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

Research in the Semantic Web, especially in modeling virtual communities, has provided models useful to represent the richness of these social network interactions. The SIOC Semantically-Interlinked Online Communities vocabulary provides concepts and properties that can be used to describe information from online communities e.g., message boards, wikis, weblogs, etc.. However, the SIOC ontology does not consider social aspects nor the higher order semantics hidden in linkages between community members. This paper describes SIOC-SNA-DM, an extension of the SIOC vocabulary. SIOC-SNA-DM's model is tri-partite, consisting of People, Policies, and Purposes which are social aspects observable in most social communities. A challenge to using our model is how to populate these aspects, since higher order semantics from interactions need to be extracted. Thus, we explain how this population is done with advanced text mining using a latent semantic technique over a large virtual community called Plexilandia.cl with more than 2500 musicians working on the site.Our previous work, in this area, has shown how including these social aspects help to outperform results generated by state-of-the-art techniques. One of the novelties of this present work is the introduction and the elucidation of SIOC-SNA-DM, and how to populate the ontology in order to support the social aspects needed to enhance results of Social Network Mining techniques.

Leveraging social network analysis with topic models and the Semantic Web extended

Digital harms can manifest across any interface. Key problems in addressing these harms include the high individuality of harms and the fast-changing nature of digital systems. We put forth GreaseVision, a collaborative human-in-the-loop learning framework that enables end-users to analyze their screenomes to annotate harms as well as render overlay interventions. We evaluate HITL intervention development with a set of completed tasks in a cognitive walkthrough, and test scalability with one-shot element removal and fine-tuning hate speech classification models. The contribution of the framework and tool allow individual end-users to study their usage history and create personalized interventions. Our contribution also enables researchers to study the distribution of multi-modal harms and interventions at scale.

GreaseVision: Rewriting the Rules of the Interface

The design and maintenance of an aero-engine generates a significant amount of documentation. When designing new engines, engineers must obtain knowledge gained from maintenance of existing engines to identify possible areas of concern. Firstly, this paper investigate the use of advanced business intelligence tenchniques to solve the problem of knowledge transfer from maintenance to design of aeroengines. Based on data availability and quality, various models were deployed. An association model was used to uncover hidden trends among parts involved in maintenance events. Classification techniques comprising of various algorithms was employed to determine severity of events. Causes of high severity events that lead to major financial loss was traced with the help of summarization techniques. Secondly this paper compares and evaluates the business intelligence approach to solve the problem of knowledge transfer with solutions available from the Semantic Web. The results obtained provide a compelling need to have data mining support on RDF/OWL-based warehoused data.

Data mining to support engineering design decisions

Preface : knowledge acquisition for planning

While it is preferable that Linked Data is published without access or licence restrictions, there will always remain certain datasets which, perhaps due to financial considerations, cannot be published as Linked Open Data. If these valuable datasets do join the Web of Linked Data, it will be as Linked Closed Data - Linked Data with access and license restrictions. In this paper, we outline a research agenda for Linked Closed Data that considers the effects that access and license restrictions may have on the Web of Linked Data. If implemented poorly, access restrictions have the potential to break URI resolvability, but even when implemented well, we can expect them to affect dataset selection processes and inter-dataset link creation rates. Additionally, there remains the technical challenge of developing and standardising access restriction and automated payment techniques for the Web of Linked Data.

Nigel Shadbolt

Papers

Leveraging social network analysis with topic models and the Semantic Web extended

GreaseVision: Rewriting the Rules of the Interface

Data mining to support engineering design decisions

Preface : knowledge acquisition for planning

A research agenda for linked closed data (vision paper)