A graphical user interface for Boolean query specification

Online information repositories commonly provide keyword search facilities through textual query languages based on Boolean logic. However, there is evidence to suggest that the syntactic demands of such languages can lead to user errors and adversely affect the time that it takes users to form queries. Users also face difficulties because of the conflict in semantics between AND and OR when used in Boolean logic and English language. Analysis of usage logs for the New Zealand Digital Library (NZDL) show that few Boolean queries contain more than three terms, use of the intersection operator dominates and that query refinement is common. We suggest that graphical query languages, in particular Venn-like diagrams, can alleviate the problems that users experience when forming Boolean expressions with textual languages. A study of the utility of Venn diagrams for query specification indicates that with little or no training users can interpret and form Venn-like diagrams in a consistent manner which accurately correspond to Boolean expressions. We describe VQuery, a Venn-diagram based user interface to the New Zealand Digital Library (NZDL). In a study which compared VQuery with a standard textual Boolean interface, users took significantly longer to form queries and produced more erroneous queries when using VQuery. We discuss the implications of these results and suggest directions for future work. Received: 15 December 1997 / Revised: June 1999     

An Introduction to Using XML for the Management of Laboratory Data

Extensible Markup Language (XML) and the XML Path Language (XPath) are introduced with software examples demonstrating how one can use them to write laboratory data management programs. Topics explored include XML document creation, manipulation, and searching. Programming examples make use of the Microsoft® XML Parser library and the Visual Basic programming language. The problem of managing microplate screening data is used as an illustration. Source code for all examples can be downloaded from http://www.labprogrammer.net.     

Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression

In this paper, we propose the application of the Kernel Principal Component Analysis (PCA) technique for feature selection in a high-dimensional feature space, where input variables are mapped by a Gaussian kernel. The extracted features are employed in the regression problems of chaotic Mackey–Glass time-series prediction in a noisy environment and estimating human signal detection performance from brain event-related potentials elicited by task relevant signals. We compared results obtained using either Kernel PCA or linear PCA as data preprocessing steps. On the human signal detection task, we report the superiority of Kernel PCA feature extraction over linear PCA. Similar to linear PCA, we demonstrate de-noising of the original data by the appropriate selection of various nonlinear principal components. The theoretical relation and experimental comparison of Kernel Principal Components Regression, Kernel Ridge Regression and ε-insensitive Support Vector Regression is also provided.     

Designing Agent-Based Electronic Employment Markets

Two modes of matching people with jobs prevail at present: 1) hierarchical planning and 2) distributed markets. Each has strengths and limitations, but few systems have been designed to take advantage of strengths corresponding to both. With evolving information technology, however, the job-matching process could be accomplished far more equitably and efficiently using web-based markets within the firm, and intelligent agents offer excellent potential to help both potential employees and employers find one another in a distributed, electronic marketplace. But realizing this potential goes well beyond simply changing the rules of internal job matching or making agent technology available to job searchers. Rather, the corresponding markets and technologies must be designed, together, to mutually accomplish the desired results (e.g., efficient and effective matching) and conform to necessary properties (e.g., market clearing). Through the research described in this paper, we draw from Game Theory results to assess the feasibility of using two-sided matching algorithms to address this market-design problem. We also draw from current agent research to address the information technology dimension of the problem by implementing a proof-of-concept multi-agent system to enact, automate and support the corresponding market solution. This paper integrates the key economic and technological elements required to design robust electronic employment markets. And the corresponding research provides new knowledge and insight into co-development of the requisite economic markets and agent technologies.     

Iterated Phantom Induction: A Knowledge-Based Approach to Learning Control

We advance a knowledge-based learning method that allows prior domain knowledge to be effectively utilized by machine learning systems. The domain knowledge is incorporated not into the learning algorithm itself but instead affects only the training data. The domain knowledge is used to explain and then transform the actual training examples into a more informative set of imaginary, or phantom examples. These phantom examples are added to the training set; the experienced examples are discarded. A new control policy is induced from the phantom training set. This policy is then exercised, yielding additional training points, and the process repeats.We investigate the performance of this method in a stylized air-hockey domain which demands a difficult nonlinear control policy. Our experiments show that, surprisingly, an accurate policy can be learned even if the domain theory is only imprecise and approximate. We advance an interpretation which indicates that the information available from a plausible qualitative domain theory is sufficient for robust successful learning. This interpretation is used to make a number of predictions which are tested in subsequent experiments. The outcomes confirm the interpretation and the robustness of the approach.     

Alleviating convergence problems in Group Support Systems

Not all Group Support Systems are identical, as is demonstrated by their software implementations. We discuss two existing implementations of group support tools and the process models underlying them. We demonstrate that fundamental to both processes is the merging or integration of individual data. Based on this and other empirical research, the Shared Context Model (SCM) of cooperative work is adopted and we show that it supports existing processes and others. We expect that groups will find merging their work easier with the SCM. This model is presented and embedded in the architecture and implementation of four group tools. Because these tools are destined to be used by dispersed groups, synchronously or asynchronously, an object-based communication and control mechanism is incorporated. Finally, as graphics and multi-tasking have been shown to be increasingly important, the tools are implemented in Microsoft Windows for personal computers attached to local area networks.     

Integrating natural language understanding with document structure analysis

Document understanding, the interpretation of a document from its image form, is a technology area which benefits greatly from the integration of natural language processing with image processing. We have developed a prototype of an Intelligent Document Understanding System (IDUS) which employs several technologies: image processing, optical character recognition, document structure analysis and text understanding in a cooperative fashion. This paper discusses those areas of research during development of IDUS where we have found the most benefit from the integration of natural language processing and image processing: document structure analysis, optical character recognition (OCR) correction, and text analysis. We also discuss two applications which are supported by IDUS: text retrieval and automatic generation of hypertext links     

Designing and equipping a usability laboratory

The goal of usability lab design is to create a space where high quality data capture occurs in an environment that looks and feels like the workplace of the product that is being tested. The lab must be part recording studio and part flexible work environment. Since most work environments are not recording studios, achieving a balance between simulation of the work environment and high quality data capture presents a challenge to usability lab designers. Steps can be taken with a user-centred design process to insure that a usability lab design meets this goal. This paper describes such a user-centred design process and how it. in combination with practical architectural and equipment guidelines derived from the authors' past experience, can be used in the design and redesign of future labs. The authors also discuss what changed in the present lab at NCR as a result of these guidelines.     

Breaking away from the conventional 'usability lab': the Customer-Centered Design Group at Tektronix, Inc.

The conventional usability lab is primarily responsible for testing prototypes and products to determine if customers will accept a new design. Often this testing comes too late in the development cycle to allow major design or product changes to occur. In the Customer-Centered Design Group at Tektronix Labs, the usability lab is a small part of our group's involvement in the entire design life cycle of a Tektronix product. We work with design groups to bring the benefits of a usability lab to all phases of design, beginning with understanding our customer's current system and work processes to assessing the competitor's strengths and weaknesses to simulating and evaluating design alternatives. Our 'lab' is often on the road; meeting with customers where they work, working with design teams to simulate and prototype designs, and evaluating designs with our customers. To keep in touch with customers and to keep product development focused, we feel a usability group must break down the barriers inherent in a conventional testing suite. By breaking these barriers we can better determine what customers need and how these needs are addressed throughout the entire product life cycle.     

MANAGEMENT SUPPORT SYSTEM AT PROMUS Using IT to Ensure Customer Satisfaction

Information-based organizations are structured to function with as small and efficient a staff as possible. To this end, executives at The Promus Companies are using IT to spread decision-making authority and responsibility for customer satisfaction and customer service throughout the organization.