Agent-mining interaction: an emerging area
03 Jun 2007-pp 60-73
TL;DR: This paper draws a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family, and summarizes key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent- mining interaction.
Abstract: In the past twenty years, agents (we mean autonomous agent and multi-agent systems) and data mining (also knowledge discovery) have emerged separately as two of most prominent, dynamic and exciting research areas. In recent years, an increasingly remarkable trend in both areas is the agent-mining interaction and integration. This is driven by not only researcher's interests, but intrinsic challenges and requirements from both sides, as well as benefits and complementarity to both communities through agent-mining interaction. In this paper, we draw a high-level overview of the agent-mining interaction from the perspective of an emerging area in the scientific family. To promote it as a newly emergent scientific field, we summarize key driving forces, originality, major research directions and respective topics, and the progression of research groups, publications and activities of agent-mining interaction. Both theoretical and application-oriented aspects are addressed. The above investigation shows that the agent-mining interaction is attracting everincreasing attention from both agent and data mining communities. Some complicated challenges in either community may be effectively and efficiently tackled through agent-mining interaction. However, as a new open area, there are many issues waiting for research and development from theoretical, technological and practical perspectives.
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20 Jan 2010TL;DR: Domain Driven Data Mining enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence.
Abstract: In the present thriving global economy a need has evolved for complex data analysis to enhance an organizations production systems, decision-making tactics, and performance. In turn, data mining has emerged as one of the most active areas in information technologies. Domain Driven Data Mining offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. About this book: Enhances the actionability and wider deployment of existing data-centered data mining through a combination of domain and business oriented factors, constraints and intelligence. Examines real-world challenges to and complexities of the current KDD methodologies and techniques. Details a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications. Bridges the gap between business expectations and research output through detailed exploration of the findings, thoughts and lessons learned in conducting several large-scale, real-world data mining business applications Includes techniques, methodologies and case studies in real-life enterprise data mining Addresses new areas such as blog mining Domain Driven Data Mining is suitable for researchers, practitioners and university students in the areas of data mining and knowledge discovery, knowledge engineering, human-computer interaction, artificial intelligence, intelligent information processing, decision support systems, knowledge management, and KDD project management.
112 citations
TL;DR: Thorough and innovative retrospection and thinking are timely in bridging the gaps and promoting data mining toward next‐generation research and development: namely, the paradigm shift from knowledge discovery from data to actionable knowledge discovery and delivery.
Abstract: Actionable knowledge has been qualitatively and intensively studied in the social sciences. Its marriage with data mining is only a recent story. On the one hand, data mining has been booming for a while and has attracted an increasing variety of increasing applications. On the other, it is a reality that the so-called knowledge discovered from data by following the classic frameworks often cannot support meaningful decision-making actions. This shows the poor relationship and significant gap between data mining research and practice, and between knowledge, power, and action, and forms an increasing imbalance between research outcomes and business needs. Thorough and innovative retrospection and thinking are timely in bridging the gaps and promoting data mining toward next-generation research and development: namely, the paradigm shift from knowledge discovery from data to actionable knowledge discovery and delivery. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.
53 citations
Book•
29 May 2015
42 citations
Cites background from "Agent-mining interaction: an emergi..."
...Metasynthetic Computing and Engineering Based on massive working experience in many different domains and large national engineering projects, Qian and his colleagues proposed the methodology qualitative-to-quantitative metasynthesis through building a Hall for Workshop of Metasynthetic Engineering, or in shortmetasynthetic engineering [1, 2, 55, 58, 64, 65], to address the system complexities in open complex giant systems....
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...Other applications of metasynthesis include Chinese character recognition [62], business intelligent systems [63], intelligent building systems [64], agile supply chain management [65], regional sustainability [66], population, the analysis and evaluation of public opinion on the Internet [67], resources and environmental economics [68], and digital urban planning [69]....
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TL;DR: It is shown that the KA field is increasingly active due to the higher and higher pace of change in human activity, and the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution are discussed.
Abstract: This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers' and practitioners' efforts in knowledge acquisition for more than a century.We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution.
29 citations
01 Jan 2009
TL;DR: This chapter proposes an Evolutionary Computation approach to the problem of automatically learn software entities based on Genetic Algorithms and regular expressions, also called wrappers, that will be able to extract some kind of Web data structures from examples.
Abstract: Data Extraction from the World Wide Web is a well known, unsolved, and critical problem when complex information systems are designed. These problems are related to the extraction, management and reuse of the huge amount ofWeb data available. These data usually has a high heterogeneity, volatility and low quality (i.e. format and content mistakes), so it is quite hard to build reliable systems. This chapter proposes an Evolutionary Computation approach to the problem of automatically learn software entities based on Genetic Algorithms and regular expressions. These entities, also called wrappers, will be able to extract some kind of Web data structures from examples.
28 citations
Additional excerpts
...The main difference with other closer approaches [1,4,6] is the utilization of Regular Expressions using a multiagent system to generate them and extract information [ 2 ]....
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References
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08 Sep 2000TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data
23,600 citations
Book•
12 Jun 2002
TL;DR: A multi-agent system is a distributed computing system with autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.
Abstract: The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The books main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology
4,042 citations
Book•
01 Nov 2001
TL;DR: A multi-agent system (MAS) as discussed by the authors is a distributed computing system with autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.
Abstract: From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.
3,003 citations
01 Jan 1999
225 citations
01 Jan 2006
TL;DR: Learning and Information Processing -- Data Mining, Retrieval and Management -- Bioinformatics and Bio-inspired Models -- Agents and Hybrid Systems -- Financial Engineering -- Special Session on Nature-Inspired Date Technologies.
169 citations