eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2009-05-13
8471
1
14
10.4230/DagSemProc.08471.1
article
08471 Report – Geographic Privacy-Aware Knowledge Discovery and Delivery
Kuijpers, Bart
Pedreschi, Dino
Saygin, Yucel
Spaccapietra, Stefano
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol08471/DagSemProc.08471.1/DagSemProc.08471.1.pdf
Spatio-temporal databases
data mining
privacy-preserving mining
data visualization
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2009-05-13
8471
1
35
10.4230/DagSemProc.08471.2
article
Propagating and measuring anchor uncertainty in space-time prisms on road networks
Kuijpers, Bart
Miller, Harvey J.
Neutens, Tijs
Othman, Walied
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol08471/DagSemProc.08471.2/DagSemProc.08471.2.pdf
Space-time prisms
beads
prisms
uncertainty
flexibility
time-geography
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2009-05-13
8471
1
0
10.4230/DagSemProc.08471.3
article
Semantic Trajectory Data Mining: a User Driven Approach
Bogorny, Vania
Alvares, Luis Otavio
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol08471/DagSemProc.08471.3/DagSemProc.08471.3.pdf
Spatio-temporal data mining
trajectory data mining
trajectory sequential patterns
trajectory association rules
trajectory generalization
trajecto
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2009-05-13
8471
1
15
10.4230/DagSemProc.08471.4
article
Temporal Support of Regular Expressions in Sequential Pattern Mining
Vaisman, Alejandro
Gómez, Leticia I.
Kuijpers, Bart
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.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol08471/DagSemProc.08471.4/DagSemProc.08471.4.pdf
Temporal support
sequential pattern mining