Return to search

CrowdCloud: Combining Crowdsourcing with Cloud Computing for SLO Driven Big Data Analysis

The evolution of structured data from simple rows and columns on a spreadsheet to more complex unstructured data such as tweets, videos, voice, and others, has resulted in a need for more adaptive analytical platforms. It is estimated that upwards of 80% of data on the Internet today is unstructured. There is a drastic need for crowdsourcing platforms to perform better in the wake of the tsunami of data. We investigated the employment of a monitoring service which would allow the system take corrective action in the event the results were trending in away from meeting the accuracy, budget, and time SLOs. Initial implementation and system validation has shown that taking corrective action generally leads to a better success rate of reaching the SLOs. Having a system which can dynamically adjust internal parameters in order to perform better can lead to more harmonious interactions between humans and machine algorithms and lead to more efficient use of resources.

Identiferoai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-3249
Date01 December 2017
CreatorsFlatt, Taylor
PublisherOpenSIUC
Source SetsSouthern Illinois University Carbondale
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses

Page generated in 0.0018 seconds