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40
Discovering Chasing Behavior in Moving Object Trajectories
"... With the increasing use of mobile devices, a lot of tracks of movement of objects are being collected. The advanced trajectory data mining research has allowed the discovery of many types of patterns from these data, like flocks, leadership, avoidance, frequent sequences, and other types of patterns ..."
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With the increasing use of mobile devices, a lot of tracks of movement of objects are being collected. The advanced trajectory data mining research has allowed the discovery of many types of patterns from these data, like flocks, leadership, avoidance, frequent sequences, and other types of patterns. In this paper we introduce a new kind of pattern: a chasing behavior between trajectories. We present the main characteristics of chasing and propose a new method that extracts these new kind of trajectory behavior pattern, considering time, distance, and speed as the main thresholds. Experimental results show that our method finds patterns that not are discovered by related approaches. 1
Sequential pattern mining with multiple minimum supports by MS-SPADE
- International Journal of Computer Sciences
, 2012
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Dummy-based schemes for protecting movement trajectories
- J. Inf. Sci. Eng
"... Dummy-based anonymization techniques for protecting the location privacy of mobile users have appeared in the literature. By generating dummies that move in human-like trajectories, this approach shows that the location privacy of mobile users can be preserved. However, the trajectories of mobile us ..."
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Dummy-based anonymization techniques for protecting the location privacy of mobile users have appeared in the literature. By generating dummies that move in human-like trajectories, this approach shows that the location privacy of mobile users can be preserved. However, the trajectories of mobile users can still be exposed by monitoring the long-term movement patterns of users. We argue that, once the trajectory of a user is identified, the locations of the user are exposed. Thus, it is critical to protect the movement trajectories of mobile users in order to preserve user location privacy. We propose two schemes that generate consistent movement patterns in the long run. Guided by three parameters in a user specified privacy profile, namely, short-term disclosure, long-term disclosure and distance deviation, the proposed schemes derive movement trajectories for dummies. Moreover, since a user may have multiple frequent movement trajectories, we further develop one scheme for users with multiple frequent movement trajectories. The experimental results show that our proposed schemes are more effective than existing works in protecting movement trajectories.
Vehicular Movement Patterns: A Sequential Patterns Data Mining Approach Towards Vehicular Route Prediction
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MMP-TREE FOR SEQUENTIAL PATTERN MINING WITH MULTIPLE MINIMUM SUPPORTS IN PROGRESSIVE DATABASES
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A NEW SPATIO-TEMPORAL DATA MINING METHOD AND ITS APPLICATION TO RESERVOIR SYSTEM OPERATION
, 2014
"... This thesis develops a spatio-temporal data mining method for uncertain water reservoir data. The goal of the data mining method is to learn from a history human reservoir operations in order to derive an automated controller for a reservoir system. Spatio-temporal data mining is a challenging task ..."
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This thesis develops a spatio-temporal data mining method for uncertain water reservoir data. The goal of the data mining method is to learn from a history human reservoir operations in order to derive an automated controller for a reservoir system. Spatio-temporal data mining is a challenging task due to the reasons: (1) spatio-temporal datasets are usually much larger than spatial data sets, (2) many common spatial techniques are unable to deal with objects that change location, size or shape, and (3) complex and often non-linear spatio-temporal relationships cannot be separated into pure spatial and pure temporal relationships. Support Vector Machines (SVMs) have been extensively and successfully applied in feature selection for many real-time applications. In this thesis, we use SVM feature selection to reduce redundant and non-discriminative features in order to improve the computational time of SVM-based data mining. We also propose combining Principal Component Analysis (PCA) with multi-class SVMs. We show that SVMs are invariant under PCA transformations and that PCA is a desirable dimension-reduction method for SVMs. We propose also an extension of the SVM Regression approach to be able to
PROEFSCHRIFT
"... An end-to-end data transformation process for increasing the information yield of system traces An end-to-end data transformation process for increasing the information yield of system traces ..."
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An end-to-end data transformation process for increasing the information yield of system traces An end-to-end data transformation process for increasing the information yield of system traces
OLAP for moving object data
, 2013
"... In this paper, we present an OLAP framework for moving object data. We introduce a new operator GROUP_TRAJECTORIES for group-by operations on moving object data and present two implementation alternatives for computing groups of moving objects for group-by aggregation: group by overlap and group by ..."
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In this paper, we present an OLAP framework for moving object data. We introduce a new operator GROUP_TRAJECTORIES for group-by operations on moving object data and present two implementation alternatives for computing groups of moving objects for group-by aggregation: group by overlap and group by intersection. We also present an interactive OLAP environment for resolution drill-down/roll-up on sets of trajectories and parameter browsing. We evaluate the performance of our GROUP_TRAJECTORIES operator by using generated as well as real life moving object datasets.
2008 Eighth IEEE International Conference on Data Mining Robust Time-Referenced Segmentation of Moving Object Trajectories ∗
"... Trajectory segmentation is the process of partitioning a given trajectory into a small number of homogeneous segments w.r.t. some criteria. Conventional segmentation techniques only focus on the spatial features of the movement and could lead to spatially homogeneous segments but with presumably dis ..."
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Trajectory segmentation is the process of partitioning a given trajectory into a small number of homogeneous segments w.r.t. some criteria. Conventional segmentation techniques only focus on the spatial features of the movement and could lead to spatially homogeneous segments but with presumably dissimilar temporal structures. Furthermore, trajectories could be over-segmented in the presence of outliers. In this paper, we propose a family of three trajectory segmentation methods that takes into account both geospatial and temporal structures of movement for the segmentation and is also robust with respect to time-referenced spatial outliers. The effectiveness of our methods is empirically demonstrated over three real-world datasets. 1.
Research Track Paper Trajectory Pattern Mining
"... The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we ..."
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The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different instantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.
