Big Data Analytics for Development: Events, Knowledge Graphs and Predictive Models (2015)
BibTeX
@MISC{Chakraborty15bigdata,
author = {Sunandan Chakraborty},
title = {Big Data Analytics for Development: Events, Knowledge Graphs and Predictive Models},
year = {2015}
}
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Abstract
Volatility in critical socio-economic indices can have a significant negative impact on global development. This thesis presents a suite of novel big data analytics algorithms that operate on unstructured Web data streams to automatically infer events, knowledge graphs and predictive models to understand, characterize and predict the volatility of socioeconomic indices. This thesis makes four important research contributions. First, given a large volume of diverse unstructured news streams, we present new models for capturing events and learning spatio-temporal characteristics of events from news streams. We specifically explore two types of event models in this thesis: one centered around the concept of event triggers and a probabilistic meta-event model that explicitly delineates named entities from text streams to learn a generic class of meta-events. The second contribution focuses on learning several different types of knowledge graphs from news streams and events: a) Spatio-temporal article graphs capture intrinsic relationships between different news articles; b) Event graphs characterize relationships between events and given a news query, provide a succinct summary of a time-line of events relating to a query; c) Event-phenomenon graphs that provide a condensed representation of classes of events that relate to a given phenomena at a given location and time; d) Causality testing on
Keyphrases
knowledge graph predictive model big data analytics news stream novel big data analytics significant negative impact socioeconomic index global development generic class probabilistic meta-event model unstructured web data stream news query event model event trigger named entity important research contribution succinct summary event graph different news article several different type new model second contribution large volume condensed representation text stream event-phenomenon graph critical socio-economic index diverse un-structured news stream spatio-temporal char-acteristics
