Basics of qualitative research: Grounded theory procedures and techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

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Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions

Data Mining Practical Machine Learning Tools and Techniques

How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Knowledge-Intensive Fusion for Improved Situational Awareness

This is an attempt to develop a universal socio-technical computing machine that captures and coordinates human input to let collective problem solving activities emerge on the Web without the need for an a priori composition of a dedicated task or human collective.

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A Universal Socio-Technical Computing Machine

This paper proposes that two significant and emerging problems facing our connected, data-driven
society may be more effectively solved by being framed as sensemaking challenges. The first is in
empowering individuals to take control of their privacy, in device-rich information environments
where personal information is fed transparently to complex networks of information brokers. Although
sensemaking is often framed as an analytical activity undertaken by experts, due to the fact that
non-specialist end-users are now being forced to make expert-like decisions in complex information
environments, we argue that it is both appropriate and important to consider sensemaking challenges
in this context. The second is in supporting human-in-the-loop algorithmic decision-making, in which
important decisions bringing direct consequences for individuals, or indirect consequences for groups,
are made with the support of data-driven algorithmic systems. In both privacy and algorithmic decision-making, framing the problems as sensemaking challenges acknowledges complex and illdefined
problem structures, and affords the opportunity to view these activities as both building up
relevant expertise schemas over time, and being driven potentially by recognition-primed decision
making.

The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility

Increasingly diverse and useful information repositories are being made available over the World Wide Web (Web). However, information retrieved from the Web is often of limited use for problem solving because it lacks task-relevance, structure and context. This research draws on software agency as a medium through which modeldriven knowledge engineering techniques can be applied to the Web. The IMPS (Internet-based Multi-agent Problem Solving) architecture described here involves software agents that can conduct structured on-line knowledge acquisition using distributed knowledge sources. Agent-generated domain ontologies are used to guide a flexible system of autonomous agents arranged in a server architecture. Generic problem solving methods developed within the expert system community supply structure and context.

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Knowledge Engineering with Software Agents

The CoAKTinG (Collaborative Advanced Knowledge Technologies in the Grid) project has developed a set of integrated tools to enhance collaboration between e-Scientists. As one of three case studies, these tools are being applied within the Combechem e-Science pilot project. Two levels of integration are being explored: straightforward deployment of generic CoAKTinG tools, and a “deep” integration between these tools and the Combechem grid. The deeper integration supports the publication at source research objective of Combechem, in which a digital record is maintained through the information processing chain that starts in the laboratory, supporting retrospective use in the e-Science process. In this paper we provide an overview of the tools and we focus in particular on the adaptation of one of the tools for the Combechem application.

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Nigel Shadbolt

Papers

Knowledge-Intensive Fusion for Improved Situational Awareness

A Universal Socio-Technical Computing Machine

The Need for Sensemaking in Networked Privacy and Algorithmic Responsibility

Knowledge Engineering with Software Agents

Chain ReAKTing: Collaborative Advanced Knowledge Technologies in the CombeChem Grid