Publications

Selected publications (2018-2021)

  • new Jonathan Rebane, Isak Samsten, Leon Bornemann, and Panagiotis Papapetrou, “SMILE: A feature-based temporal abstraction framework for event-interval sequence classification”. In Data Mining and Knowledge Discovery – to appear
  • new Jonathan Rebane, Isak Samsten, and Panagiotis Papapetrou, “Exploiting Complex Medical Data with Interpretable Deep Learning for Adverse Drug Event Prediction”.  In Artificial Intelligence in Medicine – to appear
  • new Zed Lee, Tony Lindgren, and Panagiotis Papapetrou, “Z-Miner: an efficient method for mining frequent arrangements of event intervals“. In ACM Knowledge Discovery and Data Mining (KDD) 2020 – to appear
  • new Zed Lee, Šarūnas Girdzijauskas, and Panagiotis Papapetrou, “Z-Embedding: A spectral representation of event intervals for efficient clustering and classification“. In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD) 2020 – to appear
  • new Alejandro Kuratomi, Tony Lindgren, and Panagiotis Papapetrou, “Prediction of Global Navigation Satellite System Positioning Errors with Guarantees“. In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD) 2020 – to appear
  • new Maria Bampa, Panagiotis Papapetrou, and Jaakko Hollmen, “A clustering framework for patient phenotyping with application to adverse drug events“. In IEEE Computer-Based Medical Systems (CBMS) 2020 – to appear
  • new John Pavlopoulos and Panagiotis Papapetrou, “Clinical predictive keyboard using statistical and neural language modeling“. In IEEE Computer-Based Medical Systems (CBMS) 2020 – to appear
  • new Zed Lee, Jonathan Rebane, and Panagiotis Papapetrou, “Mining disproportional frequent arrangements of event intervals for investigating adverse drug events“. In IEEE Computer-Based Medical Systems (CBMS) 2020 – to appear
  • new Tobias Blomberg, Nesaretnam Barr Kumarakulasinghe, Jintai Liu, Alexandra Saraiva Leao and Panagiotis Papapetrou, “Evaluating local interpretable model-agnostic explanations on clinical machine learning classification models“. In IEEE Computer-Based Medical Systems (CBMS) 2020 – to appear
  • Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou, and Aristides Gionis, “Locally and globally explainable time series tweaking“. In Knowledge and Information Systems (KAIS), 62 (5): 1671-1700 2020 [open access]
  • Amin Azari, Panagiotis Papapetrou, Stojan Denic, and Gunnar Peters, “User traffic prediction for proactive resource management: learning-powered approaches“. In the IEEE Global Communications Conference (Globecom), pp 1-6, 2019
  • Fransesco Bagattini, Isak Karlsson, Jonathan Rebane, and Panagiotis Papapetrou, “A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records“. In BMC Medical Informatics and Decision Making 19 (1), 7, 2019 [open access]
  • Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou, and Aristides Gionis, “Explainable time series tweaking via irreversible and reversible temporal transformations“. In the IEEE International Conference on Data Mining  (ICDM),  207-216, 2018 [arXiv version]
  • Luis Eduardo Velez Quintero, Papapetrou Panagiotis, John Munoz, and Uno Fors, “Implementation of Mobile-based Real-time Heart Rate Variability Detection for Personalized Healthcare“. In the IEEE International Conference on Data Mining (ICDM) Workshop on Translational Multimedia Data Mining for AI-Based Medical Diagnostics (TMDM),  ICDM Workshops Proceedings, pp 838-846, 2019
  • Maria Bampa and Panagiotis Papapetrou, “Mining Adverse Drug Events Using Multiple Feature Hierarchies and Patient History Windows“. In the IEEE International Conference on Data Mining (ICDM) Workshop on Data Mining in Biomedical Informatics and Healthcare (DMBIH),  ICDM Workshops Proceedings, pp 925-932, 2019
  • Amin Azari, Panagiotis Papapetrou, Stojan Denic, and Gunnar Peters, “Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA“. In the International Conference on Discovery Science (DS), pp 129-144, 2019
  • Jonathan Rebane, Isak Karlsson, and Panagiotis Papapetrou, “An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction“. In the IEEE International Symposium on Computer-Based Medical Systems (CBMS) 2019
  • Loes Crelaard and Panagiotis Papapetrou, “Explainable predictions of adverse drug events from electronic health records via oracle coaching“. In the IEEE ICDM Workshop on Data Mining in Biomedical Informatics and Healthcare  (ICDM DMBIH), ICDM Workshops 2018 [pdf]
  • Jonathan Rebane, Isak Karlsson, Panagiotis Papapetrou, and Stojan Denic, “Seq2Seq RNNs and ARIMA models for Cryptocurrency Prediction: A Comparative Study“.  In Proceedings of the FinTech Workshop at the International Conference of Knowledge Discovery and Data Mining (KDD FinTech Workshop), KDD Workshops, 2018 [pdf]
  • Tommy Hielscher, Henry Völzke, Panagiotis Papapetrou, Myra Spiliopoulou, “Discovering, selecting and exploiting feature sequence records of study participants for the classification of epidemiological data on hepatic steatosis“.  In Proceedings of the ACM/SIGAPP Symposium on Applied Computing (SAC) 2018: 6-13 [pdf]

All publications

Peer-reviewed

2020

  • new Jonathan Rebane, Isak Samsten, Leon Bornemann, and Panagiotis Papapetrou, “SMILE: A feature-based temporal abstraction framework for event-interval sequence classification”. In Data Mining and Knowledge Discovery – to appear
  • new Jonathan Rebane, Isak Samsten, and Panagiotis Papapetrou, “Exploiting Complex Medical Data with Interpretable Deep Learning for Adverse Drug Event Prediction”.  In Artificial Intelligence in Medicine – to appear
  • new Zed Lee, Tony Lindgren, and Panagiotis Papapetrou, “Z-Miner: an efficient method for mining frequent arrangements of event intervals“. In ACM Knowledge Discovery and Data Mining (KDD) 2020 – to appear
  • new Zed Lee, Šarūnas Girdzijauskas, and Panagiotis Papapetrou, “Z-Embedding: A spectral representation of event intervals for efficient clustering and classification“. In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD) 2020 – to appear
  • new Alejandro Kuratomi, Tony Lindgren, and Panagiotis Papapetrou, “Prediction of Global Navigation Satellite System Positioning Errors with Guarantees“. In the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML/PKDD) 2020 – to appear
  • new Maria Bampa, Panagiotis Papapetrou, and Jaakko Hollmen, “A clustering framework for patient phenotyping with application to adverse drug events“. In IEEE Computer-Based Medical Systems (CBMS) 2020 – to appear
  • new John Pavlopoulos and Panagiotis Papapetrou, “Clinical predictive keyboard using statistical and neural language modeling“. In IEEE Computer-Based Medical Systems (CBMS) 2020 – to appear
  • new Zed Lee, Jonathan Rebane, and Panagiotis Papapetrou, “Mining disproportional frequent arrangements of event intervals for investigating adverse drug events“. In IEEE Computer-Based Medical Systems (CBMS) 2020 – to appear
  • new Tobias Blomberg, Nesaretnam Barr Kumarakulasinghe, Jintai Liu, Alexandra Saraiva Leao and Panagiotis Papapetrou, “Evaluating local interpretable model-agnostic explanations on clinical machine learning classification models“. In IEEE Computer-Based Medical Systems (CBMS) 2020 – to appear
  • Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou, and Aristides Gionis, “Locally and globally explainable time series tweaking“. In Knowledge and Information Systems (KAIS), 62 (5): 1671-1700, 2020 [open access]
  • Jing Zhao, Panagiotis Papapetrou, Lars Asker, Henrik Boström, Corrigendum to “Learning from heterogeneous temporal data in electronic health records“. International Journal on Biomedical Informatics 101: 103352, 2020

2019

  •  Amin Azari, Panagiotis Papapetrou, Stojan Denic, and Gunnar Peters, “User Traffic Prediction for Proactive Resource Management: Learning-Powered Approaches“. In the IEEE Global Communications Conference (Globecom), pp 1-6, 2019
  • Luis Eduardo Velez Quintero, Panagiotis Papapetrou, John Munoz, and Uno Fors, “Implementation of Mobile-based Real-time Heart Rate Variability Detection for Personalized Healthcare“. In the IEEE International Conference on Data Mining (ICDM) Workshop on Translational Multimedia Data Mining for AI-Based Medical Diagnostics (TMDM),  ICDM Workshops Proceedings, pp 838-846, 2019
  • Luis Eduardo Velez Quintero, Papapetrou Panagiotis, and John Munoz,
    Open-Source Physiological Computing Framework using Heart Rate Variability in Mobile Virtual Reality Applications”. In the IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), pp 126-133, 2019
  • Maria Bampa and Panagiotis Papapetrou, “Mining Adverse Drug Events Using Multiple Feature Hierarchies and Patient History Windows“. In the IEEE International Conference on Data Mining (ICDM) Workshop on Data Mining in Biomedical Informatics and Healthcare (DMBIH),  ICDM Workshops Proceedings, pp 925-932, 2019
  • Amin Azari, Panagiotis Papapetrou, Stojan Denic, and Gunnar Peters, “Cellular Traffic Prediction and Classification: a comparative evaluation of LSTM and ARIMA“. In the International Conference on Discovery Science (DS), pp 129-144, 2019
  • Fransesco Bagattini, Isak Karlsson, Jonathan Rebane, and Panagiotis Papapetrou, “A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records“. In BMC Medical Informatics and Decision Making 19 (1), 7, 2019 [open access]
  • Jonathan Rebane, Isak Karlsson, and Panagiotis Papapetrou, “An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction“. In the IEEE International Symposium on Computer-Based Medical Systems (CBMS) 2019
  • Jaakko Hollmen and Panagiotis Papapetrou, “Clustering diagnostic profiles of patients“. In the International Conference on Artificial Intelligence Applications and Innovations (AIAI), 120-126, 2019
  • Corinne G. Allaart, Lena Mondrejevski, Panagiotis Papapetrou, FISUL: A Framework for Detecting Adverse Drug Events from Heterogeneous Medical Sources Using Feature Importance“. In the International Conference on Artificial Intelligence Applications and Innovations (AIAI), 139-151, 2019
  • Tony Lindgren, Panagiotis Papapetrou, Lars Asker and Isak Samsten, “Example-based feature tweaking using random forests“. In the IEEE International Conference on Information Reuse and Integration for Data Science (IRI) 2019.

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2018

  • Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou, and Aristides Gionis, “Explainable time series tweaking via irreversible and reversible temporal transformations“. In the IEEE International Conference on Data Mining  (ICDM),  207-216, 2018 [arXiv version]
  • Loes Crelaard and Panagiotis Papapetrou, “Explainable predictions of adverse drug events from electronic health records via oracle coaching“. In the IEEE ICDM Workshop on Data Mining in Biomedical Informatics and Healthcare  (ICDM DMBIH), ICDM Workshops 2018 [pdf]
  • Jonathan Rebane, Isak Karlsson, Panagiotis Papapetrou, and Stojan Denic, “Seq2Seq RNNs and ARIMA models for Cryptocurrency Prediction: A Comparative Study“.  In Proceedings of the FinTech Workshop at the International Conference of Knowledge Discovery and Data Mining (KDD FinTech Workshop), KDD Workshops, 2018 [pdf]
  • Tommy Hielscher, Henry Völzke, Panagiotis Papapetrou, Myra Spiliopoulou, “Discovering, selecting and exploiting feature sequence records of study participants for the classification of epidemiological data on hepatic steatosis“.  In Proceedings of the ACM/SIGAPP Symposium on Applied Computing (SAC) 2018: 6-13 [pdf]
  • Jaakko Hollmén, Lars Asker, Isak Karlsson, Panagiotis Papapetrou, Henrik Boström, Birgitta Norstedt Wikner, Inger Öhman, “Exploring epistaxis as an adverse effect of anti-thrombotic drugs and outdoor temperature”. In Proceedings of the International Conference on Pervasive Technologies Related to Assistive Environments (PETRA) 2018: 1-4 [pdf]

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2017

  • Orestis Kostakis and Panagiotis Papapetrou, “On Searching and Indexing Sequences of Temporal Intervals“. In the Data Mining and Knowledge Discovery Journal (DAMI), 31(3): 809-850, 2017 [pdf]
  • Orestis Kostakis and Panagiotis Papapetrou, “ABIDE: Querying Time-Evolving Sequences of Temporal Intervals“.  In Proceedings of the International Symposium on Intelligent Data Analysis (IDA), 2017 [pdf]
  • Isak Karlsson, Panagiotis Papapetrou, and Lars Asker, “KAPMiner: Mining ordered association rules with constraints“.  In Proceedings of the International Symposium on Intelligent Data Analysis (IDA), 2017 [pdf]
  • Jonathan Rebane, Isak Karlsson,  Lars Asker, Henrik Boström, and Panagiotis Papapetrou, “Learning from Administrative Health Registries“. In Proceedings of the Workshop on Data Science for Social Good (ECML/PKDD SoGood), 2017 [pdf]
  • Jing Zhao, Panagiotis Papapetrou, Lars Asker, and Henrik Boström, “Learning from Heterogeneous Temporal Data in Electronic Health Records“. Journal of Biomedical Informatics (JBI), 65, 105-119, 2017 [pdf]

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2016

  • Isak Karlsson, Panagiotis Papapetrou, and Henrik Boström, “Generalized Random Shapelet Forests“. In the Data Mining and Knowledge Discovery Journal (DAMI), Volume 30, Issue 5, pp 1053-1085, 2016 [pdf]
  • Andreas Henelius, Isak Karlson, Panagiotis Papapetrou, Antti Ukkonnen, Kai Puolamäki, “Semigeometric Tiling of Event Sequences“. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML PKDD), pp 329-344, 2016 [pdf]
  • Isak Karlsson, Panagiotis Papapetrou, and Henrik Boström, “Early Random Shapelet Forest“. In Proceedings of the International Conference on Discovery Science (DS), pp 261-276, 2016 Best Paper Award [pdf]
  • Leon Bornemann, Jason Lercef, and Panagiotis Papapetrou, “STIFE: A Framework for Feature-based Classification of Sequences of Temporal Intervals“. In Proceedings of the International Conference on Discovery Science (DS), pp 85-100, 2016 [pdf]
  • Mohammad Jaber, Peter Wood, Panagiotis Papapetrou, and Ana Gonzalez Marcos, “A Multi-granularity Pattern-based Sequence Classification Framework for Educational Data“. In Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016 [pdf]
  • Alexios Kotsifakos, Panagiotis Papapetrou, and Vassilis Athitsos, “Query-sensitive Distance Measure Selection for Time Series Nearest Neighbor Classification“. Intelligent Data Analysis Journal (IDAJ), 20(1): 5-27, 2016 [pdf]
  • Jefrey Lijffijt, Terttu Nevalainen, Tanja Saily, Panagiotis Papapetrou, Kai Puolamaki, and Heikki Mannila, “Significance Testing of Word Frequencies in Corpora“. Digital Scholarship in the Humanities (DSH), 31(2), 2016 [pdf]
  • Lars Asker, Henrik Boström, Panagiotis Papapetrou, Hans Persson, “Identifying Factors for the Effectiveness of Treatment of Heart Failure: A Registry Study“. International Symposium on Computer-Based Medical Systems (CBMS), 205-206, 2016 [pdf]
  • Lars Asker, Panagiotis Papapetrou, Henrik Boström, “Learning from Swedish Healthcare Data“. In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), 47, 2016 [pdf]
  • Myrsini Glinos, Svante Dahlberg, Nikolaos Tselas, Panagiotis Papapetrou, “FindMyDoc: a P2P platform disrupting traditional healthcare models and matching patients to doctors“. In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), 53, 2016 [pdf]

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2015

  • Alexios Kotsifakos, Isak Karlsson, Panagiotis Papapetrou, Vassilis Athitsos, and Dimitrios Gunopulos, “Embedding-based Subsequence Matching with Gaps-Range-Tolerances: a Query-By-Humming application“. In the Very Large Databases (VLDB) Journal (VLDBJ), 24(4): 519-536, 2015 [pdf]
  • Orestis Kostakis and Panagiotis Papapetrou, “Finding the Longest Common Sub-Pattern in Sequences of Temporal Intervals“. In the Data Mining and Knowledge Discovery Journal (DAMI), 29(5): 1178-1210, 2015 [pdf]
  • Alexios Kotsifakos, Alexandra Stefan, Vassilis Athitsos, Gautam Das, and Panagiotis Papapetrou, “DRESS: Dimensionality Reduction for Efficient Sequence Search“. In the Data Mining and Knowledge Discovery Journal (DAMI), 29(5): 1280-1311, 2015 [pdf]
  • Jefrey Lijffijt, Panagiotis Papapetrou, and Kai Puolamäki, “Size Matters: Choosing the Most Informative Set of Window Lengths for Mining Patterns in Event Sequences“. In the Data Mining and Knowledge Discovery Journal (DAMI), 29(6): 1838-1864, 2015 [pdf]
  • Pat Jangyodsuk, Panagiotis Papapetrou, and Vassilis Athitsos, “Optimizing Hashing Functions for Similarity Indexing in Arbitrary Metric and Nonmetric Spaces“. SIAM International Conference on Data Mining (SDM), 828-836, 2015 [pdf]
  • Isak Karlsson, Panagiotis Papapetrou, and Henrik Boström, “Forests of Randomized Shapelet Trees“. International Symposium on Learning and Data Sciences (SLDS), To Appear [pdf]
  • Andreas Henelius, Kai Puolamki, Isak Karlsson, Jing Zhao, Lars Asker, Henrik Boström, and Panagiotis Papapetrou, “GoldenEye++: a Closer Look into the Black Box“. International Symposium on Learning and Data Sciences (SLDS), To Appear [pdf]
  • Mohammad Jaber, Panagiotis Papapetrou, Ana González-Marcos, Peter T. Wood, “Analysing Online Education-based Asynchronous Communication Tools to Detect Students’ Roles“. CSEDU (2): 416-424, 2015 [pdf]
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2014
  • Andreas Henelius, Kai Puolamäki, Henrik Boström, Lars Asker, and Panagiotis Papapetrou, “A Peek into the Black Box: Exploring Classifiers by Randomization“. In the Data Mining and Knowledge Discovery Journal (DAMI), Volume 28, Issue 5-6, pp 1503-1529, 2014.[pdf]
  • Mohammad Jaber, Panagiotis Papapetrou, Sven Helmer, and Peter Wood, “Using Time-Sensitive Rooted PageRank to Detect Hierarchical Social Relationships“. In Proceedings of the International Symposium on Intelligent Data Analysis (IDA), 2014 [pdf]
  • Alexios Kotsifakos and Panagiotis Papapetrou, “Model-based Time Series Classification“. In Proceedings of the International Symposium on Intelligent Data Analysis (IDA), 2014 [pdf]
  • Panagiotis Papapetrou and George Roussos, “Social Context Discovery from Temporal App Use Patterns“. In the ACM Conference on Ubiquitous Computing Adjunct Publication Proceedings (UbiComp Adjunct), 2014 [pdf]
  • Mohammad Jaber, Peter Wood, Panagiotis Papapetrou, and Sven Helmer, “Inferring Offline Hierarchical Ties from Online Social Networks“. In Proceedings of the WWW Workshop on Connecting Online & Offline Life (WWW/COOL), To Appear [pdf]
  • Nikolaos Tselas and Panagiotis Papapetrou, “Classifying Electrocardiograms using time warping methods“. In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), To Appear [pdf]
  • Lars Asker, Henrik Boström, Isak Karlsson, Panagiotis Papapetrou, and Jing Zhao, “Mining Candidates for Adverse Drug Interactions in Electronic Patient Records“. In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), To Appear [pdf]
  • Jefrey Lijffijt, Panagiotis Papapetrou, and Kai Puolamäki, “A statistical significance testing approach to mining the most informative set of patterns“. In the Data Mining and Knowledge Discovery Journal (DAMI), Volume 28, No. 1, 2014 [pdf]
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2013
  • Thidawan Klaysri, Trevor Fenner, Oded LachishMark Levene, and Panagiotis Papapetrou, “Analysis of Cluster Structure on Large-scale English Wikipedia Category Networks“. In Proceedings of the International Symposium on Intelligent Data Analysis (IDA), 2013 [pdf]
  • Alexios Kotsifakos, Panagiotis Papapetrou, and Vassilis Athitsos, “IBSM: Interval-Based Sequence Matching“. In Proceedings of the SIAM Conference on Data Mining (SDM), 2013 [pdf]
  • Alexios Kotsifakos, Evangelos Kotsifakos, Panagiotis Papapetrou, and Vassilis Athitsos, “Genre Classification of Symbolic Music with SMBGT“, In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), 2013 [pdf]
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2012
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2011
  • Panagiotis Papapetrou, Luiz Augusto Pizzato, Aristides Gionis, Xiongcai Cai, “Proceedings of the IEEE ICDM Workshop on Community Mining and People Recommenders“, December 2011
  • Panagiotis Papapetrou, Vassilis Athitsos, Michalis Potamias, George Kollios, and Dimitrios Gunopulos, “Embedding-based Subsequence Matching in Time Series Databases“. In ACM Transactions on Database Systems (TODS), Volume 36, No. 3, Article 17, August 2011 [pdf]
  • Alexios Kotsifakos, Panagiotis Papapetrou, Jaakko Hollmen, and Dimitrios Gunopulos, “A Subsequence Matching with Gaps-Range-Tolerances Framework: A Query-By-Humming Application“. In Proceedings of the Very Large DataBases Endowement (PVLDB), Volume 4, Issue 11, pages 761-771, 2011 [pdf]
  • Panagiotis Papapetrou, Aristides Gionis, and Heikki Mannila, “A Shapley-value Approach for Influence Attribution. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML PKDD), pages 549-564, September 2011 [pdf]
  • Jefrey Lijffijt, Panagiotis Papapetrou, Kai Puolamaki, and Heikki Mannila, “Analyzing Word Frequencies in Large Text Corpora using Inter-arrival Times and Bootstrapping“. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML PKDD), pages 341-357, September 2011 [pdf]
  • Orestis Kostakis, Panagiotis Papapetrou, and Jaakko Hollmen, “Artemis: Assessing the Similarity of Event-Interval Sequences“. In Proceedings of the European Conference of Machine Learning and Principles and Practices of Knowledge Discovery in Databases (ECML PKDD), pages 229-244, September 2011 [pdf]
  • Alexios Kotsifakos, Vassilis Athitsos, Panagiotis Papapetrou, Jaakko Hollmen, and Dimitrios Gunopulos, “Model-based search in large time series databases“. In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), May 2011
  • Orestis Kostakis, Panagiotis Papapetrou, and Jaakko Hollmen, “Distance Measure for Querying Arrangements of Temporal Intervals“. In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), May 2011 [pdf]
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2010
  • Kai Puolamaki, Panagiotis Papapetrou, and Jefrey Lijffijt, “Visually-Controllable Data Mining Methods“. In Proceedings of the IEEE International Conference on Data Mining Workshop, (ICDM VAKD) 2010 [pdf]
  • Kai Puolamaki, Alessio Bertone, Roberto Theron, Otto Huisman, Jimmy Johansson, Silvia Miksch, Panagiotis Papapetrou, and Salvo Rinzivillo, “Mastering the Information Age Solving Problems with Visual Analytics, chapter Data Mining“, pages 39-56. Eurographics Association, 2010
  • Jefrey Lijffijt, Panagiotis Papapetrou, Jaakko Hollmen, and Vassilis Athitsos, “Benchmarking dynamic time warping for music retrieval“. In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), June 2010, Samos, Greece [pdf]
  • Jefrey Lijffijt, Panagiotis Papapetrou, and Jaakko Hollmen, “Tracking your steps on the track: Body sensor recordings of a controlled walking experiment“. In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), June 2010, Samos, Greece [pdf]
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2009
  • Panagiotis Papapetrou, George Kollios, Stan Sclaroff, and Dimitrios Gunopulos, “Mining Frequent Arrangements of Temporal Intervals“. In Knowledge and Information Systems (KAIS), Volume 21, Issue 2 (2009), Pages 133-171 [pdf]
  • Panagiotis Papapetrou, Vassilis Athitsos, George Kollios, and Dimitrios Gunopulos, “Reference-Based Alignment in Large Sequence Databases“, In Proceedings of the Very Large DataBases Endowment (PVLDB), Volume 2, Issue 1 (2009), Pages 205-216 [pdf][Oral presentation in VLDB 2009: ppt]
  • Panagiotis Papapetrou, Paul Doliotis and Vassilis Athitsos, “Towards Faster Activity Search Using Embedding-based Subsequence Matching“, In Proceedings of Pervasive Technologies Related to Assistive Environments (PETRA), June 2009, Corfu, Greece [pdf]
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2008
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2006
  • Panagiotis Papapetrou, Gary Benson, and George Kollios, “Discovering Frequent Poly-Regions in DNA Sequences“. In Proceedings of the IEEE International Conference on Data Mining Workshop on Data Mining in Bioinformatics (ICDM DMB), December 2006, Hong Kong [pdf] [Full Version: pdf ][talk slides in ppt]
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2005
  • Panagiotis Papapetrou, George Kollios, Stan Sclaroff, and Dimitrios Gunopulos, “Discovering Frequent Arrangements of Temporal Intervals“. In Proceedings of the IEEE International Conference on Data Mining (ICDM), November 2005, Houston, Texas, USA [pdf] [talk slides in ppt]

Technical Reports

2010
  • Jefrey Lijffijt, Panagiotis Papapetrou, Niko Vuokko, and Kai Puolamaki. “The smallest set of constraints that explains the data: a randomization approach“. TKK-ICS-R31, TKK Reports in Information and Computer Science, Espoo, 2010
2008
  • Panagiotis Papapetrou, Gary Benson, and George Kollios, “Generalized Methods for Discovering Frequent Poly-Regions in DNA Sequences“.Technical Report, Department of Computer Science, Boston University, October 21, 2008
2006
  • Ching Chang, Raymond Sweha, Panagiotis Papapetrou, “Extending snBench to Support a Graphical Programming Interface for a Sensor Network Tasking Language (STEP)“. Technical Report, Department of Computer Science, Boston University, July 14, 2006. [pdf] [Web-site]

Theses

2010
  • Embedding-based Subsequence Matching in Large Sequence Databases“, Ph.D. Thesis [pdf][defense slides in ppt]
2007
  • Constraint-based Mining of Frequent Arrangements of Temporal Intervals“, M.A. Thesis [pdf] [defense slides in ppt]
2004
  • Discovering Aggregate Usage Profiles using Clustering Methods“, B.Sc. Thesis [zip] (in Greek only)