This paper presents a project in experiential learning where students put knowledge of software engineering processes into action in a multidisciplinary project combining computer science and biology. Visualization serves as a primary element to bind the concepts of the two disciplines. Students seeking to further their experience and strengthen their skills in software engineering may choose to complete their senior capstone course working on an ongoing project to construct a toolkit for visualization of phylogenies generated from Avida experimental data. Avida provides a complex computational environment in which the evolution of digital organisms is tracked and analyzed to help find answers to a wide range of research questions. Student projects involve extensions of existing analytic and visualization techniques, as well as the addition of new, often novel, techniques. Importantly, to be successful a visualization technique must be appropriate for the domain in which it is to be used, requiring students to also understand elements of biology. It is our premise that exposing computer science students to the convergence of these two disciplines will strengthen their ability to work at different levels of abstraction and develop new conceptual frameworks to address current and future challenges in hardware and software.

Results of research in public health highly depend on the validity of data collection and internal research processes. To ensure validity, maintaining records of all assets in the research is very important. Researchers should be able to diagnose problems by tracing back the processes that leads to the problem. In this study, we design and implement BioRio system, a portable, flexible and secure information system for public health research that will reduce human errors, and increase traceability and efficiency.

This paper aims for designing algorithms for the problem of mining frequent traversal path patterns from very large Web logs with best possible efficiency. We devise two algorithms for this problem with the help of fast construction of "shallow" generalized suffix trees over a very large alphabet. These two algorithms have respectively provable linear time and sublinear complexity, and their performance is analyzed in comparison with the two a priori-like algorithms in (Chen et al., 1998) and the well-known Ukkonen algorithm for online suffix tree construction (1995). It is shown that these two algorithms are substantially efficient than the two apriori-like algorithms and the Ukkonen algorithm. The linear time algorithm has optimal performance in theory, while the sublinear time algorithm has better empirical performance.

Abstract In this article we report our research on building FEATURES—an intelligent web search engine that is able to perform real‐time adaptive feature (i.e., keyword) and document learning. Not only does FEATURES learn from the user's document relevance feedback, but it also automatically extracts and suggests indexing keywords relevant to a search query and learns from the user's keyword relevance feedback so that it is able to speed up its search process and to enhance its search performance. We design two efficient and mutual‐benefiting learning algorithms that work concurrently, one for feature learning and the other for document learning. FEATURES employs these algorithms together with an internal index database and a real‐time meta‐searcher to perform adaptive real‐time learning to find desired documents with as little relevance feedback from the user as possible. The architecture and performance of FEATURES are also discussed.

We investigate the applicability of on-line learning algorithms to the real-world problem of Web search. Consider that Web documents are indexed using n Boolean features. We first present a practically efficient online learning algorithm TW2 to search for Web documents represented by a disjunction of at most k relevant features. We then design and implement WebSail, a real-time adaptive Web search learner, with TW2 as its learning component. WebSail learns from the user's relevance feedback in real-time and helps the user to search for the desired Web documents. The architecture and performance of WebSail are also discussed.

Gis2web is a system intended to bridge the gap between GIS software and datasets and the WWW. The system operates by periodically accessing distributed datasets of GIS information and creating a common repository of WWW accessible information on a gis2web server. In this way gis2web users are provided seamless access to different datasets which may be of quite different forms. This paper discusses the architecture and communications issues of gis2web.

Article An environment for CS integrating hypertext, program design and language facilities (abstract) Share on Authors: W. A. Lawrence Fowler University of Texas, Pan American University of Texas, Pan AmericanSearch about this author , R. H. Fowler View Profile Authors Info & Claims SIGCSE '94: Proceedings of the twenty-fifth SIGCSE symposium on Computer science educationMarch 1994 https://doi.org/10.1145/191029.191216Online:12 March 1994Publication History 0citation178DownloadsMetricsTotal Citations0Total Downloads178Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Documentretrieval is a highly interactive process dealing with large amounts of information.Visual representations em both provide a means for managing the complexity of large information structures and support an interface style well suited to interactive manipulation.The system we have designed utilizes visually displayed graphic structures and a direct manipulation interface style to supply an integrated environment for retrieval.A common visually displayed network structure is used for query, document conteng and term relations.A query can be modified through direct manipulation of its visual form by incorporating terms from any other information structure the system displays An associative thesaurus of terms and an interdocument network provide information about a document collection that can complement other retrieval aids Visualization of these large data structures makes use of fisheye views and overview diagrams to help overcome some of the difficulties of orientation and navigation in large information structures