The Research Of Big Data Techniques In The Study Of IPRs Policies

In recent years, big data techniques have brought up huge changes in the social and economic areas, and has also brought new opportunities and challenges for the public sectors. In the public management practices, governments in some countries have managed to deploy big data techniques in a set of public issues, including medications, traffics, educations, public opinions monitoring and emergencies managements. Achievements have been made in those areas, however, those achievements were always in specific public affairs, and each practice was different from others. There has no general application theories of using big data techniques in public policies making. In the academic research area, the academic community in the study of public management and public policies had not yet payed enough academic attentions in the study of big data. How to use big data methods in the research of public policies had not been discussed. This paper had intended to enter in the study of big data methods applications in public policy research, especially in the field of Intellectual Property Rights (IPRs) policies.Like any other research methods, big data techniques have its limitations. Not all research problems and fields is suitable to be analyzed by using big data techniques. The research of IPRs policies is one of the reseach field that is suitable, because the data sources of IPRs policies research is highly digitized, which could be easily analyzed by big data algorithms. Also, the research of IPRs policies have the tendency to use multiple data in studies, which have generated the sub research filed of patentometrics and patent information analysis. Compared with other public policy sectors, IPRs policies is more suitable for adopting the new big data research method. Therefore, this paper have selected IPRs policies as the subject.This study have generated the knowledge from various research field and is Multidisciplinary. The main content of this paper consisted of 6 sections. Section 1 is the introduction of this study. It has discussed the importance and potentials of big data techniques in IPRs policies. Study of big data techniques could fulfill the gap of research tools in IPRs policy, which has not got enough academic attentions. Section 2 is the theoretical discussion part. In this section, the studies and theories in policy science, S&T policies, IPRs policies and big data techniques had been displayed. The policy making process theory in policy science has provided an analyzing framework in the 4’section. The knowledge in S&T policies and IPRs policies have set the boudary of this research. And the concepts and systems of big data techniques have introduced the essential analyzing tools of the following study. Section 3 is about the data sources of IPRs policy studies under the development of big data techniques, including the traditional data collecting sources of IPRs policy studies and the new sources that had been developed by big data techniques. The revolution provided by big data is starting from the changes in data sources. Section 4 had analyzed the application of big data techniques in IPRs policy studies under the research framework of Anderson’s 5 policy making processes. Some specific big data tools had been discussed in those policy making processes. Sections 4 have discussed the changes from using big data in policy making processes. Besides, big data techniques could also brought revolutions in the basic concepts of policy making, and that is discussed in section 5. Section 5 had studied 2 fundamental influences of big data in the research of IPRs policies, including the algorithmic of policy regulations, and real-time policy changing systems. Those 2 fundamental influences had challenged the basic knowledge of public policies, which could be a potential academic research field in the future. Section 6 is the conclusion part of this research. The pros and cons of big data methods have been discussed in this part. And the potential challenges in big data applications in the realistic practices have been brought up.