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Semantic Technologies as Enabler
Published in Sarika Jain, Understanding Semantics-Based Decision Support, 2021
In the early 1970s, relational, network, and hierarchical data models (information level) were deployed, with the relational data model the most widely used for structured data even today (fifty years on) because of its simplicity and structural clarity. Hierarchical database models are used to organize data in a tree structure. Network models are an extension of hierarchical models and provide a flexible graphical representation of objects and relationships. Relational database models declaratively store data in the form of two-dimensional tables to specify data and queries. Entity-relationship models are used to describe interrelated things of interest in pictorial form, which can then be converted to the relational model—with the drawback that there is no data-manipulation language. Enhanced entity-relationship models are used to precisely reflect the constraints and properties that are found in more complex databases. While very effective at searching record content, RDBMSs are very inflexible in terms of representing arbitrary and evolving relationships between records, because of their strict adherence to tabular structure; therefore they cannot be used for cognitive knowledge-level systems, stored procedures, and binary large objects.
Data Analysis and Design
Published in Sharon Yull, BTEC National for IT Practitioners: Systems units, 2010
Central to the relational database model is the concept of a table (relation) in which data is stored. Each table is made up of records (tuples) and fields (attributes). Each table has a unique name that can be used by the database to find the underlying table. Unlike previous models that have a defined hierarchy there is no specific navigational mapping. The relational model works on the basis that any manipulation of data is carried out via the data values themselves. Therefore to retrieve a row from a table you would compare the value stored within a particular column for that row with some search criteria.
Integrated information management for the FM: Building information modelling and database integration for the Italian Public Administration
Published in Jan Karlshøj, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2018
L. Pinti, S. Bonelli, A. Brizzolari, C. Mirarchi, M.C. Dejaco, A. Kiviniemi
As highlighted before, the focus of the work is the integration between database and 3D parametric models. Concerning the database the process adopts the Relational Database Management System (RDBMS) that allows data creation and elaboration from a DBA Administrator. The Relational Database Model enables a collection of structured data in columns and rows through SQL Language; it combines the formal mathematical theory with usual application with the aim to manipulate and access data.
Geographic context-aware text mining: enhance social media message classification for situational awareness by integrating spatial and temporal features
Published in International Journal of Digital Earth, 2021
Christopher Scheele, Manzhu Yu, Qunying Huang
The variety of social media data poses a challenge for traditional data management following the relational model (Huang and Xu 2014). Social media services utilize the NoSQL database model as a way to best manage their data. Unlike the traditional relational model, NoSQL implements many different data structures, such as document, graph, or key-value. The flexibility with NoSQL allows for data from multiple social media services to be stored in one location within the workflow. MongoDB (Banker 2011) was selected as the NoSQL database. In addition to the reasons state above, MongoDB stores its data in JavaScript Object Notation (JSON) which allows non-uniform fields to be added with no limitations. Most popular programming languages also easily parse JSON. Additionally, MongoDB is scalable allowing multiple servers to store and access the database. For the meteorological data, PostgreSQL was selected as PostgreSQL with the PostGIS extension can store both raster and vector data types, is open source, and provides a wide range of spatial functionality. Note other database systems could provide similar supports for social media data or spatial data management. For example, PostgreSQL also supports JSON data type and offers sufficient JSON operators and function to enable the storage of social media data.
Transformation of UML class diagram into OWL Ontology
Published in Journal of Information and Telecommunication, 2020
Minh Hoang Lien Vo, Quang Hoang
Many studies have transformed from the conceptual database model into the ontology. In relation to the transformation of the Entity-Relationship model to the OWL ontology, studies such as Fahad (2008) have proposed a method for designing OWL Ontologies from the ER model based on a set of rules transforming the components of an ER model (entities, attributes, and relationships between entities) into corresponding OWL components. Myroshnichenko and Murphy (2009) presented an automated conversion solution from the ER model to equivalently on the OWL Lite Ontology. The author presented a transformation algorithm based on five rules that map the components of the ER model to the corresponding components on OWL. Chujai, Kerdprasop, and Kerdprasop (2008) proposed an approach for constructing Ontology OWLs using Protégé from a relational database designed on a given ER model. Van Nguyen, Vo, Hoang, and Hoang (2016) introduced the fully EER model rules to the OWL.
A fingertips-based approach to select maintenance tool automatically in virtual environment
Published in International Journal of Computer Integrated Manufacturing, 2019
First, in real environments, an operator wears a data glove and sets a proper sampling frequency that can obtain the real-time data of operator’s hand gestures. Then, real hand poses are converted into virtual hand poses on the basis of a joint angle extraction algorithm, in which the two data correspond with each other. Second, the virtual hand data should be compared with the ‘valid gesture’ in the database model of maintenance gesture feature. The requirement can be determined whether it is satisfied or not based on Euclidean distances between the current gesture information and their corresponding valid gesture information. Third, the calculated real-time Euclidean distances and tolerable error are compared. The error request for the current gesture is satisfied when . Fourth, whether the current valid gesture is the required valid gesture should be determined when the error requirement is satisfied because many multiple ‘valid gestures’ are observed in the library of the maintenance gesture model. The matching process is terminated when the current gesture is a target gesture. On the contrary, other valid gestures should be matched. Fifth, the maintenance tool model that corresponds to the current gesture model is activated when the selected target gesture is confirmed. In addition, the feature points of the tool model are set to the current gesture model. Finally, automatic tool crawling is completed.