A Mathematical Theory of Big Data

This article presents a cardinality approach to big data, a fuzzy logicbased approach to big data, a similarity-based approach to big data, and a logical approach to the marketing strategy of social networking services. All these together constitute a mathematical theory of big data. This article also examines databases with infinite attributes. The research results reveal that relativity and infinity are two characteristics of big data. The relativity of big data is based on the theory of fuzzy sets. The relativity of big data leads to the continuum from small data to big data, big data-driven small data analytics to become statistical significance. The infinity of big data is based on the calculus and cardinality theory. The infinity of big data leads to the infinite similarity of big data. The proposed theory in this article might facilitate the mathematical research and development of big data, big data analytics, big data computing, and data science with applications in intelligent business analytics and business intelligence

Intelligence without Data

This article explores intelligence without data. More specifically, it reveals what the study of big data ignores in the trinity age of big data, analytics, and intelligence, and looks at DIKEW intelligence through presenting an integrated framework of intelligence. It then examines intelligence without data and wisdom algebra. It demonstrates that intelligence without data consists of information intelligence without data, knowledge intelligence without data, experience without data, intelligence without data, and wisdom intelligence without data, based on the hierarchy of wisdom. It argues that big data must incorporate intelligence without data to serve the world. At the same time, intelligence without data could enhance human intelligence, cognitive intelligence, machine intelligence, and business intelligence

A Unified Logical Model for CBR-based E-commerce Systems

This paper will examine new issues resulting from applying CBR in e-commerce and propose a unified logical model for CBR-based e-commerce systems (CECS) which consists of three cycles and covers almost all activities of applying CBR in e-commerce. This paper also decomposes case adaptation into problem adaptation and solution adaptation, which not only improves the understanding of case adaptation in the traditional CBR, but also facilitates the refinement of activity of CBR in e-commerce and intelligent support for e-commerce. It then investigates CBR-based product negotiation. This paper thus gives insight into how to use CBR in e-commerce and how to improve the understanding of CBR with its applications in e-commerce from a logical viewpoint

Multiagent Brokerage with CBR

This paper classifies multiagent-based e-commerce into multiagent-based auction, multiagent-based mediation and multiagent-based brokerage and gives a brief survey of related works in each. The paper proposes a framework of CMB, a CBR system for multiagent brokerage, which integrates CBR, intelligent agents and brokerage, in which we also propose a knowledge-based model for CBR. The key insight is that an efficient way for applying CBR in e-commerce is through intelligent agents or multiagent systems, and the work of a human broker should be done by a few intelligent agents in a cooperative way. This approach will facilitate research and development of CBR in multiagent e-commerce

The spectrum of big data analytics

Big data analytics is playing a pivotal role in big data, artificial intelligence, management, governance, and society with the dramatic development of big data, analytics, artificial intelligence. However, what is the spectrum of big data analytics and how to develop the spectrum are still a fundamental issue in the academic community. This article addresses these issues by presenting a big data derived small data approach. It then uses the proposed approach to analyze the top 150 profiles of Google Scholar, including big data analytics as one research field and proposes a spectrum of big data analytics. The spectrum of big data analytics mainly includes data mining, machine learning, data science and systems, artificial intelligence, distributed computing and systems, and cloud computing, taking into account degree of importance. The proposed approach and findings will generalize to other researchers and practitioners of big data analytics, machine learning, artificial intelligence, and data science. © 2019 International Association for Computer Information Systems

Integrating online social networks with e-commerce : a CBR approach

Integrating online social networks (OSN) with e-commerce is a part of Enterprise 2.0 and social media and is of significance for development of e-commerce and online social networking services. However, how to integrate online social networks including Facebook with e-commerce is still a big issue for companies. Case based reasoning (CBR) has a number of successful applications in e-commerce and web services. This article examines how to integrate OSN with e-commerce, how to integrate CBR with e-commerce and how to integrate CBR with OSN. This article also proposes a CBR architecture for integrating online social networks with e-commerce using CBR as an intelligent intermediary. One of the research findings indicates that the principle of CBR is a useful marketing strategy for integrating e-commerce and OSN. The approach proposed in this research will facilitate the development of e-commerce, Enterprise 3.0 and online social networking services.<br /

Demand driven web services

Web services are playing a pivotal role in e-business, service intelligence, and service science. Demand-driven web services are becoming important for web services and service computing. However, many fundamental issues are still ignored to some extent. For example, what is the demand theory for web services, what is a demand-driven architecture for web services and what is a demand-driven web service lifecycle remain open. This chapter addresses these issues by examining fundamentals for demand analysis in web services, and proposing a demand-driven architecture for web services. It also proposes a demand-driven web service lifecycle for the main players in web services: Service providers, service requestors and service brokers, respectively. It then provides a unified perspective on demand-driven web service lifecycles. The proposed approaches will facilitate research and development of web services, e-services, service intelligence, service science and service computing