Data Science for Urban Sustainability: Data Mining and Data-Analytic Thinking in the Next Wave of City Analytics

2018 ◽  
pp. 189-246
Author(s):  
Simon Elias Bibri
Keyword(s):  
Data Mining ◽  
Data Science ◽  
Urban Sustainability ◽  
Analytic Thinking ◽  
Data Analytic

Data Mining and Statistics in Data Science

2019 ◽  
Vol 5 (30) ◽  
pp. 960-968
Author(s):  
Güner Gözde KILIÇ
Keyword(s):  
Data Mining ◽  
Data Science

scholarly journals Analysis of Mobile Service Providers Performance Using Naive Bayes Data Mining Technique

2018 ◽  
Vol 8 (6) ◽  
pp. 5153
Author(s):  
M. A. Burhanuddin ◽  
Ronizam Ismail ◽  
Nurul Izzaimah ◽  
Ali Abdul-Jabbar Mohammed ◽  
Norzaimah Zainol
Keyword(s):  
Data Mining ◽  
Data Science ◽  
Service Providers ◽  
Telecommunication Service ◽  
Mobile Service ◽  
Business Growth ◽  
Data Mining Technique ◽  
R Language ◽  
Mining Technique ◽  
Industry Standard

Recently, the mobile service providers have been growing rapidly in Malaysia. In this paper, we propose analytical method to find best telecommunication provider by visualizing their performance among telecommunication service providers in Malaysia, i.e. TM Berhad, Celcom, Maxis, U-Mobile, etc. This paperuses data mining technique to evaluate the performanceof telecommunication service providers using their customers feedback from Twitter Inc. It demonstrates on how the system could process and then interpret the big data into a simple graph or visualization format. In addition, build a computerized tool and recommend data analytic model based on the collected result. From prepping the data for pre-processing until conducting analysis, this project is focusing on the process of data science itself where Cross Industry Standard Process for Data Mining (CRISP-DM) methodology will be used as a reference. The analysis was developed by using R language and R Studio packages. From the result, it shows that Telco 4 is the best as it received highest positive scores from the tweet data. In contrast, Telco 3 should improve their performance as having less positive feedback from their customers via tweet data. This project bring insights of how the telecommunication industries can analyze tweet data from their customers. Malaysia telecommunication industry will get the benefit by improving their customer satisfaction and business growth. Besides, it will give the awareness to the telecommunication user of updated review from other users.

Need for Dynamicity in Social Networking Sites

2014 ◽  
pp. 64-87
Author(s):  
Gurdeep S Hura
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Real World ◽  
Social Networking Sites ◽  
Data Science ◽  
Emerging Technology ◽  
New Applications ◽  
Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

Data Science

2018 ◽  
pp. 141-151
Author(s):  
Sabitha Rajagopal
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Research And Development ◽  
Real Time ◽  
Data Science ◽  
Data Warehousing ◽  
Holistic Approach ◽  
Digital Data ◽  
Data Application ◽  
Development Analysis

Data Science employs techniques and theories to create data products. Data product is merely a data application that acquires its value from the data itself, and creates more data as a result; it's not just an application with data. Data science involves the methodical study of digital data employing techniques of observation, development, analysis, testing and validation. It tackles the real time challenges by adopting a holistic approach. It ‘creates' knowledge about large and dynamic bases, ‘develops' methods to manage data and ‘optimizes' processes to improve its performance. The goal includes vital investigation and innovation in conjunction with functional exploration intended to notify decision-making for individuals, businesses, and governments. This paper discusses the emergence of Data Science and its subsequent developments in the fields of Data Mining and Data Warehousing. The research focuses on need, challenges, impact, ethics and progress of Data Science. Finally the insights of the subsequent phases in research and development of Data Science is provided.

Role of Urban Big Data in Travel Behavior Research

2020 ◽  
pp. 036119812097502
Author(s):  
Chihuangji Wang ◽  
Daniel Baldwin Hess
Keyword(s):  
Big Data ◽  
Survey Data ◽  
Travel Behavior ◽  
Data Science ◽  
Location Based Service ◽  
Labor Costs ◽  
Public And Private ◽  
Public And Private Sector ◽  
Data Analytic

Understanding urban travel behavior (TB) is critical for advancing urban transportation planning practice and scholarship; however, traditional survey data is expensive (because of labor costs) and error-prone. With advances in data collection techniques and data analytic approaches, urban big data (UBD) is currently generated at an unprecedented scale in relation to volume, variety, and speed, producing new possibilities for applying UBD for TB research. A review of more than 50 scholarly articles confirms the remarkable and expanding role of UBD in TB research and its advantages over traditional survey data. Using this body of published work, a typology is developed of four key types of UBD—social media, GPS log, mobile phone/location-based service, and smart card—focusing on the features and applications of each type in the context of TB research. This paper discusses in significant detail the opportunities and challenges in the use of UBD from three perspectives: conceptual, methodological, and political. The paper concludes with recommendations for researchers to develop data science knowledge and programming skills for analysis of UBD, for public and private sector agencies to cooperate on the collection and sharing of UBD, and for legislators to enforce data security and confidentiality. UBD offers both researchers and practitioners opportunities to capture urban phenomena and deepen knowledge about the TB of individuals.

scholarly journals CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Fernando Martinez-Plumed ◽  
Lidia Contreras-Ochando ◽  
Cesar Ferri ◽  
Jose Hernandez Orallo ◽  
Meelis Kull ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Science

Big Data Methods

2016 ◽  
Vol 21 (3) ◽  
pp. 525-547 ◽  
Author(s):  
Scott Tonidandel ◽  
Eden B. King ◽  
Jose M. Cortina
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Data Sources ◽  
Future Research ◽  
Organizational Science ◽  
Associated Data ◽  
Organizational Sciences

Advances in data science, such as data mining, data visualization, and machine learning, are extremely well-suited to address numerous questions in the organizational sciences given the explosion of available data. Despite these opportunities, few scholars in our field have discussed the specific ways in which the lens of our science should be brought to bear on the topic of big data and big data's reciprocal impact on our science. The purpose of this paper is to provide an overview of the big data phenomenon and its potential for impacting organizational science in both positive and negative ways. We identifying the biggest opportunities afforded by big data along with the biggest obstacles, and we discuss specifically how we think our methods will be most impacted by the data analytics movement. We also provide a list of resources to help interested readers incorporate big data methods into their existing research. Our hope is that we stimulate interest in big data, motivate future research using big data sources, and encourage the application of associated data science techniques more broadly in the organizational sciences.

scholarly journals Computational Design Map for Heterogeneous Experimental Studies

2021 ◽  
Author(s):  
Chhaya Kulkarni ◽  
Nuzhat Maisha ◽  
Leasha J Schaub ◽  
Jacob Glaser ◽  
Erin Lavik ◽  
...  
Keyword(s):  
Data Mining ◽  
Blood Loss ◽  
Decision Trees ◽  
Data Science ◽  
Computational Design ◽  
Science Methods ◽  
Frequent Patterns ◽  
Multiple Data ◽  
Mining Methods ◽  
Using Data

This paper focuses on the discovery of a computational design map of disparate heterogeneous outcomes from bioinformatics experiments in pig (porcine) studies to help identify key variables impacting the experiment outcomes. Specifically we aim to connect discoveries from disparate laboratory experimentation in the area of trauma, blood loss and blood clotting using data science methods in a collaborative ensemble setting. Trauma related grave injuries cause exsanguination and death, constituting up to 50% of deaths especially in the armed forces. Restricting blood loss in such scenarios usually requires the presence of first responders, which is not feasible in certain cases. Moreover, a traumatic event may lead to a cytokine storm, reflected in the cytokine variables. Hemostatic nanoparticles have been developed to tackle these kinds of situations of trauma and blood loss. This paper highlights a collaborative effort of using data science methods in evaluating the outcomes from a lab study to further understand the efficacy of the nanoparticles. An intravenous administration of hemostatic nanoparticles was executed in pigs that had to undergo hemorrhagic shock and blood loss and other immune response variables, cytokine response variables are measured. Thus, through various hemostatic nanoparticles used in the intervention, multiple data outcomes are produced and it becomes critical to understand which nanoparticles are critical and what variables are key to study further variations in the lab. We propose a collaborative data mining framework which combines the results from multiple data mining methods to discover impactful features. We used frequent patterns observed in the data from these experiments. We further validate the connections between these frequent rules by comparing the results with decision trees and feature ranking. Both the frequent patterns and the decision trees help us identify the critical variables that stand out in the lab studies and need further validation and follow up in future studies. The outcomes from the data mining methods help produce a computational design map of the experimental results. Our preliminary results from such a computational design map provided insights in determining which features can help in designing the most effective hemostatic nanoparticles.

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