Volume

Dagstuhl Seminar Proceedings, Volume 8471

Publication Details

  • published at: 2009-05-13
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

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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

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

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

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

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}
}

Filters

  • Authors
  • A
  • Alvares, Luis Otavio
  • B
  • Bogorny, Vania
  • G
  • Gómez, Leticia I.
  • K
  • Kuijpers, Bart
  • M
  • Miller, Harvey J.
  • N
  • Neutens, Tijs
  • O
  • Othman, Walied
  • P
  • Pedreschi, Dino
  • S
  • Saygin, Yucel
  • Spaccapietra, Stefano
  • V
  • Vaisman, Alejandro

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