@conference {17162,
	title = {Finding comparable temporal categorical records: A similarity measure with an interactive visualization},
	booktitle = {IEEE Symposium on Visual Analytics Science and Technology, 2009. VAST 2009},
	year = {2009},
	month = {2009/10/12/13},
	pages = {27 - 34},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {An increasing number of temporal categorical databases are being collected: Electronic Health Records in healthcare organizations, traffic incident logs in transportation systems, or student records in universities. Finding similar records within these large databases requires effective similarity measures that capture the searcher{\textquoteright}s intent. Many similarity measures exist for numerical time series, but temporal categorical records are different. We propose a temporal categorical similarity measure, the M\&M (Match \& Mismatch) measure, which is based on the concept of aligning records by sentinel events, then matching events between the target and the compared records. The M\&M measure combines the time differences between pairs of events and the number of mismatches. To accom-modate customization of parameters in the M\&M measure and results interpretation, we implemented Similan, an interactive search and visualization tool for temporal categorical records. A usability study with 8 participants demonstrated that Similan was easy to learn and enabled them to find similar records, but users had difficulty understanding the M\&M measure. The usability study feedback, led to an improved version with a continuous timeline, which was tested in a pilot study with 5 participants.},
	keywords = {data visualisation, Educational institutions, Feedback, Information retrieval, interactive search tool, interactive systems, interactive visualization tool, large databases, M\&M Measure, Match \& Mismatch measure, Medical services, numerical time series, parameters customization, Particle measurements, Similan, similarity measure, Similarity Search, temporal categorical databases, Temporal Categorical Records, temporal databases, Testing, Time measurement, time series, transportation, usability, very large databases, visual databases, Visualization},
	isbn = {978-1-4244-5283-5},
	doi = {10.1109/VAST.2009.5332595},
	author = {Wongsuphasawat,K. and Shneiderman, Ben}
}
@conference {16944,
	title = {A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections},
	booktitle = {IEEE Symposium on Information Visualization, 2004. INFOVIS 2004},
	year = {2004},
	month = {2004///},
	pages = {65 - 72},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Exploratory analysis of multidimensional data sets is challenging because of the difficulty in comprehending more than three dimensions. Two fundamental statistical principles for the exploratory analysis are (1) to examine each dimension first and then find relationships among dimensions, and (2) to try graphical displays first and then find numerical summaries (D.S. Moore, (1999). We implement these principles in a novel conceptual framework called the rank-by-feature framework. In the framework, users can choose a ranking criterion interesting to them and sort 1D or 2D axis-parallel projections according to the criterion. We introduce the rank-by-feature prism that is a color-coded lower-triangular matrix that guides users to desired features. Statistical graphs (histogram, boxplot, and scatterplot) and information visualization techniques (overview, coordination, and dynamic query) are combined to help users effectively traverse 1D and 2D axis-parallel projections, and finally to help them interactively find interesting features},
	keywords = {axis-parallel projections, boxplot, color-coded lower-triangular matrix, computational complexity, computational geometry, Computer displays, Computer science, Computer vision, Data analysis, data mining, data visualisation, Data visualization, Displays, dynamic query, Educational institutions, exploratory data analysis, feature detection, feature detection/selection, Feature extraction, feature selection, graph theory, graphical displays, histogram, Information Visualization, interactive systems, Laboratories, Multidimensional systems, Principal component analysis, rank-by-feature prism, scatterplot, statistical analysis, statistical graphics, statistical graphs, unsupervised multidimensional data exploration, very large databases},
	isbn = {0-7803-8779-3},
	doi = {10.1109/INFVIS.2004.3},
	author = {Seo,J. and Shneiderman, Ben}
}
@conference {17878,
	title = {Improving access to multi-dimensional self-describing scientific datasets},
	booktitle = {3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings. CCGrid 2003},
	year = {2003},
	month = {2003/05/12/15},
	pages = {172 - 179},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Applications that query into very large multidimensional datasets are becoming more common. Many self-describing scientific data file formats have also emerged, which have structural metadata to help navigate the multi-dimensional arrays that are stored in the files. The files may also contain application-specific semantic metadata. In this paper, we discuss efficient methods for performing searches for subsets of multi-dimensional data objects, using semantic information to build multidimensional indexes, and group data items into properly sized chunks to maximize disk I/O bandwidth. This work is the first step in the design and implementation of a generic indexing library that will work with various high-dimension scientific data file formats containing semantic information about the stored data. To validate the approach, we have implemented indexing structures for NASA remote sensing data stored in the HDF format with a specific schema (HDF-EOS), and show the performance improvements that are gained from indexing the datasets, compared to using the existing HDF library for accessing the data.},
	keywords = {Application software, application-specific semantic metadata, Bandwidth, Computer science, database indexing, disk I/O bandwidth, distributed databases, Educational institutions, Indexing, indexing structures, Libraries, meta data, Middleware, multidimensional arrays, multidimensional datasets, Multidimensional systems, NASA, NASA remote sensing data, Navigation, query formulation, self-describing scientific data file formats, structural metadata, very large databases},
	isbn = {0-7695-1919-9},
	doi = {10.1109/CCGRID.2003.1199366},
	author = {Nam,B. and Sussman, Alan}
}
@conference {17056,
	title = {Design and evaluation of incremental data structures and algorithms for dynamic query interfaces},
	booktitle = {IEEE Symposium on Information Visualization, 1997. Proceedings},
	year = {1997},
	month = {1997/10/21/21},
	pages = {81 - 86},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A dynamic query interface (DQI) is a database access mechanism that provides continuous real-time feedback to the user during query formulation. Previous work shows that DQIs are elegant and powerful interfaces 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 and display updates that work well with large databases, both in theory and in practice.},
	keywords = {Algorithm design and analysis, Bars, Computer science, continuous real-time feedback, Data structures, data visualisation, Data visualization, database access mechanism, Displays, DQI algorithms, dynamic query interfaces, Feedback, Graphical user interfaces, Heuristic algorithms, incremental data structures, Information Visualization, large databases, Manipulator dynamics, NASA, query formulation, query languages, Query processing, real-time systems, small databases, User interfaces, very large databases, visual databases, visual languages},
	isbn = {0-8186-8189-6},
	doi = {10.1109/INFVIS.1997.636790},
	author = {Tanin,E. and Beigel,R. and Shneiderman, Ben}
}
@article {16740,
	title = {ADMS: a testbed for incremental access methods},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	volume = {5},
	year = {1993},
	month = {1993/10//},
	pages = {762 - 774},
	abstract = {ADMS is an advanced database management system developed-to experiment with incremental access methods for large and distributed databases. It has been developed over the past eight years at the University of Maryland. The paper provides an overview of ADMS, and describes its capabilities and the performance attained by its incremental access methods. This paper also describes an enhanced client-server architecture that allows an incremental gateway access to multiple heterogeneous commercial database management systems},
	keywords = {Add-drop multiplexers, ADMS, advanced database management system, client-server architecture, commercial database management systems, Computational modeling, Database systems, distributed databases, heterogeneous DBMS, incremental access methods, incremental gateway, Information retrieval, interoperability, join index, large databases, Navigation, network operating systems, Object oriented databases, Object oriented modeling, Query processing, System testing, very large databases, view index, Workstations},
	isbn = {1041-4347},
	doi = {10.1109/69.243508},
	author = {Roussopoulos, Nick and Economou,N. and Stamenas,A.}
}
