Modern users demand fast, scalable, simple, user-friendly, and cost-effective solutions to perform complex analytics on complex and disparate data, including spatial data. The complex characteristics of spatial data have made analytics and management more challenging. This chapter highlights the overall research and development done in the area of big data management including geometry attributes. The state-of-the-art databases, frameworks, and architectures are reviewed and compared with significant parameters. It also presents issues and challenges to meet the current demand of modern users to perform spatial analytics and management.

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Big Spatial Data Management: Tools and Technologies Landscape

  • Purnima Gandhi

摘要

Modern users demand fast, scalable, simple, user-friendly, and cost-effective solutions to perform complex analytics on complex and disparate data, including spatial data. The complex characteristics of spatial data have made analytics and management more challenging. This chapter highlights the overall research and development done in the area of big data management including geometry attributes. The state-of-the-art databases, frameworks, and architectures are reviewed and compared with significant parameters. It also presents issues and challenges to meet the current demand of modern users to perform spatial analytics and management.