@article {16033,
	title = {Research Directions in Data Wrangling: Visualizations and Transformations for Usable and Credible Data},
	journal = {Information VisualizationInformation Visualization},
	volume = {10},
	year = {2011},
	month = {2011/10/01/},
	pages = {271 - 288},
	abstract = {In spite of advances in technologies for working with data, analysts still spend an inordinate amount of time diagnosing data quality issues and manipulating data into a usable form. This process of {\textquoteleft}data wrangling{\textquoteright} often constitutes the most tedious and time-consuming aspect of analysis. Though data cleaning and integration arelongstanding issues in the database community, relatively little research has explored how interactive visualization can advance the state of the art. In this article, we review the challenges and opportunities associated with addressing data quality issues. We argue that analysts might more effectively wrangle data through new interactive systems that integrate data verification, transformation, and visualization. We identify a number of outstanding research questions, including how appropriate visual encodings can facilitate apprehension of missing data, discrepant values, and uncertainty; how interactive visualizations might facilitate data transform specification; and how recorded provenance and social interaction might enable wider reuse, verification, and modification of data transformations.},
	keywords = {data cleaning, data quality, data transformation, Uncertainty, Visualization},
	isbn = {1473-8716, 1473-8724},
	doi = {10.1177/1473871611415994},
	url = {http://ivi.sagepub.com/content/10/4/271},
	author = {Kandel,Sean and Heer,Jeffrey and Plaisant, Catherine and Kennedy,Jessie and Van Ham,Frank and Riche,Nathalie Henry and Weaver,Chris and Lee,Bongshin and Brodbeck,Dominique and Buono,Paolo}
}
@conference {12167,
	title = {Using uncertainty as a model selection and comparison criterion},
	booktitle = {Proceedings of the 5th International Conference on Predictor Models in Software Engineering},
	series = {PROMISE {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {18:1{\textendash}18:9 - 18:1{\textendash}18:9},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Over the last 25+ years, software estimation research has been searching for the best model for estimating variables of interest (e.g., cost, defects, and fault proneness). This research effort has not lead to a common agreement. One problem is that, they have been using accuracy as the basis for selection and comparison. But accuracy is not invariant; it depends on the test sample, the error measure, and the chosen error statistics (e.g., MMRE, PRED, Mean and Standard Deviation of error samples). Ideally, we would like an invariant criterion. In this paper, we show that uncertainty can be used as an invariant criterion to figure out which estimation model should be preferred over others. The majority of this work is empirically based, applying Bayesian prediction intervals to some COCOMO model variations with respect to a publicly available cost estimation data set coming from the PROMISE repository.},
	keywords = {accuracy, Bayesian prediction intervals, Calibration, cost estimation, cost model, model evaluation, model selection, prediction interval, Uncertainty},
	isbn = {978-1-60558-634-2},
	doi = {10.1145/1540438.1540464},
	url = {http://doi.acm.org/10.1145/1540438.1540464},
	author = {Sarcia{\textquoteright},Salvatore Alessandro and Basili, Victor R. and Cantone,Giovanni}
}
@conference {16069,
	title = {Aligning temporal data by sentinel events: discovering patterns in electronic health records},
	booktitle = {Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems},
	series = {CHI {\textquoteright}08},
	year = {2008},
	month = {2008///},
	pages = {457 - 466},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Electronic Health Records (EHRs) and other temporal databases contain hidden patterns that reveal important cause-and-effect phenomena. Finding these patterns is a challenge when using traditional query languages and tabular displays. We present an interactive visual tool that complements query formulation by providing operations to align, rank and filter the results, and to visualize estimates of the intervals of validity of the data. Display of patient histories aligned on sentinel events (such as a first heart attack) enables users to spot precursor, co-occurring, and aftereffect events. A controlled study demonstrates the benefits of providing alignment (with a 61\% speed improvement for complex tasks). A qualitative study and interviews with medical professionals demonstrates that the interface can be learned quickly and seems to address their needs.},
	keywords = {electronic health record, Evaluation, Information Visualization, search, temporal data, Uncertainty},
	isbn = {978-1-60558-011-1},
	doi = {10.1145/1357054.1357129},
	url = {http://doi.acm.org/10.1145/1357054.1357129},
	author = {Wang,Taowei David and Plaisant, Catherine and Quinn,Alexander J. and Stanchak,Roman and Murphy,Shawn and Shneiderman, Ben}
}
@conference {13402,
	title = {Representing Tuple and Attribute Uncertainty in Probabilistic Databases},
	booktitle = {Seventh IEEE International Conference on Data Mining Workshops, 2007. ICDM Workshops 2007},
	year = {2007},
	month = {2007/10/28/31},
	pages = {507 - 512},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {There has been a recent surge in work in probabilistic databases, propelled in large part by the huge increase in noisy data sources-sensor data, experimental data, data from uncurated sources, and many others. There is a growing need to be able to flexibly represent the uncertainties in the data, and to efficiently query the data. Building on existing probabilistic database work, we present a unifying framework which allows a flexible representation of correlated tuple and attribute level uncertainties. An important capability of our representation is the ability to represent shared correlation structures in the data. We provide motivating examples to illustrate when such shared correlation structures are likely to exist. Representing shared correlations structures allows the use of sophisticated inference techniques based on lifted probabilistic inference that, in turn, allows us to achieve significant speedups while computing probabilities for results of user-submitted queries.},
	keywords = {attribute uncertainty, Computer science, Conferences, correlation structures, data mining, Data models, database management systems, Educational institutions, inference mechanisms, noisy data sources, probabilistic database, probabilistic inference, Probability distribution, Query processing, Relational databases, Sensor phenomena and characterization, tuple representation, Uncertainty, uncertainty handling},
	isbn = {978-0-7695-3019-2},
	doi = {10.1109/ICDMW.2007.11},
	author = {Sen,P. and Deshpande, Amol and Getoor, Lise}
}
@conference {12381,
	title = {Exploring the probabilistic design space of multimedia systems},
	booktitle = {14th IEEE International Workshop on Rapid Systems Prototyping, 2003. Proceedings},
	year = {2003},
	month = {2003/06/09/11},
	pages = {233 - 240},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we propose the novel concept of probabilistic design for multimedia systems and a methodology to quickly explore such design space at an early design stage. The probabilistic design is motivated by the challenge of how to design, but not over-design, multimedia embedded systems while systematically incorporating such application{\textquoteright}s performance requirements, uncertainties in execution time, and tolerance for reasonable execution failures. Our goal is to bridge the gap between real-time analysis and embedded software implementation for rapid and economic (multimedia) system prototyping. Our method takes advantage of multimedia system{\textquoteright}s unique features mentioned above to relax the rigid hardware requirements for software implementation and eventually avoid over-designing the system.},
	keywords = {economic system prototyping, embedded software implementation, Embedded system, Energy consumption, execution time uncertainties, Hardware, multimedia embedded systems, multimedia system prototyping, multimedia systems, performance requirements, probabilistic design space, rapid system prototyping, Real time systems, real-time analysis, reasonable execution failure tolerance, Resource management, software prototyping, Space exploration, Streaming media, systems analysis, Timing, Uncertainty},
	isbn = {0-7695-1943-1},
	doi = {10.1109/IWRSP.2003.1207053},
	author = {Shaoxiong Hua and Gang Qu and Bhattacharyya, Shuvra S.}
}
@article {17809,
	title = {Temporal probabilistic object bases},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {15},
	year = {2003},
	month = {2003/08//july},
	pages = {921 - 939},
	abstract = {There are numerous applications where we have to deal with temporal uncertainty associated with objects. The ability to automatically store and manipulate time, probabilities, and objects is important. We propose a data model and algebra for temporal probabilistic object bases (TPOBs), which allows us to specify the probability with which an event occurs at a given time point. In explicit TPOB-instances, the sets of time points along with their probability intervals are explicitly enumerated. In implicit TPOB-instances, sets of time points are expressed by constraints and their probability intervals by probability distribution functions. Thus, implicit object base instances are succinct representations of explicit ones; they allow for an efficient implementation of algebraic operations, while their explicit counterparts make defining algebraic operations easy. We extend the relational algebra to both explicit and implicit instances and prove that the operations on implicit instances correctly implement their counterpart on explicit instances.},
	keywords = {algebra;, algebraic, bases;, constraints;, data, database, database;, databases;, distribution, explicit, functions;, handling;, implicit, instances;, integrity;, intervals;, management;, model;, models;, object, object-oriented, operations;, probabilistic, probability, probability;, relational, temporal, theory;, Uncertainty, uncertainty;},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2003.1209009},
	author = {Biazzo,V. and Giugno,R. and Lukasiewicz,T. and V.S. Subrahmanian}
}
@conference {12766,
	title = {Probabilistic recognition of human faces from video},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {I-41 - I-44 vol.1 - I-41 - I-44 vol.1},
	abstract = {Most present face recognition approaches recognize faces based on still images. We present a novel approach to recognize faces in video. In that scenario, the face gallery may consist of still images or may be derived from a videos. For evidence integration we use classical Bayesian propagation over time and compute the posterior distribution using sequential importance sampling. The probabilistic approach allows us to handle uncertainties in a systematic manner. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach in both still-to-video and video-to-video scenarios with appropriate model choices.},
	keywords = {Bayes, Bayesian, CMU;, distribution;, Face, faces;, gallery;, handling;, human, image, images;, importance, likelihood;, methods;, NIST/USF;, observation, posterior, probabilistic, probability;, processing;, propagation;, recognition;, sampling;, sequential, signal, still, Still-to-video, Uncertainty, video, Video-to-video},
	doi = {10.1109/ICIP.2002.1037954},
	author = {Chellapa, Rama and Kruger, V. and Zhou,Shaohua}
}
@conference {13616,
	title = {On musical score recognition using probabilistic reasoning},
	booktitle = {Document Analysis and Recognition, 1999. ICDAR {\textquoteright}99. Proceedings of the Fifth International Conference on},
	year = {1999},
	month = {1999/09//},
	pages = {115 - 118},
	abstract = {We present a probabilistic framework for document analysis and recognition and illustrate it on the problem of musical score recognition. Our system uses an explicit descriptive model of the document class to find the most likely interpretation of a scanned document image. In contrast to the traditional pipeline architecture, we carry out all stages of the analysis with a single inference engine, allowing for an end-to-end propagation of the uncertainty. The global modeling structure is similar to a stochastic attribute grammar, and local parameters are estimated using hidden Markov models},
	keywords = {analysis;document, attribute, class;document, descriptive, document, engine;local, estimation;musical, grammar;attribute, grammars;character, handling;, image, interpretation;stochastic, Markov, mechanisms;music;uncertainty, model;global, modeling, models;inference, parameter, processing;image, propagation;explicit, reasoning;scanned, recognition;document, recognition;end-to-end, recognition;inference, recognition;probabilistic, score, structure;hidden, Uncertainty},
	doi = {10.1109/ICDAR.1999.791738},
	author = {Stuckelberg,M.V. and David Doermann}
}
@conference {14839,
	title = {Error propagation in full 3D-from-2D object recognition},
	booktitle = {Computer Vision and Pattern Recognition, 1994. Proceedings CVPR {\textquoteright}94., 1994 IEEE Computer Society Conference on},
	year = {1994},
	month = {1994/06//},
	pages = {892 - 898},
	abstract = {Robust recognition systems require a careful understanding of the effects of error in sensed features. Error in these image features results in uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty when model poses are based on matching three image and model points. This result applies to objects that are fully three-dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute this uncertainty when poses are based on any number of initial matches},
	keywords = {3D-from-2D, algorithm;, error, extraction;, feature, features;, handling;, image, initial, linear, matches;, object, Programming, programming;, propagation;, recognition, recognition;, robust, sequences;, systems;, Uncertainty, uncertainty;},
	doi = {10.1109/CVPR.1994.323920},
	author = {Alter,T. D. and Jacobs, David W.}
}
@article {14914,
	title = {Uncertainty Propagation in Model-Based Recognition.},
	year = {1994},
	month = {1994/12//},
	institution = {MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LAB},
	abstract = {Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three-dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three-dimensional objects, robust implementations of alignment interpretation-tree search, and transformation clustering. (AN)},
	keywords = {*IMAGE PROCESSING, *PATTERN RECOGNITION, algorithms, APPROXIMATION(MATHEMATICS), CYBERNETICS, ERROR CORRECTION CODES, image registration, Linear programming, MATCHING, MATHEMATICAL MODELS, PIXELS, PROJECTIVE TECHNIQUES., regions, THREE DIMENSIONAL, TWO DIMENSIONAL, Uncertainty},
	url = {http://stinet.dtic.mil/oai/oai?\&verb=getRecord\&metadataPrefix=html\&identifier=ADA295642},
	author = {Jacobs, David W. and Alter,T. D.}
}
