Storage and synchronization for distributed continuous media

Advances in local area ATM networking make it possible to integrate digital audio and video, or continuous media (CM), into distributed computing environments. The temporal nature of CM introduces new requirements for acceptable end-to-end delay and jitter. This and an increased demand for bandwidth affect the design of modern, general purpose, operating systems. This paper addresses distributed system support for CM applications, focusing on the design and implementation of facilities for secondary storage and synchronization. We describe the relevant issues, along with details of our research project and its relationship to existing work. We emphasize experimentation and present results obtained with a prototype in an ATM testbed. These provide fresh insights into the role of operating system support for multimedia.     

Minimum cost VRP with time-dependent speed data and congestion charge

A heuristic algorithm, called LANCOST, is introduced for vehicle routing and scheduling problems to minimize the total travel cost, where the total travel cost includes fuel cost, driver cost and congestion charge. The fuel cost required is influenced by the speed. The speed for a vehicle to travel along any road in the network varies according to the time of travel. The variation in speed is caused by congestion which is greatest during morning and evening rush hours. If a vehicle enters the congestion charge zone at any time, a fixed charge is applied. A benchmark dataset is designed to test the algorithm. The algorithm is also used to schedule a fleet of delivery vehicles operating in the London area.     

A framework for synchronous delivery of time-dependent multimedia data

Multimedia data often have time dependencies that must be satisfied at presentation time. To support a general purpose multimedia information system, these timing relationships must be managed to provide utility to both the data presentation system and the multimedia author. Timing management encompasses specification, data representation, temporal access control, playout scheduling, and run-time intermedia synchronization. In this paper we describe the components of our framework for supporting time-dependent multimedia data encompassing these areas and how they are assembled into a unified system.     

Visualization of time-dependent data with feature tracking and event detection

This paper presents an innovative method to analyze and visualize time-dependent evolution of features. The analysis and visualization of time-dependent data are complicated because of the immense number of data involved. However, if the scientist's main interest is the evolution of certain features, it suffices to show the evolution of these features. The task of the visualization method is to extract the features from all frames, to determine the correspondences between features in successive frames, to detect significant events or stages in the evolution of the features, and, finally, to visualize the results. The method described here performs all these steps, and it is applied to a number of applications.     

A taxonomy and survey of big data in social media

Examining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high-quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data-trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1-score, and 5% run time complexity.     

Continuous data aggregation and capacity in probabilistic wireless sensor networks

Due to the existence of many probabilistic lossy links in Wireless Sensor Networks (WSNs) (Liu et al., 2010)  [25], it is not practical to study the network capacity issue under the Deterministic Network Model (DNM). A more realistic one is actually the Probabilistic Network Model (PNM). Therefore, we study the Snapshot Data Aggregation (SDA) problem, the Continuous Data Aggregation (CDA) problem, and their achievable capacities for probabilistic WSNs under both the independent and identically distributed (i.i.d.) node distribution model and the Poisson point distribution model in this paper. First, we partition a network into cells and use two vectors to further partition these cells into equivalent color classes. Subsequently, based on the partitioned cells and equivalent color classes, we propose a Cell-based Aggregation Scheduling (CAS) algorithm for the SDA problem in probabilistic WSNs. Theoretical analysis of CAS and the upper bound capacity of the SDA problem show that the achievable capacities of CAS are all order optimal in the worst case, the average case, and the best case. For the CDA problem in probabilistic WSNs, we propose a Level-based Aggregation Scheduling (LAS) algorithm. LAS gathers the aggregation values of continuous snapshots by forming a data aggregation/transmission pipeline on the segments and scheduling all the cell-levels in a cell-level class concurrently. By theoretical analysis of LAS and the upper bound capacity of the CDA problem, we prove that LAS also successfully achieves order optimal capacities in all the cases. The extensive simulation results further validate the effectiveness of CAS and LAS.     

一种自适应的P2P流媒体数据调度算法

针对现有算法在有限带宽情况下数据块易丢失的问题,提出了一种新颖的数据调度算法——最少优先调度算法（LFSA）。该算法易于实现,并且可以根据网络的异构性,带宽抖动以及P2P网络节点的动态性进行自动调整。实验结果表明,LFSA能够有效缩短系统播放启动延迟,而且可以在不知道伙伴节点带宽和没有任何负载平衡机制的情况下取得比传统策略更好的性能。     

基于Linux的文件系统对连续媒体的支持

从操作系统角度考虑了底层文件系统对连续媒体的支持,并详细介绍了Linux下设计和实现的BLOB(Binary Large Obiject)文件系统的设计思想及其实现技术细节,并讨论它对连续媒体的支持情况。     

基于流形学习的社会化媒体网络数据分类

社会化媒体提供了海量的、大尺度的异质网络数据,如何对网络数据进行分类是一个亟待解决的新问题。基于潜在社会维模型,提出利用流形学习中的拉普拉斯特征映射算法进行社会维抽取。实验表明,在精确匹配率、微平均、宏平均等性能指标上,均优于基于模块度最大化的原有社会维模型。该算法能更好地获取用户的隐性联系,从而更好地分析网络用户行为。     

浅谈连续数据保护解决方案在医院中应用

由于在医院中会有很多对于病案、物资、药剂等需要进行统计的东西,尽管也应用了HIS、LIS、RIS等的多个信息管理的系统,建立生产中心机房和灾备中心机房。虽然这些举措切实增强了工作效率和便于医生进行决策,提高了医院整体的管理效率。那么为了更好的全面防御故障与灾难,以及确保医院的业务运行不中断,医院决定采用美国飞康公司所生产的CDP连续数据保护方案,这个方案能够提供一系列的措施来全面保护医院一关键业务系统：     

基于流数据技术的连续查询处理

提出了一个流查询处理方案,同时满足普通用户和高级用户(有计算机语言基础),并从几个重要的指标来闸述如何提高查询效率,减少系统瓶颈,并在原型系统中得到了验证。     

连续媒体网络发布的体系结构研究

该文针对连续媒体的网络发布，研究了活跃服务的体系结构，在采用软状态协议和轻量级会话体系结构基础上，设计了一个课件访问控制协议SCACP，并研究了代理洪水抑制，访问控制带宽限制，系统容错和错误恢复，会话的分裂和重组等问题。     

Efficient striping techniques for variable bit rate continuous media file servers

The performance of striped disk arrays is governed by two parameters: the stripe unit size and the degree of striping. In this paper, we describe techniques for determining the stripe unit size and degree of striping for disk arrays storing variable bit rate continuous media data. We present an analytical model to determine the optimal stripe unit size in redundant and non-redundant disk arrays. We then use the model to study the effect of various system parameters on the optimal stripe unit size. To determine the degree of striping, we first demonstrate that striping a continuous media stream across all disks in the array causes the number of clients supported to increase sub-linearly with increase in the number of disks. To overcome this limitation, we propose a technique that partitions a disk array and stripes each media stream across a single partition. We then propose an analytical model to determine the optimal partition size and maximize the number of clients supported by the array.     

演化数据流上的连续异常检测

基于滑动窗口的异常检测是数据流挖掘研究的一个重要课题,在许多应用中数据流通常在一个分布网络上传输,解决这类问题时常采用分布计算技术,以便获得实时高质量的计算结果。对分布演化数据流上连续异常检测问题,进行形式化地阐述,提出了两个基于核密度估计的异常检测定义和算法,并通过大量真实数据集的实验,表明该算法具有良好的高效性和可扩展性,完全适应数据流应用的需求。     

基于流数据技术的连续查询处理

网络提供了一个无限的、变化的信息源,使得数据库由传统的静态存储变成了动态的存储,数据变成了流数据,查询变成了连续查询。为了有一个完善的系统来处理大量的数据及查询,提出了一个流查询处理方案,同时满足普通用户和高级用户(有计算机语言基础),并从几个重要的指标来阐述了如何提高查询效率,减少了系统瓶颈,并在原型系统中得到了验证。     

The Continuous Media Web: a distributed multimedia information retrieval architecture extending the World Wide Web

The World Wide Web, with its paradigms of surfing and searching for information, has become the predominant system for computer-based information retrieval. Media resources, however information-rich, only play a minor role in providing information to Web users. While bandwidth (or the lack thereof) may be an excuse for this situation, the lack of surfing and searching capabilities on media resources are the real issue. We present an architecture that extends the Web to media, enabling existing Web infrastructures to provide seamless search and hyperlink capabilities for time-continuous Web resources, with only minor extensions. This makes the Web a true distributed information system for multimedia data. The article provides an overview of the specifications that have been developed and submitted to the IETF for standardization. It also presents experimental results with prototype applications.     

Map-Reduce在媒资系统数据收集中的应用

在传统媒体资源管理系统中,数据收集工作通常是基于C/S或B/S架构设计的,这对服务器的要求比较高。为了提高服务器的性能,传统的模式就是购买更高性能的服务器,然而,近几年出现的云计算也是一个很好解决办法。采用Apache公司的Hadoop Map-Reduce框架来实现数据收集功能,并通过实验,将数据收集工作在传统的单线程模式（传统实现模式）、Hadoop伪分布模式和全分布模式下所需时间进行比较,并对执行结果进行了分析。研究表明：使用Map-Reduce的这种云模式,确实可以缩短执行时间,以达到提高服务器端性能的目的。     

连续媒体存储技术的研究

分析了连续媒体存储面临的问题和技术现状，提出了一种创新的存储软件/硬件架构－虚拟分级存储架构(VHSA)。VHSA是分级存储技术和存储虚拟化技术的结合，支持异构的存储系统结构，可提高复杂存储系统的性能和效率，针对巨大的视听内容进行数字归档，并可与媒体资产管理系统实现无缝集成。     

持续时态数据挖掘的研究

基于一阶线性时态逻辑。形式化定义时态数据挖掘中的主要概念。利用线性状态结构对每个时间点上的一阶语言符号进行赋值。并度量公式的真值范围．按照挖掘段概念．开发持续挖掘过程模型,用于归纳局部一阶规则与推导高阶规则．基于信息扩散原理．提出一阶规则的度量值估计方法和规则泛化算法．最后通过算例说明了扩散估计和算法的有效性．     

Generating timeline summaries with social media attention

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users’ collective interests into considerations to generate timelines.We consider utilizing social media attention to address these two problems due to the facts: 1) social media is an important pool of real users’ collective interests; 2) the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user’s collective interests which are learnt from social media into a unified timeline generation algorithm.We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics.We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presentation of timelines, i.e., phase based timelines, which can potentially improve user experience.