scholarly journals Human Activity Patterns in Big Data for Healthcare Applications

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1101-1103
Keyword(s):  
Big Data ◽  
Human Development ◽  
Activity Patterns ◽  
Normal Model ◽  
Healthcare Applications ◽  
Fine Grained ◽  
Huge Data ◽  
Mining Works ◽  
Human Activity Patterns ◽  
Ultimate Objective

Nowadays, there is a consistently growing migration of people to urban domains. Therapeutic administrations organizations are a champion among the most testing viewpoints that is massively impacted by the colossal surge of people to downtown territories. In this manner, urban zones far and wide are placing enthusiastically in cutting edge change with a ultimate objective to give progressively profitable organic network to people. In such change, a large number homes are being equipped with canny contraptions (for example splendid meters, sensors, etc.) which produce massive volumes of fine-grained and indexical data that can be penniless down to help sharp city organizations. In this paper, we propose a model that utilizations splendid home huge data as techniques for picking up and finding human development structures for restorative administrations applications. We propose the use of normal model mining, bunch examination and gauge to measure and dismember essentialness use changes begun by occupants' direct. Since people's penchants are generally recognized by standard timetables, finding these calendars empowers us to see strange activities that may exhibit people's inconveniences in taking oversee to themselves, for instance, not arranging sustenance or not using shower/shower. Our places of business the need to analyze transient imperativeness use structures at the machine level, which is clearly related to human activities. The data from sharp meters is recursively mined in the quantum/data cut of 24 hours, and the results are kept up across over dynamic mining works out. [1,2,3,4,5]

Big Data Analysis and Implementation in Different Areas Using IoT

2017 ◽  
Vol 1 (2) ◽  
pp. 12-25
Author(s):  
Aqeel ur Rehman ◽  
Muhammad Fahad ◽  
Rafi Ullah ◽  
Faisal Abdullah
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Healthcare Applications ◽  
New Techniques ◽  
Standard Data ◽  
Huge Data ◽  
Data Rates ◽  
Definition Of

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

scholarly journals Smart Home based Big Data Analysis for Healthcare Applications

2020 ◽  
Vol 9 (1) ◽  
pp. 275-282
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analysis ◽  
Smart Home ◽  
Health Care Services ◽  
Activity Patterns ◽  
Big Data Analysis ◽  
City Centre ◽  
Data Types ◽  
Healthcare Applications

At present, there is a constant migration of people is encountered in urban regions. Health care services are considered as a confronting challenging factors, there is an extremely influenced by huge arrival of people to city centre. Subsequently, places all around the world are spending in digital evolution in an attempt to offer healthy eco-system for huge people. With this transformation, enormous homes are equipped with smarter devices (for example, sensors, smart sensors and so on) which produce huge amount of indexical data and fine-grained that is examined to assist smart city services. In this work, a model has been anticipated to utilize smart home big data analysis as a discovering and learning human activity patterns for huge health care applications. This work describes and highlights the experimentation with the analysis of vigorous data analysis process that assists healthcare analytics. This procedure comprises of subsequent stages: understanding, collection, cleaning, validation, enrichment, integration and storage. It has been resourcefully utilized to processing of data types variety comprising clinical data from EHR.

Big Data Analysis and Implementation in Different Areas Using IoT

2020 ◽  
pp. 1096-1111
Author(s):  
Aqeel ur Rehman ◽  
Muhammad Fahad ◽  
Rafi Ullah ◽  
Faisal Abdullah
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Healthcare Applications ◽  
Standard Data ◽  
Huge Data ◽  
Data Rates ◽  
Definition Of ◽  
Huge Challenge

This article describes how in IoT, data management is a major issue because of communication among billions of electronic devices, which generate the huge dataset. Due to the unavailability of any standard, data analysis on such a large amount of data is a complex task. There should be a definition of IoT-based data to find out what is available and its applicable solutions. Such a study also directs the need for new techniques to cope up with such challenges. Due to the heterogeneity of connected nodes, different data rates, and formats, it is a huge challenge to deal with such a variety of data. As IoT is providing processing nodes in the form of smart nodes; it is presenting a good platform to support the big data study. In this article, the characteristics of data mining requirements for data mining analysis are highlighted. The associated challenges of facts generation, as well as the plausible suitable platform of such huge data analysis is also underlined. The application of IoT to support big data analysis in healthcare applications is also presented.

scholarly journals The rhythms of the night: increase in online night activity and emotional resilience during the spring 2020 Covid-19 lockdown

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Maria Castaldo ◽  
Tommaso Venturini ◽  
Paolo Frasca ◽  
Floriana Gargiulo
Keyword(s):  
Large Scale ◽  
Activity Patterns ◽  
Daily Rhythms ◽  
Online Activity ◽  
Emotional Resilience ◽  
Fine Grained ◽  
Exogenous Shocks ◽  
Content Consumption ◽  
Youtube Videos ◽  
Human Activity Patterns

Abstract Context The lockdown orders established in multiple countries in response to the Covid-19 pandemic are arguably one of the most widespread and deepest shock experienced by societies in recent years. Studying their impact trough the lens of social media offers an unprecedented opportunity to understand the susceptibility and the resilience of human activity patterns to large-scale exogenous shocks. Firstly, we investigate the changes that this upheaval has caused in online activity in terms of time spent online, themes and emotion shared on the platforms, and rhythms of content consumption. Secondly, we examine the resilience of certain platform characteristics, such as the daily rhythms of emotion expression. Data Two independent datasets about the French cyberspace: a fine-grained temporal record of almost 100 thousand YouTube videos and a collection of 8 million Tweets between February 17 and April 14, 2020. Findings In both datasets we observe a reshaping of the circadian rhythms with an increase of night activity during the lockdown. The analysis of the videos and tweets published during lockdown shows a general decrease in emotional contents and a shift from themes like work and money to themes like death and safety. However, the daily patterns of emotions remain mostly unchanged, thereby suggesting that emotional cycles are resilient to exogenous shocks.

scholarly journals Monitoring streets through tweets: Using user-generated geographic information to predict gentrification and displacement

2021 ◽  
pp. 239980832110253
Author(s):  
Karen Chapple ◽  
Ate Poorthuis ◽  
Matthew Zook ◽  
Eva Phillips
Keyword(s):  
Big Data ◽  
San Francisco ◽  
Census Data ◽  
San Francisco Bay Area ◽  
Demographic Data ◽  
Activity Patterns ◽  
Neighborhood Change ◽  
Housing Affordability ◽  
Bay Area ◽  
Media Data

The new availability of big data sources provides an opportunity to revisit our ability to predict neighborhood change. This article explores how data on urban activity patterns, specifically, geotagged tweets, improve the understanding of one type of neighborhood change—gentrification—by identifying dynamic connections between neighborhoods and across scales. We first develop a typology of neighborhood change and risk of gentrification from 1990 to 2015 for the San Francisco Bay Area based on conventional demographic data from the Census. Then, we use multivariate regression to analyze geotagged tweets from 2012 to 2015, finding that outsiders are significantly more likely to visit neighborhoods currently undergoing gentrification. Using the factors that best predict gentrification, we identify a subset of neighborhoods that Twitter-based activity suggests are at risk for gentrification over the short term—but are not identified by analysis with traditional census data. The findings suggest that combining Census and social media data can provide new insights on gentrification such as augmenting our ability to identify that processes of change are underway. This blended approach, using Census and big data, can help policymakers implement and target policies that preserve housing affordability and protext tenants more effectively.

scholarly journals A Multiscale Survival Process for Modeling Human Activity Patterns

PLoS ONE ◽  
2016 ◽  
Vol 11 (3) ◽  
pp. e0151473 ◽  
Author(s):  
Tianyang Zhang ◽  
Peng Cui ◽  
Chaoming Song ◽  
Wenwu Zhu ◽  
Shiqiang Yang
Keyword(s):  
Human Activity ◽  
Activity Patterns ◽  
Human Activity Patterns
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