scholarly journals Real-time data assimilation potential to connect micro-smart water test bed and hydraulic model

2019 ◽  
Vol 2 (1) ◽  
pp. 71-82 ◽  
Author(s):  
Jiada Li ◽  
Shuangli Bao ◽  
Steven Burian
Keyword(s):  
Real Time ◽  
Hydraulic Model ◽  
Test Bed ◽  
Time Data ◽  
Hydraulic Simulation ◽  
Smart Water ◽  
Sensing Technology ◽  
Real Time Data ◽  
Water Test ◽  
Set Up

Abstract Recently, smart water application has gained worldwide attention, but there is a lack of understanding of how to construct smart water networks. This is partly because of the limited investigation into how to combine physical experiments with model simulations. This study aimed to investigate the process of connecting micro-smart water test bed (MWTB) and a ‘two-loop’ EPANET hydraulic model, which involves experimental set-up, real-time data acquisition, hydraulic simulation, and system performance demonstration. In this study, a MWTB was established based on the flow sensing technology. The data generated by the MWTB were stored in Observations Data Model (ODM) database for visualization in RStudio environment and also archived as the input of EPANET hydraulic simulation. The data visualization fitted the operation scenarios of the MWTB well. Additionally, the fitting degree between the experimental measurements and modeling outputs indicates the ‘two-loop’ EPANET model can represent the operation of MWTB for better understanding of hydraulic analysis.

scholarly journals HYBRIDJOIN for Near-Real-Time Data Warehousing

2011 ◽  
Vol 7 (4) ◽  
pp. 21-42 ◽  
Author(s):  
M. Asif Naeem ◽  
Gillian Dobbie ◽  
Gerald Weber
Keyword(s):  
Real Time ◽  
Data Stream ◽  
Time Integration ◽  
Synthetic Data ◽  
Performance Measurements ◽  
Time Data ◽  
Join Algorithm ◽  
Real Time Data ◽  
Set Up ◽  
Nested Loop

An important component of near-real-time data warehouses is the near-real-time integration layer. One important element in near-real-time data integration is the join of a continuous input data stream with a disk-based relation. For high-throughput streams, stream-based algorithms, such as Mesh Join (MESHJOIN), can be used. However, in MESHJOIN the performance of the algorithm is inversely proportional to the size of disk-based relation. The Index Nested Loop Join (INLJ) can be set up so that it processes stream input, and can deal with intermittences in the update stream but it has low throughput. This paper introduces a robust stream-based join algorithm called Hybrid Join (HYBRIDJOIN), which combines the two approaches. A theoretical result shows that HYBRIDJOIN is asymptotically as fast as the fastest of both algorithms. The authors present performance measurements of the implementation. In experiments using synthetic data based on a Zipfian distribution, HYBRIDJOIN performs significantly better for typical parameters of the Zipfian distribution, and in general performs in accordance with the theoretical model while the other two algorithms are unacceptably slow under different settings.

scholarly journals A Methodology for Heterogeneous Sensor Data Organization and Near Real-Time Data Sharing by Adopting OGC SWE Standards

2019 ◽  
Vol 8 (4) ◽  
pp. 167 ◽  
Author(s):  
Bartolomeo Ventura ◽  
Andrea Vianello ◽  
Daniel Frisinghelli ◽  
Mattia Rossi ◽  
Roberto Monsorno ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Weather Data ◽  
Time Data ◽  
Heterogeneous Sensors ◽  
Heterogeneous Datasets ◽  
Real Time Data ◽  
Sensor Registration ◽  
Set Up ◽  
Minimum Interaction

Finding a solution to collect, analyze, and share, in near real-time, data acquired by heterogeneous sensors, such as traffic, air pollution, soil moisture, or weather data, represents a great challenge. This paper describes the solution developed at Eurac Research to automatically upload data, in near real-time, by adopting Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) standards to guarantee interoperability. We set up a methodology capable of ingesting heterogeneous datasets to automatize observation uploading and sensor registration, with minimum interaction required of the user. This solution has been successfully tested and applied in the Long Term (Socio-)Ecological Research (LT(S)ER) Matsch-Mazia initiative, and the code is accessible under the CC BY 4.0 license.

scholarly journals HYBRIDJOIN for Near-Real-Time Data Warehousing

2013 ◽  
pp. 280-302
Author(s):  
M. Asif Naeem ◽  
Gillian Dobbie ◽  
Gerald Weber
Keyword(s):  
Real Time ◽  
Input Data ◽  
Time Integration ◽  
Synthetic Data ◽  
Performance Measurements ◽  
Time Data ◽  
Join Algorithm ◽  
Real Time Data ◽  
Set Up ◽  
Nested Loop

An important component of near-real-time data warehouses is the near-real-time integration layer. One important element in near-real-time data integration is the join of a continuous input data stream with a disk-based relation. For high-throughput streams, stream-based algorithms, such as Mesh Join (MESHJOIN), can be used. However, in MESHJOIN the performance of the algorithm is inversely proportional to the size of disk-based relation. The Index Nested Loop Join (INLJ) can be set up so that it processes stream input, and can deal with intermittences in the update stream but it has low throughput. This paper introduces a robust stream-based join algorithm called Hybrid Join (HYBRIDJOIN), which combines the two approaches. A theoretical result shows that HYBRIDJOIN is asymptotically as fast as the fastest of both algorithms. The authors present performance measurements of the implementation. In experiments using synthetic data based on a Zipfian distribution, HYBRIDJOIN performs significantly better for typical parameters of the Zipfian distribution, and in general performs in accordance with the theoretical model while the other two algorithms are unacceptably slow under different settings.

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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