@conference {13422,
	title = {An animated multivariate visualization for physiological and clinical data in the ICU},
	booktitle = {Proceedings of the 1st ACM International Health Informatics Symposium},
	series = {IHI {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {771 - 779},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Current visualizations of electronic medical data in the Intensive Care Unit (ICU) consist of stacked univariate plots of variables over time and a tabular display of the current numeric values for the corresponding variables and occasionally an alarm limit. The value of information is dependent upon knowledge of historic values to determine a change in state. With the ability to acquire more historic information, providers need more sophisticated visualization tools to assist them in analyzing the data in a multivariate fashion over time. We present a multivariate time series visualization that is interactive and animated, and has proven to be as effective as current methods in the ICU for predicting an episode of acute hypotension in terms of accuracy, confidence, and efficiency with only 30-60 minutes of training.},
	keywords = {computational physiology, Information Visualization, multivariate, time series},
	isbn = {978-1-4503-0030-8},
	doi = {10.1145/1882992.1883109},
	url = {http://doi.acm.org/10.1145/1882992.1883109},
	author = {Ord{\'o}{\~n}ez,Patricia and desJardins, Marie and Lombardi,Michael and Lehmann,Christoph U. and Fackler,Jim}
}
@conference {14227,
	title = {Learning shift-invariant sparse representation of actions},
	booktitle = {2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2010},
	month = {2010/06/13/18},
	pages = {2630 - 2637},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A central problem in the analysis of motion capture (MoCap) data is how to decompose motion sequences into primitives. Ideally, a description in terms of primitives should facilitate the recognition, synthesis, and characterization of actions. We propose an unsupervised learning algorithm for automatically decomposing joint movements in human motion capture (MoCap) sequences into shift-invariant basis functions. Our formulation models the time series data of joint movements in actions as a sparse linear combination of short basis functions (snippets), which are executed (or {\textquotedblleft}activated{\textquotedblright}) at different positions in time. Given a set of MoCap sequences of different actions, our algorithm finds the decomposition of MoCap sequences in terms of basis functions and their activations in time. Using the tools of L1 minimization, the procedure alternately solves two large convex minimizations: Given the basis functions, a variant of Orthogonal Matching Pursuit solves for the activations, and given the activations, the Split Bregman Algorithm solves for the basis functions. Experiments demonstrate the power of the decomposition in a number of applications, including action recognition, retrieval, MoCap data compression, and as a tool for classification in the diagnosis of Parkinson (a motion disorder disease).},
	keywords = {action characterization, Action recognition, action retrieval, action synthesis, Character recognition, data compression, human motion capture, HUMANS, Image matching, Image motion analysis, image representation, Image sequences, Information retrieval, joint movements, large convex minimizations, learning (artificial intelligence), learning shift-invariant sparse representation, Matching pursuit algorithms, minimisation, Minimization methods, MoCap data compression, Motion analysis, motion capture analysis, motion disorder disease, motion sequences, orthogonal matching pursuit, Parkinson diagnosis, Parkinson{\textquoteright}s disease, Pursuit algorithms, shift-invariant basis functions, short basis functions, snippets, sparse linear combination, split Bregman algorithm, time series, time series data, Unsupervised learning, unsupervised learning algorithm},
	isbn = {978-1-4244-6984-0},
	doi = {10.1109/CVPR.2010.5539977},
	author = {Li,Yi and Ferm{\"u}ller, Cornelia and Aloimonos, J. and Hui Ji}
}
@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 {18658,
	title = {Analysis of Computer Security Incident Data Using Time Series Models},
	year = {2008},
	month = {2008/11//},
	pages = {77 - 86},
	abstract = {Organizations face increasing challenges in addressing and preventing computer and network security incidents. There are financial consequences from security incidents. These include lost time and resources used during recovery, possible theft of personal and/or proprietary information, and reputational damage that may negatively impact stock prices or reduce consumer confidence in a company. Being able to understand and predict trends in computer and network security incidents can aid an organization with resource allocation for prevention of such incidents, as well as evaluation of mitigation strategies. We look at using time series models with a large set of security incident data. We examine appropriateness of the data for modeling and consider needed transformations. Parameter search and model selection criteria are discussed. Then, forecasts from time series models are compared to forecasts from Non-Homogeneous Poisson Process (NHPP) software reliability growth (SRG) models.},
	keywords = {Computer networks, computer security incident data, NETWORK SECURITY, resource allocation, security of data, telecommunication security, time series, time series model},
	doi = {10.1109/ISSRE.2008.39},
	author = {Condon,E. and He,A. and Michel Cukier}
}
@conference {17360,
	title = {Similarity-Based Forecasting with Simultaneous Previews: A River Plot Interface for Time Series Forecasting},
	booktitle = {Information Visualization, 2007. IV {\textquoteright}07. 11th International Conference},
	year = {2007},
	month = {2007/07/04/6},
	pages = {191 - 196},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Time-series forecasting has a large number of applications. Users with a partial time series for auctions, new stock offerings, or industrial processes desire estimates of the future behavior. We present a data driven forecasting method and interface called similarity-based forecasting (SBF). A pattern matching search in an historical time series dataset produces a subset of curves similar to the partial time series. The forecast is displayed graphically as a river plot showing statistical information about the SBF subset. A forecasting preview interface allows users to interactively explore alternative pattern matching parameters and see multiple forecasts simultaneously. User testing with 8 users demonstrated advantages and led to improvements.},
	keywords = {data driven forecasting method, data visualisation, Data visualization, Economic forecasting, forecasting preview interface, Graphical user interfaces, historical time series dataset, Laboratories, new stock offerings, partial time series, pattern matching, pattern matching search, Predictive models, river plot interface, Rivers, similarity-based forecasting, Smoothing methods, Technological innovation, Testing, time series, time series forecasting, Weather forecasting},
	isbn = {0-7695-2900-3},
	doi = {10.1109/IV.2007.101},
	author = {Buono,P. and Plaisant, Catherine and Simeone,A. and Aris,A. and Shneiderman, Ben and Shmueli,G. and Jank,W.}
}
@article {16081,
	title = {Exploring auction databases through interactive visualization},
	journal = {Decision Support Systems},
	volume = {42},
	year = {2006},
	month = {2006/12//},
	pages = {1521 - 1538},
	abstract = {We introduce AuctionExplorer, a suite of tools for exploring databases of online auctions. The suite combines tools for collecting, processing, and interactively exploring auction attributes (e.g., seller rating), and the bid history (price evolution represented as a time series). Part of AuctionExplorer{\textquoteright}s power comes from its coupling of the two information structures, thereby allowing exploration of relationships between them. Exploration can be directed by hypothesis testing or exploratory data analysis. We propose a process for visual data analysis and illustrate AuctionExplorer{\textquoteright}s operations with a dataset of eBay auctions. Insights may improve seller, bidder, auction house, and other vendors{\textquoteright} understanding of the market, thereby assisting their decision making process.},
	keywords = {Auction dynamics, Bid history, Online auctions, time series, user interface},
	isbn = {0167-9236},
	doi = {10.1016/j.dss.2006.01.001},
	url = {http://www.sciencedirect.com/science/article/pii/S0167923606000042},
	author = {Shmueli,Galit and Jank,Wolfgang and Aris,Aleks and Plaisant, Catherine and Shneiderman, Ben}
}
@article {17109,
	title = {Dynamic Query Tools for Time Series Data Sets: Timebox Widgets for Interactive Exploration},
	journal = {Information VisualizationInformation Visualization},
	volume = {3},
	year = {2004},
	month = {2004/03/20/},
	pages = {1 - 18},
	abstract = {Timeboxes are rectangular widgets that can be used in direct-manipulation graphical user interfaces (GUIs) to specify query constraints on time series data sets. Timeboxes are used to specify simultaneously two sets of constraints: given a set of N time series profiles, a timebox covering time periods x1{\textellipsis}x2 (x1 <= x2) and values y1{\textellipsis}y2 (y1 <= y2) will retrieve only those n√N that have values y1 <= y2 during all times x1 <= x <= x2. TimeSearcher is an information visualization tool that combines timebox queries with overview displays, query-by-example facilities, and support for queries over multiple time-varying attributes. Query manipulation tools including pattern inversion and {\textquoteleft}leaders \& laggards{\textquoteright} graphical bookmarks provide additional support for interactive exploration of data sets. Extensions to the basic timebox model that provide additional expressivity include variable time timeboxes, which can be used to express queries with variability in the time interval, and angular queries, which search for ranges of differentials, rather than absolute values. Analysis of the algorithmic requirements for providing dynamic query performance for timebox queries showed that a sequential search outperformed searches based on geometric indices. Design studies helped identify the strengths and weaknesses of the query tools. Extended case studies involving the analysis of two different types of data from molecular biology experiments provided valuable feedback and validated the utility of both the timebox model and the TimeSearcher tool. Timesearcher is available at http://www.cs.umd.edu/hcil/timesearcher},
	keywords = {angular queries, Bioinformatics, dynamic query, Graphical user interfaces, temporal data, time series, timeboxes, TimeSearcher, visual query},
	isbn = {1473-8716, 1473-8724},
	doi = {10.1057/palgrave.ivs.9500061},
	url = {http://ivi.sagepub.com/content/3/1/1},
	author = {Hochheiser,Harry and Shneiderman, Ben}
}
@conference {16921,
	title = {A dynamic query interface for finding patterns in time series data},
	booktitle = {CHI {\textquoteright}02 extended abstracts on Human factors in computing systems},
	series = {CHI EA {\textquoteright}02},
	year = {2002},
	month = {2002///},
	pages = {522 - 523},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Identification of patterns in time series data sets is a task that arises in a wide variety of application domains. This demonstration presents the timebox model of rectangular regions that specify constraints for dynamic queries over time series data sets, and the TimeSearcher application, which uses timeboxes as the basis of an interactive query tool.},
	keywords = {dynamic queries, Information Visualization, time series},
	isbn = {1-58113-454-1},
	doi = {10.1145/506443.506460},
	url = {http://doi.acm.org/10.1145/506443.506460},
	author = {Hochheiser,Harry and Shneiderman, Ben}
}
