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With the development of GPS and the popularity of smart phones and wearable devices, users can easily log their daily trajectories. Prior works have elaborated on mining trajectory patterns from raw trajectories. However, trajectory patterns do not have explicit time information or semantic information. To enrich trajectory patterns, we propose STS-TPs (standing for Spatial-Temporal Semantic Trajectory...
One of the most fundamental challenges when mining gestural patterns in 3D motion capture databases is the definition of spatiotemporal similarity between two gestural patterns. While time-elastic similarity models such as the Gesture Matching Distance on gesture signatures are able to leverage the spatial and temporal characteristics of gestural patterns, the applicability of such distance-based...
Taxis provide a flexible and indispensable service to satisfy the urban travel demand of public commuters. Understanding taxi supply and commuter demand, especially the imbalance between the supply and the demand, would directly help to improve the quality of taxi service and eventually increase a city's traffic system efficiency. In this paper, we consider the taxi demand from a region during a period...
In this paper, we present the current work and future directions on spatiotemporal frequent pattern mining algorithms for mining solar data. The current spatiotemporal pattern mining algorithms focus on spatiotemporal co-occurrence patterns. We reveal four types of spatiotemporal concepts that can be mined from solar data: event sequences, periodicity, spatiotemporal convergence and propagation. Throughout...
There are different functional regions in cities such as tourist attractions, shopping centers, workplaces and residential places. The human mobility patterns for different functional regions are different, e.g., people usually go to work during daytime on weekdays, and visit shopping centers after work. In this paper, we analyse urban human mobility patterns and infer the functions of the regions...
We examine the use of electronic healthcare reimbursement claims (EHRC) for analyzing healthcare delivery and practice patterns across the United States (US). We show that EHRCs are correlated with disease incidence estimates published by the Centers for Disease Control. Further, by analyzing over 1 billion EHRCs, we track patterns of clinical procedures administered to patients with autism spectrum...
With the advent of ubiquitous computing and sensor technologies, indoor trajectory data of individuals can readily be collected to support analysis of their underlying movement behaviors. Different methods for activity recognition have been proposed where supervised learning algorithms are often adopted. In many applications like elderly care, the behaviors to be characterized are often not known...
The explosion of available positioning information associated with the inferred or user-declared semantics of the respective locations, already contributes in what is called the big data era, posing new challenges to the mobility data management and mining research community. In this paper, motivated by a series of challenges set in [11], we present a unified framework for the management and the analysis...
The improvement in Maritime Situational Awareness (MSA), or the capability of understanding events, circumstances and activities within and impacting the maritime environment, is nowadays of paramount importance for safety and security. Enhancing coverage of existing technologies such as Automatic Identification System (AIS) provides the possibility to integrate and enrich services and information...
Space information network is a kind of delay-tolerant networks. Recently, with possible participation of many network nodes, maintaining efficient and dynamic topology becomes crucial. In space information networks, the underlying topology is lack of continuous connectivity, and has the drawbacks of network segmentation, long delay and unreliable connections, which makes the routing design face severe...
In order to design stable walking for a bipedal robot over uneven terrain, advanced control methods such as nonlinear control and receding-horizon control, and exact hybrid dynamics are needed. They are too complicated to be used in the many applications. In this paper, we use data mining techniques, locally weighted learning, principal component regression and regression clustering, and combine with...
The availability of massive volumes of trajectory data has made it convenient for the study of different types of movement behaviors. Among them, bi-directional movement behaviors exist ubiquitously in our daily life, from urban traffic to animal migration, and from sports to wars. To analyze bi-directional movement behaviors, people need to compare movements in two directions simultaneously for detecting...
The clustering process in pattern mining of spatio-temporal trajectories is an important research content. Although there has existed extensive research on trajectory clustering, the efficiency of these algorithms is not able to meet the efficiency requirements faced with the large volumes of position data from moving objects. In order to improve the efficiency of clustering, this paper proposes two...
The increase of scale and complexity exceeds of Internet big data presents unprecedented opportunities on deeply mining and taking full advantage of the big value. Meanwhile, the discovery, propagation and influence evaluation of web event become popular. The incompleteness and incredibility of Internet big data are a challenge issues for eastimating to the scope of event influence. To solve the above...
Discovering anomalies at sea is one of the critical tasks of Maritime Situational Awareness (MSA) activities and an important enabler for maritime security operations. This paper proposes a data-driven approach to anomaly detection, highlighting challenges specific to the maritime domain. This work builds on unsupervised learning techniques which provide models for normal traffic behaviour. A methodology...
This paper provides a solution for anomaly detection in maritime traffic domain based on the clustering results presented in a previous work. That work created clusters for vessels moving close to shores by associating vessel movements with International Maritime Organization Rules (especially Traffic Separation Scheme Boundaries). In this paper, we show how three division distances with the clusters...
With the rising number of vehicles equipped with GPS system, a lot of vehicle trajectories are produced every day. At present researchers begin to study the vehicles' trajectory data. But the current trajectory compression technology cannot be very simple and will lose much information. Therefore, this paper puts forward a data warehousing design of vehicle trajectory. It can store compressed vehicle...
The paper presents a data mining technique for qualitative analysis of functional differential equations of compartmental type. As a result we get the decision tree able to classify the system behaviour depending on relations between initial conditions and between rate constants. Antitumour immunity example is presented.
A great deal of research efforts has been invested in temporal aspects of big data management during last years, with alternate fortune. This line of research aims at capturing, formally modeling and successfully exploiting all the time-dependent characteristics of the fundamental big data model ranging from state model to query model. Temporal big data management thus poses novel research challenges...
Ballistic target recognition is a key process in anti-missile combat. In this paper, the movement characteristics and physical characteristics of ballistic target are analyzed, some ideas or methods to save extracted information, to create template sequence and to recognize ballistic target on grey relational analysis are proposed. Based on these, we give the method to calculate the grey incidence...
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