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Journal ArticleDOI

MoveMine: Mining moving object data for discovery of animal movement patterns

TLDR
A moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis is introduced, which will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies.
Abstract
With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic control, mobile communication management, and climatological forecast. In this article, we focus our study on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis. In this system, two interesting moving object pattern mining functions are newly developed: (1) periodic behavior mining and (2) swarm pattern mining. For mining periodic behaviors, a reference location-based method is developed, which first detects the reference locations, discovers the periods in complex movements, and then finds periodic patterns by hierarchical clustering. For mining swarm patterns, an efficient method is developed to uncover flexible moving object clusters by relaxing the popularly-enforced collective movement constraints.In the MoveMine system, a set of commonly used moving object mining functions are built and a user-friendly interface is provided to facilitate interactive exploration of moving object data mining and flexible tuning of the mining constraints and parameters. MoveMine has been tested on multiple kinds of real datasets, especially for MoveBank applications and other moving object data analysis. The system will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies. Moreover, it will benefit researchers to realize the importance and limitations of current techniques and promote future studies on moving object data mining. As expected, a mastery of animal movement patterns and trends will improve our understanding of the interactions between and the changes of the animal world and the ecosystem and therefore help ensure the sustainability of our ecosystem.

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Journal ArticleDOI

Mobile crowdsensing: current state and future challenges

TL;DR: The need for a unified architecture for mobile crowdsensing is argued and the requirements it must satisfy are envisioned.
BookDOI

Computing with Spatial Trajectories

Yu Zheng, +1 more
TL;DR: This book presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks.
Journal ArticleDOI

The environmental-data automated track annotation ( Env - DATA ) system: linking animal tracks with environmental data

TL;DR: Env-DATA as mentioned in this paper is a publicly available system that automates annotation of movement trajectories with ambient atmospheric observations and underlying landscape information to facilitate new understanding and predictive capabilities of spatiotemporal patterns of animal movement in response to dynamic and changing environments.
Journal ArticleDOI

Semantic trajectories: Mobility data computation and annotation

TL;DR: A semantic model and a computation and annotation platform for developing a semantic approach that progressively transforms the raw mobility data into semantic trajectories enriched with segmentations and annotations is presented.

The Environmental-Data Automated Track Annotation (Env-DATA) System: Linking Animal Tracks with Environmental Data

TL;DR: The new Env-DATA system enhances Movebank, an open portal of animal tracking data, by automating access to environmental variables from global remote sensing, weather, and ecosystem products from open web resources.
References
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Proceedings Article

A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise

TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Proceedings Article

A density-based algorithm for discovering clusters in large spatial Databases with Noise

TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Journal ArticleDOI

Kernel methods for estimating the utilization distribution in home-range studies

B. J. Worton
- 01 Feb 1989 - 
TL;DR: Kernel methods are of flexible form and can be used where simple parametric models are found to be inappropriate or difficult to specify and give alternative approaches to the Anderson (1982) Fourier transform methods.
Journal ArticleDOI

Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

TL;DR: A novel frequent-pattern tree (FP-tree) structure is proposed, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and an efficient FP-tree-based mining method, FP-growth, is developed for mining the complete set of frequent patterns by pattern fragment growth.