Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Shneiderman, BenSubject: search.filters.subject.direct manipulation

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    Interface and Data Architecture for Query Preview in Network Information Systems
    (1997) Doan, Khoa; Plaisant, Catherine; Shneiderman, Ben; Bruns, Tom; ISR
    There are numerous problems associated with formulating queries on networked information systems. These include data diversity, data complexity, network growth, varied user base, and slow network access. This paper proposes a new approach to a network query user interface which consists of two phases: query preview and query Refinement. This new approach is based on the concepts of dynamic queries and query previews, which guides users in rapidly and dynamically eliminating undesired datasets, reducing the data volume to manageable size, and refining queries locally before submission over a network. Examples of 2 applications are given: a Restaurant Finder and prototype with NASA's Earth Observing Systems -- Data Information Systems (EOSDIS). Data architecture is discussed and user's feedback is presented. Dynamic queries and query previews provide solutions to many existing problems in querying networked information systems.
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    Design and Evaluation of Incremental Data Structures and Algorithms for Dynamic Query Interfaces
    (1997) Tanin, Egemen; Beigel, Richard; Shneiderman, Ben; ISR
    Dynamic query interfaces (DQI) are a recently developed database access mechanism that provides continuous real-time feedback to the user during query formulation. Previous work shows that DQI are an elegant and powerful interface to small databases. Unfortunately, when applied to large databases, previous DQI algorithms slow to a crawl. We present a new incremental approach to DQI algorithms that works well with large databases, both in theory and in practice.