다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Highway Capacity Manual改訂の動向--テイラ-教授の講演より

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.

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Trajectory Data Mining: An Overview

Nowadays, there is a trend to design complex, yet secure systems. In this context, the Trusted Execution Environment (TEE) was designed to enrich the previously defined trusted platforms. TEE is commonly known as an isolated processing environment in which applications can be securely executed irrespective of the rest of the system. However, TEE still lacks a precise definition as well as representative building blocks that systematize its design. Existing definitions of TEE are largely inconsistent and unspecific, which leads to confusion in the use of the term and its differentiation from related concepts, such as secure execution environment (SEE). In this paper, we propose a precise definition of TEE and analyze its core properties. Furthermore, we discuss important concepts related to TEE, such as trust and formal verification. We give a short survey on the existing academic and industrial ARM TrustZone-based TEE, and compare them using our proposed definition. Finally, we discuss some known attacks on deployed TEE as well as its wide use to guarantee security in diverse applications.

https://hal.archives-ouvertes.fr/hal-01246364/document

Trusted Execution Environment: What It is, and What It is Not

Vehicular fog computing extends the fog computing paradigm to conventional vehicular networks. This allows us to support more ubiquitous vehicles, achieve better communication efficiency, and address limitations in conventional vehicular networks in terms of latency, location awareness, and real-time response (typically required in smart traffic control, driving safety applications, entertainment services, and other applications). Such requirements are particularly important in adversarial environments (e.g., urban warfare and battlefields in the Internet of Battlefield Things involving military vehicles). However, there is no one widely accepted definition for vehicular fog computing and use cases. Thus, in this article, we formalize the vehicular fog computing architecture and present a typical use case in vehicular fog computing. Then we discuss several key security and forensic challenges and potential solutions.

Vehicular Fog Computing: Architecture, Use Case, and Security and Forensic Challenges

Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This paper presents five traffic rerouting strategies designed to be incorporated in a cost-effective and easily deployable vehicular traffic guidance system that reduces travel time. The proposed strategies proactively compute individually tailored rerouting guidance to be pushed to vehicles when signs of congestion are observed on their route. The five proposed strategies are the dynamic shortest path (DSP), the A* shortest path with repulsion (AR*), the random k shortest path (RkSP), the entropy-balanced kSP (EBkSP), and the flow-balanced kSP (FBkSP). Extensive simulation results show that the proposed strategies are capable of reducing the travel time as much as a state-of-the-art dynamic traffic assignment (DTA) algorithm while avoiding the issues that make DTA impractical, such as the lack of scalability and robustness, and high computation time. Furthermore, the variety of proposed strategies allows tuning the system to different levels of tradeoffs between rerouting effectiveness and computational efficiency. In addition, the proposed traffic guidance system can significantly improve the traffic even if many drivers ignore the guidance or if the system adoption rate is relatively low.

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Proactive Vehicular Traffic Rerouting for Lower Travel Time

Centralized solutions for vehicular traffic re-routing to alleviate congestion suffer from two intrinsic problems: scalability, as the central server has to perform intensive computation and communication with the vehicles in real-time; and privacy, as the drivers have to share their location as well as the origins and destinations of their trips with the server. This article proposes DIVERT, a distributed vehicular re-routing system for congestion avoidance. DIVERT offloads a large part of the re-routing computation at the vehicles, and thus, the re-routing process becomes practical in real-time. To take collaborative re-routing decisions, the vehicles exchange messages over vehicular ad hoc networks. DIVERT is a hybrid system because it still uses a server and Internet communication to determine an accurate global view of the traffic. In addition, DIVERT balances the user privacy with the re-routing effectiveness. The simulation results demonstrate that, compared with a centralized system, the proposed hybrid system increases the user privacy by 92 percent on average. In terms of average travel time, DIVERT's performance is slightly less than that of the centralized system, but it still achieves substantial gains compared to the no re-routing case. In addition, DIVERT reduces the CPU and network load on the server by 99.99 and 95 percent, respectively.

https://hal.inria.fr/hal-01426424/document

DIVERT: A Distributed Vehicular Traffic Re-Routing System for Congestion Avoidance

Traffic congestion causes driver frustration and costs billions of dollars annually in lost time and fuel consumption. This paper presents three traffic re-routing strategies designed to be incorporated in a cost-effective and easily deployable vehicular traffic guidance system that reduces the effect of traffic congestions. This system collects real-time traffic data from vehicles and road-side sensors and computes proactive, individually-tailored re-routing guidance which is pushed to vehicles when signs of congestion are observed on their route. Extensive simulation results over two urban road networks show that all three strategies, namely multipath load balancing considering future vehicle positions (EBkSP), random multipath load balancing (RkSP), and dynamic shortest path (DSP), significantly decrease the average travel time. EBkSP is the best, with as much as 104% improvement compared to the "no re-routing" baseline. Additionally, it lowers with 34% the re-routing frequency compared to the other strategies. Finally, all strategies offer good improvements even when many drivers ignore the guidance or when the system adoption rate is relatively low.

Proactive Vehicle Re-routing Strategies for Congestion Avoidance

Spatial data mining is an active topic in spatial databases. This paper proposes a new clustering method for moving object trajectories databases. It applies specifically to trajectories that only lie on a predefined network. The proposed algorithm (NETSCAN) is inspired from the well-known density based algorithms. However, it takes advantage of the network constraint to estimate the object density. Indeed, NETSCAN first computes dense paths in the network based on the moving object count, then, it clusters the sub-trajectories which are similar to the dense paths. The user can adjust the clustering result by setting a density threshold for the dense paths, and a similarity threshold within the clusters. This paper describes the proposed method. An implementation is reported, along with experimental results that show the effectiveness of our approach and the flexibility allowed by the user parameters.

Clustering Algorithm for Network Constraint Trajectories

Indexing moving objects (MO) is a hot topic in the field of moving objects databases since many years. An impressive number of access methods have been proposed to optimize the processing of MO-related queries. Several methods have focused on spatio-temporal range queries, which represent the foundation of MO trajectory queries. Surprisingly, only a few of them consider that the objects movements are constrained. This is an important aspect for several reasons ranging from better capturing the relationship between the trajectory and the network space to more accurate trajectory representation with lower storage requirements. In this paper, we propose T-PARINET, an access method to efficiently retrieve the trajectories of objects moving in networks. T-PARINET is designed for continuous indexing of trajectory data flows. The cornerstone of T-PARINET is PARINET, an efficient index for historical trajectory data. The structure of PARINET is based on a combination of graph partitioning and a set of composite B+-tree local indexes. Because the network can be modeled using graphs, the partitioning of the trajectory data makes use of graph partitioning theory and can be tuned for a given query load and a given data distribution in the network space. The tuning process is built on a good quality cost model that is supplied with PARINET. The advantage of having a cost model is twofold; it allows a better integration of the index into the query optimizer of any DBMS, and it permits tuning the index structure for better performance. The tuning process can be performed before the index creation in the case of historical data or online in the case of indexing data flows. In fact, massive online updates can degrade the index quality, which can be measured by the cost model. We propose a specific maintenance process that results into T-PARINET. We study different types of queries and provide an optimized configuration for several scenarios. T-PARINET can easily be integrated into any RDBMS, which is an essential asset particularly for industrial or commercial applications. The experimental evaluation under an off-the-shelf DBMS shows that our method is robust. It also significantly outperforms the reference R-tree-based access methods for in-network trajectory databases.

Iulian Sandu Popa

Papers

Proactive Vehicular Traffic Rerouting for Lower Travel Time

DIVERT: A Distributed Vehicular Traffic Re-Routing System for Congestion Avoidance

Proactive Vehicle Re-routing Strategies for Congestion Avoidance

Clustering Algorithm for Network Constraint Trajectories

Indexing in-network trajectory flows