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This article studies the optimization of parallel clustering throughput in the context of variant-based parallelism, which exploits commonalities and reuse among variant computations for multithreading scalability. This direction is motivated by challenging scientific applications where scientists have to execute multiple runs of clustering algorithms with different parameters to determine which ones best explain phenomena observed in empirical data. To make this process more efficient, we propose a novel set of optimizations to maximize the throughput of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), a frequently used algorithm for scientific data mining in astronomy, geoscience, and many other fields. Our approach executes multiple algorithm variants in parallel, computes clusters concurrently, and leverages heuristics to maximize the reuse of results from completed variants. As scientific datasets continue to grow, maximizing clustering throughput with our techniques may accelerate the search and identification of natural phenomena of interest with computational support, i.e., Computer-Aided Discovery. We present evaluations on a whole spectrum of datasets, such as geoscience data on space weather phenomena, astronomical data from the Sloan Digital Sky Survey on intermediate-redshift galaxies, as well as synthetic datasets to characterize performance properties. Selected results show a 1,115 percent performance improvement due to indexing tailored for variant-based clustering, and a 2,209 percent performance improvement when applying all of our proposed optimizations.

This paper studies a form of parallelism termed variant-based parallelism, which exploits commonalities and reuse among variant computations in order to improve multithreading scalability. The problem is motivated by space weather studies that aim to identify changes in the Earth's ionosphere caused by auroral activity, tsunamis, and earthquakes. Today it is common to execute cluster algorithm variants with different parameters in order to determine which ones best explain phenomena in empirical data. We propose a novel approach and a set of optimizations to maximize throughput in such clustering algorithms. This is achieved by executing multiple clustering algorithm variants in parallel and developing efficient approaches to concurrently cluster data and maximize the reuse of results from completed variants. We present evaluations on real-world space weather datasets with up to 5 million ionospheric total electron content data points as well as synthetic datasets with up to a million data points. Results show a 1101% performance improvement due to indexing tailored for variant-based clustering, and a 2209% performance improvement when applying all of our proposed optimizations. Our optimizations enable new approaches in computer-aided discovery and could enable the short run times required for early warning systems for natural hazards.

This paper describes the development of a networked collaborative hypermedia system intended to support groups of writers and scholars in writing and publishing hypertext fiction and criticism. The current system supports the importation of individually-developed Storyspace hypertext documents into a MUD-based collaborative workspace for integration and expansion, and allows for the immediate publication of these dynamically-generated multimedia documents onto the World-Wide Web. In addition, we provide a forms-based writing and linking interface to the text, so that writers can write using either the MUD-based or the forms-based authoring tools. Since the HTML is generated dynamically from our underlying database, we have added the capabilities to: allow for user negotiation of content and bandwidth (e.g., only small versions of the pictures, with the audio and text in German, with movies), provide the bandwidth-intensive media from distributed mirror sites. This system is being used by dozens of students, writers, and theorists around the world to support such projects as: hypertext writing and theory classes at Brown and Vassar, a hypermedia version of David Blair's feature-length film, WAX or the Discovery of Television among the Bees, a collaborative women's hypertext fiction writing group, and the creation of an electronic journal to discuss the impact of technology on writing practice.

This paper describes our experiments with narrative structure in nonlinear, interactive and networked forms of media. Through these explorations, we have begun to anticipate what types of tools will be useful to tell stories in multi-user 3D immersive environments incorporating synthetic actors.

We describe the structure and development of WAXweb, a dynamic MOO-based hypermedia database which is being used as a VRML server. We also discuss the future of 3D MUD-like systems, describing the particular problems of highly-interactive, distributed 3D scenes. So that we can begin to experiment with these areas using commonly-available VRML browsers, this paper describes several extensions for VRML 1.x which will allow for simple dynamic multiuser interactions.