README.yml

---
owner:
    hid: 334
    name: Peter Russell
    firstname: Peter
    lastname: Russell
    longitude: 74.3632 W
    latitude: 40.5432 S
    city: Metuchen, NJ, U.S.A.
    url: https://github.com/bigdata-i523/hid334
paper1:
    abstract: >
        Executives are constantly looking for ways to find the  pulse of their
        competitive landscape along with ways to  gauge the sentiment among their
        customers. The emergence of  the Big Data movement has given businesses the
        unique  opportunity to gain perspective on these fronts, in  addition to many
        others. Amazon Web Services has placed  itself at the epicenter of this data
        movement and now  offers tools that allows decision makers to quantify their businesses
        in ways that were previously computationally  impossible or were  prohibitively
        expensive. As a result,  with Amazon Web Services, companies now have the
        ability to  gain deep insights into customer activity, which can be  used
        as real-time feedback or guidance to make future  experiences more personalized.
    author:
        - Peter Russell
    chapter: Technology
    hid:
        - 334
    status: 10/28/2017
    title: AWS in support of Big Data Applications and Analytics
    type: latex
    url: https://github.com/bigdata-i523/hid334/paper1/paper1.pdf
paper2:
    abstract: >
        This paper will explore how the expansion of big data has affected
        US military  capabilities through their drone program.
    author:
        - Peter Russell
    hid:
        - 334
    status: in progress
    title: Advancements in Drone Technology for the US Military
    type: latex
    url: https://github.com/bigdata-i523/hid334/paper2/paper2.pdf
project:
    abstract: >
        In the modern investing environment, a seismic shift is taking in place in terms of investing style. Previously, prior to the big data movement, qualitative macroeconomic investing was the dominant strategy as investors had to piece together anecdotal evidence with concrete, but infrequent government data sets. These major economic releases tend to occur monthly, but could be as infrequent as quarterly, as is the case with gross domestic product. However, with services such as GDELT, investors can now insights at a much more granular level to gauge sentiment. GDELT, short for Global Database of Events, Language, and Tone, collects news stories from around the world and publishes a 250,000 row daily file for analysis. The utilization and visualization of this data as it relates to investing will be primary theme researched.
    author:
        - Peter Russell
    hid:
        - 334
    status: in progress
    title: Sentiment based Macroeconomic Investing using GDELT
    type: latex
    url: https://github.com/bigdata-i523/hid334/project/project.pdf