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Data Collection, Storage, and Retrieval
Published in Ali Soofastaei, Data Analytics Applied to the Mining Industry, 2020
Data retrieval refers to how the desired data are specified and retrieved from a data store. The process of retrieving relevant information from BD evolves three significant issues: efficiency, effectiveness, and execution time. Data retrieval systems aim to support the storage process and the general organization of the information to meet user’s necessities to access the generated documentation. Typically, data retrieval activities begin with a simple query created by the user. In this process, indexing is considered an essential component. The most critical parts of data retrieval systems are as follows: (1) query subsystem, where the user creates a query based on his needs; (2) matching function, applied to compare the query and documents located in the database; and (3) documentary database, which is the place designed to store all the documents [24].
Applications and Performance of Current Technology
Published in John Holmes, Wendy Holmes, Speech Synthesis and Recognition, 2002
We use the term “data entry” to refer to the input of information to a computer’s data file (rather than dictation, which involves direct transcription of the words spoken). Data retrieval is the reverse process of accessing information that is stored in a computer system. Aside from telephone applications, which we will consider separately in the next section, typical application areas for data entry and retrieval via speech recognition involve hands-busy, eyes-busy scenarios similar to those already mentioned in Section 15.6.1. For example, speech recognition can be used in manufacturing to enter quality control information while inspecting product parts, and in dentistry to allow a dentist to carry out an oral examination of a patient and record the results at the same time without needing an assistant.
A data-driven predictive maintenance model for hospital HVAC system with machine learning
Published in Building Research & Information, 2023
Raid Al-Aomar, Marah AlTal, Jochen Abel
For data collection, field devices measure, collect the AHU’s sensor data and send signals to the DDC. The DDC processes the sensor data and transmits it to the Graphical User Interface (GUI) of BMS and CMMS database for condition monitoring and prediction. The databases is saved on a SQL server. Data collected from the field devices of AHUs include the temperature, pressure, CO2, and airflow sensor data in addition to unit name and location. Sensor data is sent to the DDC which converts them into numerical information and transmits them to the GUI of the BMS and the CMMS database. As shown in Figure 2, the DDC sends sensor data using the Building Automation Control Network (BACnet) protocol, which is designed to meet the communication requirements of the building automation and control systems such as HVAC, lighting, access, and fire detection control systems. The BACnet protocol provides mechanisms for computerized equipment with various functions to exchange information (ASHRAE, 2019). The data is transferred via a plug-in created and installed in the CMMS software to import and store data from the DDC. It should be noted that the attribute names of the imported data and the CMMS should match in order to facilitate the mapping of the imported data from the BMS to the CMMS system. The CMMS and condition databases are designed using the SQL data model and are stored on a server for centralized management. Data retrieval from the database is facilitated using SQL queries, a standard mechanism for requesting and processing specific data from relational databases.
NewSQL Database Management System Compiler Errors: Effectiveness and Usefulness
Published in International Journal of Human–Computer Interaction, 2022
SQL is a language initially designed for data retrieval. However, in the decades following the initial release of the SQL standard, the language has evolved to encompass data manipulation, database structure definition, access control, and transaction management (Chamberlin, 2012). Data retrieval remains the most studied aspect of SQL (Taipalus & Seppänen, 2020), and because of this more established research background, this study focuses solely on data retrieval.