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Data dissemination in large-cardinality social graphs
Linnaeus University, Faculty of Technology, Department of Computer Science.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Near real-time event streams are a key feature in many popular social media applications. These types of applications allow users to selectively follow event streams to receive a curated list of real-time events from various sources. Due to the emphasis on recency, relevance, personalization of content, and the highly variable cardinality of social subgraphs, it is extremely difficult to implement feed following at the scale of major social media applications. This leads to multiple architectural approaches, but no consensus has been reached as to what is considered to be an idiomatic solution. As of today, there are various theoretical approaches exploiting the dynamic nature of social graphs, but not all of them have been applied in practice. In this paper, large-cardinality graphs are placed in the context of existing research to highlight the exceptional data management challenges that are posed for large-scale real-time social media applications. This work outlines the key characteristics of data dissemination in large-cardinality social graphs, and overviews existing research and state-of-the-art approaches in industry, with the goal of stimulating further research in this direction.

Place, publisher, year, edition, pages
2015. , 30 p.
Keyword [en]
Data dissemination, message delivery, social graph, big data, large scale, feed following, materialized views, social network analysis, community structure detection, graph theory, database theory.
National Category
Computer Systems
URN: urn:nbn:se:lnu:diva-48268OAI: diva2:880547
Subject / course
Computer Science
Educational program
Software Technology Programme, Master Programme, 120 credits
Available from: 2015-12-10 Created: 2015-12-09 Last updated: 2015-12-10Bibliographically approved

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