Aggregation and comparison of trajectories

Meratnia, Nirvana; By, R.A. de

doi:10.1145/585147.585158

Cited by 33 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While the support of the set F is equal or greater than the minimum, the algorithm continues. Finally (lines [21][22], the algorithm repeats the sequence of phases performed inside the loop, but using C instead of C'.…”

Section: Stpm(t Oi C Minsupport)mentioning

confidence: 99%

Mining semantic trajectories

Gómez

Vaisman

2013

IDA

View full text Add to dashboard Cite

A typical problem in the field of moving object (MO) databases consists in discovering interesting trajectory patterns. To solve this problem, data mining techniques are commonly used. Due to the huge volume of these trajectory data, some form of compression facilitates the data processing. One of such compression techniques is based on the notion of stops and moves. In this approach, a set of places that are relevant to the application, denoted Places of Interest (POIs) is selected. If a moving object spends a pre-defined amount of time in a place of interest, this place is considered a stop for the object's trajectory. Thus, raw trajectories given by (O id , t, x, y)-tuples can be replaced by a sequence of application-relevant stops. This leads to the concept of semantic trajectory, in short, a trajectory obtained by replacing raw trajectory data with a sequence of stops, and enriched with metadata of the POIs corresponding to such stops. We present a language based on regular expressions over constraints, denoted RE-SPaM, that can intensionally express sequential patterns. The constraints in RE-SPaM are defined as conjunctions of equalities over metadata of the POIs. In addition, we introduce a data mining algorithm, based on sequential pattern mining techniques, where uninteresting sequences are pruned in advance making use of the automaton that accepts a RE-SPaM expression. This makes the task of the analyst easier, and the mining algorithm more efficient. We also show that RE-SPaM can be extended to support spatial functions, thus integrating spatial data in a moving object setting (proposals so far only account for the MO trajectories themselves). We denote the resulting language RE-SPaM +S . We show that the overhead of this extension is negligible, due to caching techniques that we explain in the paper. We close the paper with a case study over which we perform experiments to study the main variables that impact the performance of the mining algorithm.

show abstract

Section: Stpm(t Oi C Minsupport)mentioning

confidence: 99%

Mining semantic trajectories

Gómez

Vaisman

2013

IDA

View full text Add to dashboard Cite

show abstract

“…Meratnia and de By [9] have tackled the topic of aggregation of trajectories, identifying similar trajectories and merging them in a single one, by dividing the area of study into homogeneous spatial units. Papadias et al [12] index historical aggregate information about moving objects.…”

Section: Related Workmentioning

confidence: 99%

Aggregation languages for moving object and places of interest

Gómez

Kuijpers

Vaisman

2008

Proceedings of the 2008 ACM Symposium on Applied Computing

View full text Add to dashboard Cite

We address aggregate queries over GIS data and moving object data, where non-spatial information is stored in a data warehouse. We propose a formal data model and query language to express complex aggregate queries. Next, we study the compression of trajectory data, produced by moving objects, using the notions of stops and moves. We show that stops and moves are expressible in our query language and we consider a fragment of this language, consisting of regular expressions to talk about temporally ordered sequences of stops and moves. This fragment can be used not only for querying, but also for expressing data mining and pattern matching tasks over trajectory data.

show abstract

“…Meratnia and de By [10] have tackled the topic of aggregation of trajectories. They identify similar trajectories and merge them in a single one, by dividing the area of study into homogeneous spatial units; each unit is associated to an integer, representing the number of times any object passes through it.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Moving objects representation and computing have received a fair share of attention over recent years in the database community [2,3,10,18]. In particular, we are interested in aggregation of moving objects data.…”

Section: Introductionmentioning

confidence: 99%