GGM: efficient navigation and mining in distributed genomedical data

The integration of genomics and patient related data is considered as one of the most promising investigation topic in health care research. Started in 2004, the Grid for Geno Medicine (GGM) project aims at providing a comprehensive grid software infrastructure designed to allow biologists to mine and analyze relationships between medical, genetic, and genomic data stored in distributed datawarehouses. The proposed layered service oriented architecture offers a number of independent but compliant services that can be deployed in a grid environment. This paper presents these services insisting on their integration into a common software platform, the use case that is carried out. It also presents the current state of the developments and of the performance evaluations.     

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population, globalization, and industrialization has led to increased challenges for existing energy infrastructure. Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable, cheap, and easily available sources of energy. Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions. But the integration of distributed energy sources and increase in electric demand enhance instability in the grid. Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid. Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control, reinforcement of the grid demand, and generation balancing with cost reduction. But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data. Machine learning and artificial intelligence have recognized more accurate and reliable load forecasting methods based on historical load data. The purpose of this study is to model the electrical load of Jajpur, Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression (GPR). The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past, current, and future dependent variables, factors, and the relationship among data. Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead. The study is very helpful in grid stability and peak load control management.     

Weaving an agent-based Semantic Grid for distributed collaborative manufacturing

The development and maintenance of distributed collaborative manufacturing systems is faced with increasing challenges caused by the technical difficulties and ontological issues in distributed computing, resource integration and knowledge sharing over heterogeneous computing platforms. This paper presents an agent-based Semantic Grid for distributed manufacturing collaboration across ubiquitous virtual enterprises. A hierarchy of autonomous, adaptive agents forming a ‘super peer’ based peer-to-peer network is laid out in the Semantic Grid to provide, in an open, dynamic, loosely coupled and scalable manner, the service publication, discovery and reuse for management of applications that need to utilise Grid resources. The operation and experimental evaluation of the system are presented to validate the implementation of the proposed approach.     

A conceptual approach for an architecture of distributed objects for the integration of heterogeneous data processing systems in manufacturing companies

A new integration concept for heterogeneous legacy systems is developed. The concept consists of a model (business object model), an implementation of business objects (Internet or Java/CORBA) and an information structure (structure of the graphical user interface). The architecture separates the dialog level from a logical application level, the business objects. Legacy systems should be integrated into a company-wide IT (Information Technology) architecture. This can be done through the encapsulation of these systems into several business objects. The IT implementation of the encapsulation will be implemented on the one hand using internet standards, and on the other hand using Java and CORBA. Information transparency for all users in a corporate and user-friendly way is developed and described in an information structure. Within the information structure the software engineer creates new applications using business objects as well as encapsulated legacy systems and applications. These legacy systems are integrated in the new architecture of distributed objects, and will be re-used, encapsulated and transformed into an object-oriented architecture.     

Guest Editorial: Special Section on Grid, Web Services, Software Agents, and Ontology Applications for Life Sciences

The ten papers in this special section focus on grid, web services, software agents, and ontology applications for life sciences. While most of the papers focus on Grid and distributed computation, others are concerned with advanced techniques and methodologies, like Web services and agent-based systems.     

An integrated fuzzy approach for information technology planning in collaborative product development

Collaborative product development (CPD) processes are generally based on technological infrastructures. Various information technologies (IT) are proposed every day to facilitate collaboration, integration, co-design and co-development processes. In this highly uncertain environment, a systematic methodology is essential to plan the IT infrastructure needed to start and maintain a collaborative process. This study offers an integrated IT planning methodology combining fuzzy quality function deployment, fuzzy axiomatic design and fuzzy rule-based systems. The methodology is tested in a CPD case and the outcome presents an improvement path for IT for each of the collaborative parties.     

基于XML技术的农产品异构数据集成研究

本文主要对如何利用Web Service技术和XML技术实现农产品物流异构数据集成进行了研究.本文首先介绍了Web Service技术的概念及其对Web数据的访问原理和对XML的支持,然后介绍了XML技术的组成、特点和XML数据模型.阐述了农产品物流技术的概念、重要性和我国农产品物流信息化的现状、问题及发展趋势.提出了面向农产品物流异构数据集成系统体系结构.在分析了现代农产品物流信息化所面临的新问题的基础上,提出了建立基于XML技术的农产品物流异构数据集成框架的基本思路,重点研究了基于XML技术的农产品物流异构数据集成框架,并对其中的数据交换中心做了进一步说明.最后介绍了S OA P技术及其信息交换模型,比较了SOAP与其他分布式对象协议的不同,阐述了传统的分布式多层Web发布系统的不足,说明了在动态Web发布中使用XML的优势,并提出了基于XML的动态Web信息发布的数据模型.本文的研究成果对农产品物流异构数据集成的实现具有一定的理论和实用价值.     

虚拟制造组织中服务定位机制研究

提出一种基于公共对象请求代理结构(Common Object Request Broker Architecture,CORBA)的多自主体系统(Multi-agent System,MAS)体系结构作为虚拟制造组织(Virtual Manufacturing Organization,VMO)的组织框架和信息基础设施,以跨越成员企业之间在位置、网络、协议、平台、语言以及数据结构和类型等方面的异构性,实现在语义和应用层上的互操作。在此基础上提出一种基于中间Agent的服务定位机制(Setvice Location Mechanism,SLM),以支持虚拟制造组织的构建以及成员企业间的通信、互操作和资源／服务共享,并在开发的VMO概念原型系统上进行了实现和验证。     

Building the TeraGrid

On 1 October 2004, the most ambitious high-performance Grid project in the United States-the TeraGrid-became fully operational. Resources at nine sites--the San Diego Supercomputer Center, the California Institute of Technology, the National Center for Supercomputing Applications, the University of Chicago/Argonne National Laboratory, Pittsburgh Supercomputing Center, Texas Advanced Computing Center, Purdue University, Indiana University and Oak Ridge National Laboratory-were joined via an ultra-fast optical network, unified policies and security procedures and a sophisticated distributed computing software environment. Funded by the National Science Foundation, the TeraGrid enables scientists and engineers to combine distributed, multiple data sources with computation at any of the sites or link massively parallel computer simulations to extreme-resolution visualizations at remote sites. A single shared utility lets multiple resources be easily leveraged and provides improved access to advanced computational capabilities. One of the demonstrations of this new model for using distributed resources, Teragyroid, linked the infrastructure of the TeraGrid with computing resources in the United Kingdom via a transatlantic data fibre link. Once connected, the software framework of the RealityGrid project was used to successfully explore lattice-Boltzmann simulations involving lattices of over one billion sites.     

Optimal resource allocation on grid systems for maximizing service reliability using a genetic algorithm

Grid computing system is different from conventional distributed computing systems by its focus on large-scale resource sharing and open architecture for services. The global grid technologies and the Globus Toolkit in particular, are evolving toward an open grid service architecture (OGSA) with which a grid system provides an extensible infrastructure so that various organizations can offer their own services and integrate their resources. Hence, this paper aims at solving the problem of optimally allocating services on the grid to maximize the grid service reliability. Since no existing study has analyzed the grid service reliability, this paper develops initial modeling and evaluation algorithms to evaluate the grid service reliability. Based on the grid service reliability evaluation, we present an optimization model for the grid service allocation problem and develop a genetic algorithm (GA) to effectively solve it. A numerical example is given to show the modeling procedures and efficiency of the GAs.     

Information resources reorganization and sharing platform of national scientific & technical document service in China

Reorganization of network information resources for scientific and technical (scitech for abbreviation)documents and development of information infrastructure have brought a great change in China's scitech documentation information sharing.It integrated not only information resources, but also systems, technologies and funds.High quality services and effective resources sharing are the main goals of the platform construction.The paper describes briefly the principles and aims of the integration of the scitech information resources and networks.The services of the sharing platforms have been also introduced.     

MMLUP: Multi-Source & Multi-Task Learning for User Profiles in Social Network

With the rapid development of the mobile Internet, users generate massive data in different forms in social network every day, and different characteristics of users are reflected by these social media data. How to integrate multiple heterogeneous information and establish user profiles from multiple perspectives plays an important role in providing personalized services, marketing, and recommendation systems. In this paper, we propose Multi-source & Multi-task Learning for User Profiles in Social Network which integrates multiple social data sources and contains a multi-task learning framework to simultaneously predict various attributes of a user. Firstly, we design their own feature extraction models for multiple heterogeneous data sources. Secondly, we design a shared layer to fuse multiple heterogeneous data sources as general shared representation for multi-task learning. Thirdly, we design each task’s own unique presentation layer for discriminant output of specific-task. Finally, we design a weighted loss function to improve the learning efficiency and prediction accuracy of each task. Our experimental results on more than 5000 Sina Weibo users demonstrate that our approach outperforms state-of-the-art baselines for inferring gender, age and region of social media users.     

供应链管理中的信息集成

供应链管理是“横向一体化”管理思想的典型代表。在分析供应链和供应链管理出现的背景的基础上，讨论了信息技术在集成化供应链管理中的应用，并提出了基于Internet/Intranet的供应链管理信息系统的技术支撑体系。     

Design of collaboration framework for distributed CIM data activities

Together with the development of computer network technology, the automatic collaboration of CIM data activities is becoming an important research issue. Two models are proposed for the research issue: a data activity flow network model called DAF-Net for coordinating interdependent data activities and an agent-based integration model of information systems called AIMIS for integrating distributed and heterogeneous information systems on demand. Additionally, this paper addresses the key design factors of the DAF-Net and also the AIMIS: the structural type of the DAF-Net and the agent interaction protocol of the AIMIS. Statistical methods and PVM (Parallel Virtual Machine) software are employed for the design of the DAF-Net and the AIMIS.     

气象通信网络和计算机系统工程

气象通信网络和计算机系统作为我国气象事业的信息基础设施,经过近30年的建设和发展,在网络化和计算机应用方面已经取得长足的进步。面向21世纪,中国气象事业在现有基础上将有较大的飞跃和发展,气象信息基础设施将随着国家信息化的发展进程而得到进一步的巩固和发展。章通过对气象通信网络和计算机系统的发展介绍,来说明气象部门的现代化系统工程建设及其工程化应用状况。     

面向网络化协同设计的合作对象管理研究

针对敏捷制造及其模式,提出一个以协同设计思想为指导的产品协同设计环境,用于支持分布式异地协同设计,协调设计过程中各设计人员之间交换、共享产品设计信息和知识的过程.在此基础上,从CSCW支撑平台的角度分析角色-权限管理功能,进而提出应用系统所需要的角色定义及其合作伙伴管理功能.通过对应用工具集成、协同过程控制、通信与信息共享、产品数据管理等功能的实现达到信息集成、过程集成和组织集成的目的,从而提高产品设计过程及其设计决策的效率和质量.     

Data measurement in research information systems: metrics for the evaluation of data quality

In recent years, research information systems (RIS) have become an integral part of the university’s IT landscape. At the same time, many universities and research institutions are still working on the implementation of such information systems. Research information systems support institutions in the measurement, documentation, evaluation and communication of research activities. Implementing such integrative systems requires that institutions assure the quality of the information on research activities entered into them. Since many information and data sources are interwoven, these different data sources can have a negative impact on data quality in different research information systems. Because the topic is currently of interest to many institutions, the aim of the present paper is firstly to consider how data quality can be investigated in the context of RIS, and then to explain how various dimensions of data quality described in the literature can be measured in research information systems. Finally, a framework as a process flow according to UML activity diagram notation is developed for monitoring and improvement of the quality of these data; this framework can be implemented by technical personnel in universities and research institutions.     

Information Technology Measurement and Testing Activities at NIST

Our high technology society continues to rely more and more upon sophisticated measurements, technical standards, and associated testing activities. This was true for the industrial society of the 20th century and remains true for the information society of the 21st century. Over the last half of the 20th century, information technology (IT) has been a powerful agent of change in almost every sector of the economy. The complexity and rapidly changing nature of IT have presented unique technical challenges to the National Institute of Standards and Technology (NIST) and to the scientific measurement community in developing a sound measurement and testing infrastructure for IT. This measurement and testing infrastructure for the important non-physical and non-chemical properties associated with complex IT systems is still in an early stage of development. This paper explains key terms and concepts of IT metrology, briefly reviews the history of the National Bureau of Standards/National Institute of Standards and Technology (NBS/NIST) in the field of IT, and reviews NIST’s current capabilities and work in measurement and testing for IT. It concludes with a look at what is likely to occur in the field of IT over the next ten years and what metrology roles NIST is likely to play.     

Big data and machine learning: A roadmap towards smart plants

Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.