Special Issue on Data Exploration in the Web 3.0 Age
• 大类 : 工程技术 - 2区
• 小类 : 计算机：理论方法 - 2区
Currently emerging Web 3.0 environments have provided a strong potential for the integration of data sources, applications and tools. In such a pervasive and highly dynamic scenario, existing techniques for accessing and managing web content seem to be actually inadequate to satisfy the user needs and more automatic ways of exploring, joining and sharing information are needed to improve the usability of web resources.
This raises several important challenges for future data and web mining methods. Such challenges range from the analysis of poorly structured information, such as annotations and tags, to the provision of intelligent methods that support users in searching and integrating information offered by web resources. The overall goal of these challenges is not limited to enhance information retrieval but also includes exploiting the enriched semantics a dataset acquires when used in conjunction with other sources of information. The synergy of different technologies, including semantic web, natural language search, machine learning, recommendation agents and artificial intelligence, can be especially fruitful in this perspective.
Furthermore, in the era of big data and Internet of things, we are increasingly dealing with a huge amount of information generated by heterogeneous sources. Indeed, almost every individual leaves digital traces when interacting with sensor networks, cloud services and positioning services, through a variety of mobile devices and smart objects. A growing attention is thus devoted to the design of suitable approaches for exploring this kind of data, in order to extract actionable knowledge about people, things, and their interactions.
More generally, the dimensionality and the complexity of gathered data is fast increasing in almost all applications domains, giving rise to the need of innovative data analysis approaches.
The goal of this FGCS special issue is to foster the dissemination of top-notch results in all the areas related to Data Exploration in a very broad sense, including contributions from data mining, query languages, semantic analysis, data visualization, graph databases and other fields related to the analysis and exploitation of data.
Topics of Interest
The topics of the call regard original contributions focusing on challenging aspects of data exploration in modern scenarios, in a broad sense. These may for instance be related to the followings:
Text and data mining, knowledge discovery
Faceted search and browsing
Data visualization and ux for web 3.0 data
Querying interfaces and languages including constrained natural languages
Entity recognition and merging, type classification, record linkage and property ranking
Privacy and security issues in data exploration
Recommendation agents and artificial intelligence technologies
High-dimensional data analysis
Machine learning and statistical methods for data analysis and processing
Natural language processing for data extraction
Platforms and applications exploring data in all domains including social, web, bioinformatics and finance
Knowledge graph creation, reasoning, and exploration