Big Data
Graduate Category: Engineering and Technology Degree Level: PhD Abstract ID# 505
Advanced Solutions to Big Data Management in the Study and Analysis of Time-to-Event and Survival Data Keivan Sadeghzadeh, Nasser Fard
Abstract In the era of big data and advanced information technology, analysis of complex and huge data expends time and money, may cause error and misinterpretation. Consequently, inaccurate and erroneous reasoning possibly lead to poor inference and decision making, sometimes irreversible and catastrophic events in many fields such as biomedical science, engineering, sociology, economics and business.
Applying Data Dimensionality Reduction Methods
Big Data Mining
Veracity
Volume
Big Data
Database Technology
Applying Data Clustering Methods Statistics
Machine Learning
Velocity
Variety
Information Science
Introduction Time-to-event and survival data analysis have an inevitable role in predicting the probability of many events occurrence such as response to a treatment, failure of a device or component as well as change of customer satisfaction rate. Thus, necessity of optimal solutions for analysis and management of complex large scale data which is measured and generated rapidly nowadays is not only obvious but desired.
Approaches
Visualization
Hierarchical
Data Mining
Partitional
Other Disciplines
Next Steps Data Mining Process Problem
Data
Analysis
Model
Verification
Insights
We would like to provide an optimal solution to extract appropriate and significant data variables and observations from big data sets in order to study and analysis time-to-event and survival data for multidisciplinary purposes.
References Objective The objective of this research is to apply big data management methods and techniques as practical solutions to reduce volume of multivariate and high dimension data appropriately in order to avoid data analysis and decision making difficulties and facilitate time-to-event and survival data study and analysis.
Time-to-Event and Survival Data
Healthcare and Biomedical Sciences
Engineering Sciences Survival Data Analysis
•
Hosmer et al., Applied Survival Analysis: Regression Modeling of Time to Event Data, 2009.
•
Lee et al., Statistical Methods for Survival Data Analysis, 2013.
•
Jolliffe, Principal Component Analysis, 2005.
•
Han et al., Data Mining: Concepts and Techniques, 2011.
•
Jajuga et al., Classification, Clustering, and Data Analysis: Recent Advances and Applications, 2002.
•
Rajaraman et al., Mining of Massive Datasets, 2011.
Business and Economics Sociology and Psychology
Acknowledgements Northeastern University, Mechanical and Industrial Engineering Department
Advanced Solutions to Big Data Management in the Study and Analysis of Time-to-Event and Survival Data Keivan Sadeghzadeh, Nasser Fard
Abstract In the era of big data and advanced information technology, analysis of complex and huge data expends time and money, may cause error and misinterpretation. Consequently, inaccurate and erroneous reasoning possibly lead to poor inference and decision making, sometimes irreversible and catastrophic events in many fields such as biomedical science, engineering, sociology, economics and business.
Applying Data Dimensionality Reduction Methods
Big Data Mining
Veracity
Volume
Big Data
Database Technology
Applying Data Clustering Methods Statistics
Machine Learning
Velocity
Variety
Information Science
Introduction Time-to-event and survival data analysis have an inevitable role in predicting the probability of many events occurrence such as response to a treatment, failure of a device or component as well as change of customer satisfaction rate. Thus, necessity of optimal solutions for analysis and management of complex large scale data which is measured and generated rapidly nowadays is not only obvious but desired.
Approaches
Visualization
Hierarchical
Data Mining
Partitional
Other Disciplines
Next Steps Data Mining Process Problem
Data
Analysis
Model
Verification
Insights
We would like to provide an optimal solution to extract appropriate and significant data variables and observations from big data sets in order to study and analysis time-to-event and survival data for multidisciplinary purposes.
References Objective The objective of this research is to apply big data management methods and techniques as practical solutions to reduce volume of multivariate and high dimension data appropriately in order to avoid data analysis and decision making difficulties and facilitate time-to-event and survival data study and analysis.
Time-to-Event and Survival Data
Healthcare and Biomedical Sciences
Engineering Sciences Survival Data Analysis
•
Hosmer et al., Applied Survival Analysis: Regression Modeling of Time to Event Data, 2009.
•
Lee et al., Statistical Methods for Survival Data Analysis, 2013.
•
Jolliffe, Principal Component Analysis, 2005.
•
Han et al., Data Mining: Concepts and Techniques, 2011.
•
Jajuga et al., Classification, Clustering, and Data Analysis: Recent Advances and Applications, 2002.
•
Rajaraman et al., Mining of Massive Datasets, 2011.
Business and Economics Sociology and Psychology
Acknowledgements Northeastern University, Mechanical and Industrial Engineering Department
