In-Memory Database Design and Modeling to Support Guided- and Self-Service Visual Discovery in Big-Data Context: An Autism Spectrum Disorder (ASD) Application Case
Department
Business and Information Technology
Major
Information Science and Technology
Research Advisor
Lea, Bih-Ru
Advisor's Department
Business and Information Technology
Funding Source
Opportunity for Undergraduate Research (OURE); Center for Enterprise Resource Planning (ERP)
Abstract
This research identifies how in-memory technology can be leveraged to understand big data in a case study using Autism Spectrum Disorder (ASD) data from Simons Foundation Autism Research Initiative (SFARI) database. Literature review was first conducted to compare and contrast different In-memory database design architectures, to define the role of in-memory computing in Big-Data Analytics, and to address the role of different types of Self-Service Business Intelligence (BI) in visual discovery. A data warehouse schema and related data modeling framework (i.e., attribute views, analytical views, and calculation views) were proposed and implemented to support guided- and self-service visual discovery at the data discovery stage utilizing SAP HANA In-memory Appliance. Finally, experiments were conducted to test the proposed models and framework with users ranging from novice to computer savvy. The data were analyzed and recommendations were provided for future research improvement.
Biography
Sean Howell is a senior in Information Science and Technology. He plans to graduate from Missouri University of Science and Technology in Dec. 2015, with minors in Business and Enterprise Resource Planning. Sean works for the Center for Enterprise Resource Planning as a Student Research Assistant.
Presentation Type
OURE Fellows Final Oral Presentation
Document Type
Presentation
Location
Gasconade Room
Presentation Date
15 Apr 2015, 2:00 pm - 2:20 pm
In-Memory Database Design and Modeling to Support Guided- and Self-Service Visual Discovery in Big-Data Context: An Autism Spectrum Disorder (ASD) Application Case
Gasconade Room
This research identifies how in-memory technology can be leveraged to understand big data in a case study using Autism Spectrum Disorder (ASD) data from Simons Foundation Autism Research Initiative (SFARI) database. Literature review was first conducted to compare and contrast different In-memory database design architectures, to define the role of in-memory computing in Big-Data Analytics, and to address the role of different types of Self-Service Business Intelligence (BI) in visual discovery. A data warehouse schema and related data modeling framework (i.e., attribute views, analytical views, and calculation views) were proposed and implemented to support guided- and self-service visual discovery at the data discovery stage utilizing SAP HANA In-memory Appliance. Finally, experiments were conducted to test the proposed models and framework with users ranging from novice to computer savvy. The data were analyzed and recommendations were provided for future research improvement.
