Answering the Min-Cost Quality-Aware Query on Multi-Sources in Sensor-Cloud Systems

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.

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Getting High Quality Data to Drive Programs: How is the Quality of the Data Collection System Associated with the Quality of Routine Health Data in Malawi?

Annals of Global Health ◽

10.1016/j.aogh.2017.03.010 ◽

2017 ◽

Vol 83 (1) ◽

pp. 5

Author(s):

R. O'Hagan ◽

M. Marx ◽

K. Finnegan ◽

P. Naphini ◽

K. Ng'ambi ◽

...

Keyword(s):

Data Collection ◽

Health Data ◽

Quality Data ◽

Collection System ◽

High Quality ◽

High Quality Data ◽

Data Collection System

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Improving Data Quality Using Amazon Mechanical Turk Through Platform Setup

Cornell Hospitality Quarterly ◽

10.1177/19389655211025475 ◽

2021 ◽

pp. 193896552110254

Author(s):

Lu Lu ◽

Nathan Neale ◽

Nathaniel D. Line ◽

Mark Bonn

Keyword(s):

Best Practices ◽

Data Quality ◽

Mechanical Turk ◽

Amazon Mechanical Turk ◽

Quality Data ◽

High Quality ◽

High Quality Data ◽

Amazon's Mechanical Turk ◽

Associated Data

As the use of Amazon’s Mechanical Turk (MTurk) has increased among social science researchers, so, too, has research into the merits and drawbacks of the platform. However, while many endeavors have sought to address issues such as generalizability, the attentiveness of workers, and the quality of the associated data, there has been relatively less effort concentrated on integrating the various strategies that can be used to generate high-quality data using MTurk samples. Accordingly, the purpose of this research is twofold. First, existing studies are integrated into a set of strategies/best practices that can be used to maximize MTurk data quality. Second, focusing on task setup, selected platform-level strategies that have received relatively less attention in previous research are empirically tested to further enhance the contribution of the proposed best practices for MTurk usage.

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Building a Sample Frame of SMEs Using Patent, Search Engine, and Website Data

Journal of Official Statistics ◽

10.2478/jos-2021-0001 ◽

2021 ◽

Vol 37 (1) ◽

pp. 1-30

Author(s):

Sanjay K. Arora ◽

Sarah Kelley ◽

Sarvothaman Madhavan

Keyword(s):

Low Cost ◽

Data Sources ◽

Quality Data ◽

Reasonable Accuracy ◽

High Quality ◽

High Quality Data ◽

Patent Search ◽

Data Matching ◽

Data Collections ◽

Future Work

Abstract This research outlines the process of building a sample frame of US SMEs. The method starts with a list of patenting organizations and defines the boundaries of the population and subsequent frame using free to low-cost data sources, including search engines and websites. Generating high-quality data is of key importance throughout the process of building the frame and subsequent data collection; at the same time, there is too much data to curate by hand. Consequently, we turn to machine learning and other computational methods to apply a number of data matching, filtering, and cleaning routines. The results show that it is possible to generate a sample frame of innovative SMEs with reasonable accuracy for use in subsequent research: Our method provides data for 79% of the frame. We discuss implications for future work for researchers and NSIs alike and contend that the challenges associated with big data collections require not only new skillsets but also a new mode of collaboration.

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Survival-Span Method: How to Qualitatively Estimate Lifespan to Improve the Study of Aging, and not Disease, in Aging Studies

Frontiers in Aging ◽

10.3389/fragi.2021.724794 ◽

2021 ◽

Vol 2 ◽

Author(s):

Julia Adelöf ◽

Jaime M. Ross ◽

Madeleine Zetterberg ◽

Malin Hernebring

Keyword(s):

Animal Welfare ◽

Late Stage ◽

Aging Process ◽

Quality Data ◽

High Quality ◽

High Quality Data ◽

Aging Studies ◽

Novel Method ◽

Per Se

Lifespan analyses are important for advancing our understanding of the aging process. There are two major issues in performing lifespan studies: 1) late-stage animal lifespan analysis may include animals with non-terminal, yet advanced illnesses, which can pronounce indirect processes of aging rather than the aging process per se and 2) they often involves challenging welfare considerations. Herein, we present an option to the traditional way of performing lifespan studies by using a novel method that generates high-quality data and allows for the inclusion of excluded animals, even animals removed at early signs of disease. This Survival-span method is designed to be feasibly done with simple means by any researcher and strives to improve the quality of aging studies and increase animal welfare.

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Science and Policy in Regulating Pesticides: Will They Collide or Merge?

Weed Technology ◽

10.1017/s0890037x00028888 ◽

1994 ◽

Vol 8 (4) ◽

pp. 883-886 ◽

Cited By ~ 1

Author(s):

Janet L. Andersen

Keyword(s):

Environmental Protection Agency ◽

Experimental Methods ◽

Quality Data ◽

Comparative Efficacy ◽

Protection Agency ◽

High Quality ◽

High Quality Data ◽

Science And Policy ◽

Regulatory Decision

The Environmental Protection Agency (EPA) is required by law to assure that the use of pesticides does not cause unreasonable risks to humans or the environment when risks are compared with benefits. Weed scientists conduct hundreds of comparative efficacy tests each year, but the results are often of little use to the Agency in benefit assessments because the tests are unpublished or otherwise unavailable to the Agency, the tests are conducted in a manner unusable for regulatory purposes, or there are inconsistencies between tests conducted year to year or at different sites. Despite the lack of high quality data, the Agency is compelled to make the best regulatory decision possible with the information at hand, and it may appear to some that decisions are based more on policy than science. EPA is looking for experimental methods that will improve the quality of benefits data available to the Agency.

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IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices

Sensors ◽

10.3390/s19091978 ◽

2019 ◽

Vol 19 (9) ◽

pp. 1978 ◽

Cited By ~ 12

Author(s):

Argyro Mavrogiorgou ◽

Athanasios Kiourtis ◽

Konstantinos Perakis ◽

Stamatios Pitsios ◽

Dimosthenis Kyriazis

Keyword(s):

Data Quality ◽

Medical Devices ◽

Holistic Approach ◽

Quality Data ◽

Quality Estimation ◽

High Quality ◽

Data Interoperability ◽

High Quality Data ◽

Continuous Growth

It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.

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A comparative analysis of butterfly richness detection capacity of Pollard transects and general microhabitat surveys

The Canadian Entomologist ◽

10.4039/tce.2012.64 ◽

2012 ◽

Vol 144 (5) ◽

pp. 727-731

Author(s):

Isabelle Létourneau ◽

Maxim Larrivée ◽

Antoine Morin

Keyword(s):

Citizen Science ◽

Conservation Biology ◽

Low Cost ◽

Quality Data ◽

Butterfly Species ◽

High Quality ◽

High Quality Data ◽

Science Projects ◽

Butterfly Assemblages ◽

Number Of Individuals

AbstractAssessing biodiversity is essential in conservation biology but the resources needed are often limited. Citizen science, by which volunteers gather data at low cost, represents a potential solution for the lack of resources if it produces usable data for scientific means. Scientific inventories for butterflies are often performed with a Pollard transect, a standardised surveying technique that generates high-quality data. General microhabitat surveys (GMSs) are potentially more appealing to amateurs participating in citizen science projects because they are less constrained. We compare estimates of butterfly species richness acquired by Pollard transects to those obtained by GMSs. We demonstrate that GMSs allow surveyors to detect more butterfly species and a more complete portrait of local butterfly assemblages for the same number of individuals captured.

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Gas sensors for climate research

Journal of Sensors and Sensor Systems ◽

10.5194/jsss-7-535-2018 ◽

2018 ◽

Vol 7 (2) ◽

pp. 535-541 ◽

Cited By ~ 3

Author(s):

Louisa Scholz ◽

Alvaro Ortiz Perez ◽

Benedikt Bierer ◽

Jürgen Wöllenstein ◽

Stefan Palzer

Keyword(s):

Spatial Scales ◽

Low Cost ◽

Optical Path Length ◽

Quality Data ◽

High Temporal Resolution ◽

High Quality ◽

High Quality Data ◽

Climate Simulations ◽

High Measurement ◽

Carbon Dioxide Co2

Abstract. The availability of datasets providing information on the spatial and temporal evolution of greenhouse gas concentrations is of high relevance for the development of reliable climate simulations. However, current gas detection technologies do not allow for obtaining high-quality data at intermediate spatial scales with high temporal resolution. In this regard the deployment of a wireless gas sensor network equipped with in situ gas analysers may be a suitable approach. Here we present a novel, non-dispersive infrared absorption spectroscopy (NDIR) device that can possibly act as a central building block of a sensor node to provide high-quality data of carbon dioxide (CO2) concentrations under field conditions at a high measurement rate. Employing a gas-based, photoacoustic detector we demonstrate that miniaturized, low-cost, and low-power consuming CO2 sensors may be built. The performance is equal to that of standard NDIR devices but at a much reduced optical path length. Because of the spectral properties of the photoacoustic detector, no cross-sensitivities to humidity exist.

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Data Management Portal

10.48060/tghn.9 ◽

2020 ◽

Author(s):

◽

Keyword(s):

Data Management ◽

Management Plan ◽

Data Capture ◽

Quality Data ◽

Quality Of Data ◽

High Quality ◽

High Quality Data ◽

Data Management Systems ◽

Extensive Collection

Good data management is essential for ensuring the validity and quality of data in all types of clinical research and is an essential precursor for data sharing. The Data Management Portal has been developed to provide support to researchers to ensure that high-quality data management is fully considered, and planned for, from the outset and throughout the life of a research project. The steps described in the portal will help identify the areas which should be considered when developing a Data Management Plan, with a particular focus on data management systems and how to organise and structure your data. Other elements include best practices for data capture, entry, processing and monitoring, how to prepare data for analysis, sharing, and archiving, and an extensive collection of resources linked to data management which can be searched and filtered depending on their type.

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