| We have entered the data driven intelligence age from the information age,and data has become a new logical starting point for people to understand and solve problems."Data-driven" breaks the shackles of solving problems based on knowledge and forms a new understanding of methodology that use data to solve problems.This research paradigm pushes the digital library knowledge discovery services return from the exploration of the root of the problems to the true nature of the knowledge services.Directing to the users,managements and services from data,so to speak,provides a new idea for the reform of the supply-side of knowledge discovery services in digital libraries in big data environments.Knowledge discovery services should not only change the management technologies,management rules or service forms,but also involve the entire management philosophies and service systems.But in the big data environments,there are more and more sources of digital library information,the data output is getting larger and larger,the growth rate of digital resources is getting faster and faster,the data heterogeneity is more and more obvious,and the data aging is more serious.The contradictions between the hidden dangers of knowledge hunger because of low-value density and data tsunami are becoming more and more prominent,and the demands of users for discovery services are becoming more and more diversified.The data resources in digital library are facing the challenge of being rediscovered.About the oncoming changes and challenges,the knowledge discovery services of digital libraries not only need to complete the transformation of knowledge organizations from document digitalization to content datamation,but also realize the value exploitation and wisdom insight of digital resources from content datamation to data intelligence.The research paradigm of data-driven opens up a new path for knowledge discovery and opens up a new era of knowledge services of digital libraries.Exploring the new form of the digital library knowledge discovery service models based on the data-driven requires learning and internalizing the relevant theories of data science.It is necessary to analyze the data-driven factors and the mechanism of knowledge discovery.It is necessary to break the rigid models of traditional resource discovery and create an ecological function circle of service innovation.Combining the dual technological advantages of data-driven and knowledge discovery,the innovation models of knowledge discovery services in digital libraries should seek the new synergy of user data,content resource data and expert data from aspects of datamation,semantization,association,visualization and intelligentization.And exploit new applications such as the user portraits,research design fingerprints,and accurate document recommendations,strengthen data cluster integration,enhance the green connectivity of platforms,achieve user-friendly interaction.Make digital libraries an intelligent service system that supports the explorations,the discoveries and creations of knowledge for users,maximize data resources for value developments and knowledge transformations,and enable users to benefit from digital libraries' the efficient,convenient,friendly and intelligent knowledge discovery service experiences.Therefore,this paper defines the core concept of data-driven digital library knowledge discovery services through tracing the research results of the data-driven and the knowledge discovery.With the comprehensive application of literature analyses,surveys and interviews,simulation experiments,model trainings and other methods,analyze the data environments,driving mechanisms,innovation models,model applications and innovation strategies of digital library knowledge discovery service innovations.Focusing on the main research contents,the third chapter of this paper analyzes the opportunities and challenges of knowledge discovery services driven by data from the characteristics of data environments,data environment changes and data environment developments.The fourth chapter combines data elements,data-driven processes,and data-driven dimensions to explore the data-driven dynamic mechanism,the stream mechanism,the synergetic driven mechanism and the data-driven control mechanism of digital library knowledge discovery services.Chapter Five points out the construction requirements,construction foundations and construction processes of the digital library knowledge discovery service innovation models by analyzing the inherent missions of the innovations and evolutions of the knowledge discovery service models of digital libraries.Chapter Six realizes specific applications,such as user portraits,research design fingerprints,text recommendations,and multi-granularity retrieval decision-makings for the digital library knowledge discovery service innovation models.Aiming at the specific bottlenecks of the digital library knowledge discovery service innovation models,chapter seven provides the solution strategies for each driven dimensions.The specific contents are explained as follows:Chapter 3 Data-Driven Data Environment Analyses of Knowledge Discovery Service in Digital LibraryThis chapter is a contextual deconstruction of the digital library knowledge discovery service domain in big data driven environments.Firstly,based on the 4V characteristics of big data,this chapter from the perspectives of the all-data oriented analyzes the characteristics of digital library knowledge discovery services in terms of data states,existence modes,storage modes,storage contents and data value.Secondly,this chapter discusses the advantages and disadvantages of digital library knowledge discovery service innovations under the influence of datamation,new generation information technologies,data analysis thinkings and data-intensive scientific discovery paradigms.Finally,based on the two-way state of environmental characteristics and environmental changes,the development directions of digital library knowledge discovery services are positioned.While clarifying the research purpose of this article,the main research tasks of chapters 4,5,6,and 7 are drawn.Chapter 4 Analyses of Data Driven Mechanisms of Knowledge Discovery Service Innovation in Digital LibraryAs paving the way for chapter 5,this chapter analyzes the data elements and driving forms of the digital library knowledge discovery service platforms in detail.Through the classifications of user data,resource content data,and expert data,provides data foundations for service model applications such as research user portraits,research design fingerprints,and accurate document recommendations of Chapter 6.Through the hierarchical analyses of datamation and semantization,association,visualization and intelligentization,lays the optimization main lines for the formulations of innovation strategies of Chapter 7.Based on the data elements,the driven processes and the driven dimensions,present the interactive catalytic reactions of data-driven and knowledge discovery services from the analyses of dynamic mechanisms of the internal and external forces,the input-output flow mechanism,and the data-fusion synergetic driven mechanism and the data-driven control mechanism specificallyChapter 5 The Research on Data Driven the Innovation Models of Knowledge Discovery Service in Digital LibraryOn the basis of the previous researches,this chapter firstly analyzes the internal logics of the data-driven innovation models of digital library knowledge discovery services.Secondly,this chapter from the external bases,expounds the realization of digital library knowledge discovery service innovations from the aspects of resource discovery existing models,knowledge products and technical supports.Finally,the internal logics and external foundations are integrated,the initial deconstructions both of the innovation model's basic frameworks and the architectures of platforms are carried out.And on this basis,the data-driven digital library knowledge discovery service innovation function circle is constructed.Chapter 6 Application Researches of Data-driven Knowledge Discovery Service Innovation Models in Digital LibraryBased on the innovation models proposed in Chapter 5,this chapter uses the research user data to construct the research user portraits of Baidu discovery in the digital library,and uses the literature data to construct the research design fingerprints with research objects,research problems and research methods as the core elements.And combined with user portraits and research design fingerprints to achieve accurate literature recommendations,and verify the advantages of the multi-granular retrieval decisions through user retrieval experiments.Chapter 7 Researches on Data-driven Innovation Strategies of Knowledge Discovery Service in Digital LibraryBased on the analyses of data-driven dimensions and driving mechanisms in Chapter 4,this chapter aims to clarify the directions of innovations in datamation and semantization,association,visualization and intelligentization,and to design corresponding optimization paths for digital library knowledge discovery services.Based on the innovative constraints,this chapter gives practical solutions and countermeasures.Under the big data environments,redefining the content of digital library knowledge discovery services driven by data,exploring the data-driven mechanisms of digital library knowledge discovery services,and the existing resource discovery service models of innovative digital libraries are beneficial to provide theoretical support for the supply-side reforms of knowledge discovery services in digital libraries from the perspective of methodologies.The significances of the digital library knowledge discovery services lies not only in their unified retrievals and their extension functions,but also on the bases of the evidence-based decision-makings,intelligent managements and knowledge re-creation services value of scientific discoveries.On the road of human beings constantly exploring the unknown and trying to understand the unknown,the catalytic responses of data-driven +knowledge discovery not only provide a feasible reference for the explorations of scientific discovery methods,and also promotes the knowledge discovery services of digital libraries in the process of continuous innovations to benefit more knowledge-seeking users. |
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