Implementation Of Data Management Research Articles

MSARI: A Database for Large Volume Storage and Utilisation of Maritime Data

The volume of maritime vessel data, such as available from the Automatic Identification System (AIS), places considerable burden on systems designed and developed to manage data pertaining to maritime traffic. A properly designed and implemented data management infrastructure can provide benefits to the maritime domain awareness research community by supporting data volumes, diverse user needs, and product management. Such an infrastructure has been constructed on a modest budget by utilising open-source technologies. This paper describes the Maritime Situational Awareness Research Infrastructure (MSARI), and the design of the underlying database to meet data volume and user analysis needs. The resulting infrastructure currently handles input rates of approximately two billion vessel reports per month. This work is of potential benefit to those in the navigational community interested in the long-term storage and usage of global vessel data such as that available from AIS.

A Taxonomy of Data Management Models in Distributed and Grid Environments

The distributed environments vary largely in their architectures, from tightly coupled cluster environment to loosely coupled Grid environment and completely uncoupled peer-to-peer environment, and thus differ in their working environments as well as performance. To meet the specific needs of these environments for data organization, replication, transfer, scheduling etc. the data management systems implement different data management models. In this paper, major data management tasks in distributed environments are identified and a taxonomy of the data management models in these environments is presented. The taxonomy is used to highlight the specific data management requirements of each environment and highlight the strengths and weakness of the implemented data management models. The taxonomy is followed by a survey of different distributed and Grid environments and the data management models they implement. The taxonomy and the survey results are used to identify the issues and challenges of data management for future exploration.

Decision Making for the Adoption of Cloud Computing for Sensor Data: From the Viewpoint of Industrial Security

Utilizing cloud computing platform to meet the new business requirements brought several business and technical benefits including cost-saving, high scalability, and business risk reductions. The new requirements of sensor-based systems for computing and communication also need cloud platforms. Based on the survey results of Korean companies, we analyze important factors of decision making for cloud platform adoption. Survey results show the cost efficiency is the most important factor for cloud platform adoption. With the economic benefits of cloud computing utilization, we provide basic arguments on data sharing issue with infrastructure providers, which requires additional investments to implement data management policies in cloud computing environment.

Big data analytics: The case of the social security administration

Public agencies are investing significant resources in big data analytics to mine valuable information, predict future outcomes, and make data-driven decisions. In order to foster a strong understanding of the opportunities and challenges associated with the adoption of big data analytics in the public sphere, we analyze various efforts undertaken by the United States Social Security Administration (SSA). The SSA, which is commonly referred to as the face of the government, collects, manages, and curates large volumes of data to provide Social Security services to US citizens and beneficiaries living abroad. The agency has made great strides in the burgeoning big data space to improve administration and delivery of services. This has included: (1) improving its arcane legacy system, (2) developing employee and end-user capability, (3) implementing data management strategies and organizational architecture, (4) managing security and privacy issues, and (5) advocating for increased investment in big data analytics. Despite these efforts, the SSA is still in the early stages of developing capability in the domain of big data analytics. By outlining challenges and opportunities facing the SSA, we discuss policy implications and explore issues to consider when public agencies begin to develop their capacity to analyze big data.

<i>C&RL</i> Spotlight

role of librarians in providing data services to researchers and the role of the library in establishing and implementing data management plans as a component of the broader academic research enterprise. The combination of uncertainty and optimism about the librarian’s role in a swiftly changing research landscape reported by the participants in this study reflects themes also prominent in reports of the ALA Summit, including the need to embrace new roles for the library that remain consistent with core values and the need to consider the professional skill set(s) that will characterize the successful academic library of the future. The future of staffing for library services is also a theme reflected in Julie Mitchell and Nathalie Soini’s “Student Involvement for Student Success: Student Staff in the Learning Commons,” a study of the learning commons service models at libraries that depend on undergraduate students to answer “the diverse and complex questions” received at public service desks. These and other issues discussed at the Summit (and at

국외 정부연구비지원기관의 연구데이터 관리정책 분석 - 미국, 영국, 캐나다, 호주를 중심으로 -

ABSTRACT This study aims to analyze data management policies offered by 15 government funding agencies in the U.S., UK, Canada, and Australia, and to make recommendations for deve loping data management policies in Korea. For the analysis of data management policies, five criteria wer e suggested based on literature review as follows: 1) the definition of research data, 2) principles of data manag ement, 3) data management plan, 4) the implementation of data management, 5) legal and ethical issues. It was found t hat there was no policy that covers all the criteria for the analysis. Several funding agencies, however, commonly d ealt with each criteria in their data management policies. Based on the findings from the analysis, this study m ade the following suggestions: First, data policies provide definitions and types of research data based on the und erstanding of data creation in the fields of funding interests. Second, data policies include principles of data management applicable to data practices in Korea. Third, data policies implement data management plans to promote responsibility of researchers for managing data. Fourth, data policies specify data management implementat ions to facilitate and support data sharing practices. Fifth, data policies should minimize legal and ethic al challenges in data sharing through the review of the applicability of related laws and regulations and their improvement. 키워드: 연구데이터, 과학데이터, 데이터관리정책, 데이터 공유, 정부연구비지원기관Research Data, Scientific Data, Data Management Policies, Data Sharing, Government Funding Agencies

Analyzing the Success Factors on the Implementation of Product Data Management: A Case Study of Electronic Manufacturing Industry

For promoting competitive advantages, many companies seek new ways to decrease costs in global markets. Information products contain more functionality, and their developments are continuous process to meet customer requirements. Developers have to manage and control a range of technology and increasing amount of product data. This research selects an old established enterprise named T-company in Taiwan as the subject. We found out 21 theoretical critical success factors (CSFs) from 6 case studies via three research and development departments of T-company. We pointed out 10 most important CSFs should be the critical point before and during the product data management implementation, which can be a reference model to executives of related electronic businesses.

A Grid-enabled web platform for integrated digital biobanking in paediatrics

A Grid-enabled web platform for integrated digital biobanking in paediatrics

De-Mystifying the Data Management Requirements of Research Funders.

Research libraries have sought to apply their information management expertise to the management of digital research data. This focus has been spurred in part by the policies of two major funding agencies in the United States, which require grant recipients make research outputs, including publications and research data, openly available. As many academic libraries are beginning to offer or are already offering assistance in writing and implementing data management plans, it is important to consider how best to support researchers. Our research examined the current data management requirements of major US funding agencies to better understand data management requirements facing researchers and the implications for libraries offering data management services for researchers. [ABSTRACT FROM AUTHOR]

A Distributed Low-Cost RIP for Digital Presses

As commercial printing presses become faster, cheaper and more efficient, so too must the RIPs that process and feed them data to print. Previously [1], we presented a single machine GPU-accelerated RIP system that harnesses the massive parallel computational power of programmable Graphic Processing Units (GPUs). This project builds on this work and leverages the Apache Hadoop framework to construct a distributed RIP system designed to scale efficiently and meet current and future RIP requirements for high speed commercial digital presses. Hadoop is an open source package already deployed in mission critical application at major internet companies designed for massively distributed processing of large static data sets. It provides job management, load balancing, and error recovery services which are well suited to building a stable and comprehensive distributed RIP system. We found, however, that the Hadoop processing and data management models do not match the streaming media processing nature of an EPID (“Every Page is Different”[2]) printing infrastructure. We have therefore implemented custom processing and data management modules to support distributed ripping of jobs and dynamic streaming of PDFs, images and ripped jobs through the system. Lastly, a custom front end was developed which allowed the system to be integrated with a digital press. The result is a solution combining open source software and a cluster of GPU-equipped commodity PC workstations which significantly reduces the cost and energy consumption of the DFE while scaling to meet the DFE needs of a wide range of commercial digital presses.

Preface to the Special Issue on Grid Data Management

Initially developed for the scientific community, Grid computing is now breaking into other important fields such as enterprise information systems, thus making data management more critical than ever. New Grid applications are characterized with high heterogeneity, high autonomy and large-scale distribution of computing and data resources. Managing and transparently accessing large numbers of autonomous, heterogeneous data resources efficiently is an open problem. Furthermore, different Grids may have different requirements with respect to autonomy, query expressiveness, efficiency, quality of service, fault-tolerance, security, etc. Thus, different solutions need be investigated, ranging from extensions of distributed and parallel computing techniques to more decentralized, self-adaptive techniques such as Peer-to-peer (P2P). This special issue on Grid data management is based on papers selected from the International Workshop on High-Performance Data Management in Grid Environments (HPDGrid 2006), co-located with the VecPar Conference, July 13, 2006, Rio de Janeiro (http://vecpar.fe.up.pt/hpdg06). HPDGrid 2006 brought together researchers and practitioners from the high-performance computing, distributed systems and database communities to discuss the challenges and propose novel solutions in the design and implementation of data management in Grid environments. This special issue includes four papers which reflect the main discussions and results of the workshop. All four papers have been reviewed according to the rules of JOGC (the first paper was directly handled by the editor-in-chief). The first paper “Grid Data Management: open problems and new issues,” by Esther Pacitti, Patrick Valduriez and Marta Mattoso, is an introduction to the special issue, focusing on the main open problems and new issues related to Grid data management. First, it recalls the main principles behind data management in distributed systems and the basic techniques. Then, it makes precise the requirements for Grid data management and discusses the main techniques needed to address these requirements. This implies revisiting distributed database techniques in major ways, in particular, using P2P techniques. The second paper “Enterprise Grids: Challenges Ahead,” by Ricardo Jimenez-Peris, Marta PatinoMartinez and Bettina Kemme, discusses the use of Grid technology in enterprise computing where the Grid is viewed as a new architecture for data centers. The paper identifies the significant gap between what is required by enterprise Grids and what is currently J Grid Computing (2007) 5:271–272 DOI 10.1007/s10723-007-9078-4

Prototype Preservation Environments

The Persistent Archive Testbed and National Archives and Records Administration (NARA) research prototype persistent archive are examples of preservation environments. Both projects are using data grids to implement data management infrastructure that can manage technology evolution. Data grids are software systems that provide persistent names to digital entities, manage data that are distributed across multiple types of storage systems, and provide support for preservation metadata. A persistent archive federates multiple data grids to provide the fault tolerance and disaster recovery mechanisms essential for long-term preservation. The capabilities of the prototype persistent archives will be presented, along with examples of how the capabilities are used to support the preservation of email, Web crawls, office products, image collections, and electronic records.

Process and impact of product data management implementation

According to the definition of CIMdata, Product Data Management (PDM) supports management of both data and the product development process during the total life cycle of the product. However, several problems exist with the adoption and implementation of PDM functionality in organisations. To learn more about the consequences of selecting and implementing PDM functionality, we have performed a survey in Dutch organisations already using PDM functionality. The survey is a step in our research aimed at developing a methodology for selecting and implementing PDM functionality in a specific context.

Spatial–Temporal Traffic Data Analysis Based on Global Data Management Using MAS

The spatial-temporal traffic data analysis based on global data management is a newly developed and crucial approach to help traffic managers having the global view of urban traffic status in the level of road network, which is very clearly useful in traffic control and route guidance. The multiagent systems are used in traffic data management with full consideration of the characteristics of traffic data and the cooperation and workflow among them. In software implementation of data management, the agent-based common object request broker architecture is adopted taking the distributed urban traffic data in the large area under network environments into account. Based on the global traffic data, the approach of visualized spatial-temporal analysis is then induced. The similarity of traffic data is analyzed first for each link and its profile is achieved to undertake the primary processing of urban traffic data. Furthermore, analysis results are shown on the basis of the geographic information systems for transportation. The two types of visualization, pseudocolor and contour maps, are adopted in the demonstration to display the traffic status graphically and its changing frames. Among the applications in some big cities in China, the case of urban traffic analysis for Beijing is studied to demonstrate the implementation of the approach.

Process and impact of product data management implementation

According to the definition of CIMdata, Product Data Management (PDM) supports management of both data and the product development process during the total life cycle of the product. However, several problems exist with the adoption and implementation of PDM functionality in organisations. To learn more about the consequences of selecting and implementing PDM functionality, we have performed a survey in Dutch organisations already using PDM functionality. The survey is a step in our research aimed at developing a methodology for selecting and implementing PDM functionality in a specific context.