Goethals, Bart

Subjects
ComputingMethodologies_PATTERNRECOGNITION, Human medicine
Abstract

Finding patterns in long event sequences is an important data mining task. In the past, research focused on finding all frequent patterns, where the anti-monotonic property of frequency was used to design efficient algorithms. Recently, research focused on producing a smaller output containing only the most interesting patterns. In this thesis, we discover patterns using cohesion and quantile-based cohesion. Cohesion measures how close the items making up the pattern are on average. Quantile-based cohesion measures the proportion of pattern occurrences that are cohesive. We tackle the fact that both measures are not anti-monotonic by developing an upper bound to prune the search space. Experiments show that our method efficiently discovers important patterns that existing state-of-the-art methods fail to discover. In the second part of this thesis, we focus on multi-label classification which is important in different applications such as text categorisation, scene classification and bioinformatics. In machine learning, multi-label classification is the problem of identifying a set of labels for a new instance, based on a training database of labelled instances. Traditionally, methods learn a separate model for each label, however, this is not feasible for datasets with millions of labels. We propose a new algorithm that predicts labels using a linear ensemble of instance- and feature-based nearest neighbours. We tackle the problem of computing cosine similarity and similarity weighted predictions on large datasets using an inverted index and sparse optimisation. In addition, we propose a new top-k query with pruning based on a partition of the training database. Experiments show that our method is more accurate and orders of magnitude faster than state-of-the-art methods and requires less than 20 ms per instance to predict labels for extreme datasets consisting of hundreds of thousands of labels without the need for expensive hardware.

Published
2020

18. Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI): A Prospective Longitudinal Observational Study

Author

Maas, Andrew I. R., Menon, David K., Andelic, Nada, Kolibay, Felix, Kondziella, Daniel, Koskinen, Lars-Owe, Lagares, Alfonso, Lanyon, Linda, Laureys, Steven, Lecky, Fiona, Lefering, Rolf, Levi, Leon, Lightfoot, Roger, Andreassen, Lasse, Lingsma, Hester F., Loeckx, Dirk, Lohkamp, Laura-Nanna, Lötjönen, Jyrki, Lumenta, Christiano, Lyttle, Mark, Maas, Andrew, Maegele, Marc, Majdan, Marek, Manara, Alex, Andrews, Peter, Manley, Geoffrey T., Maréchal, Hugues, Martino, Costanza, Mascia, Luciana, Mauritz, Walter, McMahon, Catherine, Menovsky, Tomas, Mitchell, Patrick, Mladenov, Nikolay, Audibert, Gérard, Morganti-Kossmann, Cristina, Nelson, David, Neugebauer, Eddy, Newcombe, Virginia F., Oddo, Mauro, Oresic, Matej, Orzalesi, Vanni, Outtrim, Joanne G., Palotie, Aarno, Parizel, Paul, Audny, Anke, Payen, Jean-François, Perlbarg, Vincent, Peul, Wilco, Pichon, Nicolas, Piippo, Anna, Floury, Sébastien Pili, Ples, Horia, Polinder, Suzanne, Preiksaitis, Aidanas, Psota, Marek, Azouv, Philippe, Pullens, Pim, Puybasset, Louis, Ragauskas, Arminas, Raj, Rahul, Reiner, Michael, Rhodes, Jonathan K. J., Richardson, Sylvia, Ripatti, Samuli, Rocka, Saulius, Roosenfeld, Jeffrey, Barzó, Pál, Rosand, Jonathan, Rosenlund, Christina, Rosenthal, Guy, Rossaint, Rolf, Rossi, Sandra, Rueckert, Daniel, Rusnák, Martin, Rynkowski, Michal A., Sahuquillo, Juan, Sakowitz, Oliver, Beer, Ronny, Sandor, Janos, Schmidt, Silke, Schoechl, Herbert, Schou, Rico, Skandsen, Toril, Sonne, Morten, Schwendenwein, Elisabeth, Smeets, Dirk, Smieleweski, Peter, Söderberg, Jeannette, Bellander, Bo-Michael, Stamatakis, Emmanuel, Stanworth, Simon, Stefini, Roberto, Stevens, Robert, Stewart, William, Steyerberg, Ewout W., Stocchetti, Nino, Stummer, Walter, Szabó, József, Tascu, Alexandru, Belli, Antonio, Tenovuo, Olli, Theadom, Alice, Tibboel, Dick, Tolias, Christos M., Unterberg, Andreas, Vajkoczy, Peter, Vargiolu, Alessia, van der Naalt, Joukje, van Essen, Thomas, Van Hecke, Wim, Benali, Habib, Van Praag, Dominique, Van Roost, Dirk, Vandenbulcke, Tim, vande Hauwe, Luc, Van der Jagt, Mathieu, Vega, Emmanuel, Verheyden, Jan, Verma, Vishwajit, Vespa, Paul M., Vik, Anne, Berardino, Maurizio, Vilcinis, Rimantas, von Steinbüchel, Nicole, Vulekovic, Peter, Wang, Kevin K. W., Wildschut, Eno, Williams, Guy, Wilson, Mark, Wilson, Lindsay, Wolf, Stefan, Ylén, Peter, Beretta, Luigi, Zaaroor, Menashe, Zolfaghari, Parjam, Martin, Didier, D'Orio, Vincenzo, Damas, Pierre, Tshibanda, Jean-Flory Luaba, Brazinová, Alexandra, Binder, Harald, Brehar, Felix, Buki, Andras, Bullinger, Monika, Cakmak, Ela, Callebaut, Ina, Citerio, Giuseppe, Cameron, Peter, Lozano, Guillermo Carbayo, Carpenter, Keri L. H., Chieregato, Arturo, Coburn, Mark, Coles, Jonathan P., Cooper, Jamie, Cnossen, Maryse, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, Dahyot-Fitzelier, Claire, Damas, Francois, Dawes, Helen, De Keyser, Véronique, De Luca, Alessandra, de Ruiter, Godard C. W, De Witte, Olivier, Della Corte, Francesco, Demeter, Béla, Depreitere, Bart, Dippel, Diederik W. J., Dizdarevic, Kemal, Dreier, Jens P., Eapen, George, Ercole, Ari, Esser, Patrick, Fabricius, Martin, Feremans, Len, Hill, Sean, Feigin, Valery L., Fossi, Francesca, Forsyth, Faye, Florian, Stefan, Frisvold, Shirin Kordasti, Frosini, Caterina, Furmanov, Alex, Frantzén, Janek, Gadda, Davide, Gagliardo, Pablo, Legrand, Valerie, Galanaud, Damien, Gao, Guoyi, Ghuysen, Alexandre, Godbolt, Alison, Gonsorová, Veronika, Grigore, Zapulih, Gruen, Russel, Haagsma, Juanita A., Hallaert, Giorgio, Hadzic, Ermin, Sorgner, Annina, Haitsma, Ian, Hartings, Jed A., Helbok, Raimund, Helseth, Eirik, Hoefer, Stefan, Holling, Markus, Hunfeld, Maaike, Hutchinson, Peter J., Illés, Robert, CENTER-TBI Participants and Investigators, Janssens, Koen, Bovend'Eerdt, Thamar J. H., Jiang, Ji-yao, Jones, Kelly M., Kalala, Jean-Pierre, Kalovits, Ferenc, Kasprian, Gregor, Katila, Ari, Ketharanathan, Naomi, Kolias, Angelos G., Maas, A, Menon, D, Steyerberg, E, Citerio, G, Lecky, F, Manley, G, Hill, S, Legrand, V, Sorgner, A, CENTER-TBI Participants, Public Health, Maas, Ai, Menon, Dk, Steyerberg, Ew, Manley, Gt, Sorgner, A (CENTER-TBI Participants), Beretta, L., and Molecular Neuroscience and Ageing Research (MOLAR)

Subjects
Research design, Adult, Male, medicine.medical_specialty, Pathology, Comparative Effectiveness Research, Neurologi, Comparative effectiveness research, ta3112, Clinical study, Quality of life (healthcare), Traumatic brain injury, Health care, Outcome Assessment, Health Care, Medicine and Health Sciences, Protocol, Medicine, Humans, ddc:610, Longitudinal Studies, Prospective Studies, Israel, Intensive care medicine, Child, OUTCOMES, business.industry, Orvostudományok, Precision medicine, TRENDS, Clinical trial, Europe, Systematic review, Neurology, Research Design, Brain Injuries, Surgery, Observational study, Female, Neurology (clinical), Human medicine, Egészségtudományok, business, Tomography, X-Ray Computed
Abstract

Neurosurgery : official journal of the Congress of Neurological Surgeons 76(1), 67-80 (2015). doi:10.1227/NEU.0000000000000575, Published by Lippincott Williams & Wilkins, Baltimore, Md.

Published
2015
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