Velocity‐Preserving Trajectory Compression Based on Retrace Point Detection

The trajectory data of vessel AIS (automatic identification system) has important theoretical and application value for information supporting decisions. However, large sizes lead to difficulties in storing, querying, and processing. To solve the problems of high compression ratio and longtime consumption of the existing online trajectory compression algorithm, an SPM (scan-pick-move) trajectory data compression algorithm added sliding window is proposed. In order to better compress vessel trajectory data regarding compression efficiency, the sliding window is added to the classical SPM algorithm. In order to reduce trajectory data storage space, the maximum offset distance reference trajectory point is used as the criterion of whether the current trajectory point can be compressed. In this paper, the multi-dimensional space-time characteristics of trajectory data, such as distance error, compression ratio and compression time, are selected to evaluate the trajectory compression method from three levels: geometric characteristics, motion characteristics and compression efficiency. Compared with the existing SPM trajectory data compression algorithm, parallel experiments are conducted based on AIS data gathered over the duration of a month in the Japan Osaka Bay. The SPM trajectory compression algorithm added sliding window can significantly reduce the compression time and outperforms other existing trajectory compression algorithms in term of average compression error at high compression strengths. Also, the proposed method has high compression efficiency in the range of commonly used compression thresholds.

Data mining approach to shipping route characterization and anomaly detection based on AIS data

The pervasiveness of GPS-enabled devices and wireless communication technologies results in massive trajectory data, incurring expensive cost for storage, transmission, and query processing. To relieve this problem, in this paper we propose a novel framework for compressing trajectory data, REST ( Re ference-based S patio- t emporal trajectory compression), by which a raw trajectory is represented by concatenation of a series of historical (sub-)trajectories (called reference trajectories) that form the compressed trajectory within a given spatio-temporal deviation threshold. In order to construct a reference trajectory set that can most benefit the subsequent compression, we propose three kinds of techniques to select reference trajectories wisely from a large dataset such that the resulting reference set is more compact yet covering most footprints of trajectories in the area of interest. To address the computational issue caused by the large number of combinations of reference trajectories that may exist for resembling a given trajectory, we propose efficient greedy algorithms that run in the blink of an eye and dynamic programming algorithms that can achieve the optimal compression ratio. Compared to existing work on trajectory compression, our framework has few assumptions about data such as moving within a road network or moving with constant direction and speed, and better compression performance with fairly small spatio-temporal loss. In addition, by indexing the reference trajectories directly with an in-memory R-tree and building connections to the raw trajectories with inverted index, we develop an extremely efficient algorithm that can answer spatio-temporal range queries over trajectories in their compressed form. Extensive experiments on a real taxi trajectory dataset demonstrate the superiority of our framework over existing representative approaches in terms of both compression ratio and efficiency.

The widespread use of locatable devices leads to a sharp increase in the storage of trajectory data, and redundant storage of similar trajectories wastes a large amount of storage resources. The state-of-the-art multiple trajectory compression algorithms are developed to strip the partial information of trajectory; however, these algorithms have low compression efficiency because they do not eliminate the redundancy within a single trajectory as much as possible, as well as high time overhead due to matching of reference sub-trajectories. In this study, we propose a new spatio-temporal trajectory compression technique, consisting of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intra-trajectory error balancing</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inter-trajectory feature point clustering</i>. Intra-trajectory error balancing is achieved through retaining high score (an aggregated metric) trajectory points (i.e. feature points). Furthermore, inter-trajectory feature point clustering realizes the fusion of similar trajectories and extracts the commonality between trajectories. Experiments are performed on five real trajectory datasets, including two road datasets, one airline dataset, and one walking dataset. Compared with the state-of-the-art methods, our compression technique improves the compression ratio by an average of 24.9% under the same error, and reduces the time overhead by at least an order of magnitude.

Recently, smart phones such as the Galaxy S and IPhone, and tablet PCs such as the Galaxy Tab have been introduced and become widespread in the market, thus the environment for wireless Internet services has been improving. As the market has improved, consumer preferences for mobile devices and the relationship between these devices and wireless Internet services regarding consumer preferences have become a very important issue. However, although there have been a considerable number of studies on the demand for mobile devices, it is not easy to find previous studies on the relationship between consumer preferences for specific attributes of wireless Internet services and the demand for mobile devices. The main purpose of this study is to analyze which mobile device is more likely to be preferred or become the main channel for wireless Internet services and how the demand for each mobile device is influenced by consumer preferences for specific attributes of wireless broadband Internet services used on it, and to induce managerial implications based on the main findings. To derive the results on consumer preferences, conjoint analysis is used. The survey was conducted using 1,000 Korean consumers. In the survey, respondents are allowed to choose more than one hypothetical alternative representing a mobile device combined with a certain type of wireless broadband Internet service if they desired. To estimate the consumer preferences quantitatively, a multivariate probit model is used. According to the estimation results, owing to their portability, smart phones are more likely to become a major channel for wireless Internet services despite their drawbacks in terms of display size and performance. Portability is the most important factor affecting the choice of a mobile device when using wireless Internet services. Therefore, manufacturers of mobile devices for wireless Internet services should improve the overall functionality and performances of mobile devices without negatively affecting portability. However, at the same time, consumer preferences for specific attributes of wireless Internet services are found to affect the demand of each mobile device much differently. Therefore, the prediction that smart phones will be the prevalent mobile device for use of wireless Internet services can be somewhat changed, depending on which and how wireless Internet services are provided to each mobile device and how they affect the demand for each mobile device. As mentioned above, there are significant differences between mobile devices with regard to the effects of mobility, data transfer speed, type of contents or Internet interface for wireless Internet services. It is found that the mobility of wireless Internet service is highly valued for smaller devices, but not much for notebook PCs. In contrast, data transfer speed and similarities in the type of contents or Internet interface to those provided by fixed broadband Internet services become more important for larger mobile devices. Thus, it is necessary for wireless Internet service providers to differentiate and optimize their contents and Internet interfaces properly for various mobile devices. Additionally, it is reasonable that manufacturers of smart phones focus on mobility and portability (or convenience) rather than adding sophisticated functions or pursuing higher-level specifications. Further, for smart phones, for which mobility and convenience are important, wireless Internet service providers should develop and provide easy and convenient programs, rather than advanced programs that require high device performance. Lastly, with respect to individual demographics, young and highly educated people are more likely to be consumers of newly introduced mobile devices for using wireless Internet services. Therefore, these young and highly educated consumers should be considered a major target for manufacturers of mobile devices for wireless Internet services.

This paper provides a comprehensive empirical performance analysis of various trajectory compression algorithms for offline compression of trajectory data of sea borne objects which include boats, trawlers, ships etc. Our empirical study uses data from a surveillance application. The surveillance application receives data from various sensors which include GPS integrated sensors, Radars, AIS, S-AIS etc. The Algorithms under consideration include the classical Douglas Peucker Algorithm, SQUISH, OLDCAT, SPM. These algorithms have been compared with respect to the capability to preserve the spatio-temporal information and execution time.

Effects of errors in velocity tilt on maximum longitudinal compression during neutralized drift compression of intense beam pulses: I. general description

Emerging automotive engineering solutions extensively rely on navigation tools. Shortest path evaluation is core competency of optimal path planning, one of the most critical component of any navigation application. Fibonacci heap variant of state-of-the-art Dijkstra’s algorithm solve single source shortest path problems in asymptotic O(E + V log V) worst-case time. In spite Fibonacci heap is theoretically dominant in time efficiency, it might not work much better empirically with real-world data, because although large and intricate, the real-world road network graphs are sparse and the graph topology greatly affects the performance of the algorithms. SGO algorithm, a more efficient approch to address single source shortest path problem, is applied to solve optimal path planning problem in automotive navigation applications. Each road segment of the road graph leads to a particular location thus will hold unique distance values all the time. The SGO calculates the total distance from start location to the end of the each road segment and evaluates shortest path based on these calculations. The SGO algorithm and the Fibonacci and Binary heap variants of Dijkstra’s algorithm were tested on real-world intercity and intra-city road network graphs to evaluate running time efficiency of the algorithms in automotive navigation systems. The experimental results show that the SGO algorithm outperformed both the variants. The empirical superiority of the SGO algorithm suggests its usefulness in automotive navigation systems.

As the daily usage time of smart phone increases, battery life time becomes increasingly important. Recently, a complaint is raised for frequent battery drain in smart phones due to applications running in the background. In most of the mobile applications, after end of the data transfer, longer idle time occurs in a Transmission Control Protocol (TCP) connection for connection closure. Usually such idle TCP connections are terminated by servers based on a static timeout value configured at the server side. This results in frequent transition from idle Radio Frequency (RF) state to active RF state thereby increasing mobile network signalling and device battery power consumption. To address this problem, we propose a novel TCP connection control mechanism in view of power saving, called Low Power TCP (LPTCP). Our solution is lightweight, application agnostic, a client deployed solution. LPTCP identifies inactive TCP connections that cause the delayed server close and pro-actively closes those TCP connections. We evaluated LPTCP with the latest Samsung smart phones such as Galaxy S8 and S9. LPTCP achieved up to 18&#x0025; of power gain and significantly reduced 24&#x0025; of mobile network signalling overhead.

The rapid growth of mobile device (e.g., smart phone and bracelet) has spawned a lot of new applications, during which the requirements of applications are increasing, while the capacities of some mobile devices are still limited. Such contradiction drives the emergency of computation migration among mobile edge devices, which is a lack of research currently. In this article, we focus on addressing the computation migration oriented resource allocation problem among mobile edge devices. Specifically, we first construct a framework for Mobile Social Cloud(MSC), in which the mobile devices with rich resources are abstracted as resource suppliers and those resource-lacking devices are abstracted as resource demanders. Then, a mathematical model is formulated and an evolutionary algorithm is proposed to effectively solve this model based on decomposition, dominance, and genetic operations. Moreover, the parallel computing is introduced to further improve the efficiency of the proposed algorithm. The experimental results indicate that the proposed algorithm outperforms the other state-of-the-art methods and it improves the calculation efficiency by about 178 percent (2 cores) and 262 percent (3 cores) by introducing parallel computing.

Many real-world complex systems can be modeled as dynamic networks with real-valued vertex/edge attributes. Examples include users' opinions in social networks and average speeds in a road system. When managing these large dynamic networks, compressing attribute values becomes a key requirement, since it enables the answering of attribute-based queries regarding a node/edge or network region based on a compact representation of the data. To address this problem, we introduce a lossy network compression scheme called Slice Tree (ST), which partitions a network into smooth regions with respect to node/edge values and compresses each value as the average of its region. ST applies a compact representation for network partitions, called slices, that are defined as a center node and radius distance. We propose an importance sampling algorithm to efficiently prune the search space of candidate slices in the ST construction by biasing the sampling process towards the node values that most affect the compression error. The effectiveness of ST in terms of compression error, compression rate, and running time is demonstrated using synthetic and real datasets. ST scales to million-node instances and removes up to 87% of the error in attribute values with a 103 compression ratio. We also illustrate how ST captures relevant phenomena in real networks, such as research collaboration patterns and traffic congestions.

Mobile devices are very common these days. It is rare to meet a person that does not own a mobile phone. In the past these devices were mainly used for voice calls. This changed however with the introduction of the so called smart-phones. Their powerful processors and large displays allowed a large number of new applications. Today these devices are used to check e-mails, surf the Internet or other applications that mainly use data-transmissions instead of voice calls. Today mobile standards like HSPA+ or LTE are therefore designed to provide very high data-rates. However these data-rates can only be achieved when the signal quality between mobile and base-station allows it. Since signal quality depends on a number of factors like distance to the base-station, shadowing and multipath fading, simulators for mobile communication are usually very complex. This complexity leads to long run times, which is not always acceptable. This work will start by introducing a simulation model for mobile data-transmission, which is derived from existing work and specifications. This model will then be used to evaluate a number of heuristics, which will be introduced in the remainder of the document. These heuristics will simulate mobile communication over larger time scales, and will therefore perform faster.

Mobile devices have enabled the development of a new breed of enterprise solutions. Oracle, SAP, IBM, and others are offering mobile clients (e.g. ERP, BI, CRM) for iPhone and Android devices. Nonetheless, in the field of workflow management systems (WfMS) the progresses do not support well mobile workers. In this paper we explore how metaphors can be used to drive the development of mobile workflow systems. Our approach relies on the use of the TomTom metaphor to establish an isomorphism between car navigation systems and WfMS. Based on the isomorphism, we used the Technology Acceptance Model (TAM) to provide a first validation of the approach. The positive results led us to implement an early prototype to be used as a proof of concept and to identify important requirements such as context information.

Mobile devices such as smart phones, the iPhone, and the iPad have become more popular in recent years. With access to the Internet through cellular or WIFI networks, these mobile devices can make use of the great source of information available on the Internet. Unlike a desktop or laptop computer, an Internet enabled mobile device is designed to be carried around and available to the owner almost instantly at any moment of the day. Despite having such great advantage and potential, searching for information with a mobile device remains a difficult task. Mobile device users have to juggle between different search engines through the web browser to seek useful information. The small screen of most mobile devices makes it very difficult to interact with the search engines using the device's web browser. This project focuses on the development of a mobile application that attempts to make information searching easier for mobile device users. In addition, the application is designed to answer search queries that are related to the medical field. Search query in the medical field requires more selective results and that the results come from trusted 4 sources before it can be used. The application addresses this problem by introducing a database of community knowledge designed to store credible medically related articles that could help answer medical specific search queries. This would help minimize the need to look for answers from the Internet, which often could contain unreliable information. In addition to answering search queries, the application also allows user to store personal medical records and conveniently share that information as needed using email.

&amp;lt;p&amp;gt;The projection into 2030 of the climate data volume increase brings an important challenge to the climate science community. This is particularly true for the CMIP7 that is projected to need about an Exabyte of storage capacity. Error-bounded lossy compression is explored as a potential solution to the above problem by different climate research teams. Several lossy compression schemes have been proposed leveraging different forms of decorrelation (transforms, prediction, HoSVD, DNN), quantization (linear, non-linear, vector), and encoding (dictionary-based, variable length, etc.) algorithms. Our experience with different applications shows that the compression methods often need to be customized and optimized to fit the specificities of the datasets to compress and the user requirements on the compression quality, ratio, and throughput. However, none of the existing lossy compression software for scientific data has been designed to be customizable. To address this issue, we developed SZ3, an innovative customizable, modular compression framework. SZ3 is a full C++ refactoring of SZ2 enabling the specialization, addition, or removal of each stage of the lossy compression pipeline to fit the specific characteristics of the datasets to compress and the use-case requirements. This extreme flexibility allows adapting SZ3 to many different use-cases, from ultra-high compression for visualization to ultra-high-speed compression between the CPU (or GPU) and the memory. Thanks to its unique set of features: customization, high compression ratio, high compression throughput, and excellent accuracy preservation, SZ3 won a 2021 R&amp;amp;D100 award. In this presentation, we present SZ3 and a new data prediction-based decorrelation method that significantly improves the compression ratios for climate datasets over the state-of-the-art lossy compressors, while preserving the same data accuracy. Experiments based on CESM datasets show that SZ3 can lead to up to 300% higher compression ratios than SZ2 with the same compression error bound and similar compression throughput.&amp;lt;/p&amp;gt;