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Atrak: a MapReduce-based data warehouse for big data

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Abstract

As warehouse data volumes expand, single-node solutions can no longer analyze the immense volume of data. Therefore, it is necessary to use shared nothing architectures such as MapReduce. Inter-node data segmentation in MapReduce creates node connectivity issues, network congestion, improper use of node memory capacity and inefficient processing power. In addition, it is not possible to change dimensions and measures without changing previously stored data and big dimension management. In this paper, a method called Atrak is proposed, which uses a unified data format to make Mapper nodes independent to solve the data management problem mentioned earlier. The proposed method can be applied to star schema data warehouse models with distributive measures. Atrak increases query execution speed by employing node independence and the proper use of MapReduce. The proposed method was compared to established methods such as Hive, Spark-SQL, HadoopDB and Flink. Simulation results confirm improved query execution speed of the proposed method. Using data unification in MapReduce can be used in other fields, such as data mining and graph processing.

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  1. Mohammad Hossein Barkhordari Query Language.

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Authors and Affiliations

  1. Advance Information System Research Group for Information and Communication Technology Research Centre, No 5, Saeedialley, College Intersection, Enghelab Street, Tehran, 1599616313, Iran

    Mohammadhossein Barkhordari & Mahdi Niamanesh

Authors
  1. Mohammadhossein Barkhordari
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  2. Mahdi Niamanesh
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Correspondence to Mohammadhossein Barkhordari.

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Barkhordari, M., Niamanesh, M. Atrak: a MapReduce-based data warehouse for big data. J Supercomput 73, 4596–4610 (2017). https://doi.org/10.1007/s11227-017-2037-3

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