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Preparing the Data
Published in Richard J. Roiger, Just Enough R!, 2020
Database management systems (DBMS) store and manipulate transactional data. The computer programs in a DBMS are able to quickly update and retrieve information from a stored database. The data in a DBMS is often structured using the relational model. A relational database represents data as a collection of tables containing rows and columns. Each column of a table is known as an attribute, and each row of the table stores information about one data record. The individual rows are called tuples.All tuples in a relational table are uniquely identified by a combination of one or more table attributes.
Databases for Planning and Manufacturing
Published in Ulrich Rembold, Robot Technology and Applications, 2020
Klaus R. Dittrich, Alfons Kemper, Peter C. Lockemann
Obviously, relationships between objects have to be expressed—like anything else—as relations in NF2. As in the pure relational model, an attribute or a combination of attributes that uniquely identifies the objects to be related (foreign key) is selected for representing them. In DODM, there is a separate construct for relationships. The relates-clause introduces the participating object types by naming them and associating a role attribute with them. From a user standpoint, this should be more natural because one simply considers the whole object to participate in the relationship.
Introduction to the SAS- and R-Based Table-Driven Environment
Published in Tanya Kolosova, Samuel Berestizhevsky, Supervised Machine Learning, 2020
Tanya Kolosova, Samuel Berestizhevsky
A table is a fundamental entity in the relational model. A table can be represented as a two-dimensional object arranged in rows and columns. The structure of a table is very similar to that of an SAS dataset or R data frame.
Are NoSQL Databases Affected by Schema?
Published in IETE Journal of Research, 2023
Neha Bansal, Shelly Sachdeva, Lalit K. Awasthi
A data model is a collection of tools used to describe data and its relationships, constraints, and semantics. This section briefly introduces the modelling perspective of three categories of NoSQL databases. a) Document Store, b) Column Store, and c) Key-Value Store. Table 1 presents the terminology translation from the relational database to three different NoSQL data models named Document (MongoDB), Column (Cassandra), and Key-Value (Redis) corresponding to the relational model. Table 1 explains that MongoDB stores the data in a database as a set of collections consisting of entities as documents. Cassandra stores the data in a Key-space as one or more column families. Whereas Redis stores the data in basic key-value pairs where the key for each entity is unique.
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 general, the relational database (RD) played a significant and leading role in technology-based data storage for the past few years. The relational models are utilized by the relational database where it allows to process and create a relational database ?>management system by means of a structured query language (SQL) [1]. The non-relational databases are introduced to overcome the shortcomings of the relational databases. The non-relational databases are classified under three main categories namely object-oriented databases, XML databases, as well as NoSQL databases [1]. The XML databases are further classified into XML enabled database, Native XML database, as well as Hybrid XML database. Due to numerous changes in demand, data processing has emerged with new processing, data storage, and retrieval mechanisms. Among those mechanisms, not only structured query language databases (NoSQL-DB) are developed in labeling two different types of data stores namely distributed data stores and non-relational data stores. Nevertheless, it becomes difficult to query the traditional relational table for achieving numerous transactions, combining several relational tables, and minimizing the query computational time. Also, an object-relational database [2] is the modified form of the relational model that helps to overcome the query issues. Thus, the object-relational model preserves a relationship among two different types of data approaches such as object-oriented approach and the data-oriented approach, thereby supporting the major methods [3–10].
Analysing the past to prepare for the future: Writing a literature review a roadmap for release 2.0
Published in Journal of Decision Systems, 2020
Richard T. Watson, Jane Webster
A graph is composed of nodes and edges. In the domain of literature reviewing, an element of interest (e.g. a concept or process) is a node and a relationship between a pair of elements is an edge. A labelled graph properties database allows nodes and relationships to have properties (Negro, 2018; Robinson et al., 2013). A property of an element might be a concept’s name (e.g. information asymmetry) and its type (e.g. a concept). The property of an edge relationship could be a descriptor of the relationship, such as ‘precedes‘ in the case of a process diagram or ‘causes’ for a causal model. Another property could indicate the nature of a relationship, such as causal or temporal. Nodes can also have one or more labels, which are used to group nodes together and indicate one or more roles. Thus, all elements of the same type (e.g. processes) could be so labelled to group them. We suggest that a graph description language (GDL) could help in this regard in defining elements and nomological relationship maps. A graph query language (GQL) is used to query a graph database and provides features similar to SQL for the relational model. ISO is working on specifying a standard GQL based on openCypher and similar languages.3 In this article, we use openCypher (or simply Cypher), which is currently the most widely adopted open query language for graph databases.4 Cypher can be used to define and manipulate property graphs (Appendix A). We now consider relationship maps and their descriptions.