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Online Analytical Processing for Business Intelligence in Big Data.

Jigna Ashish Patel1, Priyanka Sharma2

  • 1Department of CSE, Institute of Technology, Nirma University, Ahmedabad, India.

Big Data
|December 21, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces OLAP on Hadoop by Indexing (OOHI), a novel approach to address big data challenges in Online Analytical Processing (OLAP). OOHI demonstrates superior performance in data storage and operations compared to existing Hadoop-based OLAP systems.

Area of Science:

  • Computer Science
  • Data Management
  • Business Intelligence

Background:

  • Exponential data growth from sources like IoT and social media presents significant storage and speed challenges for traditional Online Analytical Processing (OLAP).
  • Existing business intelligence solutions struggle to efficiently handle the scale and complexity of big data in multidimensional analysis.

Purpose of the Study:

  • To propose and implement a novel OLAP framework, OLAP on Hadoop by Indexing (OOHI), designed to overcome the storage and speed limitations of big data.
  • To demonstrate the data independence and efficiency of the OOHI model across diverse datasets.

Main Methods:

  • Developed OOHI, a system with a simplified multidimensional model storing dimensions on a schema server and measures on a Hadoop cluster.
  • Implemented modules including Data Storage Module (DSM) using serialization/deserialization, Dimension Encoding Module (DEM) using integer encoding, Cube Segmentation Module, Segment Selection Module (SSM), and Block Selection and Process (BSAP) module.
Keywords:
HadoopMOLAPOLAPOOHIbig data

Related Experiment Videos

  • Utilized MapReduce for indexing and parallel processing to ensure fault tolerance and efficient data handling.

    • Main Results:

    • OOHI demonstrated effective space utilization through efficient data storage and retrieval mechanisms.
    • Integer encoding in DEM successfully addressed the sparsity problem inherent in multidimensional big data.
    • Comparative analysis showed OOHI significantly outperformed existing Hadoop-based OLAP solutions in data storage, dice, slice, and roll-up operations.

    Conclusions:

    • The OOHI framework provides an efficient and scalable solution for big data OLAP, addressing critical storage and speed challenges.
    • The proposed model is data-independent, validated through real-time oceanography and supermarket datasets, highlighting its broad applicability.