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.Tanin, Egemen

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Now showing 1 - 4 of 4
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    Browsing Large Online Data Tables Using Generalized Query Previews (2001)
    (2005) Tanin, Egemen; Shneiderman, Ben; ISR
    Companies, government agencies, and other organizations are making their data available to the world over the Internet. They often use large online relational tables for this purpose. Users query such tables with front-ends that typically use menus or form fillin interfaces, but these interfaces rarely give users information about the contents and distribution of the data. Such a situation leads users to waste time and network resources posing queries that have zero-hit or mega-hit results. Generalized query previews enable efficient browsing of large online databases by supplying data distribution information to the users. The data distribution information provides continuous feedback about the size of the result set as the query is being formed. Our paper presents a user interface architecture and discusses recent experimental findings. Our prototype system, ExpO, provides a flexible user interface for research and testing. The user study shows that for exploratory querying tasks, generalized query previews speed user performance and reduce network load.
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    Broadening Access to Large Online Databases by Generalizing Query Previews (2000)
    (2005) Tanin, Egemen; Plaisant, Catherine; Shneiderman, Ben; ISR
    Companies, government agencies, and other types of organizations are making their large databases available to the world over the Internet. Current database front-ends do not give users information about the distribution of data. This leads many users to waste time and network resources posing queries that have either zero-hit or mega-hit result sets. Query previews form a novel visual approach for browsing large databases. Query previews supply data distribution information about the database that is being searched and give continuous feedback about the size of the result set for the query as it is being formed. On the other hand, query previews use only a few pre-selected attributes of the database. The distribution information is displayed only on these attributes. Unfortunately, many databases are formed of numerous relations and attributes. This paper introduces a generalization of query previews. We allow users to browse all of the relations and attributes of a database using a hierarchical browser. Any of the attributes can be used to display the distribution information, making query previews applicable to many public online databases.
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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.
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    Incremental Data Structures and Algorithms for Dynamic Query Interfaces
    (1997) Tanin, Egemen; Beigel, Richard; Shneiderman, Ben; ISR
    Dynamic query interfaces (DQIs) are a recently developed form of database access that provides continuous realtime feedback to the user during the query formulation process. Previous work shows that DQIs 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 approach to DQI algorithms that works well with large databases.