Online interactive thematic mapping: Applications and techniques for socio-economic research

Recent advances in public sector open data and online mapping software are opening up new possibilities for interactive mapping in research applications. Increasingly there are opportunities to develop advanced interactive platforms with exploratory and analytical functionality. This paper reviews tools and workflows for the production of online research mapping platforms, alongside a classification of the interactive functionality that can be achieved. A series of mapping case studies from government, academia and research institutes are reviewed. The conclusions are that online cartography's technical hurdles are falling due to open data releases, open source software and cloud services innovations. The data exploration functionality of these new tools is powerful and complements the emerging fields of big data and open GIS. International data perspectives are also increasingly feasible. Analytical functionality for web mapping is currently less developed, but promising examples can be seen in areas such as urban analytics. For more presentational research communication applications, there has been progress in story-driven mapping drawing on data journalism approaches that are capable of connecting with very large audiences.     

Generic support for distributed applications

In the late 1980s, software designers introduced middleware platforms to support distributed computing systems. Since then, the rapid evolution of technology has caused an explosion of distributed-processing requirements. Application developers now routinely expect to support multimedia systems and mobile users and computers. Timely response to asynchronous events is crucial to such applications, but current platforms do not adequately meet this need. Another need of existing and emerging applications is the secure interoperability of independent services in large-scale, widely distributed systems. Information systems serving organizations such as universities, hospitals, and government agencies require cross-domain interaction. To meet the needs of these applications, Cambridge University researchers developed middleware extensions that provide a flexible, scalable approach to distributed-application development. This article details the extensions they developed, explaining their distributed software approach and the support it has provided for emerging applications     

运载火箭测发控软件的中间件技术应用

由于不同型号硬件平台和软件平台的多样性,跨型号大规模融合应用系统通常要求在拥有多种软硬件平台(异构平台)的分布式环境下运行。借助中间件(Middleware)技术改进软件重用形式,提高软件重用程度,以实现支持分布式应用有效开发、部署、运行和管理的目的。本文在对现有火箭测发控系统软件的子系统功能聚类和程序架构分析基础上,开发基于中间件模型的通用火箭测发控系统软件。     

Grids and Grid technologies for wide‐area distributed computing

The last decade has seen a substantial increase in commodity computer and network performance, mainly as a result of faster hardware and more sophisticated software. Nevertheless, there are still problems, in the fields of science, engineering, and business, which cannot be effectively dealt with using the current generation of supercomputers. In fact, due to their size and complexity, these problems are often very numerically and/or data intensive and consequently require a variety ofheterogeneous resources that are not available on a single machine. A number of teams have conducted experimental studies on the cooperative use of geographically distributed resources unified to act as a single powerful computer. This new approach is known by several names, such as metacomputing, scalable computing, global computing, Internet computing, and more recently peer‐to‐peer or Grid computing. The early efforts in Grid computing started as a project to link supercomputing sites, but have now grown far beyond their original intent. In fact, many applications can benefit from the Grid infrastructure, including collaborative engineering, data exploration, high‐throughput computing, and of course distributed supercomputing. Moreover, due to the rapid growth of the Internet and Web, there has been a rising interest in Web‐based distributed computing, and many projects have been started and aim to exploit the Web as an infrastructure for running coarse‐grained distributed and parallel applications. In this context, the Web has the capability to be a platform for parallel and collaborative work as well as a key technology to create a pervasive and ubiquitous Grid‐based infrastructure. This paper aims to present the state‐of‐the‐art of Grid computing and attempts to survey the major international efforts in developing this emerging technology. Copyright © 2002 John Wiley & Sons, Ltd.     

Expanded cloud plumes hiding Big Data ecosystem

Today, a paradigm shift is being observed in science, where the focus is gradually shifting away from operation to data, which is greatly influencing the decision making also. The data is being inundated proactively from several sources in various forms; especially social media and in modern data science vocabulary is being recognized as Big Data. Today, Big Data is permeating through the bigger aspect of human life for scientific and commercial dependencies, especially for massive scale data analytics of beyond the exabyte magnitude. As the footprint of Big Data applications is continuously expanding, the reliability on cloud environments is also increasing to obtain appropriate, robust and affordable services to deal with Big Data challenges. Cloud computing avoids any need to locally maintain the overly scaled computing infrastructure that include not only dedicated space, but the expensive hardware and software also. Several data models to process Big Data are already developed and a number of such models are still emerging, potentially relying on heterogeneous underlying storage technologies, including cloud computing. In this paper, we investigate the growing role of cloud computing in Big Data ecosystem. Also, we propose a novel XCLOUDX {XCloudX, X…X}classification to zoom in to gauge the intuitiveness of the scientific name of the cloud-assisted NoSQL Big Data models and analyze whether XCloudX always uses cloud computing underneath or vice versa. XCloudX symbolizes those NoSQL Big Data models that embody the term “cloud” in their name, where X is any alphanumeric variable. The discussion is strengthen by a set of important case studies. Furthermore, we study the emergence of as-a-Service era, motivated by cloud computing drive and explore the new members beyond traditional cloud computing stack, developed in the past couple of years.     

RIoTBench: An IoT benchmark for distributed stream processing systems

The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage physical, environmental, and human systems in real time. The inherent closed‐loop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed stream processing systems (DSPS) hosted in cloud data centers are becoming the vital engine for real‐time data processing and analytics in any IoT software architecture. But the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT applications and data streams. Here, we propose RIoTBench , a real‐time IoT benchmark suite, along with performance metrics, to evaluate DSPS for streaming IoT applications. The benchmark includes 27 common IoT tasks classified across various functional categories and implemented as modular microbenchmarks. Further, we define four IoT application benchmarks composed from these tasks based on common patterns of data preprocessing, statistical summarization, and predictive analytics that are intrinsic to the closed‐loop IoT decision‐making life cycle. These are coupled with four stream workloads sourced from real IoT observations on smart cities and smart health, with peak streams rates that range from 500 to 10 000 messages/second from up to 3 million sensors. We validate the RIoTBench suite for the popular Apache Storm DSPS on the Microsoft Azure public cloud and present empirical observations. This suite can be used by DSPS researchers for performance analysis and resource scheduling, by IoT practitioners to evaluate DSPS platforms, and even reused within IoT solutions.     

基于国产软硬件的OpenCL计算平台研究

随着智能计算和大数据应用的发展,人们对GPU等加速部件的需求不断增长。基于国产基础软硬件平台运行显控应用做加速计算的需求,研究了OpenCL计算平台的移植和实现途径,就国产软硬件平台进行GPU计算做出了初步探索。研究的计算平台包括Mesa、ROCm、Pocl和Beignet,最后给出了如何将ROCm在国产平台上移植适配的思路和解决方案。     

MidHDC: Advanced Topics on Middleware Services for Heterogeneous Distributed Computing. Part 1

Middleware solutions for Heterogeneous Distributes System aim to respond to high requirements of large scale distributed applications related to performance, flexibility, extensibility, portability, availability, reliability, safety, security, trust, and scalability, in the context of high number of users, and large geographic distribution of heterogeneous hardware and software resources. The solutions used in the design, implementation, and deployment of systems with such capabilities are based on monitoring, scheduling, optimization, sharing, balancing, discovery, and synchronization methods and techniques that are continuously improved.This special issue presents advances in virtual machine management solutions in Clouds, object storage platforms, HPC heterogeneous platforms, middleware for Android systems and reliability and performances in large scale distributed applications.     

J2EE数据持久化技术的研究

研究了J2EE架构下的数据持久化技术，并对各种持久化技术作了分析比较，提出了J2EE应用程序持久化的一般原则，为应用程序的开发提供帮助。     

Semantic enrichment of building and city information models: A ten-year review

Building Information Models (BIMs) and City Information Models (CIMs) have flourished in building and urban studies independently over the past decade. Semantic enrichment is an indispensable process that adds new semantics such as geometric, non-geometric, and topological information into existing BIMs or CIMs to enable multidisciplinary applications in fields such as construction management, geoinformatics, and urban planning. These two paths are now coming to a juncture for integration and juxtaposition. However, a critical review of the semantic enrichment of BIM and CIM is missing in the literature. This research aims to probe into semantic enrichment by comparing its similarities and differences between BIM and CIM over a ten-year time span. The research methods include establishing a uniform conceptual model, and sourcing and analyzing 44 pertinent cases in the literature. The findings plot the terminologies, methods, scopes, and trends for the semantic enrichment approaches in the two domains. With the increasing availability of data sources, algorithms, and computing power, they cross the border to enter each other’s domain. Future research will likely gain new momentums from the demands of value-added applications, development of remote sensing devices, intelligent data processing algorithms, interoperability between BIM and CIM software platforms, and emerging technologies such as big data analytics.