Intelligent Urban Transport Decision Analysis System Based on Mining in Big Data Analytics and Data Warehouse

Abstract

This paper conduct a study on the augmentation of the current capabilities of the intelligent urban mobility and road transport in terms of the analytics dimension focusing on the data mining and big data analytics methodologies. A federated or a hybrid approach leverages the strengths and mitigates the weaknesses of both data warehouse and big data analytics. We discuss the challenges, requirements, integrated models, components, scenarios and proposed solutions to the performance, efficiency, availability, security and privacy concerns in the context of smart cities. Our approach relies on several layers that run in parallel to collect and manage all collected data and create several scenarios that will be used to assist urban mobility. The data warehouse and big data analytics can serve as means to support clustering, classification, recommending systems, frequent item set mining. The challenge here is to populate the repository architecture with the schema, view definitions, metadata and specify/integrate the types of this architecture (Centralized Metadata repository, Distributed Metadata repository, Federated or Hybrid Metadata repository).