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Methods and techniques for acquiring manufacturing knowledge
Published in Justyna Patalas-Maliszewska, Managing Manufacturing Knowledge in Europe in the Era of Industry 4.0, 2023
Structured Query Language (SQL) is most often used to manage relational databases. SQL is one of the most popular interactive programming languages used by users to store, search, add and modify information in databases (Raatz et al., 2012). SQL also belongs to the group of open-source software. To use the data stored in the database, a database server is required. There are even several dozen different types of database servers on the current market. Popular relational database management systems that use SQL are Oracle Database, Microsoft SQL Server, MySQL, PostgreSQL, Sybase and DB2. Universal, relational databases are used in all layers of enterprises and can be commercial database management system (DBMS) solutions (e.g. Oracle, MS SQL Server), open-source database systems or solutions created on behalf of the client.
Concept Structure of Database Management System (DBMS) Portal for Real-Time Tracking and Controlling the Spread of Coronavirus
Published in Ram Shringar Raw, Vishal Jain, Sanjoy Das, Meenakshi Sharma, Pandemic Detection and Analysis Through Smart Computing Technologies, 2022
Abhishek M. Thote, Rajesh V. Patil
DBMS organizes the data with a particular logic and structure which is called DBMS model. There are different types of DBMS models such as hierarchical, network, entity-relationship, and relational [30]. Out of these models, relational DBMS (RDBMS) model, also called RDBMS is widely used. In RDBMS, the data is stored in the form of two-dimensional tables with different attributes (properties) stored in columns of the table. One table is linked to another with a common attribute. In the rows of the table, the all the information (attributes) of a particular component or product is stored [30]. Thus, tables are called relations in RDBMS. The different RDBMS software are currently available such as MySQL, Microsoft SQL Server, Oracle database, IBM Db2, Amazon Aurora, PostgreSQL, Amazon relational database service (RDS), IBM Informix, Google Cloud SQL, Maria DB, SQLite, memSQL, etc., [31].
Data Lakes: A Panacea for Big Data Problems, Cyber Safety Issues, and Enterprise Security
Published in Mohiuddin Ahmed, Nour Moustafa, Abu Barkat, Paul Haskell-Dowland, Next-Generation Enterprise Security and Governance, 2022
A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Abu Barkat Ullah
Data warehouses are business intelligence tools and technologies and are designed to analyze large datasets. Data warehouses have been successfully used in various application domains, such as healthcare, retail, and marketing. In general, two types of operations are involved with data processing: (1) transactional and (2) analytical. Daily operations, for example, online transactional processing (OLTP), is managed through create, replicate, update, and delete operations on data on a daily basis. These data types are usually structured and stored in a SQL database, for instance, Oracle Database8. In the context of Big Data, structured data are processed and stored, and other types of data, including unstructured and semi-structured data, are processed and stored in NoSQL databases, such as MongoDB9. Big Data is also selected, cleaned, integrated, summarized, and transformed based on the structure of the data warehouse schema definition for the analytical purpose. Data warehouses are the currently dominant approach of providing analytical data, and they store only transformed data [16].
Design of intelligent manufacturing system based on digital twin for smart shop floors
Published in International Journal of Computer Integrated Manufacturing, 2023
Mengke Sun, Zongyan Cai, Ningning Zhao
In the information interaction network architecture, OPC UA server is embedded in field devices such as programmable control devices, sensors RFIDs, QR code and bar code readers. In order to improve the generality of data, OPC UA server converts the format of real-time data into XML format after gathering them. After data management and logical operation, OPC UA client provides corresponding services. When SCADA system obtains data, it adopts the concept of joint server and client, that is, SCADA system provides data for information systems and digital twin system as a server up and obtains data from the lower proxy server as a client down. These real-time data are stored in digital twin database after cleaning, filtering, integrating and mining. MySQL database, Oracle database or Microsoft SQL Server with the ability to read and generate XML data can be used as digital double databases. The simulation software or intelligent algorithms are used to drive the digital twin models simulation of various elements for further analysis and decision making. CAPP system, MES, ERP system and other upper application systems accept decision results to update current process information, planning and scheduling information and monitoring information.
Requirements of a data storage infrastructure for effective land administration systems: case study of Victoria, Australia
Published in Journal of Spatial Science, 2022
Davood Shojaei, Farshad Badiee, Hamed Olfat, Abbas Rajabifard, Behnam Atazadeh
Another issue was regarding geometries including arcs/curves which are not indexed in the database. In the Oracle database, the arc geometry can be stored in the spatial database table. However, if any geometry consists of arcs, then no index can be generated for the geometry. Having an index for all the geometries is crucial and a solution should be provided to address this issue. One of the common solutions is approximating the arc with several line segments. This approach was selected using a densifier function for the arc geometry to break any single arc into many line segments using the arc tolerance. The attributes of the arc are fully stored, and the only change is densifying the arcs to many line segments when the SDO_GEOMETRY is created to preserve the geometry in database format.
Building an efficient storage model of spatial-temporal information based on HBase
Published in Journal of Spatial Science, 2019
Ke Wang, Guolin Liu, Min Zhai, Zhiwei Wang, Chuanyi Zhou
This study used urban road data in Guilin, China, as an example. The size of the data is approximately 1.9 GB and they contain 957,460 surface vectors. Each data vector contains 20 fields, such as the location of the road, the time of construction and the administrative area. The data are stored in the Oracle database and the HBase database. First, 200 rectangular areas with different sizes are set up, where the size of each rectangle contains different amounts of data. Then, the spatial query experiment in Oracle is performed and the database is distributed, subsequently, by utilizing the client for the simulation of multiple users. The concurrency level varies when using more threads. The model runs every 20 min, continuously, under different concurrency levels and calculates the average time for each spatial query. The temporal consumption results in two system architectures at different concurrent levels, as shown in Figure 10.