Abstract
Social Media offer golden opportunity for information mining. However, it is a challenge to find useful knowledge from these massive data. This paper describes a solution for a common problem using top-x querying in Online Social Networks (OSNs). Seventy-five GB data were collected from Twitter, reorganized into dataset in a distributed computing platform with Hadoop. By adopting Aggregate-Rank-Delete algorithm, we used MapReduce solution to develop an algorithm, called MapFollowee & ReduceFollower to query the Top-X members who retweet and that have largest number of followers. This proposed approach is to effectively accelerate the querying process, in which not only the performance is faster than the matrix algorithm but also faster than the original algorithm in the stand-alone version. It also reduced the data storage from the original dataset. This result is important because it provides a new parallel paradigm as an application of MapReduce with an efficient way to resolve the practical problem in OSNs.
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Jianya, Z., Weigang, L., Uden, L. (2014). Top-X Querying in Online Social Networks with MapReduce Solution. In: Uden, L., Wang, L., Corchado RodrÃguez, J., Yang, HC., Ting, IH. (eds) The 8th International Conference on Knowledge Management in Organizations. Springer Proceedings in Complexity. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7287-8_32
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DOI: https://doi.org/10.1007/978-94-007-7287-8_32
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