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Big Data

Vasant Dhar, Sam Bowman
No abstract text is available yet for this article.
March 2017: Big Data
Matthew G Tuck, Farrokh Alemi, John F Shortle, Sanj Avramovic, Charles Hesdorffer
This article demonstrates how time-dependent, interacting, and repeating risk factors can be used to create more accurate predictive medicine. In particular, we show how emergence of anemia can be predicted from medical history within electronic health records. We used the Veterans Affairs Informatics and Computing Infrastructure database to examine a retrospective cohort of 9,738,838 veterans over an 11-year period. Using International Clinical Diagnoses Version 9 codes organized into 25 major diagnostic categories, we measured progression of disease by examining changes in risk over time, interactions in risk of combination of diseases, and elevated risk associated with repeated hospitalization for the same diagnostic category...
March 2017: Big Data
Lifu Huang, Jonathan May, Xiaoman Pan, Heng Ji, Xiang Ren, Jiawei Han, Lin Zhao, James A Hendler
The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases...
March 2017: Big Data
Arash Barfar, Balaji Padmanabhan
In a recent article by Barfar and Padmanabhan (2015), we demonstrated how television viewership data could predict presidential election outcomes in the United States. In this article, we examine predictive models using a snapshot of Nielsen's national data on television viewership. The study is conducted with high-dimensional low sample size (HDLSS) data, whereby we conduct a comparative analysis with and without feature reduction on the data from the 2012 elections. We find that simple "single-show models" often provided more insights and predictive accuracies than models from feature reduction...
March 2017: Big Data
Jay Aikat, Thomas M Carsey, Karamarie Fecho, Kevin Jeffay, Ashok Krishnamurthy, Peter J Mucha, Arcot Rajasekar, Stanley C Ahalt
The era of "big data" has radically altered the way scientific research is conducted and new knowledge is discovered. Indeed, the scientific method is rapidly being complemented and even replaced in some fields by data-driven approaches to knowledge discovery. This paradigm shift is sometimes referred to as the "fourth paradigm" of data-intensive and data-enabled scientific discovery. Interdisciplinary research with a hard emphasis on translational outcomes is becoming the norm in all large-scale scientific endeavors...
March 2017: Big Data
Verica Buchanan, Yafeng Lu, Nathan McNeese, Michael Steptoe, Ross Maciejewski, Nancy Cooke
Historically, domains such as business intelligence would require a single analyst to engage with data, develop a model, answer operational questions, and predict future behaviors. However, as the problems and domains become more complex, organizations are employing teams of analysts to explore and model data to generate knowledge. Furthermore, given the rapid increase in data collection, organizations are struggling to develop practices for intelligence analysis in the era of big data. Currently, a variety of machine learning and data mining techniques are available to model data and to generate insights and predictions, and developments in the field of visual analytics have focused on how to effectively link data mining algorithms with interactive visuals to enable analysts to explore, understand, and interact with data and data models...
March 2017: Big Data
Bart Baesens, Wouter Verbeke, Cristián Bravo
No abstract text is available yet for this article.
March 2017: Big Data
Philip N Howard, Gillian Bolsover
No abstract text is available yet for this article.
March 2017: Big Data
Nathan Ratliff, Franziska Meier, Daniel Kappler, Stefan Schaal
It has long been hoped that model-based control will improve tracking performance while maintaining or increasing compliance. This hope hinges on having or being able to estimate an accurate inverse dynamics model. As a result, substantial effort has gone into modeling and estimating dynamics (error) models. Most recent research has focused on learning the true inverse dynamics using data points mapping observed accelerations to the torques used to generate them. Unfortunately, if the initial tracking error is bad, such learning processes may train substantially off-distribution to predict well on actual observed acceleration rather than the desired accelerations...
December 2016: Big Data
Jeannette Bohg, Matei Ciocarlie, Javier Civera, Lydia E Kavraki
No abstract text is available yet for this article.
December 2016: Big Data
Yongqiang Huang, Matteo Bianchi, Minas Liarokapis, Yu Sun
Data sets is crucial not only for model learning and evaluation but also to advance knowledge on human behavior, thus fostering mutual inspiration between neuroscience and robotics. However, choosing the right data set to use or creating a new data set is not an easy task, because of the variety of data that can be found in the related literature. The first step to tackle this issue is to collect and organize those that are available. In this work, we take a significant step forward by reviewing data sets that were published in the past 10 years and that are directly related to object manipulation and grasping...
December 2016: Big Data
Juan Pablo Mendoza, Reid Simmons, Manuela Veloso
Autonomous robots often rely on models of their sensing and actions for intelligent decision making. However, when operating in unconstrained environments, the complexity of the world makes it infeasible to create models that are accurate in every situation. This article addresses the problem of using potentially large and high-dimensional sets of robot execution data to detect situations in which a robot model is inaccurate-that is, detecting context-dependent model inaccuracies in a high-dimensional context space...
December 2016: Big Data
Adrian Boteanu, Aaron St Clair, Anahita Mohseni-Kabir, Carl Saldanha, Sonia Chernova
This work seeks to leverage semantic networks containing millions of entries encoding assertions of commonsense knowledge to enable improvements in robot task execution and learning. The specific application we explore in this project is object substitution in the context of task adaptation. Humans easily adapt their plans to compensate for missing items in day-to-day tasks, substituting a wrap for bread when making a sandwich, or stirring pasta with a fork when out of spoons. Robot plan execution, however, is far less robust, with missing objects typically leading to failure if the robot is not aware of alternatives...
December 2016: Big Data
Matthias Plappert, Christian Mandery, Tamim Asfour
Linking human motion and natural language is of great interest for the generation of semantic representations of human activities as well as for the generation of robot activities based on natural language input. However, although there have been years of research in this area, no standardized and openly available data set exists to support the development and evaluation of such systems. We, therefore, propose the Karlsruhe Institute of Technology (KIT) Motion-Language Dataset, which is large, open, and extensible...
December 2016: Big Data
Vasant Dhar
No abstract text is available yet for this article.
September 2016: Big Data
Evangelos E Papalexakis, Christos Faloutsos
Multiaspect data are ubiquitous in modern Big Data applications. For instance, different aspects of a social network are the different types of communication between people, the time stamp of each interaction, and the location associated to each individual. How can we jointly model all those aspects and leverage the additional information that they introduce to our analysis? Tensors, which are multidimensional extensions of matrices, are a principled and mathematically sound way of modeling such multiaspect data...
September 2016: Big Data
Brittany Megan Bogle, Sanjay Mehrotra
Synthetic data are becoming increasingly important mechanisms for sharing data among collaborators and with the public. Multiple methods for the generation of synthetic data have been proposed, but many have short comings with respect to maintaining the statistical properties of the original data. We propose a new method for fully synthetic data generation that leverages linear and integer mathematical programming models in order to match the moments of the original data in the synthetic data. This method has no inherent disclosure risk and does not require parametric or distributional assumptions...
September 2016: Big Data
Steven Thompson, Stephen Varvel, Maciek Sasinowski, James P Burke
Big data and advances in analytical processes represent an opportunity for the healthcare industry to make better evidence-based decisions on the value generated by various tests, procedures, and interventions. Value-based reimbursement is the process of identifying and compensating healthcare providers based on whether their services improve quality of care without increasing cost of care or maintain quality of care while decreasing costs. In this article, we motivate and illustrate the potential opportunities for payers and providers to collaborate and evaluate the clinical and economic efficacy of different healthcare services...
September 2016: Big Data
Vasant Dhar, Nandan Nilekani, Shankar Maruwada, Nagaraju Pappu
No abstract text is available yet for this article.
September 2016: Big Data
Michal Ozery-Flato, Liat Ein-Dor, Naama Parush-Shear-Yashuv, Ranit Aharonov, Hani Neuvirth, Martin S Kohn, Jianying Hu
The availability of electronic health records creates fertile ground for developing computational models of various medical conditions. We present a new approach for detecting and analyzing patients with unexpected responses to treatment, building on machine learning and statistical methodology. Given a specific patient, we compute a statistical score for the deviation of the patient's response from responses observed in other patients having similar characteristics and medication regimens. These scores are used to define cohorts of patients showing deviant responses...
September 2016: Big Data
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