Smooth Data Flow Research Articles

The U.K. environmental change network database: An integrated information resource for long-term monitoring and research

An integrated approach to information resource development has been adopted by the Environmental Change Network (ECN) to underpin environmental change research and policy requirements within the U.K. This approach views the data management process as an integrated system in order to achieve a smooth flow of data from capture through to access within the short time-scales required, incorporating the necessary quality assurance specifications. ECN is the U.K.'s long-term environmental monitoring programme, established in 1992 to provide comparable, reliable, multi-disciplinary, long-term runs of data on environmental change. The ECN site network operates a standardized programme of data capture for a range of physical, chemical and biological variables which drive and respond to change. Data quality standards are maintained through quality control, assessment and data validation procedures, and the resulting information accompanies the data. A central database, meta-database and geographic information system provide an integrated information resource for ECN. The meta-database is an essential component, providing “lower level” data specifications, dimensions and quality information, as well as developing “higher-level” links to other related data sources and monitoring networks. Remote network access systems provide rapid access to ECN data: a direct interface to the ECN summary database using the World Wide Web enables users to browse text and images, and progress to guided data query, retrieval and display in a single system. Increasing demand for timely access to reliable environmental information puts further pressure on information budgets; it is important that vulnerable areas such as data quality are not compromised.

Rohrleitungsplanung und Materialwirtschaft mit unterschiedlichen Datenstrukturen der Rohrleitungsklassen von Anlagenbetreibern

AbstractPipework Planning and Material Management with Differing Data Structures of the Pipe Classes Employed by Plant Users. Engineering quality depends not least on good coordination and smooth flow of information between the individual disciplines. A major aim of CAD – apart from rationalization – is to improve the quality of engineering work. This is why all the plant users and engineering firms have introduced computer systems to support their piping work. Pipe classes had to be stored in accordance with the data structures prescribed by the respective computer systems. This has led to differences in the definitions of piping items. Different identifiers and data structures are an obstacle to cooperation. Economic aspects and comprehensive documentation requirements imposed by new regulations call for suitable structures to ensure smooth data flow between the various systems.

On the design of a pipelined/systolic finite element system

A parallel finite element system is suggested based on the idea of pipelining the computations corresponding to the different finite elements. The systolic architecture is used extensively in the design to satisfy a regular and smooth flow of data in the pipe. Also a node numbering algorithm is developed in order to allow for the application of a frontal technique in the solution of the linear system of equations resulting from the analysis.

A data‐driven execution control scheme and its experimental study

AbstractThe authors have proposed a high‐level parallel processing system representing the data‐drive principle (including history‐sensitive processing by diagrammatical language) and have been investigating its realization. Realization of high‐level processing by the data‐driven principle requires a load and function distribution scheme to secure the smooth data flow corresponding to the input stream into the system. Together with the results of experiment, this paper describes a systematic control scheme for load and function distribution of the data‐driven execution function through the construction of clusters adapted to the hierarchical diagrammatical program structure. In other words, the proposed system realizes the data‐driven execution functions of the hierarchical system by an iterative structure composed of fundamental functions, namely input‐output, function processing, history‐sensitive processing and firing control. This paper first describes the load and function distribution scheme for the fundamental function in the data‐driven execution control. Then, using an experimental system with common‐bus multiprocessor structure, it is shown that the proposed system is useful in realizing high‐level parallel processing. Lastly, based on the geometrical connection structure of the diagrammatical program, a semiquantitative guideline is presented for the distributed assignment of fundamental functions in the hierarchical cluster structure.