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Software and Technology Standards as Tools
Published in Jim Goodell, Janet Kolodner, Learning Engineering Toolkit, 2023
Jim Goodell, Andrew J. Hampton, Richard Tong, Sae Schatz
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.
Blockchain Architecture and Policy for Transforming Healthcare Industry
Published in Pushpa Singh, Divya Mishra, Kirti Seth, Transformation in Healthcare with Emerging Technologies, 2022
Most people misunderstand blockchain architecture as something really complicated, but it is quite simple. A blockchain is nothing but a type of database. A database is a collection of data or information stored in a structured manner. For instance, it could be a table format to allow for simple searching and filtering for specific information. Blockchain is nothing but a chain of data or a chain of the transaction as blocks being chained together with each of them having a cryptographic signature, where each signature is called a hash, which is saved in multiple shared ledgers. It is supported by a connected process of nodes, which creates a network.
Database querying using SQL
Published in Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, Modern Data Science with R, 2021
Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
SQL (Structured Query Language) is a programming language for relational database management systems. Originally developed in the 1970s, it is a mature, powerful, and widely used storage and retrieval solution for data of many sizes. Google, Facebook, Twitter, Reddit, LinkedIn, Instagram, and countless other companies all access large datastores using SQL.
An Enhanced Entity Model for Converting Relational to Non-Relational Documents in Hospital Management System Based on Cloud Computing
Published in IETE Technical Review, 2022
A. Samydurai, K. Revathi, L. Karthikeyan, B. Vanathi, K. Devi
In addition to this, the relational database system comprises four different processes that include SELECTION, INSERTION, DELETION, and UPDATION. In a relational database system, the SELECTION option is employed in the retrieval of data that further encompassed projections and joints. Also, the INSERTION, DELETION, and UPDATION processes are employed to vary the main command and data. SQL is considered a declarative language that is used by the relational database system to store the data. Here, the data are retrieved, stored, as well as modified by means of different types of SQL in a relational database system. Moreover, a relational model was introduced by Edgar Frank Codd; in which he provides a solution in overcoming the difficulty that occurred during accessing and storing data by means of twelve different rules that are referred to as E.F. Codd’s twelve rules [32]. The usability and the benefits of the relational databases are explained by means of an experimental database system that further developed a declarative language referred to as SQL which is known for standard database interaction.
End-user engineering of ontology-based knowledge bases
Published in Behaviour & Information Technology, 2022
Audrey Sanctorum, Jonathan Riggio, Jan Maushagen, Sara Sepehri, Emma Arnesdotter, Mona Delagrange, Joery De Kock, Tamara Vanhaecke, Christophe Debruyne, Olga De Troyer
To explain the concept of an ontology-based knowledge base, we will compare it with a classical database. A database organizes data according to a specific data schema, also called a data model. In this way, the database is an ‘instantiation’ of the data model, as at each moment, the stored data can be considered as an instance of the data model. Similarly, a knowledge base (KB) also stores data (or rather, knowledge), but the data/knowledge is not stored according to a strict data schema but in general in the form of a knowledge graph, which is a collection of connected descriptions. An ontology can be used to specify which type of knowledge can be stored in the knowledge graph (Hepp 2008). An ontology describes concepts in a domain and their properties, as well as relationships between the concepts and domain rules that apply to them. In this way, the ontology can be seen as the schema/model, and the knowledge stored can be considered as an instantiation of the ontology (Hepp 2008; Chasseray et al. 2021) (see right container box in Figure 1). Using an ontology to define the model of a knowledge base has the advantage of providing formal definitions of the knowledge that can be stored, its meaning, and possible restrictions on what can be stored. This not only provides an unambiguous description of the knowledge, but it also allows humans, as well as computers, to process the information and infer new knowledge.
Improving mine-to-mill by data warehousing and data mining
Published in International Journal of Mining, Reclamation and Environment, 2019
Mustafa Erkayaoglu, Sean Dessureault
Continuous improvement (CI) has been the focus of various researchers, is defined as the on-going effort taken in advancing processes by evaluation [13] and focuses on improvement on a corporate level [14–16]. Incorporating key personnel of different departments for CI targets requires a reliable and integrated data layer. The consistent effort towards higher productivity in mining industry reveals the necessity of a central and integrated repository of operational data. Data warehousing can be defined as a collection of databases in an integrated way where the time perspective can be historical or real-time [17]. Its benefits in management and decision-making have been proven in many industries. Mining is a highly complex production field where technology is utilised heavily on equipment. Mobile equipment that is part of the fleet management system provides relational data, whereas mineral processing plant equipment generates non-relational process data. Integration of these unique data sources for business intelligence (BI) on a real-time basis is a challenge.