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

Business Model: What It Is and What It Is Not

Traditional approaches to business process modeling and wo rkflow are based on activity flows (with data often an afterthought) or documents (with processing o ften an afterthought). In contrast, an emerging approach uses(business) artifacts, that combine data and process in an holistic manner as the ba sic building block. These correspond to key business entities w hich evolve as they pass through the business’s operation. This short paper motivates the approach,surveys research and its applications, and discusses how principles and techniques from database mana gement research can further develop the artifact-centric paradigm.

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Business Artifacts: A Data-centric Approach to Modeling Business Operations and Processes

The term “business model” has been misinterpreted and misused over the years, resulting in it being inadequately understood and applied by both practitioners and scholars. It is frequently confused with other popular terms in the management literature such as strategy, business concept, revenue model, economic model or even business process modeling. Our findings suggest that while business model describes what an organization currently is, it needs to be complemented with a strategy and capabilities in order to face upcoming changes. Besides clarifying the meaning and use of the business model terminology, we theorize about its roots through a combination of the resource-based view and transaction cost economics. Finally, we identify new avenues for further research such as the investigation of path dependency in a business model and the meaning of business model innovation. “The definition of a business model is murky at best. Most often, it seems to refer to a loose conception of how a company does business and generates revenue... The business model approach to management becomes an invitation for faulty thinking and self-delusion” (Porter, 2001, p. 73).“While the term ‘business model’ has gained widespread use in the practice community, the academic literature on this topic is fragmented and confounded by inconsistent definitions and construct boundaries” (George & Bock, 2011, p. 83).

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2181113_code1937431.pdf?abstractid=2181113&mirid=1

Business Model: What it is and What it is Not

A data-centric approach to business process and workflow modeling has been emerging over the past several years This short paper presents a structured framework for a class of data-centric business process models, which are based on "business artifacts" The paper provides a brief survey of research results on artifact-centric business process, and identifies a broad array of remaining research challenges

Artifact-Centric Business Process Models: Brief Survey of Research Results and Challenges

This paper introduces a new class of interactive interfaces that can be moved around to appear on ordinary objects and surfaces anywhere in a space. By dynamically adapting the form, function, and location of an interface to suit the context of the user, such steerable interfaces have the potential to offer radically new and powerful styles of interaction in intelligent pervasive computing spaces. We propose defining characteristics of steerable interfaces and present the first steerable interface system that combines projection, gesture recognition, user tracking, environment modeling and geometric reasoning components within a system architecture. Our work suggests that there is great promise and rich potential for further research on steerable interfaces.

Steerable interfaces for pervasive computing spaces

Fast production of a solution is a necessity in the world of competitive IT consulting business today. In engagements where early user interface design mock-ups are needed to visualize proposed business processes, the need to quickly create UI becomes prominent very early in the process. Our work aims to speed up the UI design process, enabling rapid creation of low-fidelity UI design with traditional user-centered design thinking but different tooling concepts. This paper explains the approach and the rationale behind our model and tools. One key focal point is in leveraging business process models as a starting point of the UI design process. The other focal point is on using a model-driven approach with designer-centered tools to eliminate some design overheads, to help manage a large design space, and to cope with changes in requirements. We used examples from a real business engagement to derive and strengthen this work.

/pdf/user-centered-design-and-business-process-modeling-cross-2dayz3famf.pdf

User-centered design and business process modeling: cross road in rapid prototyping tools

How can interactive computer interfaces be created anywhere in a space without wiring or modifying objects or people? We propose using steerable projector-camera systems employing computer vision to realize such “steerable interfaces.” In this paper, we illustrate the potential of the new kinds of applications enabled by steerable interfaces and discuss the challenges imposed on computer vision through the presentation of four application prototypes: a collaborative assembly task coordinator; a multi-surface presentation viewer; a ubiquitous product finder for retail environments; and an interactive merchandise shelf.

/pdf/applications-of-steerable-projector-camera-systems-iqjq70y01z.pdf

Applications of Steerable Projector-Camera Systems

This paper describes a prototype retail environment in which information interactions occur in situ, within the actual space of the merchandise. By combining a steerable projected display and recognition of user gestures and actions, and user position tracking through peripheral cameras, we have developed several innovative interaction techniques designed to augment the reality of a retail store.

Augmenting a retail environment using steerable interactive displays

User interface (UI) design is an integral part of the software design process. The UI design not only outlines the look and feel of the system, but also helps in flushing out the requirements - by identifying what data is visible to and processed by different users. However, in any SOA methodology, UI design is typically considered out of scope. In this paper, we highlight the importance of UI design specification in the SOA landscape, from a service- identification perspective. Service identification, which is a key activity in any SOA-based development, involves specification of business requirements as a set of granular service definitions. We propose an approach for harvesting the UI design specification to define service requirements for the intended system; more specifically in terms of information and business service requirements. Our approach consists of the following steps: (1) capture user interface design in a format amenable to automated analysis, with appropriate references to data and process models, (2) identify requirements for information services from data that is displayed in the user interface, and (3) identify business service requirements from the UI navigation flow and the links between the UI and the business process model. To illustrate our approach, we present a case study using the Amazon associate Web services. The study demonstrates how the use of UI designs can lead to better service identification. The proposed approach can complement any existing SOA methodology that follows a top-down approach to identify services.

Noi Sukaviriya

Papers

Steerable interfaces for pervasive computing spaces

User-centered design and business process modeling: cross road in rapid prototyping tools

Applications of Steerable Projector-Camera Systems

Augmenting a retail environment using steerable interactive displays

Using User Interface Design to Enhance Service Identification