# hMDAP: A Hybrid Framework for Multi-paradigm Data Analytical Processing on Spark

## TL;DR

hMDAP is a hybrid framework designed for large-scale, multi-paradigm data processing on Spark, integrating OLAP, machine learning, and graph analysis in distributed environments, demonstrated through traffic scenario case studies.

## Contribution

It introduces a three-layer data process framework and a business process module to support diverse analytical paradigms on Spark.

## Key findings

- Effective handling of multi-paradigm data processing in Spark.
- Demonstrated applicability in real-world traffic scenarios.
- Enhanced flexibility for large-scale distributed data analysis.

## Abstract

We propose hMDAP, a hybrid framework for large-scale data analytical processing on Spark, to support multi-paradigm process (incl. OLAP, machine learning, and graph analysis etc.) in distributed environments. The framework features a three-layer data process module and a business process module which controls the former. We will demonstrate the strength of hMDAP by using traffic scenarios in a real world.