Concept Structure of Database Management System (DBMS) Portal for Real-Time Tracking and Controlling the Spread of Coronavirus
Ram Shringar Raw, Vishal Jain, Sanjoy Das, Meenakshi Sharma in Pandemic Detection and Analysis Through Smart Computing Technologies, 2022
A database is a data storage hub or warehouse where data of persons, products, and people is stored in a structured and sequential manner [25, 26]. Database management system (DBMS) is a software platform which access the database. The users of the DBMS are called clients. The clients can access the DBMS to retrieve the data from the DBMS, but in a controlled manner. DBMS securely provides the data to the clients with certain login and password credentials. The DBMS software system is programmed in such a manner that it provides only specific type of a data based on the type of client. For example, only the design engineer of a particular organization can view or edit the detail design parameters of a product on which he or she is working and can update the database. In another example, the quality engineer can only update the product quality data after inspecting the part or product. For an organization, there are different types of clients such as engineers, workers, suppliers, customers, marketing persons, etc. They access the same database through DBMS but only specific data can be retrieved or updated according to authority granted to a client. In the recent years, as organizations are growing rapidly, they need to store large amount of data. Hence, the use of DBMS is nowadays widely adopted by most of the organizations. The DBMS system is applicable in different fields such as airlines, railways, buses, banks, sales, engineering industries, telecommunication services, educational systems, finance, economics, human resources (HR), etc., [27].
Software and Technology Standards as Tools
Jim Goodell, Janet Kolodner in Learning Engineering Toolkit, 2023
Data architectures address the structure of data and the capabilities of associated data platforms. Data platforms are the various components required to acquire, store, prepare, deliver, and manage your data (along with the requisite security).10 Most data platforms include one or more databases, including relational databases (for example, built using SQL) and / or non-relational or NoSQL databases (such as object databases, graph databases, document stores, key / value stores, triple / quad stores, and hybrid platforms). The different database types each have strengths and weaknesses. For instance, SQL needs predefined schema and structured data, while NoSQL can handle dynamic schema and unstructured data. The different database types also scale and perform differently, depending on how they’re accessed and how data are structured within them.11 For example, object databases are a convenient choice for applications built using object query languages, and they can handle complex relationships between objects. Meanwhile, graph databases excel at managing highly connected data and complex queries where the relationships between data elements are as important to the data elements themselves.
Storage and databases for big data
Jun Deng, Lei Xing in Big Data in Radiation Oncology, 2019
With Figures 3.2 and 3.3 in mind, one can see why the incentive for the utilization of big data technologies is lesser in the scope of conventional clinical research. There, the data is separated across a diverse set of databases with a well-defined relational data model that allows taking advantage of structured query language (SQL) for data definition and manipulation. A wide variety of available literature sources have covered the relational model in detail (e.g., Codd 1970, 1991). For the purpose of this chapter, it is useful to recapitulate that the relational database consists of a set of tables that defines classes of stored entities, where each table defines a primary key, identifying an entity record and a set of columns (attributes) of an entity. Each row of a table represents an instance of an entity (record). Relations can be represented by referring to the primary key of the specific entity within another foreign entity or relationship table.
Radiation databases and archives – examples and comparisons
Published in International Journal of Radiation Biology, 2019
Alia Zander, Tatjana Paunesku, Gayle Woloschak
Descriptive work observing the effects of ionizing radiation dates back to the discovery of X-rays in 1895 (Hall and Giaccia 2012). Initially, observational notes were created from patient samples collected after outwardly adverse radiation events. As interest in the field grew, more accurate and detailed information describing the impact of ionizing radiation was necessary. Eventually, researchers and doctors recognized that there is a significant delay in expression of radiation pathologies following ionizing radiation exposures. This resulted in the collection of data and materials from radiation-exposed subjects who did not exhibit any noticeable and/or immediate complications. Data collection and storage methods gradually improved over time, and ultimately led to valuable archives that we still use today. In current terms, a dataset is the actual data that has been collected, while a database is an organized way to store datasets, typically controlled by a management system. Once all of the data have been collected and there will be no new alterations, it can be stored as a searchable archive for future reference and analysis. This review provides some examples of open and closed databases and archives available for studying the impact ionizing radiation has on health (Figure 1).
Use of Routinely Collected Registry Data for Undergraduate and Postgraduate Medical Education in Denmark
Published in Journal of European CME, 2021
Kasper Bonnesen, Cecilia Hvitfeldt Fuglsang, Søren Korsgaard, Katrine Hjuler Lund, Natascha Gaster, Vera Ehrenstein, Morten Schmidt
Administrative databases register individuals from a certain geographic area or attending a certain health service (e.g. hospital department or out-patient clinic). The CRS is an administrative database. Other types of information in Danish administrative databases include hospital encounters [13], prescription redemptions [14], and laboratory results [15]. Figure 2 displays examples of such databases. Health databases include, e.g. disease registries containing information on the time of diagnosis or treatment for a specific disease (e.g. the Danish Cancer Registry) [16], procedure registries containing information on time and type of procedure and other procedure-specific data (e.g. the Western Denmark Heart Registry) [17], and biological biobanks containing blood and tissue samples. Clinical quality databases aim to use clinical care data to improve treatment of specific diseases or clinical procedures, to improve management of specific departments, and for research [18,19]. Currently, the Danish Clinical Quality Program – National Clinical Registries (RKK) has listed 84 clinical quality databases [20] categorised into (1) heart/vascular, surgery, and emergency (e.g. the Danish in-hospital cardiac arrest registry) [21], (2) cancer and cancer screening (e.g. the Danish Colorectal Cancer Group Database) [22], and (3) psychiatry, gynecology/obstetrics, and chronic diseases (e.g. the Danish Depression Database) [23].
Provision of data from the clinical database and of biological material from the tumor bank of the Danish Breast Cancer Cooperative Group 2008–2017
Published in Acta Oncologica, 2018
Henning Mouridsen, Peer Christiansen, Maj-Britt Jensen, Anne-Vibeke Laenkholm, Henrik Flyger, Birgitte Offersen, Ilse Vejborg, Bent Ejlertsen
The data of the database are unique. They are individual based, and longitudinal with successive dates of therapeutic interventions and events. And the database, following improvement over time, is now close to have a complete coverage of the Danish breast cancer population. This has been achieved by the development of an effective system of reminders, based partly on identification of gabs in the reporting and by linkage to the Danish Pathology Registry, which registers data from every pathology report performed by the Danish departments of pathology. Thus patients not registered from the departments can be identified and enquiries sent to the departments. And finally, the database is constructed to give advice to the clinicians, based on the reported data of the clinical, histopathological and genetic characteristics about the recommended oncological treatment according to current evidence based guidelines.
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