Research electronic data capture (REDCap)-A metadata-driven methodology and workflow process for providing translational research informatics support

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

Social Network Analysis

[서평]「The Unified Modeling Language User Guide」

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Designing and maintaining Web applications is one of the major challenges for the software industry of the year 2000. In this paper we present Web Modeling Language (WebML), a notation for specifying complex Web sites at the conceptual level. WebML enables the high-level description of a Web site under distinct orthogonal dimensions: its data content (structural model), the pages that compose it (composition model), the topology of links between pages (navigation model), the layout and graphic requirements for page rendering (presentation model), and the customization features for one-to-one content delivery (personalization model). All the concepts of WebML are associated with a graphic notation and a textual XML syntax. WebML specifications are independent of both the client-side language used for delivering the application to users, and of the server-side platform used to bind data to pages, but they can be effectively used to produce a site implementation in a specific technological setting. WebML guarantees a model-driven approach to Web site development, which is a key factor for defining a novel generation of CASE tools for the construction of complex sites, supporting advanced features like multi-device access, personalization, and evolution management. The WebML language and its accompanying design method are fully implemented in a pre-competitive Web design tool suite, called ToriiSoft.

Web Modeling Language (WebML): a modeling language for designing Web sites

FOREWORD by Adam Bosworth. PREFACE. PART ONE Technology Overview: Technologies for Web Applications. PART TWO Models for Designing Web Applications: Data Model. Hypertext Model. Content Management Model. Advanced Hypertext Model. PART THREE Design of Web Applications: Overview of the Development Process. Requirements Specifications. Data Design. Hypertext Design. PART FOUR Implementation of Web Applications: Architecture Design. Data Implementation. Hypertext Implementation. Advanced Hypertext Implementation. Tools for Model-Based Development of Web Applications. APPENDIX: Summary of WebML Elements. WebML Syntax. OCL Syntax. Summary of WebML Elements Implementation. REFERENCES. INDEX.

Designing Data-Intensive Web Applications

The exponential growth and capillar diffusion of the Web are nurturing a novel generation of applications, characterized by a direct business-to-customer relationship. The development of such applications is a hybrid between traditional IS development and Hypermedia authoring, and challenges the existing tools and approaches for software production. This paper investigates the current situation of Web development tools, both in the commercial and research fields, by identifying and characterizing different categories of solutions, evaluating their adequacy to the requirements of Web application development, enlightening open problems, and exposing possible future trends.

Tools and approaches for developing data-intensive Web applications: a survey

Information retrieval is a discipline that deals with the representation, storage, organization, and access to information items. The goal of information retrieval is to obtain information that might be useful or relevant to the user: library card cabinets are a “traditional” information retrieval system, and, in some sense, even searching for a visiting card in your pocket to find out a colleague’s contact details might be considered as an information retrieval task. In this chapter we introduce information retrieval as a scientific discipline, providing a formal characterization centered on the notion of relevance. We touch on some of its challenges and classic applications and then dedicate a section to its main evaluation criteria: precision and recall.

Piero Fraternali

Papers

Web Modeling Language (WebML): a modeling language for designing Web sites

Designing Data-Intensive Web Applications

Tools and approaches for developing data-intensive Web applications: a survey

An Introduction to Information Retrieval

XML-GL: A Graphical Language for Querying and Restructuring XML Documents.