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Steed, Chad Allen

Publications and source records attributed to Steed, Chad Allen.

Method and system to discover and recommend interesting documents

Disclosed are several examples of systems that can read millions of news feeds per day about topics (e.g., your customers, competitors, markets, and partners), and provide a small set of the most relevant items to read to keep current with the overwhelming amount of information currently available. Topics of interest can be chosen by the user of the system for use as seeds. The seeds can be vectorized and compared with the target documents to determine their similarity. The similarities can be sorted from highest to lowest so that the most similar seed and target documents are at the top of the list. This output can be produced in XML format so that an RSS Reader can format the XML. This allows for easy Internet access to these recommendations.

99 GENERAL AND MISCELLANEOUS↗

Exploratory Data analysis ENvironment eXtreme scale (EDENx)

EDENx is a multivariate data visualization tool that allows interactive user driven analysis of large-scale data sets with high dimensionality. EDENx builds on our earlier system, called EDEN to enable analysis of more dimensions and larger scale data sets. EDENx provides an initial overview of summary statistics for each variable in the data set under investigation. EDENx allows the user to interact with graphical summary plots of the data to investigate subsets and their statistical associations. These plots include histograms, binned scatterplots, binned parallel coordinate plots, timeline plots, and graphical correlation indicators. From the EDENx interface, a user can select a subsample of interest and launch a more detailed data visualization via the EDEN system. EDENx is best suited for high-level, aggregate analysis tasks while EDEN is more appropriate for detail data investigations.

Steed, Chad Allen↗

Twitter Stream Archiver

The Twitter Archiver system allows a user to enter their Twitter developer account credentials (obtained separately from the Twitter developer website) and read from the freely available Twitter sample stream. The Twitter sample stream provides a random sample of the overall volume of tweets that are contributed by users to the system. The Twitter Archiver system consumes the stream and serializes the information to text files at some predefined interval. A separate utility reads the text files and creates a searchable index using the open source Apache Lucene text indexing system.

Steed, Chad Allen↗