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40
Discovering spatio-temporal causal interactions in traffic data streams
- In SIGKDD
, 2011
"... The detection of outliers in spatio-temporal traffic data is an important research problem in the data mining and knowl-edge discovery community. However to the best of our knowl-edge, the discovery of relationships, especially causal inter-actions, among detected traffic outliers has not been inves ..."
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Cited by 25 (5 self)
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The detection of outliers in spatio-temporal traffic data is an important research problem in the data mining and knowl-edge discovery community. However to the best of our knowl-edge, the discovery of relationships, especially causal inter-actions, among detected traffic outliers has not been inves-tigated before. In this paper we propose algorithms which construct outlier causality trees based on temporal and spa-tial properties of detected outliers. Frequent substructures of these causality trees reveal not only recurring interac-tions among spatio-temporal outliers, but potential flaws in the design of existing traffic networks. The effectiveness and strength of our algorithms are validated by experiments on a very large volume of real taxi trajectories in an urban road network.
Mining individual life pattern based on location history
- In Proc. of the 10th IEEE Int. Conf. on Mobile Data Management
, 2009
"... Abstract — The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enables people to conveniently log their location history into spatial-temporal data, thus giving rise to the necessity as well as opportunity to discovery valuable knowledge from this type of data ..."
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Cited by 23 (1 self)
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Abstract — The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) enables people to conveniently log their location history into spatial-temporal data, thus giving rise to the necessity as well as opportunity to discovery valuable knowledge from this type of data. In this paper, we propose the novel notion of individual life pattern, which captures individual’s general life style and regularity. Concretely, we propose the life pattern normal form (the LPnormal form) to formally describe which kind of life regularity can be discovered from location history; then we propose the LP-Mine framework to effectively retrieve life patterns from raw individual GPS data. Our definition of life pattern focuses on significant places of individual life and considers diverse properties to combine the significant places. LP-Mine is comprised of two phases: the modelling phase and the mining phase. The modelling phase pre-processes GPS data into an available format as the input of the mining phase. The mining phase applies separate strategies to discover different types of pattern. Finally, we conduct extensive experiments using GPS data collected by volunteers in the real world to verify the effectiveness of the framework. I.
A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets
"... Abstract—Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, mining spatio-temporal sequential patterns identifies significant event-type sequences. Such spatio-temporal sequential patterns are crucial to the investigati ..."
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Cited by 17 (1 self)
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Abstract—Given a large spatio-temporal database of events, where each event consists of the fields event ID, time, location, and event type, mining spatio-temporal sequential patterns identifies significant event-type sequences. Such spatio-temporal sequential patterns are crucial to the investigation of spatial and temporal evolutions of phenomena in many application domains. Recent research literature has explored the sequential patterns on transaction data and trajectory analysis on moving objects. However, these methods cannot be directly applied to mining sequential patterns from a large number of spatio-temporal events. Two major research challenges still remain: 1) the definition of significance measures for spatio-temporal sequential patterns to avoid spurious ones and 2) the algorithmic design under the significance measures, which may not guarantee the downward closure property. In this paper, we propose a sequence index as the significance measure for spatio-temporal sequential patterns, which is meaningful due to its interpretability using spatial statistics. We propose a novel algorithm called Slicing-STS-Miner to tackle the algorithmic design challenge using the spatial sequence index, which does not preserve the downward closure property. We compare the proposed algorithm with a simple algorithm called STS-Miner that utilizes the weak monotone property of the sequence index. Performance evaluations using both synthetic and real-world data sets show that the Slicing-STS-Miner is an order of magnitude faster than STS-Miner for large data sets. Index Terms—Spatio-temporal sequential pattern, density ratio, sequence index, slicing, performance. Ç 1
F.Giannotti. Hiding sequences
- In ICDM workshop
"... The process of discovering relevant patterns holding in a database, was first indicated as a threat to database security by O ’ Leary in [20]. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent itemsets mini ..."
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Cited by 8 (2 self)
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The process of discovering relevant patterns holding in a database, was first indicated as a threat to database security by O ’ Leary in [20]. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent itemsets mining. Following many real-world data and applications demands, in this paper we shift the problem of knowledge hiding to contexts where both the data and the extracted knowledge have a sequential structure. We provide problem statement, some theoretical issues including NP-hardness of the problem, a polynomial sanitization algorithm and an experimental evaluation. Finally we discuss possible extensions that will allow to use this work as a basic building block for more complex kinds of patterns and applications. 1.
Modeling Herds and Their Evolvements from Trajectory Data
"... Abstract. A trajectory is the time-stamped path of a moving entity through space. Given a set of trajectories, this paper proposes new conceptual definitions for a spatio-temporal pattern named Herd and four types of herd evolvements: expand, join, shrink, and leave based on the definition of a rela ..."
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Cited by 5 (0 self)
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Abstract. A trajectory is the time-stamped path of a moving entity through space. Given a set of trajectories, this paper proposes new conceptual definitions for a spatio-temporal pattern named Herd and four types of herd evolvements: expand, join, shrink, and leave based on the definition of a related term flock. Herd evolvements are identified through measurements of Precision, Recall, and F-score. A graph-based representation, Herd Interaction Graph, or Herding, for herd evolvements is described and an algorithm to generate the graph is proposed and implemented in a Geographic Information System (GIS) environment. A data generator to simulate herd movements and their interactions is proposed and implemented as well. The results suggest that herds and their interactions can be effectively modeled through the proposed measurements and the herd interaction graph from trajectory data.
Introduction to Remote Sensing
, 1996
"... Traffic-related data analysis plays an important role in urban and spatial planning. Infrared video cameras have capabilities to operate at day and night and to acquire the scene sampled with video frame rate, but at the cost of geometric resolution. In this paper, an approach for the estimation of ..."
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Cited by 4 (0 self)
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Traffic-related data analysis plays an important role in urban and spatial planning. Infrared video cameras have capabilities to operate at day and night and to acquire the scene sampled with video frame rate, but at the cost of geometric resolution. In this paper, an approach for the estimation of vehicle motion and the assessment of traffic activity from airborne IR video data is presented. This strategy is based on the separate handling of detection and tracking vehicle in the video, which differs from the common method developed to extract the object motion. The reason for it is that static vehicles are also intended to be detected. A single vehicle detector is firstly applied to find the vehicles in the image frames of video successively. Sensor movement is compensated by coregistering the image sequence under the selected geometric constraint. Afterwards, a progressive grouping concept considering temporal coherence and geometric relation is designed to recover the vehicle trajectories and classify them into static, moving and uncertain type. Image matching and the topology of trajectory are integrated into grouping process to aid the verification. Testing the algorithm on an IR video of urban area show us a promising result that 83 % of moving vehicles are successfully extracted which is able to serve as basis for traffic density analysis. 1.
k-STARs: Sequences of Spatio-Temporal Association Rules
, 2006
"... A Spatio-Temporal 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 Spatio-Temporal 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 k-STAR pattern which describes the sequences of STARs that objects obey. Since a k-STAR captures sequences of object movements it solves these problems. We also allow space and time gaps between successive STARs, as well as supporting ‘replenishable ’ k-STARs so we are able to capture the rich set of patterns that exist in real world data. We define a lattice on the k-STARs 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; min-l-support and min-l-confidence that allow us to achieve the above. This paper gives a rigorous theoretical treatment of k-STARs, proving various anti-monotonic and weakly anti-monotonic properties that can be exploited to mine k-STARs efficiently. We describe an algorithm, k-STARMiner, that uses these results to mine the lattice of k-STARs 1. 1
Mining complex spatio-temporal sequence patterns
- In Proceedings of the Ninth SIAM International Conference on Data Mining, John Ascuaga’s Nugget
"... Mining sequential movement patterns describing group behaviour in potentially streaming spatio-temporal data sets is a challenging problem. Movements are typically noisy and often overlap each other. This makes a set of simple patterns difficult to interpret and sequences difficult to mine. Furtherm ..."
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Cited by 4 (0 self)
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Mining sequential movement patterns describing group behaviour in potentially streaming spatio-temporal data sets is a challenging problem. Movements are typically noisy and often overlap each other. This makes a set of simple patterns difficult to interpret and sequences difficult to mine. Furthermore, group behaviour is complex. Objects in a group may behave similarly for a period of time (an interesting pattern sequence), then split up – either spatially, temporally or both; making a series of uninteresting movements before rejoining again. This behaviour must be captured in a single pattern for that group, rather than a number of unconnected pattern sequences. Secondly, it often occurs that individual objects only move along segments of a path, perhaps between intersections in a road or highway. However, the entire path is interesting when all such behaviours are taken together. Therefore, a pattern describing such behaviour should be found, rather than just a number of short sequences. This paper solves these challenges, among others, by mining sequences of Spatio-Temporal Association Rules. Theoretical results are exploited in order to develop an efficient algorithm, which is demonstrated to have linear run time in the number of interesting sequences discovered. A lattice for drill down and roll up exploratory analysis of the sequence patterns is proposed. Finally, verifiable and interesting patterns possessing the above characteristics are found in a real world animal tracking data set. 1
OLAP for Trajectories
, 2008
"... In this paper, we present an OLAP framework for trajectories of moving objects. We introduce a new operator GROUP TRAJEC-TORIES for group-by operations on trajectories and present three implementation alternatives for computing groups of trajectories for group-by aggregation: group by overlap, gro ..."
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Cited by 3 (0 self)
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In this paper, we present an OLAP framework for trajectories of moving objects. We introduce a new operator GROUP TRAJEC-TORIES for group-by operations on trajectories and present three implementation alternatives for computing groups of trajectories for group-by aggregation: group by overlap, group by intersection, andgroup by overlap and intersection. We also present an interactive OLAP environment for resolution drill-down/roll-up on sets of trajectories and parameter browsing. Using generated and real life moving data sets, we evaluate the performance of our GROUP TRAJECTORIES operator. An implementation of our new interactive OLAP environment for trajectories can be accessed at
Detecting and Tracking Coordinated Groups in Dense, Systematically Moving, Crowds
"... We address the problem of detecting and tracking clusters of moving objects in very noisy environments. Monitoring a crowded football stadium for small groups of individuals acting suspiciously is an example instance of this problem. In this example the vast majority of individuals are not part of a ..."
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Cited by 2 (0 self)
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We address the problem of detecting and tracking clusters of moving objects in very noisy environments. Monitoring a crowded football stadium for small groups of individuals acting suspiciously is an example instance of this problem. In this example the vast majority of individuals are not part of a suspicious group and are considered as noise. Existing spatio-temporal cluster algorithms are only capable of detecting small clusters in extreme noise when the noise objects are moving randomly. In reality, including the example cited, the noise objects move more systematically instead of moving randomly. The members of the suspicious groups attempt to mimic the behaviors of the crowd in order to blend in and avoid detection. This significantly exacerbates the problem of detecting the true clusters. We propose the use of Support Vector Machines (SVMs) to differentiate the true clusters and their members from the systematically moving noise objects. Our technique utilizes the relational history of the moving objects, implicitly tracked in a relationship graph, and a SVM to increase the accuracy of the clustering algorithm. A modified DBSCAN algorithm is then used to discover clusters of highly related objects from the relationship graph. We evaluate our technique experimentally on several data sets of mobile objects. The experiments show that our technique is able to accurately and efficiently identify groups of suspicious individuals in dense crowds. 1
