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22
A Hybrid Prediction Model for Moving Objects
 In ICDE. IEEE
, 2008
"... Abstract — Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object’s movements can be represented by some mathematical formula ..."
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Cited by 52 (5 self)
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Abstract — Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object’s movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object’s movements are more complicated than what the mathematical formulas can represent. Prediction based on an object’s trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object’s future locations based on its pattern information as well as existing motion functions using the object’s recent movements. Specifically, an object’s trajectory patterns which have adhoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonstrate that proposed techniques are more accurate and efficient than existing forecasting schemes. I.
Reporting flock patterns
 IN PROCEEDINGS OF THE 14TH EUROPEAN SYMPOSIUM ON ALGORITHMS (ESA 2006
, 2006
"... Data representing moving objects is rapidly getting more available, especially in the area of wildlife GPS tracking. It is a central belief that information is hidden in large data sets in the form of interesting patterns, where a pattern can be any configuration of some moving objects in a certain ..."
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Cited by 45 (9 self)
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Data representing moving objects is rapidly getting more available, especially in the area of wildlife GPS tracking. It is a central belief that information is hidden in large data sets in the form of interesting patterns, where a pattern can be any configuration of some moving objects in a certain area and/or during a certain time period. One of the most common spatiotemporal patterns sought after is flocks. A flock is a large enough subset of objects moving along paths close to each other for a certain predefined time. We give a new definition that we argue is more realistic than the previous ones, and by the use of techniques from computational geometry we present fast algorithms to detect and report flocks. The algorithms are analysed both theoretically and experimentally.
Detecting Commuting Patterns by Clustering Subtrajectories
, 2008
"... In this paper we consider the problem of detecting commuting patterns in a trajectory. For this we search for similar subtrajectories. To measure spatial similarity we choose the Fréchet distance and the discrete Fréchet distance between subtrajectories, which are invariant under differences in spee ..."
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Cited by 30 (14 self)
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In this paper we consider the problem of detecting commuting patterns in a trajectory. For this we search for similar subtrajectories. To measure spatial similarity we choose the Fréchet distance and the discrete Fréchet distance between subtrajectories, which are invariant under differences in speed. We give several approximation algorithms, and also show that the problem of finding the ‘longest’ subtrajectory cluster is as hard as MaxClique to compute and approximate.
Dimensionality reduction for long duration and complex spatiotemporal queries
, 2006
"... In this paper we present an approach to mine and query spatiotemporal data with the aim of finding interesting patterns and understanding the underlying data generating process. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths cl ..."
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Cited by 19 (7 self)
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In this paper we present an approach to mine and query spatiotemporal data with the aim of finding interesting patterns and understanding the underlying data generating process. An important class of queries is based on the flock pattern. A flock is a large subset of objects moving along paths close to each other for a certain predefined time. One approach to process a “flock query ” is to map spatiotemporal data into a high dimensional space and reduce the query into a sequence of standard range queries which can be presented using a spatial indexing structure. However, as is well known, the performance of spatial indexing structures drastically deteriorates in high dimensional space. In this paper we propose a preprocessing strategy which consists of using a random projection to reduce the dimensionality of the transformed space. Our experimental results show, for the first time, the possibility of breaking the curse of dimensionality in a spatiotemporal setting.
Reporting Leaders and Followers Among Trajectories of Moving Point Objects
"... Abstract. Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. In this paper we investigate spatio ..."
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Cited by 16 (5 self)
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Abstract. Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects, opening new options for a better understanding of the processes involved. In this paper we investigate spatiotemporal movement patterns in large tracking data sets. We present a natural definition of the pattern ‘one object is leading others’, which is based on behavioural patterns discussed in the behavioural ecology literature. Such leadership patterns can be characterised by a minimum time length for which they have to exist and by a minimum number of entities involved in the pattern. Furthermore, we distinguish two models (discrete and continuous) of the time axis for which patterns can start and end. For all variants of these leadership patterns, we describe algorithms for their detection, given the trajectories of a group of moving entities. A theoretical analysis as well as experiments show that these algorithms efficiently report leadership patterns.
Constrained free space diagrams: a tool for trajectory analysis
 Int. J. of Geogr. Inform. Sci
"... Abstract. We propose a new and powerful tool for the analysis of trajectories, which in particular allows for more temporally aware analyses. Time plays an important role in the analysis of moving object data. For many applications it is neither sufficient to only compare objects at exactly the same ..."
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Cited by 14 (7 self)
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Abstract. We propose a new and powerful tool for the analysis of trajectories, which in particular allows for more temporally aware analyses. Time plays an important role in the analysis of moving object data. For many applications it is neither sufficient to only compare objects at exactly the same times, nor to consider only the geometry of the trajectories. We show how to leverage between these two approaches by extending a tool from curve analysis, the free space diagram. Our approach also allows to take further attributes of the objects like speed or direction into account. We demonstrate the usefulness of the new tool by applying it to the problem of detecting single file movement. A single file is a set of moving entities, which are following each other, one behind the other. This is the first algorithm for detecting this movement pattern. For this application, we analyse and demonstrate the performance of our tool both theoretically and experimentally. 1
Dynamic modeling of trajectory patterns using data mining and reverse engineering
 In TwentySixth International Conference on Conceptual Modeling  ER2007  Tutorials, Posters, Panels and Industrial Contributions
, 2007
"... The constant increase of moving object data imposes the need for modeling, processing, and mining trajectories, in order to find and understand the patterns behind these data. Existing works have mainly focused on the geometric properties of trajectories, while the semantics and the background geogr ..."
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Cited by 8 (2 self)
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The constant increase of moving object data imposes the need for modeling, processing, and mining trajectories, in order to find and understand the patterns behind these data. Existing works have mainly focused on the geometric properties of trajectories, while the semantics and the background geographic information has rarely been addressed. We claim that meaningful patterns can only be extracted from trajectories if the geographic space where trajectories are located is considered. In this paper we propose a reverse engineering framework for mining and modeling semantic trajectory patterns. Since trajectory patterns are data dependent, they may not be modeled in conceptual geographic database schemas before they are known. Therefore, we apply data mining to extract general trajectory patterns, and through a new kind of relationships, we model these patterns in the geographic database schema. A case study shows the power of the framework for modeling semantic trajectory patterns in the geographic space.
Towards Semantic Trajectory Knowledge Discovery
"... Abstract. Trajectory data play a fundamental role to an increasing number of applications, such as transportation management, urban planning and tourism. Trajectory data are normally available as sample points. However, for many applications, meaningful patterns cannot be extracted from sample point ..."
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Cited by 7 (0 self)
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Abstract. Trajectory data play a fundamental role to an increasing number of applications, such as transportation management, urban planning and tourism. Trajectory data are normally available as sample points. However, for many applications, meaningful patterns cannot be extracted from sample points without considering the background geographic information. In this paper we propose a novel framework for semantic trajectory knowledge discovery. We propose to integrate trajectory sample points to the geographic information which is relevant to the application. Therefore, we extract the most important parts of trajectories, which are stops and moves, before applying data mining methods. Empirically we show the application and usability of our approach. 1.
FINDING POPULAR PLACES
, 2008
"... Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects. We investigate spatiotemporal movement patterns in large tracking data sets, i.e. in large sets of polygonal paths. Sp ..."
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Cited by 6 (3 self)
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Widespread availability of location aware devices (such as GPS receivers) promotes capture of detailed movement trajectories of people, animals, vehicles and other moving objects. We investigate spatiotemporal movement patterns in large tracking data sets, i.e. in large sets of polygonal paths. Specifically, we study socalled ‘popular places’, that is, regions that are visited by many entities. Given a set of polygonal paths with a total of ¯n vertices, we look at the problem of computing such popular places in two different settings. For the discrete model, where only the vertices of the polygonal paths are considered, we propose an O(¯nlog ¯n) algorithm; and for the continuous model, where also the straight line segments between the vertices of a polygonal path are considered, we develop an O(¯n²) algorithm. We also present lower bounds and hardness results.
kSTARs: Sequences of SpatioTemporal Association Rules
, 2006
"... A SpatioTemporal Association Rule (STAR) describes how objects move between regions over time. Since they describe only a single movement between two regions, it is very difficult to see larger patterns in the dataset by considering only the set of STARs. It is especially difficult on complex datas ..."
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Cited by 4 (1 self)
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A SpatioTemporal Association Rule (STAR) describes how objects move between regions over time. Since they describe only a single movement between two regions, it is very difficult to see larger patterns in the dataset by considering only the set of STARs. It is especially difficult on complex datasets where the underlying patterns overlap. At best we will miss important patterns being unable to “see the forest for the trees”, and at worst this can lead to false interpretations. We introduce the kSTAR pattern which describes the sequences of STARs that objects obey. Since a kSTAR captures sequences of object movements it solves these problems. We also allow space and time gaps between successive STARs, as well as supporting ‘replenishable ’ kSTARs so we are able to capture the rich set of patterns that exist in real world data. We define a lattice on the kSTARs that allows the user to drill down and drill up in order to explore the patterns in detail, or view them at a higher level. We introduce two important measures; minlsupport and minlconfidence that allow us to achieve the above. This paper gives a rigorous theoretical treatment of kSTARs, proving various antimonotonic and weakly antimonotonic properties that can be exploited to mine kSTARs efficiently. We describe an algorithm, kSTARMiner, that uses these results to mine the lattice of kSTARs 1. 1