scholarly journals Building Context Aware Network of Wireless Sensors Using a Scalable Distributed Estimation Scheme for Real-time Data Manipulation

10.5772/13756 ◽  
2010 ◽  
Author(s):  
Amir Basirat ◽  
Asad I.
Keyword(s):  
Real Time ◽  
Wireless Sensors ◽  
Distributed Estimation ◽  
Context Aware ◽  
Time Data ◽  
Data Manipulation ◽  
Real Time Data ◽  
Estimation Scheme

scholarly journals Kajian Pendekatan Binary Log dalam Change Data Capture

2017 ◽  
pp. 11
Author(s):  
Muhammad Febrian Rachmadhan Amri ◽  
I Made Sukarsa ◽  
I Ketut Adi Purnawan
Keyword(s):  
Data Management ◽  
Real Time ◽  
Data Warehouse ◽  
Data Capture ◽  
Problem Solution ◽  
Time Data ◽  
Quality Of Data ◽  
Data Manipulation ◽  
Change Data Capture ◽  
Real Time Data

The online business era causes the form of transactions to occur so quickly that the information stored in the data warehouse becomes invalid. Companies are required to have a strong system, which is a system that is real time in order to be able to perform data loading into the media repository that resides on different hosts in the near-real time. Data Warehouse is used as a media repository of data that has the nature of subject-oriented, integrated, time-variant, and is fixed. Data Warehouse can be built into real time management with the advantages possessed and utilize Change Data Capture. Change Data Capture (CDC) is a technique that can be used as problem solution to build real time data warehousing (RTDW). The binary log approach in change data capture is made to record any data manipulation activity that occurs at the OLTP level and is managed back before being stored into the Data Warehouse (loading process). This can improve the quality of data management so that the creation of the right information, because the information available is always updated. Testing shows that Binary Log approach in Change Data Capture (BinlogCDC) is able to generate real time data management, valid current information, dynamic communication between systems, and data management without losing any information from data manipulation.

A Wireless Sensors Network System for Local Multipoint Storm Surge Measurements

2018 ◽  
Vol 52 (4) ◽  
pp. 32-41 ◽  
Author(s):  
Sunil Chintalapati ◽  
Chelakara S. Subramanian
Keyword(s):  
Real Time ◽  
Storm Surge ◽  
Wireless Sensors ◽  
Prediction Models ◽  
Water Levels ◽  
Time Data ◽  
Network Systems ◽  
Efficient System ◽  
Real Time Data ◽  
Wireless Sensors Network

AbstractReal-time data of storm surge are much needed for developing effective prediction models and nowcasting of impending hazard potential. The crucial aspect of monitoring and transmitting information for extreme weather conditions in near real time is vitally important and would benefit from accurate, robust, and relatively inexpensive wireless sensing network systems. This article presents a detailed overview of the design, operation modes, system performance, and field testing of a prototype wireless sensors network (WSN) system for local multipoint storm surge measurements. Key differentiators for the prototype WSN system when compared to the existing infrastructure for monitoring water levels used by National Oceanic and Atmospheric Administration are (1) real-time data transmission, (2) ultra low cost, and (3) power-efficient system. The WSN system offers reliable field measurement employing single or multiple sensors and with features to upload data to either a local laptop or to the cloud for easy concurrent access to data for users located in any part of the world.

399-P: Nurse-Driven Intervention Using Real-Time Data to Improve Hypoglycemia Rates in a Respiratory Care Unit

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 399-P
Author(s):  
ANN MARIE HASSE ◽  
RIFKA SCHULMAN ◽  
TORI CALDER
Keyword(s):  
Real Time ◽  
Respiratory Care ◽  
Time Data ◽  
Real Time Data

MHA releases largest real‐time data from MH help‐seekers

2021 ◽  
Vol 31 (6) ◽  
pp. 7-7
Author(s):  
Valerie A. Canady
Keyword(s):  
Real Time ◽  
Time Data ◽  
Real Time Data

Why Ecological Momentary Assessment Surveys Go Incomplete?: When It Happens and How It Impacts Data

2020 ◽  
Author(s):  
Yu-Hsiang Wu ◽  
Jingjing Xu ◽  
Elizabeth Stangl ◽  
Shareka Pentony ◽  
Dhruv Vyas ◽  
...  
Keyword(s):  
Real Time ◽  
Self Report ◽  
Data Logging ◽  
Time Data ◽  
Real Time Data ◽  
Report Data ◽  
Self Report Data ◽  
Ecological Momentary ◽  
The Impact ◽  
Momentary Assessment

Abstract Background Ecological momentary assessment (EMA) often requires respondents to complete surveys in the moment to report real-time experiences. Because EMA may seem disruptive or intrusive, respondents may not complete surveys as directed in certain circumstances. Purpose This article aims to determine the effect of environmental characteristics on the likelihood of instances where respondents do not complete EMA surveys (referred to as survey incompletion), and to estimate the impact of survey incompletion on EMA self-report data. Research Design An observational study. Study Sample Ten adults hearing aid (HA) users. Data Collection and Analysis Experienced, bilateral HA users were recruited and fit with study HAs. The study HAs were equipped with real-time data loggers, an algorithm that logged the data generated by HAs (e.g., overall sound level, environment classification, and feature status including microphone mode and amount of gain reduction). The study HAs were also connected via Bluetooth to a smartphone app, which collected the real-time data logging data as well as presented the participants with EMA surveys about their listening environments and experiences. The participants were sent out to wear the HAs and complete surveys for 1 week. Real-time data logging was triggered when participants completed surveys and when participants ignored or snoozed surveys. Data logging data were used to estimate the effect of environmental characteristics on the likelihood of survey incompletion, and to predict participants' responses to survey questions in the instances of survey incompletion. Results Across the 10 participants, 715 surveys were completed and survey incompletion occurred 228 times. Mixed effects logistic regression models indicated that survey incompletion was more likely to happen in the environments that were less quiet and contained more speech, noise, and machine sounds, and in the environments wherein directional microphones and noise reduction algorithms were enabled. The results of survey response prediction further indicated that the participants could have reported more challenging environments and more listening difficulty in the instances of survey incompletion. However, the difference in the distribution of survey responses between the observed responses and the combined observed and predicted responses was small. Conclusion The present study indicates that EMA survey incompletion occurs systematically. Although survey incompletion could bias EMA self-report data, the impact is likely to be small.

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