Use of Data Science for Promotion Optimization in Convenience Chain

2019 ◽  
pp. 649-660
Author(s):  
Sławomir Mazurowski ◽  
Elżbieta Lewańska
Keyword(s):  
Data Science ◽  
Use Of Data

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Author(s):  
Sitti Zuhaerah Thalhah ◽  
Mohammad Tohir ◽  
Phong Thanh Nguyen ◽  
K. Shankar ◽  
Robbi Rahim
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science.

scholarly journals Leveraging Data Science for a Personalized Haemodialysis

2020 ◽  
Vol 6 (6) ◽  
pp. 385-394
Author(s):  
Miguel Hueso ◽  
Lluís de Haro ◽  
Jordi Calabia ◽  
Rafael Dal-Ré ◽  
Cristian Tebé ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Kidney Disease ◽  
Personalized Medicine ◽  
Data Collection ◽  
Randomized Controlled Trials ◽  
Data Science ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Use Of Data ◽  
The Right

<b><i>Background:</i></b> The 2019 Science for Dialysis Meeting at Bellvitge University Hospital was devoted to the challenges and opportunities posed by the use of data science to facilitate precision and personalized medicine in nephrology, and to describe new approaches and technologies. The meeting included separate sections for issues in data collection and data analysis. As part of data collection, we presented the institutional ARGOS e-health project, which provides a common model for the standardization of clinical practice. We also pay specific attention to the way in which randomized controlled trials offer data that may be critical to decision-making in the real world. The opportunities of open source software (OSS) for data science in clinical practice were also discussed. <b><i>Summary:</i></b> Precision medicine aims to provide the right treatment for the right patients at the right time and is deeply connected to data science. Dialysis patients are highly dependent on technology to live, and their treatment generates a huge volume of data that has to be analysed. Data science has emerged as a tool to provide an integrated approach to data collection, storage, cleaning, processing, analysis, and interpretation from potentially large volumes of information. This is meant to be a perspective article about data science based on the experience of the experts invited to the Science for Dialysis Meeting and provides an up-to-date perspective of the potential of data science in kidney disease and dialysis. <b><i>Key messages:</i></b> Healthcare is quickly becoming data-dependent, and data science is a discipline that holds the promise of contributing to the development of personalized medicine, although nephrology still lags behind in this process. The key idea is to ensure that data will guide medical decisions based on individual patient characteristics rather than on averages over a whole population usually based on randomized controlled trials that excluded kidney disease patients. Furthermore, there is increasing interest in obtaining data about the effectiveness of available treatments in current patient care based on pragmatic clinical trials. The use of data science in this context is becoming increasingly feasible in part thanks to the swift developments in OSS.

COVID and social media

2021 ◽  
pp. 1-20
Author(s):  
Manish Puri ◽  
Zachary Dau ◽  
Aparna S. Varde
Keyword(s):  
Social Media ◽  
Public Perception ◽  
Data Science ◽  
The Public ◽  
Use Of Data ◽  
Technological Advances ◽  
The Government ◽  
Key Resources ◽  
Online Sources

The Coronavirus pandemic is one of the most devastating encounters in modern times. Over 175 million cases have been recorded globally with over 3.5 million deaths. Disseminating information to billions of people during the pandemic has been challenging, and social media has been one of the key resources for the public during these excruciating circumstances. Social media and other online sources have made it easier to access information on a variety of topics. This article presents an exploration of social media trends pertinent to information on the COVID-19 pandemic, the use of several technological advances, as well as methods for evaluating their effectiveness in combating COVID-19. We examine global case studies on the use of data from various sources to tackle COVID-19, address the issue of trust between the government and the public, and shed light on the manner in which it influences the public perception of information. We delve into the role of advances in web technology and data science in curbing COVID-19 while also touching upon the impacts in the field of smart living and healthcare. We examine studies from regions around the world, explore how the pandemic has affected people from different walks of life, and peek into the utilization of advances for disseminating information as well as curbing the spread of the virus. Additionally, we briefly discuss how the works investigated here can open pathways of research to help in further enhancing the situation as we all head towards the light at the end of the tunnel, and strive to restore global normalcy.

Port Dada Integration

2021 ◽  
pp. 1-22
Author(s):  
José Luís Cacho ◽  
Adalberto Tokarski ◽  
Elizabete Thomas ◽  
Valentina Chkoniya
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Cloud Computing ◽  
Supply Chain ◽  
Data Integration ◽  
Data Science ◽  
Just In Time ◽  
Use Of Data ◽  
Digital Infrastructure ◽  
Digital Transition

The maritime supply chain is growing in complexity. Ports are at the crossroads of many activities, modes, and stakeholders, and are actively becoming digital hubs. Today, digital and physical connectivity go hand in hand. The port could benefit from taping the opportunities arising from digitalization and data integration since it helps to leverage external knowledge, engage stakeholders, create new decision-making anchors, lower the risk of certain investments, boost productivity and cut costs, and accelerate greening and digital transition, generating possibilities for just-in-time operations and optimizations. The chapter aims to apprehend the use of data science in the port sector. The state of the art in Brazil and Portugal are different. Even inside Brazil, there is no homogeneity of ports in the usage of digital infrastructure, cloud computing, or artificial intelligence. The existing inequalities hinder general cooperation between nations but, at the same time, reveal opportunities to approach specific nodes in the international supply chain.

Exploratory data analysis for cybersecurity

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Julián Darío Miranda-Calle ◽  
Vikranth Reddy C. ◽  
Parag Dhawan ◽  
Prathamesh Churi
Keyword(s):  
Network Flow ◽  
Data Science ◽  
Cyber Attacks ◽  
Statistical Hypothesis ◽  
Data Set ◽  
Content Type ◽  
Security Controls ◽  
Use Of Data ◽  
Proactive Measures ◽  
The Impact

Purpose The impact of cyberattacks all over the world has been increasing at a constant rate every year. Performing exploratory analysis helps organizations to identify, manage and safeguard the information that could be vulnerable to cyber-attacks. It encourages to the creation of a plan for security controls that can help to protect data and keep constant tabs on threats and monitor their organization’s networks for any breaches. Design/methodology/approach The purpose of this experimental study is to state the use of data science in analyzing data and to provide a more detailed view of the most common cybersecurity attacks, what are the most accessed logical ports, visible patterns, as well as the trends and occurrence of attacks. The data to be processed has been obtained by aggregating data provided by a company’s technology department, which includes network flow data produced by nine different types of attacks within every day user activities. This could be insightful for many companies to measure the damage caused by these breaches but also gives a foundation for future comparisons and serves as a basis for proactive measures within industry and organizations. Findings The most common cybersecurity attacks, most accessed logical ports and their visible patterns were found in the acquired data set. The strategies, which attackers have used with respect to time, type of attacks, specific ports, IP addresses and their relationships have been determined. The statistical hypothesis was also performed to check whether attackers were confined to perform random attacks or to any specific machines with some pattern. Originality/value Policies can be suggested such that if an attack is conducted on a specific machine, which can be prevented by identifying the machine, ports and duration of the attacks on which the attacker is targeting and to formulate such policies that the organization should follow to tackle these targeted attacks in the future.

Things and Data in Recent Biology

2018 ◽  
Vol 48 (5) ◽  
pp. 648-658
Author(s):  
Soraya de Chadarevian
Keyword(s):  
Life Cycle ◽  
Biological Material ◽  
Data Science ◽  
Data Driven ◽  
Special Issue ◽  
Data Link ◽  
Use Of Data ◽  
Data Practices ◽  
In Silico Biology ◽  
Biological World

There is much talk about data-driven and in silico biology, but how exactly does it work? This essay reflects on the relation of data practices to the biological things from which they are abstracted. Looking at concrete examples of computer use in biology, the essay asks: How are biological things turned into data? What organizes and limits the combination, querying, and re-use of data? And how does the work on data link back to the organismic or biological world? Considering the life cycle of data, the essay suggests that data remain linked to the biological material and the concrete context from which they are extracted and to which they always refer back. Consequently, the transition to data science is never complete. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.

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