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Traditional economic theory revolves around information asymmetry, where some possess more knowledge than others. However, the digital age is upending this paradigm. This research proposes a groundbreaking shift: a transition from an Asymmetric Information Economy to a Symmetric Knowledge and Wisdom Economy, fueled by the ubiquitous availability of data and digital communication. The shifting is worked through DIKWP-12 Chains framework (Data-Information-Knowledge-Wisdom-Purpose), which serves as a key pillar supporting this paradigm shift. It integrates Data, Information, Knowledge, Wisdom, and Purpose into a unified semantic processing approach. This novel approach empowers us to navigate this transformative era. Furthermore, this study goes beyond theory by leveraging the DIKWP chains to construct an Intelligent Consultant System for Real Estate Investment. This system analyzes customer behavior data and captures the wisdom behind their purchasing decisions, offering a unique and data-driven approach to real estate investment consulting.

Artificial intelligence (AI) offers digital tools that advertisers can use to create ads and automate various advertising processes. It has reorganized and upgraded traditional advertising and specifically improved advertising efficiency for tourist destinations. Adopting the computers-are-social-actors paradigm and spreading–activation theory, this study examined how the relationship between declared creator types (AI vs. human) and advertising appeals (rational vs. emotional) affects individuals' visit intentions. Three scenario-based experiments were conducted. They revealed that advertisements with rational appeals improved visit intention for AI-generated ads more effectively. In contrast, those with emotional appeals were more attractive when the declared creator was human. This effect was mediated by information processing fluency. The results also confirmed the moderating role of tourists’ involvement in these relationships. This research expands the range of applications of AI-generated content in advertising and can inform practical marketing strategies for tourist destinations.

Web service is a popular solution to integrate components when building a software system, or to allow communication between a system and third-party users, providing a flexible and reusable mechanism to access its functionalities. Scientists have been proposing numerous models for defining anything 'as a service (aaS)', including discussions of products, processes, data and information management, and security as a service. Various web service-based systems are prevailing in health service, personalised recommendation provision. We propose a framework towards constructing and searching typed resources in terms of data, information and knowledge through a hierarchy composing data graph, information graph and knowledge graph in order to improve performance in accessing and processing resources. We use cases to illustrate the mechanism of the framework.

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. A knowledge graph is a graph constructed by representing each item, entity and user as nodes, and linking those nodes that interact with each other via edges. Knowledge graphs have abundant natural semantics and can contain various and more complete information. It is an expression mechanism close to natural language. However, we still lack a unified definition and standard expression form of knowledge graph. The authors propose to clarify the expression of knowledge graph as a whole. They clarify the architecture of knowledge graph from data, information, knowledge, and wisdom aspects respectively. The authors also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

Empirical rules are among the most widely employed approaches for processing view abstraction for UML, which can support model simplification, consistency checking, and complexity reduction. However, empirical rules face challenges such as completeness validation, consistency among rules, and composition priority arrangement. The challenge of composition is enlarged in the environment of distributed model-driven development for web service-based systems, where redundant information/data is emphasised. The same redundant information can be expressed in different forms that comprise different topological structures representing the same part of the system. Such variation will result in choosing different compositions of rules executed in different orders, which will increase the severity of the current non-determinism from the empirical probability of some rules. We investigate the effect of redundancy on rule application through designing a simulated distributed storage for an example model. We construct finite-state automaton to unify empirical rules while relieving the side effects caused by redundancy.

Many developer recommendation techniques have been developed in the literature. Among existing studies, most of them are performed based on exploring the historical commit repository. The thought behind them is that developers who submit similar historical commits relevant to the incoming issue are more probably to be the candidates for the current issue resolution. But whether such a thought is always useful for developer recommendation? This paper aims at this problem by conducting a set of empirical studies on four real open-source projects. The results show that, 1) historical commit messages do well reflect the historical experience of the maintenance task of developers and can be used for developer recommendation in most of the time; 2) the number of historical commits submitted by the recommended developer(s) and the similarity value used to select the relevant historical commits should be carefully considered to recommend developers for issue resolution; 3) The efficiency of issue resolution process can be improved if some associated source code files relevant to this issue can be also recommended; and 4) developer recommendation techniques that rank the recommended developers based on the times of co-changed source code files cannot always produce correct recommendation results.

Firstly this article presents a thorough discussion of semantics formalization related issues in model driven engineering (MDE). Then motivated for the purpose of software implementation, and attempts to overcome the shortcomings of incompleteness and context-sensitivity in the existing models, we propose to study formalization of semantics from a cognitive background. Issues under study cover the broad scope of overlap vs. incomplete vs. complete, closed world assumption (CWA) vs. open world assumption (OWA), Y(Yes)/N(No) vs. T(True)/F(False), subjective (SUBJ) vs. objective (OBJ), static vs. dynamic, unconsciousness vs. conscious, human vs. machine aspects, and so forth. A semantics formalization approach called EID-SCE (Existence Identification Dualism-Semantics Cosmos Explosion) is designed to meet both the theoretical investigation and implementation of the proposed formalization goals. EID-SCE supports the measure/evaluation in a {complete, no overlap} manner whether a given concept or feature is an improvement. Some elementary cases are also shown to demonstrate the feasibility of EID-SCE.

Machine learning (ML) techniques and algorithms have been successfully and widely used in various areas including software engineering tasks. Like other software projects, bugs are also common in ML projects and libraries. In order to more deeply understand the features related to bug fixing in ML projects, we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories, fixing patterns, fixing scale, fixing duration, and types of maintenance. The results show that (1) there are commonly seven types of bugs in ML programs; (2) twelve fixing patterns are typically used to fix the bugs in ML programs; (3) 68.80% of the patches belong to micro-scale-fix and small-scale-fix; (4) 66.77% of the bugs in ML programs can be fixed within one month; (5) 45.90% of the bug fixes belong to corrective activity from the perspective of software maintenance. Moreover, we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical study. The results of our empirical study are basically consistent with the feedback from developers. The findings from the empirical study provide useful guidance and insights for developers and users to effectively detect and fix bugs in ML projects.