Data Base Environmennt for Power System Simulation Models

Very powerful data base management systems (DBMS) now exist which allow medical personnel access to patient record data bases. DBMS's make it easy to retrieve either complete or abbreviated records of patients with similar characteristics. In addition, statistics on data base records are immediately accessible. However, the price of this power is a large computer with the inherent problems of access, response time, and reliability. If a general purpose, time-shared computer is used to get this power, the response time to a request can be either rapid or slow, depending upon loading by other users. Furthermore, if the computer is accessed via dial-up telephone lines, there is competition with other users for telephone ports. If either the DBMS or the host machine is replaced, the medical users, who are typically not sophisticated in computer usage, are forced to learn the new system. Microcomputers, because of their low cost and adaptability, lend themselves to a solution of these problems. A microprocessor-based intelligent terminal has been designed and implemented at the USAF School of Aerospace Medicine to provide a transparent interface between the user and his data base. The intelligent terminal system includes multiple microprocessors, floppy disks, a CRT terminal, and a printer. Users interact with the system at the CRT terminal using menu selection (framing). The system translates the menu selection into the query language of the DBMS and handles all actual communication with the DBMS and its host computer, including telephone dialing and sign on procedures, as well as the actual data base query and response. Retrieved information is stored locally for CRT display, hard copy production, and/or permanent retention. Microprocessor-based communication units provide security for sensitive medical data through encryption/decryption algorithms and high reliability error detection transmission schemes. Highly modular software design permits adapation to a different DBMS and/or host computer with only minor localized software changes. Importantly, this portability is completely transparent to system users. Although the terminal system is independent of the host computer and its DBMS, it has been linked to a UNIVAC 1108 computer supporting MRI's SYSTEM 2000 DBMS.

The PHLOX project leads to three Data Base Management System (DBMS) packages for micro-computers: PHLOX1 for individual machines, PHLOX2 for servers managing data bases shared between several users in a local network, and PHLOX3, a Distributed DBMS for interconnected work stations. These packages have the essential possibilities of DBMSs that existed only (and not always) on bigger machines previously. The description of a data base can be given, checked and modified at three levels (relational, network and physical). The data manipulation language allows any operation using the relational or the network model, by direct interpretation or from host precompiled application programs. The system automatically and transparently insures the data base's consistency, in any situation (user's error, conflict of access or hardware incident). Thanks to their low cost, their reliability and their ease of operation, such systems can be used for integrated office automation, business management in small companies or documentation centers, as well as in computer aided design installations or medical centers.

Introduction. The banking sector assigns high priority to data storage, as it is a critical aspect of business operations. The volume of data in this area is steadily growing. With the increasing volume of data that needs to be stored, processed and analyzed, it is critically important to select a suitable data storage solution and develop the required architecture. The presented research is aimed at filling the gap in the existing knowledge of the data base management system (DBMS) suitable for the banking sector, as well as to suggest ways for a fault-tolerant data storage cluster. The purpose of the work is to analyze the key DBMS for analytical queries, determine the priorities of the DBMS for the banking sector, and develop a fault-tolerant data storage cluster. To meet the performance and scalability requirements, a data storage solution with a fault-tolerant architecture that meets the requirements of the banking sector has been proposed.Materials and Methods. Domain analysis allowed us to create a set of characteristics that a DBMS for analytical queries (OnLine Analytical processing — OLAP) should correspond to, compare some popular DBMS OLAP, and offer a fault-tolerant cluster configuration written in xml, supported by the ClickHouse DBMS. Automation was done using Ansible Playbook. It was integrated with the Gitlab version control system and Jinja templates. Thus, rapid deployment of the configuration on all nodes of the cluster was achieved.Results. For OLAP databases, criteria were developed and several popular systems were compared. As a result, a reliable cluster configuration that met the requirements of analytical queries has been proposed for the banking industry. To increase the reliability and scalability of the DBMS, the deployment process was automated. Detailed diagrams of the cluster configuration were also provided.Discussion and Conclusions. The compiled criteria for the DBMS OLAP allowed us to determine the need for this solution in the organization. Comparison of popular DBMS can be used by organizations to minimize costs when selecting a solution. The proposed configuration of the data warehouse cluster for analytical queries in the banking sector will improve the reliability of the DBMS and meet the requirements for subsequent scalability. Automation of cluster deployment by the mechanism of templating configuration files in Ansible Playbook provides configuring a ready-made cluster on new servers in minutes.

The focus of the present paper is on the data‐knowledge integration problem, whereby data and knowledge can be made radically more useful within the same framework. Most tools for developing knowledge‐based systems do not integrate well with data bases. This lack of integration is usually hampered by slow data base queries or by overly limited data base interfaces. On the other hand, since data base management systems are already well developed, leveraging knowledge‐based systems with data base management systems would provide significant advantages like speed, security, and portability across data formats (e.g. easy conversion between dBase and ASCII formats). In the present paper, we describe a novel approach to the data‐knowledge integration problem. This approach entails the simple but powerful extension of a data base system to increase its ability to represent and manipulate knowledge. We describe Kbase, an environment for developing knowledge‐based systems using facilities and extended constructs of the familiar dBase III data base programming environment. At the end, we use an implemented example, IPDOS, to support our hypothesis.

The architecture of Geographic Information Systems (GISs) is changing: more and more systems are based on the integrated architecture, i.e. storing geometric data in the Data Base Management System (DBMS) together with administrative data. The first step in building a Geo-DBMS is by having data types and operators for simple features (i.e. geometric primitives): point, line and polygon. This has reached a level of standardisation and is now implemented in several commercial DBMSs. The next step is to have support for the topologically structured features in the DBMS, i.e. complex features. The DBMS can then check and guarantee consistency. In addition, complex operations can be executed within the DBMS. Despite the fact that topologically structured models are well known and that it is not difficult to store the topological references, it still remains an unresolved issue as to how to effectively implement these models within a relational DBMS. In this paper, we describe the design and implementation of a topologically structured management at the DBMS level. Our focus is to translate topological structures into geometric primitives. It is then possible to define a DBMS view on a topological primitive, which makes this appear as a geometric primitive. This process supports the best of both worlds: on the one hand there are advantages of the topological structure (no redundancy) and on the other hand the ease of explicit geometric primitives in querying, analysis and presentation is available.

Under the leadership of the African National Congress (ANC), the South African Government has long mooted the notion of becoming a developmental state in response to rising unemployment, inequality and poverty. This idea was moulded along the folds of the rise of economic growth and industrialisation, as experienced by East Asian developmental states such as Taiwan, Singapore, South Korea and Hong Kong. There would be an attempt to channel the state’s administrative capacities, policies and resources towards attaining national development goals. A developmental state plays an active role in guiding economic development and using the country's resources to meet the needs of the people. State-owned enterprises (SOEs) such as the Electricity Supply Commission (Eskom) were meant to be the catalyst in the National Development Plan’s (NDP) bid to reignite the country’s stagnant economy. However, the country's state institutions, administrative capacities and resources have been challenged by the state capture debacle that shed light on the corrupt relationship between the political and business elite. As a lack of effective leadership, mismanagement and a complex regulatory framework appear to be at the core of the crisis at Eskom in particular, the main objective of this paper was to determine to what extent the aforementioned constructs perpetuated the energy crisis at Eskom and in turn, negatively impacted the socio-economic aims of the NDP and South Africa’s endeavour to become a developmental state. To achieve this objective, the paper pursued a qualitative research approach where secondary information about the crisis at Eskom, and South Africa’s goal to become a developmental state, were perused. One of the article’s recommendations was that Eskom's grip on the regional power economy must end and that independent power plants must be enabled.

The similarity ainong elemcnts emerges naturally as the most adequate to ask about complex data (such as, multimdia and genomic sequenees among others). There are two basie similarity queries: Range Query and k-Nearest Neighbor Query. The increasing volume of complex data stored in Database Management Systems (DBMS), makes it neeessary to provide support for these data tvpes. One way to support complex data types in current DBMS is to include similarity queries in its query processor, and consequently, in the relational algebra. This fact leacls to produce ways to express such queries in the DBMS language as predicates in select operations. As a consequence, the two basic similarity queries cari be combined in more complex expressions involving boolean conjunctions and disjunctions among them, i.e., complex similarity queries. However, for complex similarity queries to be processed efhciently in a DBMS, it is neeessary to provide support in the optimization and runtime laycrs of the; query proeessing. There are many works involving the development, of algorithms to ariswer specific and simple similarity query whereas there is not a generic algorithm efficiently able to handle complex similarity queries. Furthermore, the similarity query optimization is a topic not frequently explored in the literature. This work establishes a structured rnethod 011 how to analyze complex similarity queries. This method is used to extend the relational algebra through algebraic rules and to determine a small set of algorithms that can be used to answer any complex similarity query. In addition, the proposed method makes it possible to formalize rules for selectivity estimai,ion of these. queries thus assisting cost estimation. To validate the concepts presented, exjjeriments are being performed on real and synthetic data sets that highlight meaningful results. The algebraic rules. algorithms and metrics to est-imate the selectivity can be employed in the optimization process of a DBMS in order to derive efficient complex similarity query execution plans. Therefore, this work deals with essential poiuts that enable the practical use of similarity (jueries in Relational Database Management Systems.

This article discusses data security and controls, primarily in the context of data base management systems (DBMSs), with an emphasis on basic principles and mechanisms that have been successfully used by practitioners in actual products and systems. The general discussion does not focus on particular products, although a later section does review several products to illustrate previously discussed concepts. The discussion of data bases is limited specifically to relational DBMSs, which store data in relations that have specific mathematical properties. All examples given are in SQL. It is assumed that readers are familiar with rudimentary concepts of relational data bases and SQL. This article begins with a review of basic security objectives, followed by a discussion of access controls in the current generation of commercially available DBMSs. The problem of multilevel security is then introduced, including a review of techniques developed specifically for multilevel security.

As data and information fusion technology and application evolves, the need is increasing to cope with large amounts and diverse types of data. Although there would be many benefits to employment of Data Base Management Systems (DBMS) in automated fusion processes the data access throughput requirements of automated fusion processes have vastly exceeded the performance of off-the-shelf DBMS's. The availability of large random access memories is allowing the development of real-time data base management systems. While these are currently being used in financial market and telecommunications applications, their ability to bring DBMS benefits to data fusion applications has yet to be explored. This paper presents results of experimentation with these emergent real-time DBMS's for automated fusion applications. We used algorithms, data characteristics, and scenarios from deployed and R&D systems. The applications-dependent data structures were converted to Entity-Relationship models and generated into real-time and conventional DBMS's.

Data base management systems are gaining rapid and widespread use by organizations attempting to improve their data management facilities. Several commercial DBMS's are available and educators are increasingly introducing students to concepts and techniques of data management. An important part of grasping data management concepts is to provide hands-on experience in using a data base management system. The Educational Data Base System is a tool designed to provide this hands-on experience in a teaching environment. It has been used successfully in this capacity for several years and has been distributed to over twenty different organizations in seven countries. This paper briefly describes the system and outlines how it is used at the University of Toronto to augment classroom teaching of data management concepts.

Data base management systems are gaining rapid and widespread use by organizations attempting to improve their data management facilities. Several commercial DBMS's are available and educators are increasingly introducing students to concepts and techniques of data management. An important part of grasping data management concepts is to provide hands-on experience in using a data base management system. The Educational Data Base System is a tool designed to provide this hands-on experience in a teaching environment. It has been used successfully in this capacity for several years and has been distributed to over twenty different organizations in seven countries. This paper briefly describes the system and outlines how it is used at the University of Toronto to augment classroom teaching of data management concepts.

In the work we present a new architecture for visualization systems that is based on data base management system (DBMS) technology. By building on the mechanisms present in a next-generation DBMS, rather than merely on the capabilities of a standard file manager, we show that a simpler and more powerful visualization system can be constructed. We retain the popular boxes and arrows programming notation for constructing visualization programs, but add a flight simulator model of movement to navigate the output of such programs. In addition, we provide a means to specify a hierarchy of abstracts of data of different types and resolutions, so that a zoom capability can be supported. The underlying DBMS support for this system, Tioga, is briefly described, as well as the current state of the implementation. >

The organization and management of the data in a computer simulation of a discrete event system can be a significant task. This task can be facilitated by linking a data base management system with the computer language used for the simulation. This paper presents such a linkage using dBase III for the data base management system and TURBO-PASCAL for the computer language. dBase provides the means to file system model parameters, simulation input data and simulation output data. Since dBase III has a capacity of about 109 records per data file, the user can store a large amount of experimental data.Data for several models can be stored and conveniently retrieved, processed and compared via dBase independently of the TURBO-PASCAL simulation model. The retrieved and processed data can be displayed in graphical or tabular form.The combined use of the data base management system and a computer language can significantly facilitate many discrete event simulation projects.This paper presents a manufacturing system simulation model as an example.

The development of information technologies requires the processing of an increasing amount of information. A great number of tools and DBMSs for working with big data have been created. Examples of DBMS are: MS SQL Server, Oracle Database, SciDB, VoltDB, PostgreSQL XL, Clusterix, etc. Most systems of the BigData class require serious computing power to ensure acceptable performance. This article discusses the TPC-H test and 22 of its queries, the execution of which allows you to analyze the performance of database management systems (DBMS). PostgreSQL, MySQL, and Microsoft SQL Server DBMS are considered and their performance is investigated based on TPC-H test queries with database (DB) of various sizes in the DbVisualizer Free version 11.0.6.

One method for performing exposure assessments is to use mathematical models that predict the transport and transformations of toxic substances in the environment. To run these models, a variety of input data on receiving water‐body properties and water‐quality parameters that define a range of environments and influence the chemical‐specific rate coefficients must be developed. A data base that contains selected hydrologic and water quality parameters has been developed by EPA for 15 major U.S. river basins. Called the Canonical Environments Data Base, the file brings together information developed by public and private organizations on each reach of the rivers as well as individual lakes and reservoirs in the basins. Coupling raw data with a data base management system in this environmental file will facilitate entry into and deletions from the data base and development of data input to the Exposure Analysis Modeling System (EXAMS) and other similar exposure assessment mathematical models. To a...