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Abstract ▼ AI-Helper

Currently, due to advances of various technologies such as social networks and sensor networks, the size of data has extremely increased, and hence, Google proposed a MapReduce framework which is working on a cluster consisting of connected commodity computers to process big data efficiently. Howeve...

Currently, due to advances of various technologies such as social networks and sensor networks, the size of data has extremely increased, and hence, Google proposed a MapReduce framework which is working on a cluster consisting of connected commodity computers to process big data efficiently. However, since the conventional data processing techniques cannot be directly applied to MapReduce frameworks, many algorithms have been re-developed on MapReduce frameworks. In this paper, we construct a quad-tree index with MapReduce frameworks to support efficient access to big data. Our technique reduces the index construction time utilizing a sampling technique to partition a data set evenly. Additionally, in our experiment, we show the efficiency of data access using our constructed quad tree and MapReduce frameworks together compared to data accessing using MapReduce frame works only.

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