Publish-subscribe architecture for building non-polling asynchronous web applications.

Abstract

Globalization of network topologies and resources has lead to the proliferation of information driven system based on the HTTP protocol. However, information driven systems have much to gain from a full duplex asynchronous approach to data delivery architectures. Asynchronous data push architectures allow information driven systems to utilize bandwidth and energy resources more efficiently, while reducing latency and data inconsistency across systems. At the time this paper is written, available asynchronous technologies are not mature enough to handle full duplex data communication required by distributed information systems. Recognizing these challenges, I propose in this thesis a publish-subscribe messaging framework for web applications. A framework which consists of a publish-subscribe server, to facilitate an asynchronous message passing interface, a web application library to access the message passing interface, and a client subscriber library. The proposed framework is designed along the multiple observer pattern [1] in software engineering to allow 1 to N and N to N message passing capabilities. This thesis consists of a theoretical part describing the proposed framework and a practical implementation of an asynchronous web application interface for communicating with mobile phone systems over short message system protocol (SMS).

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Social Network Data Mining Using Natural Language Processing and Density Based Clustering

Abstract

There is a growing need to make sense of all the raw data available on the Internet, hence, the purpose of this study is to explore the capabilities of data mining algorithms applied to social networks. We propose a system to mine public Twitter data for information relevant to obesity and health as an initial case study. This paper details the findings of our project and critiques the use of social networks for data mining purposes.

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