Big Data Framework for Analyzing Patents to Support Strategic R&D Planning

Abstract

Patents are important technical documents that are widely used to support strategic research and development (R&D) planning. With the rapid evolution of Information Technology (IT), many researchers have attempted to analyze patent data using IT-based methods such as data mining. More recently, advancements in big data technologies have increased their suitability for analyzing large-scale patent data. In this paper, we propose a big data framework to process and analyze large-scale patent data. The proposed framework consists of four layers: an aggregator layer, a storage layer, an analysis layer, and an application layer. These layers are designed to collect patent data, store the collected data, analyze the data, and present the results. The primary objectives of the proposed framework are to provide a patent analysis service platform based on big data technologies, and to support strategic R&D planning for organizations. To demonstrate the feasibility of the proposed approach, we implement a Hadoop cluster based on the proposed framework and conduct a case study to analyze patents in the Quantum Dot technology field.

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