Aggregation languages for moving object and places of interest

Gómez, Leticia I.; Kuijpers, Bart; Vaisman, Alejandro

doi:10.1145/1363686.1363885

Cited by 16 publications

(9 citation statements)

References 22 publications

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“…That means, spatial objects in thematic layers can be added, removed, split, merged, or their shape may change. In [17,32] the authors show that the Piet framework supports continuous motion, following the classification above. However, Piet works under the assumption that all objects in a layer remain unchanged across time, i.e., neither does Piet support objects with discrete changes nor objects combining continuous motion and changing shapes.…”

Section: Gis-olap Decision Supportmentioning

confidence: 77%

A data model and query language for spatio-temporal decision support

2010

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In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of introducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote L t , able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the 456 Geoinformatica (2011) 15:455-496 model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet.

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Section: Gis-olap Decision Supportmentioning

confidence: 77%

A data model and query language for spatio-temporal decision support

2010

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“…In [12], the authors studied an aggregate query language forGIS and no-spatial data stored in a data warehouse. In [13], the authors studied k-nearest neighbour search algorithm for historical moving object trajectories, thisk-nearest neighbour is one of the queries that is considered in our study.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of SQL and NoSQL Database Management Systems in a Cluster

Niyizamwiyitira¹,

Lundberg²

2017

IJDMS

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“…Due to the huge volume of these trajectory data, some form of compression facilitates data processing. For example, the notions of stops and moves [4,23,27] allow compressing trajectory data produced by moving objects using application-dependent places of interest [12,13]. In this approach, a designer selects a set of places of interest that are relevant to her application.…”

Section: Introductionmentioning

confidence: 99%

Mining semantic trajectories

Gómez

Vaisman

2013

IDA

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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.

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Aggregation languages for moving object and places of interest

Cited by 16 publications

References 22 publications

A data model and query language for spatio-temporal decision support

A data model and query language for spatio-temporal decision support

Performance Evaluation of SQL and NoSQL Database Management Systems in a Cluster

Mining semantic trajectories

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