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

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A Break in the Clouds: Towards a Cloud Definition

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.

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Web mining research: a survey

Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

Sensor-Based Activity Recognition

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented.

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Web mining for web personalization

Electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximize their marketing effort. The discovery of marketing related navigation patterns requires the development of data mining algorithms capable of discovering sequential access patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible navigation templates that can specify navigational behavior, as network structures for the capture of web site topologies, in addition to concept hierarchies and syntactic constraints. Unlike existing approaches, field dependency has been implemented, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three different types of contained-in relationships are supported, which express different types of browsing behavior. The carried out experimental evaluation have shown promising results in terms of functionality as well as scalability.

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Navigation Pattern Discovery from Internet Data

This paper investigates optimal model selection for posture recognition Accuracy and computational time are related to the trained model in a supervised classification An optimal model selection is important for a reliable activity monitoring system Conventional guidance on model training uses large instances of randomly selected data in order to characterize the classes A new approach to the training of a multiclass support vector machine (SVM) model suited to limited training sets such as used in posture recognition is provided This approach picks a small training set from misclassified data to improve an initial model in an iterative and incremental fashion In addition, a two step grid-search algorithm is used for the parameters setting The best parameters were chosen according to the testing accuracy rather than conventional validating accuracy This new approach for model selection was evaluated against conventional approaches in an activity classification study Nine everyday postures were classified from a belt-worn smart phone’s accelerometer data The classification derived from the small training set and the conventional randomly selected training set differed in two aspects: classification performance to new data (851% Pick-out small training set vs 703% conventional large training set) and computational efficiency (improved 28%)

Optimal model selection for posture recognition in home-based healthcare

Big Data are the new frontier for businesses and governments alike. Dealing with big data and extracting valuable and actionable knowledge from it poses one of the biggest challenges in computing and, simultaneously, provides one of the greatest opportunities for business, government and society alike. The content produced by the social media community and in particular the micro blogging community reflects one of the most opinion-and knowledge-rich, real-time accessible, expressive and diverse data sources, both in terms of content itself as well as context related knowledge such as user profiles including user relations. Harnessing the embedded knowledge and in particular the underlying opinion about certain topics and gaining a deeper understanding of the overall context will provide new opportunities in the inclusion of user opinions and preferences. This paper discusses a keyword-based classifier for short message based sentiment mining. It outlines a simple classification mechanism that has the potential to be extended to include additional sentiment dimensions. Eventually, this could provide a deeper understanding about user preferences, which in turn could actively and in almost real time influence further development activities or marketing campaigns.

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Keyword-Based Sentiment Mining using Twitter

Managers of electronic commerce sites need to learn as much as possible about their customers and those browsing their virtual premises, in order to maximise the return on marketing expenditure. The discovery of marketing related navigation patterns requires the development of data mining algorithms capable of the discovery of sequential access patterns from web logs. This paper introduces a new algorithm called MiDAS that extends traditional sequence discovery with a wide range of web-specific features. Domain knowledge is described as flexible navigation templates that can specify generic navigational behaviour of interest, network structures for the capture of web site topologies, concept hierarchies and syntactic constraints. Unlike existing approaches MiDAS supports sequence discovery from multidimensional data, which allows the detection of sequences across monitored attributes, such as URLs and http referrers. Three methods for pruning the sequences, resulting in three different types of navigational behaviour are presented. The experimental evaluation has shown promising results in terms of functionality as well as scalability.

User-Driven Navigation Pattern Discovery from Internet Data

Context-awareness refers to computing systems that are able to sense and to comprehend their environment in order to adapt themselves in dependence to the available and relevant contextual information they depend upon. Gathering such contextual information involves real world entities such as sensors, which, for various reasons, are often prone to certain degrees of uncertainty and inaccuracy. Nevertheless, high quality context information plays a vital role in ensuring correct system behavior as well as dynamic system and service adaptation. Thus, a set of indicators is required which allows determining the quality of contextual information, which is commonly known as Quality of Context (QoC). One of the most relevant parameter of the QoC is the Probability of Correctness (PoC), which expresses the level of confidence, that the contextual information sensed, are in fact correct or not. In this paper, we propose an approach for measuring the PoC of context information by firstly analyzing the nature of context information and, secondly, revisiting the concept of Quality of Context also discussing other QoC parameters. Finally, we present a novel approach for quantifying the PoC for specific context information and evaluate the proposed method on a concrete case study.

Matthias Baumgarten

Papers

Navigation Pattern Discovery from Internet Data

Optimal model selection for posture recognition in home-based healthcare

Keyword-Based Sentiment Mining using Twitter

User-Driven Navigation Pattern Discovery from Internet Data

Measuring the Probability of Correctness of Contextual Information in Context Aware Systems