Databases and Information Systems Commons^™

Data Management In Cloud Environments: Nosql And Newsql Data Stores, Katarina Grolinger, Wilson A. Higashino, Abhinav Tiwari, Miriam Am Capretz

Electrical and Computer Engineering Publications

: Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the …

Disaster Data Management In Cloud Environments, Katarina Grolinger

Electronic Thesis and Dissertation Repository

Facilitating decision-making in a vital discipline such as disaster management requires information gathering, sharing, and integration on a global scale and across governments, industries, communities, and academia. A large quantity of immensely heterogeneous disaster-related data is available; however, current data management solutions offer few or no integration capabilities and limited potential for collaboration. Moreover, recent advances in cloud computing, Big Data, and NoSQL have opened the door for new solutions in disaster data management.

In this thesis, a Knowledge as a Service (KaaS) framework is proposed for disaster cloud data management (Disaster-CDM) with the objectives of 1) facilitating information gathering …

Automated Extraction Of Community Mobility Measures From Gps Stream Data Using Temporal Dbscan, Sungsoon Hwang, Timothy Hanke, Christian Evans

Sungsoon Hwang

Inferring community mobility of patients from GPS data has received much attention in health research. Developing robust mobility (or physical activity) monitoring systems relies on the automated algorithm that classifies GPS track points into events (such as stops where activities are conducted, and routes taken) accurately. This paper describes the method that automatically extracts community mobility measures from GPS track data. The method uses temporal DBSCAN in classifying track points, and temporal filtering in removing noises (any misclassified track points). The result shows that the proposed method classifies track points with 88% accuracy. The percent of misclassified track points decreased …