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

How have Japanese companies become world leaders in the automotive and electronics industries, among others? What is the secret of their success? Two leading Japanese business experts, Ikujiro Nonaka and Hirotaka Takeuchi, are the first to tie the success of Japanese companies to their ability to create new knowledge and use it to produce successful products and technologies. In The Knowledge-Creating Company, Nonaka and Takeuchi provide an inside look at how Japanese companies go about creating this new knowledge organizationally. The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus on tacit knowledge. And this, the authors argue, is the key to their success--the Japanese have learned how to transform tacit into explicit knowledge. To explain how this is done--and illuminate Japanese business practices as they do so--the authors range from Greek philosophy to Zen Buddhism, from classical economists to modern management gurus, illustrating the theory of organizational knowledge creation with case studies drawn from such firms as Honda, Canon, Matsushita, NEC, Nissan, 3M, GE, and even the U.S. Marines. For instance, using Matsushita's development of the Home Bakery (the world's first fully automated bread-baking machine for home use), they show how tacit knowledge can be converted to explicit knowledge: when the designers couldn't perfect the dough kneading mechanism, a software programmer apprenticed herself withthe master baker at Osaka International Hotel, gained a tacit understanding of kneading, and then conveyed this information to the engineers. In addition, the authors show that, to create knowledge, the best management style is neither top-down nor bottom-up, but rather what they call "middle-up-down," in which the middle managers form a bridge between the ideals of top management and the chaotic realities of the frontline. As we make the turn into the 21st century, a new society is emerging. Peter Drucker calls it the "knowledge society," one that is drastically different from the "industrial society," and one in which acquiring and applying knowledge will become key competitive factors. Nonaka and Takeuchi go a step further, arguing that creating knowledge will become the key to sustaining a competitive advantage in the future. Because the competitive environment and customer preferences changes constantly, knowledge perishes quickly. With The Knowledge-Creating Company, managers have at their fingertips years of insight from Japanese firms that reveal how to create knowledge continuously, and how to exploit it to make successful new products, services, and systems.

The knowledge-creating company

Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the /spl alpha/-algorithm can successfully mine any workflow represented by a so-called SWF-net.

/pdf/workflow-mining-discovering-process-models-from-event-logs-2e3us8cswr.pdf

Workflow mining: discovering process models from event logs

Business Process Management (BPM) includes methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. It can be considered as an extension of classical Workflow Management (WFM) systems and approaches. Although the practical relevance of BPM is undisputed, a clear definition of BPM and related acronyms such as BAM, BPA, and STP are missing. Moreover, a clear scientific foundation is missing. In this paper, we try to demystify the acronyms in this domain, describe the state-of-the-art technology, and argue that BPM could benefit from formal methods/languages (cf. Petri nets, process algebras, etc.).

/pdf/business-process-management-a-survey-mkhfh265q2.pdf

Business process management: a survey

Nowadays organizations have to adjust their business processes along with the changing environment, in order to maintain a competitive advantage. Often, a change in a part of the system which is to support the business process implies a change of the whole system, which causes complex redesign activities. In this paper we present a case study involving the process redesign of a Dutch company belonging to gas industry. In order to identify appropriate redesign interventions, process mining and simulation techniques are used. This paper presents a process mining approach used in this case study, which consists of three steps: (i) data preparation, (ii) process mining and performance analysis, and (iii) simulation of the current and redesigned process models.

A Process Mining Approach to Redesign Business Processes - A Case Study in Gas Industry

http://m-hikari.com/ams/ams-2015/ams-41-44-2015/marusterAMS41-44-2015.pdf

Strong convergence of the Mann iteration for alpha-demicontractive mappings

Purpose – This paper aims to present a classification of approaches toward knowledge claim evaluation (KCE), which is the process of evaluating and testing knowledge claims in organizations, and to position KCE as a fundamental research issue for KM.Design/methodology/approach – The paper draws from a range of KM theories in the academic literature and reviews the role of KCE. Concepts and principles from epistemology provide the basis for the analysis and classification of KCE approaches. The paper's particular focus is on KCE in innovation. Furthermore, practical examples illustrate the working of KCE.Findings – KCE is a neglected process in KM theories. The conceptual underpinnings of KCE in KM theories are insufficient and empirical studies are lacking. The paper identifies three approaches towards KCE from the literature. The proposed classification shows that KCE can be dealt with in various ways, and that an understanding concerning the practical workings, the contextual factors and effects of KCE ...

Knowledge claim evaluation: a fundamental issue for knowledge management

Although sustainability is often discussed solely in ecological terms, it cannot be disconnected from the way humans behave in their social environment. This article presents a theoretical approach toward sustainability that takes a human behavior and knowledge view on sustainability as a starting point. This approach requires that human behavior should change, individually and collectively, in order to achieve sustainability. Knowledge is identified as the driving force behind human behavior and its effect on the ecological and social environment. In connecting knowledge with sustainability, two concepts are introduced: knowledge of sustainability (KoS), which refers to the sustainability content of knowledge, and sustainability of knowledge (SoK), which denotes the dynamics of the continuing process of knowledge creation and application. To apply SoK and KoS, we argue that a cognitive interpretation of human behavior should be formulated within a knowledge management approach that incorporates the stages of knowledge creation, integration, and application and that ensures the critical evaluation of created knowledge. In order to show that our new approach is practical, we use existing research from the Dutch starch potato industry to reformulate possibilities for the enhancement of sustainability in terms of KoS and SoK. In addition, the research led to the development of a mechanism for evaluating knowledge. Group interaction, information technology, and decision support systems are used to realize knowledge integration. The combination of conceptual design and domain of application is common in the engineering sciences, where a design methodology is used to make the steps from conceptual design to functional design and technical implementation. A conceptual design may show ways to improve existing practices, which in turn might result in superior practices. Of course, it is necessary to empirically test the interventions in reality. For AGROBIOKON that has not been done, yet. © 2010 M.E. Sharpe, Inc. All rights reserved.

Sense making of (Social) sustainability: A behavioral and knowledge approach

Processes such as tendering, ordering, delivery, and paying are executed by several parties in almost all supply chains. However, none of these parties has a proper overview over the whole set of activities executed. Therefore, none of the parties can take the lead in business process redesign. Business processes are often not described in an explicit manner, and therefore they are not available for analysis. However, in the information system of each supply chain party, partial information about the business process are recorded. We claim that the overall distributed process can be induced, by using this partial information of all involved parties.

/pdf/discovering-distributed-processes-in-supply-chains-44h86iq7uw.pdf

Laura Maruster

Papers

A Process Mining Approach to Redesign Business Processes - A Case Study in Gas Industry

Strong convergence of the Mann iteration for alpha-demicontractive mappings

Knowledge claim evaluation: a fundamental issue for knowledge management

Sense making of (Social) sustainability: A behavioral and knowledge approach

Discovering Distributed Processes in Supply Chains