Quantifying Insurance Agency Channel Dynamics Using Premium Sales Big Data and External Factors

Big Data ◽  
2020 ◽  
Author(s):  
Erdem Kaya ◽  
Eray Alpan ◽  
Selim Balcisoy ◽  
Burcin Bozkaya
Keyword(s):  
Big Data ◽  
External Factors ◽  
Channel Dynamics ◽  
Insurance Agency

scholarly journals Intelligent Predictive Maintenance (IPdM) in Forestry: A Review of Challenges and Opportunities

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1495
Author(s):  
Jamal Maktoubian ◽  
Mohammad Sadegh Taskhiri ◽  
Paul Turner
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Business Processes ◽  
Wood Quality ◽  
Weather Condition ◽  
Remaining Useful Life ◽  
External Factors ◽  
Predictive Maintenance ◽  
Production Costs ◽  
Biomass Waste

The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.

Find Out About 'Big Data' to Track Outcomes

ASHA Leader ◽  
2013 ◽  
Vol 18 (2) ◽  
pp. 59-59
Keyword(s):  
Big Data

Find Out About 'Big Data' to Track Outcomes

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Interactive Experience and Identification as Predictors of Attributing Responsibility in Video Games

2018 ◽  
Vol 30 (1) ◽  
pp. 3-15
Author(s):  
Nathan Walter ◽  
Yariv Tsfati
Keyword(s):  
Video Game ◽  
Psychological Research ◽  
External Factors ◽  
Theoretical Expectation ◽  
Main Character ◽  
Attribution Of Responsibility ◽  
Attributions Of Responsibility ◽  
Violent Video Game ◽  
Grand Theft Auto Iv ◽  
Interactive Experience

Abstract. This study examines the effect of interactivity on the attribution of responsibility for the character’s actions in a violent video game. Through an experiment, we tested the hypothesis that identification with the main character in Grand Theft Auto IV mediates the effect of interactivity on attributions of responsibility for the main character’s antisocial behavior. Using the framework of the fundamental attribution error, we demonstrated that those who actually played the game, as opposed to those who simply watched someone else playing it, identified with the main character. In accordance with the theoretical expectation, those who played the game and came to identify with the main character attributed the responsibility for his actions to external factors such as “living in a violent society.” By contrast, those who did not interact with the game attributed responsibility for the character’s actions to his personality traits. These findings could be viewed as contrasting with psychological research suggesting that respondents should have distanced themselves from the violent protagonist rather than identifying with him, and with Iyengar’s (1991) expectation that more personalized episodic framing would be associated with attributing responsibility to the protagonist.

“Big Data in Psychology: Methods and Applications”

2017 ◽  
Vol 225 (3) ◽  
pp. 287-288
Keyword(s):  
Big Data

An associated conference will take place at ZPID – Leibniz Institute for Psychology Information in Trier, Germany, on June 7–9, 2018. For further details, see: http://bigdata2018.leibniz-psychology.org

When the Sample Is the Population: Big Data

PsycCRITIQUES ◽  
2014 ◽  
Vol 59 (2) ◽  
Author(s):  
David J. Pittenger
Keyword(s):  
Big Data

Little sensors, big data

2015 ◽  
Author(s):  
Kirsten Weir
Keyword(s):  
Big Data
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