10 Search Results for "Pedreschi, Dino"

Document
DOI: 10.4230/LIPIcs.TIME.2025.12
On the Complexity of the Realisability Problem for Visit Events in Trajectory Sample Databases

Authors: Arthur Jansen and Bart Kuijpers

Published in: LIPIcs, Volume 355, 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)

Abstract
Trajectory sample databases store finite sequences of measured space-time locations of moving objects, along with a speed bound for each object. These databases can be seen as uncertain databases. We propose a language that allows the formulation of queries about the uncertainty in trajectory sample databases. As part of that language, we introduce the notion of visit events, which are used to describe certain constraints on the movement of an object. In our language, an atomic query asks whether a moving object can, given its limitations, realise such an event. We give complexity results for this realisability problem, in various settings.
Cite as

Arthur Jansen and Bart Kuijpers. On the Complexity of the Realisability Problem for Visit Events in Trajectory Sample Databases. In 32nd International Symposium on Temporal Representation and Reasoning (TIME 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 355, pp. 12:1-12:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{jansen_et_al:LIPIcs.TIME.2025.12,
  author =	{Jansen, Arthur and Kuijpers, Bart},
  title =	{{On the Complexity of the Realisability Problem for Visit Events in Trajectory Sample Databases}},
  booktitle =	{32nd International Symposium on Temporal Representation and Reasoning (TIME 2025)},
  pages =	{12:1--12:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-401-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{355},
  editor =	{Vidal, Thierry and Wa{\l}\k{e}ga, Przemys{\l}aw Andrzej},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2025.12},
  URN =		{urn:nbn:de:0030-drops-244586},
  doi =		{10.4230/LIPIcs.TIME.2025.12},
  annote =	{Keywords: Trajectory sample databases, uncertain databases, query languages, complexity}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.29
From Prediction to Action: A Constraint-Based Approach to Predictive Policing

Authors: Younes Mechqrane and Ismail Elabbassi

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
Crime prevention in urban environments demands both accurate crime forecasting and the efficient deployment of limited law enforcement resources. In this paper, we present an integrated framework that combines a machine learning module (i.e. PredRNN++ [Wang et al., 2018]) for spatiotemporal crime prediction with a constraint programming module for patrol route optimization. Our approach operates within the ICON loop framework [Bessiere et al., 2017], facilitating iterative refinement of predictions and immediate adaptation of patrol strategies. We validate our method using the City of Chicago Crime Dataset. Experimental results show that routes informed by crime predictions significantly outperform strategies relying solely on historical patterns or operational constraints. These findings illustrate how coupling predictive analytics with constraint programming can substantially enhance resource allocation and overall crime deterrence.
Cite as

Younes Mechqrane and Ismail Elabbassi. From Prediction to Action: A Constraint-Based Approach to Predictive Policing. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 29:1-29:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{mechqrane_et_al:LIPIcs.CP.2025.29,
  author =	{Mechqrane, Younes and Elabbassi, Ismail},
  title =	{{From Prediction to Action: A Constraint-Based Approach to Predictive Policing}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{29:1--29:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.29},
  URN =		{urn:nbn:de:0030-drops-238902},
  doi =		{10.4230/LIPIcs.CP.2025.29},
  annote =	{Keywords: Inductive Constraint Programming (ICON) Loop, Next Frame Prediction, PredRNN++}
}
Document
Academic Track
DOI: 10.4230/OASIcs.SAIA.2024.12
A View on Vulnerabilites: The Security Challenges of XAI (Academic Track)

Authors: Elisabeth Pachl, Fabian Langer, Thora Markert, and Jeanette Miriam Lorenz

Published in: OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024)

Abstract
Modern deep learning methods have long been considered as black-boxes due to their opaque decision-making processes. Explainable Artificial Intelligence (XAI), however, has turned the tables: it provides insight into how these models work, promoting transparency that is crucial for accountability. Yet, recent developments in adversarial machine learning have highlighted vulnerabilities in XAI methods, raising concerns about security, reliability and trustworthiness, particularly in sensitive areas like healthcare and autonomous systems. Awareness of the potential risks associated with XAI is needed as its adoption increases, driven in part by the need to enhance compliance to regulations. This survey provides a holistic perspective on the security and safety landscape surrounding XAI, categorizing research on adversarial attacks against XAI and the misuse of explainability to enhance attacks on AI systems, such as evasion and privacy breaches. Our contribution includes identifying current insecurities in XAI and outlining future research directions in adversarial XAI. This work serves as an accessible foundation and outlook to recognize potential research gaps and define future directions. It identifies data modalities, such as time-series or graph data, and XAI methods that have not been extensively investigated for vulnerabilities in current research.
Cite as

Elisabeth Pachl, Fabian Langer, Thora Markert, and Jeanette Miriam Lorenz. A View on Vulnerabilites: The Security Challenges of XAI (Academic Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 12:1-12:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{pachl_et_al:OASIcs.SAIA.2024.12,
  author =	{Pachl, Elisabeth and Langer, Fabian and Markert, Thora and Lorenz, Jeanette Miriam},
  title =	{{A View on Vulnerabilites: The Security Challenges of XAI}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{12:1--12:23},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-357-7},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{126},
  editor =	{G\"{o}rge, Rebekka and Haedecke, Elena and Poretschkin, Maximilian and Schmitz, Anna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.12},
  URN =		{urn:nbn:de:0030-drops-227523},
  doi =		{10.4230/OASIcs.SAIA.2024.12},
  annote =	{Keywords: Explainability, XAI, Transparency, Adversarial Machine Learning, Security, Vulnerabilities}
}
Document
Vision
DOI: 10.4230/TGDK.1.1.8
Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges

Authors: Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
The graph model is nowadays largely adopted to model a wide range of knowledge and data, spanning from social networks to knowledge graphs (KGs), representing a successful paradigm of how symbolic and transparent AI can scale on the World Wide Web. However, due to their unprecedented volume, they are generally tackled by Machine Learning (ML) and mostly numeric based methods such as graph embedding models (KGE) and deep neural networks (DNNs). The latter methods have been proved lately very efficient, leading the current AI spring. In this vision paper, we introduce some of the main existing methods for combining KGs and ML, divided into two categories: those using ML to improve KGs, and those using KGs to improve results on ML tasks. From this introduction, we highlight research gaps and perspectives that we deem promising and currently under-explored for the involved research communities, spanning from KG support for LLM prompting, integration of KG semantics in ML models to symbol-based methods, interpretability of ML models, and the need for improved benchmark datasets. In our opinion, such perspectives are stepping stones in an ultimate view of KGs as central assets for neuro-symbolic and explainable AI.
Cite as

Claudia d'Amato, Louis Mahon, Pierre Monnin, and Giorgos Stamou. Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 8:1-8:35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{damato_et_al:TGDK.1.1.8,
  author =	{d'Amato, Claudia and Mahon, Louis and Monnin, Pierre and Stamou, Giorgos},
  title =	{{Machine Learning and Knowledge Graphs: Existing Gaps and Future Research Challenges}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{8:1--8:35},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.8},
  URN =		{urn:nbn:de:0030-drops-194824},
  doi =		{10.4230/TGDK.1.1.8},
  annote =	{Keywords: Graph-based Learning, Knowledge Graph Embeddings, Large Language Models, Explainable AI, Knowledge Graph Completion \& Curation}
}
Document
Position
DOI: 10.4230/TGDK.1.1.5
Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities

Authors: Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
The term life sciences refers to the disciplines that study living organisms and life processes, and include chemistry, biology, medicine, and a range of other related disciplines. Research efforts in life sciences are heavily data-driven, as they produce and consume vast amounts of scientific data, much of which is intrinsically relational and graph-structured. The volume of data and the complexity of scientific concepts and relations referred to therein promote the application of advanced knowledge-driven technologies for managing and interpreting data, with the ultimate aim to advance scientific discovery. In this survey and position paper, we discuss recent developments and advances in the use of graph-based technologies in life sciences and set out a vision for how these technologies will impact these fields into the future. We focus on three broad topics: the construction and management of Knowledge Graphs (KGs), the use of KGs and associated technologies in the discovery of new knowledge, and the use of KGs in artificial intelligence applications to support explanations (explainable AI). We select a few exemplary use cases for each topic, discuss the challenges and open research questions within these topics, and conclude with a perspective and outlook that summarizes the overarching challenges and their potential solutions as a guide for future research.
Cite as

Jiaoyan Chen, Hang Dong, Janna Hastings, Ernesto Jiménez-Ruiz, Vanessa López, Pierre Monnin, Catia Pesquita, Petr Škoda, and Valentina Tamma. Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 5:1-5:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{chen_et_al:TGDK.1.1.5,
  author =	{Chen, Jiaoyan and Dong, Hang and Hastings, Janna and Jim\'{e}nez-Ruiz, Ernesto and L\'{o}pez, Vanessa and Monnin, Pierre and Pesquita, Catia and \v{S}koda, Petr and Tamma, Valentina},
  title =	{{Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{5:1--5:33},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.5},
  URN =		{urn:nbn:de:0030-drops-194791},
  doi =		{10.4230/TGDK.1.1.5},
  annote =	{Keywords: Knowledge graphs, Life science, Knowledge discovery, Explainable AI}
}
Document
DOI: 10.4230/OASIcs.Gabbrielli.11
Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy

Authors: Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli

Published in: OASIcs, Volume 86, Recent Developments in the Design and Implementation of Programming Languages (2020)

Abstract
This paper shows how we can combine the power of machine learning with the flexibility of constraints. More specifically, we show how machine learning models can be represented by first-order logic theories, and how to derive these theories. The advantage of this representation is that it can be augmented with additional formulae, representing constraints of some kind on the data domain. For instance, new knowledge, or potential attackers, or fairness desiderata. We consider various kinds of learning algorithms (neural networks, k-nearest-neighbours, decision trees, support vector machines) and for each of them we show how to infer the FOL formulae. Then we focus on one particular application domain, namely the field of security and privacy. The idea is to represent the potentialities and goals of the attacker as a set of constraints, then use a constraint solver (more precisely, a solver modulo theories) to verify the satisfiability. If a solution exists, then it means that an attack is possible, otherwise, the system is safe. We show various examples from different areas of security and privacy; specifically, we consider a side-channel attack on a password checker, a malware attack on smart health systems, and a model-inversion attack on a neural network.
Cite as

Moreno Falaschi, Catuscia Palamidessi, and Marco Romanelli. Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy. In Recent Developments in the Design and Implementation of Programming Languages. Open Access Series in Informatics (OASIcs), Volume 86, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{falaschi_et_al:OASIcs.Gabbrielli.11,
  author =	{Falaschi, Moreno and Palamidessi, Catuscia and Romanelli, Marco},
  title =	{{Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy}},
  booktitle =	{Recent Developments in the Design and Implementation of Programming Languages},
  pages =	{11:1--11:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-171-9},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{86},
  editor =	{de Boer, Frank S. and Mauro, Jacopo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Gabbrielli.11},
  URN =		{urn:nbn:de:0030-drops-132338},
  doi =		{10.4230/OASIcs.Gabbrielli.11},
  annote =	{Keywords: Constraints, machine learning, privacy, security}
}
Document
DOI: 10.4230/DagSemProc.08471.1
08471 Report – Geographic Privacy-Aware Knowledge Discovery and Delivery

Authors: Bart Kuijpers, Dino Pedreschi, Yucel Saygin, and Stefano Spaccapietra

Published in: Dagstuhl Seminar Proceedings, Volume 8471, Geographic Privacy-Aware Knowledge Discovery and Delivery (2009)

Abstract
The Dagstuhl-Seminar on Geographic Privacy-Aware Knowledge Discovery and Delivery was held during 16 - 21 November, 2008, with 37 participants registered from various countries from Europe, as well as other parts of the world such as United States, Canada, Argentina, and Brazil. Issues in the newly emerging area of geographic knowledge discovery with a privacy perspective were discussed in a week to consolidate some of the research questions. The Dagstuhl program included plenary sessions and special interest group meetings which continued even late in the evening with heated discussions. The plenary sessions were dedicated for the talks of some of the participants covering a variety of issues in geographic knowledge discovery and delivery. The reports on special interest group meetings (SIG) were also presented and discussed during the plenary sessions.
Cite as

Bart Kuijpers, Dino Pedreschi, Yucel Saygin, and Stefano Spaccapietra. 08471 Report – Geographic Privacy-Aware Knowledge Discovery and Delivery. In Geographic Privacy-Aware Knowledge Discovery and Delivery. Dagstuhl Seminar Proceedings, Volume 8471, pp. 1-14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{kuijpers_et_al:DagSemProc.08471.1,
  author =	{Kuijpers, Bart and Pedreschi, Dino and Saygin, Yucel and Spaccapietra, Stefano},
  title =	{{08471 Report – Geographic Privacy-Aware Knowledge Discovery and Delivery}},
  booktitle =	{Geographic Privacy-Aware Knowledge Discovery and Delivery},
  pages =	{1--14},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8471},
  editor =	{Bart Kuijpers and Dino Pedreschi and Yucel Saygin and Stefano Spaccapietra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08471.1},
  URN =		{urn:nbn:de:0030-drops-20102},
  doi =		{10.4230/DagSemProc.08471.1},
  annote =	{Keywords: Spatio-temporal databases, data mining, privacy-preserving mining, data visualization}
}
Document
DOI: 10.4230/DagSemProc.08471.2
Propagating and measuring anchor uncertainty in space-time prisms on road networks

Authors: Bart Kuijpers, Harvey J. Miller, Tijs Neutens, and Walied Othman

Published in: Dagstuhl Seminar Proceedings, Volume 8471, Geographic Privacy-Aware Knowledge Discovery and Delivery (2009)

Abstract
Space-time prisms capture all possible spatio-temporal locations of a moving object between sample points given speed limit constraints on its movement. These sample points are usually considered to be perfect measurements. In this paper we restrict ourselves to a road network and extend the notion of sample points to sample regions, which are bounded, sometimes disconnected, subsets of space-time wherein each point is a possible location, with its respective probability, where a moving object could have originated from or arrived in. This model allows us to model measurement errors, multiple possible simultaneous locations and even flexibility of a moving object. We develop an algorithm that computes the envelope of all space-time prisms that have an anchor in these sample regions and we developed an algorithm that computes for any spatio-temporal point the probability with which a space-time prism, with anchors in these sample regions, contains that point. We implemented these algorithms in Mathematica to visualise all these newly-introduced concepts.
Cite as

Bart Kuijpers, Harvey J. Miller, Tijs Neutens, and Walied Othman. Propagating and measuring anchor uncertainty in space-time prisms on road networks. In Geographic Privacy-Aware Knowledge Discovery and Delivery. Dagstuhl Seminar Proceedings, Volume 8471, pp. 1-35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{kuijpers_et_al:DagSemProc.08471.2,
  author =	{Kuijpers, Bart and Miller, Harvey J. and Neutens, Tijs and Othman, Walied},
  title =	{{Propagating and measuring anchor uncertainty in space-time prisms on road networks}},
  booktitle =	{Geographic Privacy-Aware Knowledge Discovery and Delivery},
  pages =	{1--35},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8471},
  editor =	{Bart Kuijpers and Dino Pedreschi and Yucel Saygin and Stefano Spaccapietra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08471.2},
  URN =		{urn:nbn:de:0030-drops-20072},
  doi =		{10.4230/DagSemProc.08471.2},
  annote =	{Keywords: Space-time prisms, beads, prisms, uncertainty, flexibility, time-geography}
}
Document
DOI: 10.4230/DagSemProc.08471.3
Semantic Trajectory Data Mining: a User Driven Approach

Authors: Vania Bogorny and Luis Otavio Alvares

Published in: Dagstuhl Seminar Proceedings, Volume 8471, Geographic Privacy-Aware Knowledge Discovery and Delivery (2009)

Abstract
Trajectories left behind cars, humans, birds or any other moving object are a new kind of data which can be very useful in decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery.
Cite as

Vania Bogorny and Luis Otavio Alvares. Semantic Trajectory Data Mining: a User Driven Approach. In Geographic Privacy-Aware Knowledge Discovery and Delivery. Dagstuhl Seminar Proceedings, Volume 8471, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{bogorny_et_al:DagSemProc.08471.3,
  author =	{Bogorny, Vania and Alvares, Luis Otavio},
  title =	{{Semantic Trajectory Data Mining: a User Driven Approach}},
  booktitle =	{Geographic Privacy-Aware Knowledge Discovery and Delivery},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8471},
  editor =	{Bart Kuijpers and Dino Pedreschi and Yucel Saygin and Stefano Spaccapietra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08471.3},
  URN =		{urn:nbn:de:0030-drops-20096},
  doi =		{10.4230/DagSemProc.08471.3},
  annote =	{Keywords: Spatio-temporal data mining, trajectory data mining, trajectory sequential patterns, trajectory association rules, trajectory generalization, trajecto}
}
Document
DOI: 10.4230/DagSemProc.08471.4
Temporal Support of Regular Expressions in Sequential Pattern Mining

Authors: Alejandro Vaisman, Leticia I. Gómez, and Bart Kuijpers

Published in: Dagstuhl Seminar Proceedings, Volume 8471, Geographic Privacy-Aware Knowledge Discovery and Delivery (2009)

Abstract
Classic algorithms for sequential pattern discovery,return all frequent sequences present in a database. Since, in general, only a few ones are interesting from a user's point of view, languages based on regular expressions (RE) have been proposed to restrict frequent sequences to the ones that satisfy user-specified constraints. Although the support of a sequence is computed as the number of data-sequences satisfying a pattern with respect to the total number of data-sequences in the database, once regular expressions come into play, new approaches to the concept of support are needed. For example, users may be interested in computing the support of the RE as a whole, in addition to the one of a particular pattern. As a simple example, the expression $(A|B).C$ is satisfied by sequences like A.C or B.C. Even though the semantics of this RE suggests that both of them are equally interesting to the user, if neither of them verifies a minimum support although together they do), they would not be retrieved. Also, when the items are frequently updated, the traditional way of counting support in sequential pattern mining may lead to incorrect (or, at least incomplete), conclusions. For example, if we are looking for the support of the sequence A.B, where A and B are two items such that A was created after B, all sequences in the database that were completed before A was created, can never produce a match. Therefore, accounting for them would underestimate the support of the sequence A.B. The problem gets more involved if we are interested in categorical sequential patterns. In light of the above, in this paper we propose to revise the classic notion of support in sequential pattern mining, introducing the concept of temporal support of regular expressions, intuitively defined as the number of sequences satisfying a target pattern, out of the total number of sequences that could have possibly matched such pattern, where the pattern is defined as a RE over complex items (i.e., not only item identifiers, but also attributes and functions). We present and discuss a theoretical framework for these novel notion of support.
Cite as

Alejandro Vaisman, Leticia I. Gómez, and Bart Kuijpers. Temporal Support of Regular Expressions in Sequential Pattern Mining. In Geographic Privacy-Aware Knowledge Discovery and Delivery. Dagstuhl Seminar Proceedings, Volume 8471, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{vaisman_et_al:DagSemProc.08471.4,
  author =	{Vaisman, Alejandro and G\'{o}mez, Leticia I. and Kuijpers, Bart},
  title =	{{Temporal Support of Regular Expressions in Sequential Pattern Mining}},
  booktitle =	{Geographic Privacy-Aware Knowledge Discovery and Delivery},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{8471},
  editor =	{Bart Kuijpers and Dino Pedreschi and Yucel Saygin and Stefano Spaccapietra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.08471.4},
  URN =		{urn:nbn:de:0030-drops-20087},
  doi =		{10.4230/DagSemProc.08471.4},
  annote =	{Keywords: Temporal support, sequential pattern mining}
}
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