scholarly journals Large Scale Participatory Acoustic Sensor Data Analysis: Tools and Reputation Models to Enhance Effectiveness

2011 ◽  
Author(s):  
Anthony Truskinger ◽  
Haofan Yang ◽  
Jason Wimmer ◽  
Jinglan Zhang ◽  
Ian Williamson ◽  
...  
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Sensor Data ◽  
Acoustic Sensor ◽  
Analysis Tools

Large-Scale Sensor Network Analysis

2013 ◽  
pp. 314-347 ◽  
Author(s):  
Joaquin Vanschoren ◽  
Ugo Vespier ◽  
Shengfa Miao ◽  
Marvin Meeng ◽  
Ricardo Cachucho ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Vital Signs ◽  
Sensor Data ◽  
Atmospheric Conditions ◽  
Big Data Applications ◽  
The World ◽  
Sheer Size ◽  
Effective Use

Sensors are increasingly being used to monitor the world around us. They measure movements of structures such as bridges, windmills, and plane wings, human’s vital signs, atmospheric conditions, and fluctuations in power and water networks. In many cases, this results in large networks with different types of sensors, generating impressive amounts of data. As the volume and complexity of data increases, their effective use becomes more challenging, and novel solutions are needed both on a technical as well as a scientific level. Founded on several real-world applications, this chapter discusses the challenges involved in large-scale sensor data analysis and describes practical solutions to address them. Due to the sheer size of the data and the large amount of computation involved, these are clearly “Big Data” applications.

scholarly journals Neuroscience Cloud Analysis As a Service

2020 ◽  
Author(s):  
Taiga Abe ◽  
Ian Kinsella ◽  
Shreya Saxena ◽  
Liam Paninski ◽  
John P. Cunningham
Keyword(s):  
Data Analysis ◽  
Open Source ◽  
Large Scale ◽  
Ease Of Use ◽  
Cutting Edge ◽  
Analysis Tools ◽  
Large Scale Computing ◽  
Cloud Computation ◽  
Computational Resources ◽  
Cloud Analysis

AbstractA major goal of computational neuroscience is to develop powerful analysis tools that operate on large datasets. These methods provide an essential toolset to unlock scientific insights from new experiments. Unfortunately, a major obstacle currently impedes progress: while existing analysis methods are frequently shared as open source software, the infrastructure needed to deploy these methods – at scale, reproducibly, cheaply, and quickly – remains totally inaccessible to all but a minority of expert users. As a result, many users can not fully exploit these tools, due to constrained computational resources (limited or costly compute hardware) and/or mismatches in expertise (experimentalists vs. large-scale computing experts). In this work we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully-managed infrastructure platform, based on modern large-scale computing advances, that makes state-of-the-art data analysis tools accessible to the neuroscience community. We offer NeuroCAAS as an open source service with a drag-and-drop interface, entirely removing the burden of infrastructure expertise, purchasing, maintenance, and deployment. NeuroCAAS is enabled by three key contributions. First, NeuroCAAS cleanly separates tool implementation from usage, allowing cutting-edge methods to be served directly to the end user with no need to read or install any analysis software. Second, NeuroCAAS automatically scales as needed, providing reliable, highly elastic computational resources that are more efficient than personal or lab-supported hardware, without management overhead. Finally, we show that many popular data analysis tools offered through NeuroCAAS outperform typical analysis solutions (in terms of speed and cost) while improving ease of use and maintenance, dispelling the myth that cloud compute is prohibitively expensive and technically inaccessible. By removing barriers to fast, efficient cloud computation, NeuroCAAS can dramatically accelerate both the dissemination and the effective use of cutting-edge analysis tools for neuroscientific discovery.

scholarly journals Soc-IoT: A Proof-of-Concept for Citizen-Centric Environmental Monitoring

2021 ◽  
Author(s):  
Sachit Mahajan
Keyword(s):  
Data Analysis ◽  
Environmental Monitoring ◽  
Large Scale ◽  
Low Cost ◽  
Large Data ◽  
Sensor Data ◽  
Proof Of Concept ◽  
Environment Monitoring ◽  
Systematic Analysis ◽  
Visualization Application

Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitor the ambient environment. Such systems have opened up environment monitoring opportunities to researchers and citizens while simultaneously confronting them with challenges like sensor accuracy, accumulation of large data sets, and data analysis, which itself is a formidable task that requires extensive computational resources and technical expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is proposed that combines tools for real-time environmental sensing with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit – a resource-efficient, portable and modular environment monitoring device intended for citizen sensing and complementing official environment monitoring infrastructure, and (2) exploreR – an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without any coding requirement. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by reducing technological barriers.

scholarly journals Soc-IoT: A Proof-of-Concept for Citizen-Centric Environmental Monitoring

2021 ◽  
Author(s):  
Sachit Mahajan
Keyword(s):  
Data Analysis ◽  
Environmental Monitoring ◽  
Large Scale ◽  
Low Cost ◽  
Large Data ◽  
Sensor Data ◽  
Proof Of Concept ◽  
Environment Monitoring ◽  
Systematic Analysis ◽  
Visualization Application

Cities around the world are struggling with environmental pollution. The conventional monitoring approaches are not effective for undertaking large-scale environmental monitoring due to logistical and cost-related issues. The availability of low-cost and low-power Internet of Things (IoT) devices has proved to be an effective alternative to monitor the ambient environment. Such systems have opened up environment monitoring opportunities to researchers and citizens while simultaneously confronting them with challenges like sensor accuracy, accumulation of large data sets, and data analysis, which itself is a formidable task that requires extensive computational resources and technical expertise. To address this challenge, a social, open-source, and citizen-centric IoT (Soc-IoT) framework is proposed that combines tools for real-time environmental sensing with an intuitive data analysis and visualization application. Soc-IoT has two main components: (1) CoSense Unit – a resource-efficient, portable and modular environment monitoring device intended for citizen sensing and complementing official environment monitoring infrastructure, and (2) exploreR – an intuitive cross-platform data analysis and visualization application that offers a comprehensive set of tools for systematic analysis of sensor data without any coding requirement. Developed as a proof-of-concept framework to monitor the environment at scale, Soc-IoT aims to promote environmental resilience and open innovation by reducing technological barriers.

scholarly journals Advances in two photon scanning and scanless microscopy technologies for functional neural circuit imaging

2016 ◽  
Author(s):  
Simon R. Schultz ◽  
Caroline S. Copeland ◽  
Amanda J. Foust ◽  
Peter Quicke ◽  
Renaud Schuck
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Neural Circuit ◽  
Functional Neuroimaging ◽  
Imaging Techniques ◽  
Region Of Interest ◽  
Calcium Transient ◽  
Two Photon ◽  
Analysis Tools ◽  
Pattern Size

AbstractRecent years have seen substantial developments in technology for imaging neural circuits, raising the prospect of large scale imaging studies of neural populations involved in information processing, with the potential to lead to step changes in our understanding of brain function and dysfunction. In this article we will review some key recent advances: improved fluorophores for single cell resolution functional neuroimaging using a two photon microscope; improved approaches to the problem ofscanningactive circuits; and the prospect ofscanlessmicroscopes which overcome some of the bandwidth limitations of current imaging techniques. These advances in technology for experimental neuroscience have in themselves led to technical challenges, such as the need for the development of novel signal processing and data analysis tools in order to make the most of the new experimental tools. We review recent work in some active topics, such as region of interest segmentation algorithms capable of demixing overlapping signals, and new highly accurate algorithms for calcium transient detection. These advances motivate the development of new data analysis tools capable of dealing with spatial or spatiotem-poral patterns of neural activity, that scale well with pattern size.

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

Microcomputers in Political Science

1983 ◽  
Vol 38 ◽  
pp. 1-9
Author(s):  
Herbert F. Weisberg
Keyword(s):  
Data Analysis ◽  
Political Science ◽  
Large Scale ◽  
Turnaround Time ◽  
General Purpose ◽  
Batch Mode ◽  
New Era ◽  
Large Scale Data ◽  
The Social ◽  
Frequency Counts

We are now entering a new era of computing in political science. The first era was marked by punched-card technology. Initially, the most sophisticated analyses possible were frequency counts and tables produced on a counter-sorter, a machine that specialized in chewing up data cards. By the early 1960s, batch processing on large mainframe computers became the predominant mode of data analysis, with turnaround time of up to a week. By the late 1960s, turnaround time was cut down to a matter of a few minutes and OSIRIS and then SPSS (and more recently SAS) were developed as general-purpose data analysis packages for the social sciences. Even today, use of these packages in batch mode remains one of the most efficient means of processing large-scale data analysis.

Sign in / Sign up

Export Citation Format

Share Document