Joan C. Nordbotten

The effect of graphic style on data model interpretation

Abstract. Graphic data models are commonly used as a tool for presentation of information structures in the design, implementation, use and maintenance of the databases that support information systems. The methods proposed for database design assume that the use of graphic data models will enhance understanding of system specifications by both the end‐users and the implementers of the system. For this assumption to hold, the information presented in the graphic data model must be readily comprehensible so that the design, represented by the model, can be confirmed and implemented correctly. The lack of standard representations for graphic models has led to a variety of graphic styles. To date, there has been little focus on studying the effect graphic style has on model comprehension. We have studied the effect of three graphic styles proposed for data models on model legibility and interpretation. Our study shows a significant variation in model interpretation that can be attributed to the graphic syntax used. Graphic style appears to influence which model elements are included in the interpretation, as well as the way data models are read.

Credibility: Norwegian Students Evaluate Media Studies Web Sites

This paper investigates Norwegian university students’ evaluations of web site credibility and site authors’ vested interests with respect to a text-based academic site and an informational site with commercial support. Credibility ratings were higher for some aspects of the academic site even though the non-academic sit was rated more highly in presentation design and currency. Negative correlations emerged between academic level and confidence in deciding web site credibility and in detecting misrepresentations.

Search patterns in hypertext exhibits

As access to hypermedia documents becomes generally available, it is becoming increasingly important to understand how casual users search for information. We have studied the search patterns used in a small hypermedia museum exhibit. Our goal has been to identify preferred search strategies that can be supported for effective information retrieval from Web-based information systems. Our data indicates that users make little use of the embedded links enabling associative selection. Instead, topic selection was predominantly serial, through first-in-index and next-page sequences. Further, sessions were short in both time and number of documents viewed, indicating little general interest in exploring the exhibit.

Development of image collection representations for intelligent distributed systems

Numerous social networks, such as Flickr, Picasa, Instagram, and even news agencies, encourage their users to upload content in the form of annotated images. These image collections have become extremely large, containing hundreds of millions of images from millions of users. One consequence is that retrieval of a full set of thematic images, such as ancient ruins in Italy or mountain climbing in Peru, requires multiple collection searches. This is very time‐consuming for the user, particularly because these collections have not been developed for distributed system search and typically have no externally available collection description. In this paper, we present a system that can automatically generate an image collection description suitable for distributed search. Our approach enhances the image tag sets collected by the host system for development of a collection description that provides an extended vocabulary to match search query terms. The benefit of using collection descriptions in the image retrieval process is the ability to first select collections that are relevant to the query before retrieval of relevant images from those collections. This two‐step process improves query processing efficiency, because irrelevant collections need not be searched. Copyright

Perception of statistical presentations-investigated by means of Internet experiments

A main responsibility of a national statistical office is to disseminate statistical information about the state and development of the country to its citizens. In statistical presentation, it has been assumed that statistical tables are particularly useful and required for precise analysis and research, charts are suitable for fast visual orientation of the main trends within the area on which information is requested, and that textual or vocal presentations are best suited for mass media presentation, such as in newspapers, television, or Internet news services. To date, there have been few studies of how the general public recipient perceives information in these various formats. The primary objective has been to investigate how these three presentation forms for statistical facts are perceived and interpreted by general public readers of statistical information. A second purpose has been to study the utilization of a Web based experiment built on statistical design principles.

Capturing Diverse Usage Contexts forWeb-Based Images

The rapidly increasing number of image collections on the Internet require improvements in our image retrieval systems if these collections are to be easily available for browsing and searching by information seekers. Current techniques for image retrieval have known shortcomings that make it difficult to search for images based on their semantic content. We propose that an increased use of image context information can improve identification of image semantics, and may thus contribute to closing the gap between user needs for semantic image retrieval and the capabilities of current image retrieval systems. In this paper we present a new category of image context, called usage context, describe how usage context from multiple sources can be combined, and show how usage context information can be specified using a new context descriptor vocabulary CTXT.

Recognising Graphic Detail - An Experiment in User Interpretation of Data Models

Graphic data models are used as a communication tool between system users and a system analyst to support database design. It is a tacit assumption that users understand graphic models and can confirm that the model correctly depicts important requirements for the system. For this, the data model syntax and style must facilitate understanding of the information elements, interrelationships, and constraints represented. Unfortunately, little is known about how users read and interpret data models, what level of detail is seen, or how model type or graphic style influences model comprehension. Our observations, from a pilot study in graphic data model perception, indicate that many graph details are seen by less than half of the readers and that graphic style influences both the ease with which the models are read and model comprehension.