[Paper Review] Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Hai Zhuge|arXiv (Cornell University)|Jul 18, 2015

Cognitive Computing and Networks2 references19 citations

TL;DR

This paper proposes a cognitive cyber-infrastructure framework that maps big data into knowledge space through multi-dimensional analysis, enabling knowledge discovery, sharing, and management. It establishes a methodology for big data computing by addressing its fundamental challenges and positioning big data within evolving scientific paradigms.

ABSTRACT

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.

Motivation & Objective

• To address the lack of a recognized methodology for big data computing and its fundamental challenges.
• To clarify the nature of big data and its relationship with knowledge, science paradigms, and technological innovation.
• To develop a systematic framework for representing, managing, and analyzing big data beyond traditional data processing.
• To establish a cognitive cyber-infrastructure that supports not only data management but also knowledge discovery and sharing.
• To explore how big data computing aligns with the fourth industrial revolution and the evolution of scientific paradigms.

Proposed method

• Proposes a multi-dimensional perspective on big data computing, integrating data, knowledge, and cognitive processes.
• Introduces a cognitive cyber-infrastructure as a foundational platform for managing big data and enabling knowledge extraction.
• Employs a conceptual mapping from data space to knowledge space using semantic, structural, and functional dimensions.
• Leverages artificial intelligence and semantic technologies to model relationships and infer knowledge from heterogeneous big data.
• Integrates data processing pipelines with knowledge representation and reasoning mechanisms for scalable discovery.
• Positions big data within the context of scientific paradigms, particularly the shift from empiricism to data-driven science.

Experimental results

Research questions

• RQ1 What is the true nature and fundamental challenge of big data computing?
• RQ2 How can big data be systematically mapped into knowledge space to enable discovery and sharing?
• RQ3 What kind of cyber-infrastructure is required to support both big data processing and knowledge management?
• RQ4 What is the relationship between big data and the evolution of scientific paradigms?
• RQ5 What methodology can be established for big data representation, management, and analysis?

Key findings

• Big data is not merely large-scale data but a transformative force requiring a new computational and cognitive framework.
• A cognitive cyber-infrastructure enables the transition from data to knowledge by integrating semantic modeling and AI-driven reasoning.
• The mapping from big data to knowledge space is feasible through multi-dimensional analysis and semantic enrichment.
• The fundamental challenge of big data lies in its complexity, heterogeneity, and the need for intelligent abstraction beyond traditional processing.
• Big data computing is deeply intertwined with the fourth industrial revolution and the evolution of scientific paradigms.
• The proposed framework provides a methodological foundation for future big data research and knowledge discovery systems.