China
Africa
Explore chapters and articles related to this topic
System Architecture
Published in Dobrivoje Popovic, Vijay P. Bhatkar, Distributed Computer Control for Industrial Automation, 2017
Dobrivoje Popovic, Vijay P. Bhatkar
Another problem, typical for all time-related databases like the real-time and production management databases, is the representation of time-related data. Such data has 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.
Survey and analysis of mining requirements in spatio-temporal database
Published in Debatosh Guha, Badal Chakraborty, Himadri Sekhar Dutta, Computer, Communication and Electrical Technology, 2017
D. Dasgupta, S. Roy, S. Singha Roy, A. Chakraborty
Temporal data mining deals with the analysis of events ordered by one or more dimensions of time. If a system contains multiple time lines such as valid time, transaction time, or decision time, then it will be considered as multiple dimensions of time. The spatial data mining considers the alternative path of embedded and exclusively spatial constructs using association rules, clustering, etc.
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
The temporal dimensions that are supported by TJeH are transaction time and valid time. Therefore, a temporal JSON component may have a transaction-time, valid-time, or bitemporal format, according to the temporal dimension(s) along which its history is kept.
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
The temporal dimension of the data in the database is divided into two different aspects: valid time (VT) and transaction time (TT). These two timestamp concepts are equally important and needed to capture the complete picture of the data from past, present and future. The temporal features are typically added either by: (1) extending the existing RDBMSs or (2) creating a middle layer with the time-related functionalities without making any changes to the operational, underlying databases (Arora, 2015). Although many approaches were proposed in the literature, only some were actually implemented and materialized as prototypes or in the commercial tools (Arora, 2015; Faisal et al., 2017; Radhakrishna et al., 2015) and only VT timestamps were supported – not TT timestamps.
Uncovering hidden resource allocation decisions: An application in hospital bed management
Published in IISE Transactions on Healthcare Systems Engineering, 2019
Nooshin Valibeig, Jacqueline Griffin
Time inconsistency across databases is a common data corruption problem in information systems, particularly in healthcare organizations. Therefore, we also examine the effect of time lags between valid time and transaction time of events in analyzing the robustness of the algorithm. The valid time refers to the time an event happens and the transaction time refers to the time that the event is recorded. Among the transaction times stored in the database, the request time and the assigned time are used in the algorithm to identify the allocation type. Therefore, to generate corrupted data, after randomly selecting 20% of tasks, we corrupt the selected data by adding a time lag to the request time of the task. In a separate analysis, we add the time lag to the assigned time. To evaluate the impact of the size of the time lag, the value is generated randomly using a Normal distribution with means of 15, 30, 45, and 60 minutes.
WMO: an ontology for the semantic enrichment of wetland monitoring data
Published in International Journal of Digital Earth, 2023
Xin Xiao, Hui Lin, Chaoyang Fang
In more detail, the valid time is earlier than the transaction time of an event in the wetland monitoring domain. Although numerous monitoring systems emphasize real-time, the transaction time inevitably lags due to the sampling interval and network transmission. In addition, the transaction time must be an absolute time instant; the valid time, however, may be a time interval or a time instant.