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A Temporal JSON Model to Represent Big Data in IoT-Based e-Health Systems
Published in Om Prakash Jena, Bharat Bhushan, Nitin Rakesh, Parma Nand Astya, Yousef Farhaoui, Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems, 2022
Zouhaier Brahmia, Fabio Grandi, Safa Brahmia, Rafik Bouaziz
A temporal database is a database with built-in support for defining, storing, manipulating, querying and controlling time-varying data [32–35]. Two time dimensions have been introduced for timestamping time-varying data: transaction time [36], which represents the time when data are current in the database, and valid time [37], which represents the time when data are valid in the real world. Thus, four types of temporal databases can be found: (i) transaction-time databases, which support only transaction time; (ii) valid-time databases, which support only valid time; (iii) bitemporal databases, which support both transaction time and valid time; and (iv) multitemporal databases in which coexist data of different temporal formats. Notice that, in a temporal setting, conventional (i.e., non-temporal) databases are called snapshot databases. Moreover, the timestamp of a temporal datum can be either a time interval or a time point [38].
Distributed Control Systems
Published in Richard L. Shell, Ernest L. Hall, Handbook of Industrial Automation, 2000
Another problem, typical for all time-related databases, such as the real-time and production management databases, is the representation of time-related data. Such data have to be integrated into the context of time, a capability that the conventional database management systems do not have. In the meantime, numerous proposals have been made along this line which include the time to be stored as a universal attribute. The attribute itself can, for instance, be transaction time, valid time, or any user-defined time. Recently, four types of time-related databases have been defined according to their ability to support the time concepts and to process temporal information: Snapshot databases, i.e., databases that give an instance or a state of the data stored concerning the system (plant, enterprise) at a certain instant of time, but not necessarily corresponding to the current status of the system. By insertion, deletion, replacement, and similar data manipulation a new snapshot database can be prepared, reflecting a new instance or state of the system, whereby the old one is definitely lost.Rollback databases, e.g., a series of snapshot databases, simultaneously stored and indexed by transaction time, that corresponds to the instant of time the data have been stored in the database. The process of selecting a snapshot out of a rollback database is called rollback. Also here, by insertion of new and deletion of old data (e.g., of individual snapshots) the rollback databases can be updated.Historical databases, in fact snapshot databases in valid time, i.e., in the time that was valid for the systems as the databases were built. The content of historical databases is steadily updated by deletion of invalid data, and insertion of actual data acquired. Thus, the databases always reflect the reality of the system they are related to. No data belonging to the past are kept within the database.Temporal databases are a sort of combination of rollback and historical databases, related both to the transition time and the valid time.
Uncovering hidden resource allocation decisions: An application in hospital bed management
Published in IISE Transactions on Healthcare Systems Engineering, 2019
Nooshin Valibeig, Jacqueline Griffin
Information systems gather and store many types of data in various databases. Two common types of databases are spatial and temporal databases (Nandal, 2013). Temporal databases represent time-related features or attributes (Snodgrass, 1986; Chaudhuri, 1988). Spatial databases represent geometric, geographic, or space-related data features such as size, shape, or location (Güting, 1994). Databases representing changes of spatial features over time are known as spatio-temporal databases (Erwig et al., 1998). Spatio-temporal databases represent the changing nature of the real world in various application domains, including transportation, monitoring, and environmental systems (Gutiérrez et al., 2005). In this study, we focus on two data models of spatio-temporal databases: snapshot and event-oriented. In a snapshot data model, a temporal sequence of the spatial state of a system is represented at fixed time intervals. In an event-oriented data model, every event and its components, including time and place of the event occurrence, are represented (Pelekis et al., 2004).
A survey on spatial, temporal, and spatio-temporal database research and an original example of relevant applications using SQL ecosystem and deep learning
Published in Journal of Information and Telecommunication, 2020
Kulsawasd Jitkajornwanich, Neelabh Pant, Mohammadhani Fouladgar, Ramez Elmasri
Temporal dimension of data and other time-related issues plays a very important role in applications such as accounting and banking. In fact, in many other applications, temporal data are also critical. In temporal databases, time-related component are carefully and systematically recorded and validated, which are very important in time-sensitive applications. In the last two decades, many research studies on temporal databases have been done (Arora, 2015; Jensen, 2000; 2016; Radhakrishna et al., 2015). We will discuss them next.