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Data collection, processing, and database management
Published in Zongzhi Li, Transportation Asset Management, 2018
Entity–relationship data model: An entity-relationship (E–R) model is one of the most popular object-based models studied in database literature. The E–R model is easy to understand and also powerful enough to model complex scenarios. An E–R model uses three components to describe data: entities, relationships between entities, and attributes of entities or relationships. An entity is an object that exists uniquely. It could be an event or a location. Entities in an organization cannot stay isolated. The relationship is defined to describe the association among entities. The normal relationships included in an E–R model are those of (i) belonging to; (ii) set and subset relationships; (iii) parent–child relationships; and (iv) component parts of an object. An attribute is a property of an entity type that is of interest for some purpose. An entity is thoroughly described through adding a set of attributes to be associated with it.
E
Published in Phillip A. Laplante, Dictionary of Computer Science, Engineering, and Technology, 2017
entity an object or thing about which data is to be stored. According to the International Organization for Standardization’s open systems interconnection (OSI) terminology for a layer protocol machine, an entity within a layer performs the functions of the layer within a single computer system, accessing the layer entity below and providing services to the layer entity above at local service access points. An entity may be an activity, a process, a product, an organization, a system or person, or any combination thereof. In object-oriented programming, an entity is part of the definition of a class (group) of objects. In this instance, an entity might be an attribute of the class (as feathers are an attribute of birds), or it might be a variable or an argument in a routine associated with the class. In database design, an entity is an object of interest about which data can be collected. In a retail database application, customers, products, and suppliers might be entities. An entry can subsume a number of attributes. Product attributes might be color, size, and price; customer attributes might include name, address, and credit rating. Synonymous with item.
The Data Warehouse
Published in Richard J. Roiger, Data Mining, 2017
The first step toward building a transactional database is data modeling. A data model documents the structure of the data to be placed into a system independent of how the data will be used. A common notation for data modeling is the entity relationship diagram (ERD). An ERD shows the structure of the data in terms of entities and relationships between entities. An entity is like a concept in that it represents a class of persons, places, or things. An entity may contain one or several attributes. A key represented by a combination of one or several attributes uniquely identifies each instance of an entity.
Development of a worldwide ferry safety database utilizing relational database approach
Published in Journal of Transportation Safety & Security, 2019
Siyu Xu, Hao Hu, Roberta Weisbrod
An ER model consists of entity types that classify the things of interest and identifies relationships between these entity types. In grammatical terms, entities are the equivalent of grammatical nouns, for example, vessels, accidents, or ocean regions. An entity can be defined by means of its properties called attributes. Relationships are the equivalent of verbs or associations, for example, the act of happening, or being a member of a group. A relationship can be defined based on the number of entities related to it, known as the cardinality. As shown in Figure 2, the ER diagram pictorially represents how the data relevant to ferry casualties are supposed to be related to each other. In accordance with (Chen, 1976) diagramming technique, this model consists of rectangles, diamonds and ovals, denoting the entities, relationships and attributes, respectively.
M2SA: a novel dataset for multi-level and multi-domain sentiment analysis
Published in Journal of Information and Telecommunication, 2023
Huyen Trang Phan, Ngoc Thanh Nguyen, Dosam Hwang, Yeong-Seok Seo
Sentiment analysis (SA), as expressed in comments or reviews on social networks, has piqued the interest of many natural language processing scientists. SA identifies polarities or degrees of sentiment described in documents and sentences regarding entities or emotions toward aspects of entities. An entity is any recognizable or separate object that often mentions individuals, organizations, events, products, and systems. A sentiment indicates an emotion or feeling regarding a particular entity. SA has three main characteristics (Phan et al., 2023): the sentiment target (entities, topics, or aspects), the sentiment degree (positive, negative, or neutral), and the sentiment level (document, sentence, or aspect).
A scalable mobile context-aware recommender system for a smart city administration
Published in International Journal of Parallel, Emergent and Distributed Systems, 2021
Dey and Abowd have defined a context as any information that can be used to characterize the situation of an entity. An entity is a person, place or object that is considered relevant to the interaction between a user and an application, including the user and the application themselves. [6]According to Nivala and Sarjakoski, there are several important factors acting on the context such as context location, time, physical environment, browsing history, orientation, cultural, and social background [7].