Design Concepts of Japanese-Language Data Processing Systems

This experience report describes the successful application of statistical software testing to a large-scale realworld equipment replacement project. The Data Processing System at Dungeness B nuclear power station is obsolescent and needs to be replaced by a new system involving different technology. The new Data Processing and Control System (DPCS) involves a large amount of software code, with the potential for software failures, and therefore confidence needs to be established in the dependability of the new software-based system. Statistical software testing (ST) was used as one technique to do this. The aim was to support the claim that with high confidence the higher integrity parts of the replacement software have a failure probability of better than 10-2 failures per demand (fpd). This paper describes how this has been achieved by careful design of the statistical tests and by the development of a large number of supporting tools and procedures. A total of 395 statistical tests with no failure was achieved providing at least 98% confidence that the safety claim has been met.

The ASTRI Mini-Array is an international project led by the Italian National Institute for Astrophysics (INAF) to build and operate an array of nine 4-m class Imaging Atmospheric Cherenkov Telescopes (IACTs) at the Observatorio del Teide (Tenerife, Spain). The system is designed to perform deep observations of the galactic and extragalactic gamma-ray sky in the TeV and multi-TeV energy band, with important synergies with other ground-based gamma-ray facilities in the Northern Hemisphere and space-borne telescopes. As part of the overall software system, the ASTRI (Astrofisica con Specchi a Tecnologia Replicante Italiana) Team is developing dedicated systems for Data Processing, Simulation, and Archive to achieve effective handling, dissemination, and scientific exploitation of the ASTRI Mini-Array data. Thanks to the high-speed network connection available between Canary Islands and Italy, data acquired on-site will be delivered to the ASTRI Data Center in Rome immediately after acquisition. The raw data will be then reduced and analyzed by the Data Processing System up to the generation of the final scientific products. Detailed Monte Carlo simulated data will be produced by the Simulation System and exploited in several data processing steps in order to achieve precise reconstruction of the physical characteristics of the detected gamma rays and to reject the overwhelming background due to charged cosmic rays. The data access at different user levels and for different use cases, each one with a customized data organization, will be provided by the Archive System. In this contribution we present these three ASTRI Mini-Array software systems, focusing on their main functionalities, components, and interfaces.

In this article an attempt is made to remove some of the confusions that may block understanding of the digital computer, its use and its application to the surveying profession. An outline of the history of electronic data processing is given from the earliest concepts of numerical symbols and mechanical accounting machines to the present levels of technological skills. The various functional components that collectively form a data processing system are described in simple and non-technical terms. Data representation and storage devices are considered in order to introduce a typical data processing system. Data processing methods and user responsibilities are emphasized to highlight data processing justification. More importantly the article is intended to help surveyors understand the part that electronic data processing could play in the successful introduction of the “Integrated Survey System” into New South Wales. Hence the objective of this article is educational and informative rather than technically detailed.

The article describes the development of an architecture for cloud storage of melons based on a Residue Number System (RNS). The developed architecture is evaluated in terms of the reliability of the stored data, and its comparative analysis is carried out with existing data storage methods. The architecture of a multi-cloud big data storage and processing system based on the principles of modular arithmetic is proposed. The proposed modification of the cloud storage and data processing system allows increasing the reliability and fault tolerance of the stored and processed data. Single-level and two-level models for reliable storage of big data were developed based on the simulation results. The comparative analysis showed the advantage of models based on modular arithmetic over models based on traditional arithmetic.

The functioning of modern information and communication networks is impossible without data processing. With the emergence of new network services, the amount of information that needs to be processed increases, while the requirements to the data processing quality become more and more stringent. Therefore, the problem of designing and maintaining a scalable data processing system with a flexible quality of service management is becoming more and more important for a network operator. Such data processing systems have a complex internal structure with many interrelated parameters, which makes them difficult to analyze, manage, and expand. This study proposes to use an ontological model to store, represent, and manipulate the information in the operator’s data processing system. The ontological model allows to structure and systematize the data of an information processing system, and transparently reflects the relationships between the parameters of the system to simplify its analysis and scaling. The proposed ontology of a data processing system consists of three related subsystems. The paper describes the proposed ontological model and additionally analyzes the sources of information that needs to be processed in the information and communication network.

Introducción: el artículo de revisión es producto de la investigación “Implicaciones de la estructura organizacional: organizaciones como sistema de procesamiento de información”, desarrollada durante el 2017 en la Facultad de Ingeniería Industrial de la Universidad Distrital Francisco José de Caldas. Problema: en las organizaciones se evidencia que las estructuras jerárquicas son el paradigma predominante, a pesar de que investigaciones han mostrado la precariedad de este tipo de estructuras para el procesamiento de información. Objetivo: identificar los rasgos que permitan comprender la empresa como un sistema de procesamiento de información ágil. Metodología: se revisan los rasgos estructurales de los sistemas de procesamiento de información desde diferentes disciplinas. A partir de esta revisión se plantean las implicaciones de comprender a las organizaciones empresariales y en particular el diseño de la estructura como sistemas de procesamiento de información. Resultado: se desarrolla una apreciación sobre las implicaciones de diseñar estructuras con mayor agilidad en el procesamiento de información para la ingeniería organizacional. Conclusión: la comprensión de la organización empresarial, y en particular de la estructura como sistemas de procesamiento de información, ofrece una perspectiva ampliamente diferente del paradigma tradicional. Originalidad: la investigación se encuentra en etapas tempranas y no existen modelos o evidencias empíricas frente a los planteamientos realizados. Los altos niveles de agilidad en la toma de decisiones que exhiben las disciplinas estudiadas, permiten proyectar importantes líneas de investigación al respecto. Limitaciones: la propuesta se encuentra en la fase conceptual y requieren comprobación experimental.

Rapid economic development causes increasingly fierce business competition. Moreover, the development of increasingly sophisticated technology, companies are competing to use technology to increase competitive advantage and efficiency in the company. One of the most popular information technology investments today is the Application of Systems and Products in Data Processing (SAP), systems that do business using a single database that can be accessed by all divisions within the company. The purpose of this research is to study and discuss the application of Systems and Products in Data Processing (SAP) conducted at PT. Haier Sales Indonesia. This study uses qualitative methods through in-depth interviews with IT Managers and Logistics Managers and warehouse administration staff using Pareto diagrams and fishbone diagrams. The results of the evaluation of the application of Application Systems and Products in Data Processing (SAP) in Inventory Logistics that occurred at PT. Haier Sales Indonesia is getting better, all data from every activity has been documented and integrated into the system much faster than the manual process that has been done before and can produce accurate reports. Suggestions are given in order to continue training in SAP Systems and Applications for users in developing SAP Systems and Applications.

A web-based CO2 measurement device and a data network system in Indonesia which consist of the CO2 measurement device, the local data processor system, and the central data processor system has been built. The CO2 measurement device consist of the GMM220 CO2 sensor module from Vaisala and the data logger system. The data logger output consists of location, time, date, CO2 concentration, and input voltage, which packed all up in one data form with RGPGHG as the data header. The data logger system store the data into SD Card memory and ends it to the local data processor system. The CO2 Monitor Software in the local data processor system processes the data and display the graphical data which shows the maximum, minimum and average values of the data, and also sends the data to the central data processor system. The central data processor system displays data from several local data processor systems in the form of real time web-based graphical data and text, so the internet users can access and download its data. The development result device has been applicated in Kototabang, and the CO2 measurement device has been connected as well to the central data processor system in Bandung via the internet network to perform the web-based CO2 data network system in Indonesia. According to the data stored by the central data processor system for the periode of 1 - 26 March 2010, it shows that the CO2 concentration in Kototabang have an average of 403,5 ppm, with the maximum of 476,9 ppm and the minimum of 364,7 ppm. While the CO2 concentration in Bandung have an average of 397,2 ppm, with the maximum of 502,5 ppm and the minimum of 325,3 ppm. Key words: Data logger, CO2 sensor, Web-based interface

Service-oriented architecture of adaptive, intelligent data acquisition and processing systems for long-pulse fusion experiments

The expected development of grid monitoring applications and control functionalities in low voltage distribution networks calls for advanced simulation tools for performance assessment of these applications. Power hardware-in-the-loop simulations form a cost effective approach to accurately capture the (dynamic) system behaviour. This paper describes the development of a data acquisition and processing system that enables power hardware-in-the-loop simulation of a real physical feeder within a larger simulated three-phase low voltage network simulated in real-time. With the aid of the data acquisition and processing system, the performance of newly developed monitoring and control applications can be assessed and evaluated in terms of accuracy, speed and effectiveness. To this extend, the paper shows how the data acquisition and processing system is implemented and discusses the possibilities that it creates for verifying newly developed applications for distribution grid automation.

This computerized system, which collects, processes, and stores data on and analyzes and reports the performance of production equipment and operations, is one step closer to the optimum profitability of producing properties. It is designed primarily to be used by the production man properties. It is designed primarily to be used by the production man directly responsible for day-to-day economic decisions. Introduction In any attempt at optimizing operations by means, for example, of a failure-control program, basic information is required for defining the nature and magnitude of the problems involved and for estimating the economic stakes. The information determines whether or not one should try to solve a particular problem; and if more than one solution is possible, it aids in deciding on the most profitable one. In short, the basic information makes it possible to set economic priorities to the solution of problems. Methods of obtaining basic information related to equipment performance have been the subject of much work. Many oil-producing companies have created and now operate data gathering and processing systems, all of which have two things in common: they obtain accurate and timely information and process and condense it into an easily usable and process and condense it into an easily usable and digestible form; and they use computers to process and report data. Atlantic Richfield has created a system for data gathering and handling that has been in use throughout its Continental U. S. and Canadian operations since late 1969. This system, known as the Equipment Performance Reporting System, relies heavily on the use of computers. It was developed as a cooperative effort with field personnel to the point that it is now part of the Company procedures. By late 1971 the system had more than 34,000 records in its data banks. Since its inception, the original concept of a data gathering and processing system for use by production personnel has been expanded according to needs production personnel has been expanded according to needs discovered as a result of using the system. These needs are: (1) to set better specifications for subsurface and surface equipment, (2) to predict future performance of equipment, and (3) to follow closely performance of equipment, and (3) to follow closely the economic impact of remedial action such as area-wide corrosion treating programs. The Equipment Performance Report System-Part One The Equipment Performance Report System consists of four parts. The following discussion will be limited to Part One, which is concerned with subsurface failures, and to Part Three, which handles well inhibition. Parts Two and Four will be described later. As early as 1964, Fincher and Nunn defined a usable data gathering and processing system as one that contains1. A simple report form,2. Complete data about each reported event,3. Computer processing for rapid data handling, and4. Output in the form of orderly and concise data summaries for easy review. JPT p. 185

The paper presents Data processing and optimization system for studying and making rational decisions on the formation of interstate electric power interconnections, with aim to increasing effectiveness of their functioning and expansion. The technologies for building and integrating a Data processing and optimization system including an object-oriented database and a predictive mathematical model for optimizing the expansion of electric power systems ORIRES, are described. The technology of collection and pre-processing of non-structured data collected from various sources and its loading to the object-oriented database, as well as processing and presentation of information in the GIS system are described. One of the approaches of graphical visualization of the results of optimization model is considered on the example of calculating the option for expansion of the South Korean electric power grid.

In the big data processing systems, the amount of data is increasing. At the same time, the real-time requirement of data processing and analysis is higher and higher. Therefore, it is required that the big data processing and analysis systems have better performance. Job scheduling plays an important role in improving the overall system performance in big data processing frameworks. However, job scheduling is a difficult NP-hard problem. There are many factors that need to be considered for job scheduling. For example, jobs have dependencies among stages, therefore we should not allocate resources to tasks that are not ready. Sometimes, there are constraints between jobs. These are a challenge to the scheduling performance of big data processing and analysis systems. In this paper, we try to solve the problem by translating it into Boolean Satisfiability Problem (SAT) which is an exact method. SAT-based scheduling algorithm is not a new approach, but in the past it mainly used to solve the static scheduling problems. For dynamic scheduling system, it requires all problems to be solved within a limited time, which is a challenge for SAT encoding. In this paper, we refer to the previous SAT solution to the Job Shop Scheduling Problem, and adjust the algorithm to meet the requirements of the big data processing system. At the same time, we optimized the coding approach and reduced the number of clauses. Thus, the efficiency of the problem solved is improved to meet the performance requirements. The experimental results show that the number of clauses is reduced by more than 30%, and the processing time of the SAT solver to get the solution can be reduced by more than 50%. To demonstrate its effectiveness, we have also implemented our new job scheduler in Apache Hadoop YARN, and validated its effectiveness

In this article we describe the solution to make an estimate of allowed alternations in model components parameters that make up a data processing and control system. They reflect properties of physical and information components of the aforementioned system. This solution is derived according to the limitations of possible changes in integral property that describes the system behavior in different modes of operation. The proposed solution makes it possible to solve not only the direct problem - making a conclusion whether the vulnerability of the data processing system is negligible with respect to alternations in parameters of this systems components but the inverse as well, finding tolerable levels in alterations in systems components parameters from an acceptable level of uncertainty of targeted efficiency. The proposed solution follows known demands: inner properties of the system must be so that customer demands are fulfilled.

The data processing system in the shop is still manual. Previously, the shop had not been able to predict the stock or inventory of goods. Therefore, this system predicts the inventory of goods in the shop. so that the data processing system at the shop can be handled by problems that occur and can form information quickly, precisely, and well. The use of the a priori method which will be used to calculate the prediction of the supply of goods in stock that is still there. And this a priori method will later be used to calculate odds. Data storage using SQL Server database. With the development of technology, the ability to collect and process data is also growing. Data processing has become the advantage of computers, computers have also penetrated various aspects, both in the field of education and in the business world. Competition in the global business has built tight competition between one stall and another. This algorithm is used to analyze when purchasing goods that have run out of stock by classifying which goods have been added to stock or not, as a result, the availability of goods remains stable and maintained. The author is currently making inventory predictions at the stalls to develop an existing system with the a priori method.