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Big Data Deliverables through Cross-Platform Interest-Based content discovery
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy. The goal of this study was twofold: first we address the theoretical dilemmas of a cross-platform user experience; second, we implemented an Android-based mobile application and designed a cloud architecture to account for theoretical parameters of Big Data User-centric approach and interactivity. To address cross-platform Big Data challenges, we relied on cloud computing to perform computationally intensive operations such as searching, data mining, and data processing at large scale. on and content filtering across multiple radio content streams. The streams consisted of tags from radio stations’ programming and social media content through a discovery process. User interaction was geared to enable preferred topic filtering, flexible shifting participation roles, notifications, and navigation through external data sources. We tested our application on a list of popular radio stations and their social media content streams (including Facebook, Twitter, Google+) to generate a Big Data scenario.
big data, interest-based content discovery, user-centric approach, cross-platform media, cloud
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