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Articles 391 - 420 of 2694

Full-Text Articles in Computer Sciences

Scalable Euclidean Embedding For Big Data, Zohreh S. Alavi, Sagar Sharma, Lu Zhou, Keke Chen Jul 2015

Scalable Euclidean Embedding For Big Data, Zohreh S. Alavi, Sagar Sharma, Lu Zhou, Keke Chen

Kno.e.sis Publications

Euclidean embedding algorithms transform data defined in an arbitrary metric space to the Euclidean space, which is critical to many visualization techniques. At big-data scale, these algorithms need to be scalable to massive dataparallel infrastructures. Designing such scalable algorithms and understanding the factors affecting the algorithms are important research problems for visually analyzing big data. We propose a framework that extends the existing Euclidean embedding algorithms to scalable ones. Specifically, it decomposes an existing algorithm into naturally parallel components and non-parallelizable components. Then, data parallel implementations such as MapReduce and data reduction techniques are applied to the two categories of …

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Evaluating A Potential Commercial Tool For Healthcare Application For People With Dementia, Tanvi Banerjee, Pramod Anantharam, William L. Romine, Larry Wayne Lawhorne Jul 2015

Evaluating A Potential Commercial Tool For Healthcare Application For People With Dementia, Tanvi Banerjee, Pramod Anantharam, William L. Romine, Larry Wayne Lawhorne

Kno.e.sis Publications

The widespread use of smartphones and sensors has made physiology, environment, and public health notifications amenable to continuous monitoring. Personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context, converting relevant medical knowledge into actionable information for better and timely decisions. We apply these principles in the healthcare domain of dementia. Specifically, in this study we validate one of our sensor platforms to ascertain whether it will be suitable for detecting physiological changes that may help us detect changes in people with dementia. This study shows …

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Domain Specific Document Retrieval Framework For Real-Time Social Health Data, Swapnil Soni Jul 2015

Domain Specific Document Retrieval Framework For Real-Time Social Health Data, Swapnil Soni

Kno.e.sis Publications

With the advent of the web search and microblogging, the percentage of Online Health Information Seekers (OHIS) using these online services to share and seek health real-time information has in- creased exponentially. OHIS use web search engines or microblogging search services to seek out latest, relevant as well as reliable health in- formation. When OHIS turn to microblogging search services to search real-time content, trends and breaking news, etc. the search results are not promising. Two major challenges exist in the current microblogging search engines are keyword based techniques and results do not contain real-time information. To address these challenges, …

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"Time For Dabs": Analyzing Twitter Data On Butane Hash Oil Use, Raminta Daniulaityte, Robert G. Carlson, Farahnaz Golroo, Sanjaya Wijeratne, Edward W. Boyer, Silvia S. Martins, Ramzi W. Nahhas, Amit P. Sheth Jun 2015

"Time For Dabs": Analyzing Twitter Data On Butane Hash Oil Use, Raminta Daniulaityte, Robert G. Carlson, Farahnaz Golroo, Sanjaya Wijeratne, Edward W. Boyer, Silvia S. Martins, Ramzi W. Nahhas, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

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Trust Management: Multimodal Data Perspective, Krishnaprasad Thirunarayan Jun 2015

Trust Management: Multimodal Data Perspective, Krishnaprasad Thirunarayan

Kno.e.sis Publications

No abstract provided.

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Entity Recommendations Using Hierarchical Knowledge Bases, Siva Kumar Cheekula, Pavan Kapanipathi, Derek Doran, Prateek Jain, Amit P. Sheth May 2015

Entity Recommendations Using Hierarchical Knowledge Bases, Siva Kumar Cheekula, Pavan Kapanipathi, Derek Doran, Prateek Jain, Amit P. Sheth

Kno.e.sis Publications

Recent developments in recommendation algorithms have focused on integrating Linked Open Data to augment traditional algorithms with background knowledge. These developments recognize that the integration of Linked Open Data may or better performance, particularly in cold start cases. In this paper, we explore if and how a specific type of Linked Open Data, namely hierarchical knowledge, may be utilized for recommendation systems. We propose a content-based recommendation approaches that adapts a spreading activation algorithm over the DBpedia category structure to identify entities of interest to the user. Evaluation of the algorithm over the Movielens dataset demonstrates that our method yields …

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Domain Specific Document Retrieval Framework On Near Real-Time Social Health Data, Swapnil Soni May 2015

Domain Specific Document Retrieval Framework On Near Real-Time Social Health Data, Swapnil Soni

Kno.e.sis Publications

With the advent of web search and microblogging, the percentage of Online Health Information Seekers (OHIS) using these services to share and seek health information in real-time has increased exponentially. Recently, Twitter has emerged as one of the primary mediums for sharing and seeking of the latest information related to a variety of topics, including health information. Although Twitter is an excellent information source, the identification of useful information from the deluge of tweets is one of the major challenges. Twitter search is limited to keyword-based techniques to retrieve information for a given query and sometimes the results do not …

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Analyzing The Social Media Footprint Of Street Gangs, Sanjaya Wijeratne, Derek Doran, Amit P. Sheth, Jack Dustin May 2015

Analyzing The Social Media Footprint Of Street Gangs, Sanjaya Wijeratne, Derek Doran, Amit P. Sheth, Jack Dustin

Kno.e.sis Publications

Gangs utilize social media as a way to maintain threatening virtual presences, to communicate about their activities, and to intimidate others. Such usage has gained the attention of many justice service agencies that wish to create better crime prevention and judicial services. However, these agencies use analysis methods that are labor intensive and only lead to basic, qualitative data interpretations. This paper presents the architecture of a modern platform to discover the structure, function, and operation of gangs through the lens of social media. Preliminary analysis of social media posts shared in the greater Chicago, IL region demonstrate the platform’s …

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Big Data And Smart Cities, Amit P. Sheth Apr 2015

Big Data And Smart Cities, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

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Where Do We Develop? Discovering Regions For Urban Investment In Senegal, Derek Doran, Andrew Fox, Veena Mendiratta Apr 2015

Where Do We Develop? Discovering Regions For Urban Investment In Senegal, Derek Doran, Andrew Fox, Veena Mendiratta

Computer Science and Engineering Faculty Publications

The rate of urbanization in developing countries, defined as the speed with which a population shifts from rural to urban areas, is among the highest in the world. The disproportionate number of citizens that live in a small numbers of cities places incredible pressure on the largest cities in these countries, which may already be faced with limited resources, weak industrialization, and underdeveloped infrastructures. Urban planning researchers as well as policy makers have suggested that governments in developing countries make capital investments within and surrounding smaller cities to attract citizens away from large urban centers, thereby lowering the pressure placed …

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Context-Driven Automatic Subgraph Creation For Literature-Based Discovery, Delroy H. Cameron, Ramakanth Kavuluru, Thomas Rindflesch, Amit P. Sheth, Krishnaprasad Thirunarayan, Olivier Bodenreider Apr 2015

Context-Driven Automatic Subgraph Creation For Literature-Based Discovery, Delroy H. Cameron, Ramakanth Kavuluru, Thomas Rindflesch, Amit P. Sheth, Krishnaprasad Thirunarayan, Olivier Bodenreider

Kno.e.sis Publications

Background: Literature-based discovery (LBD) is characterized by uncovering hidden associations in non-interacting scientific literature. Prior approaches to LBD include use of: 1) domain expertise and structured background knowledge to manually filter and explore the literature, 2) distributional statistics and graph-theoretic measures to rank interesting connections and 3) heuristics to help eliminate spurious connections. However, manual approaches to LBD are not scalable and purely distributional approaches may not be sufficient to obtain insights into the meaning of poorly understood associations. While several graph-based approaches have the potential to elucidate associations, their effectiveness has not been fully demonstrated. A considerable degree of …

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Compensatory Enlargement Of Ossabaw Miniature Swine Coronary Arteries In Diffuse Atherosclerosis, Jenny Susana Choy, Tong Luo, Yunlong Huo, Thomas Wischgoll, Kyle Schultz, Shawn D. Teague, Michael Sturek, Ghassan S. Kassab Mar 2015

Compensatory Enlargement Of Ossabaw Miniature Swine Coronary Arteries In Diffuse Atherosclerosis, Jenny Susana Choy, Tong Luo, Yunlong Huo, Thomas Wischgoll, Kyle Schultz, Shawn D. Teague, Michael Sturek, Ghassan S. Kassab

Computer Science and Engineering Faculty Publications

Studies in human and non-human primates have confirmed the compensatory enlargement or positive remodeling (Glagov phenomenon) of coronary vessels in the presence of focal stenosis. To our knowledge, this is the first study to document arterial enlargement in a metabolic syndrome animal model with diffuse coronary artery disease (DCAD) in the absence of severe focal stenosis. Two different groups of Ossabaw miniature pigs were fed a high fat atherogenic diet for 4 months (Group I) and 12 months (Group II), respectively. Group I (6 pigs) underwent contrast enhanced computed tomographic angiography (CCTA) and intravascular ultrasound (IVUS) at baseline and after …

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Smart Data - How You And I Will Exploit Big Data For Personalized Digital Health And Many Other Activities, Amit P. Sheth Feb 2015

Smart Data - How You And I Will Exploit Big Data For Personalized Digital Health And Many Other Activities, Amit P. Sheth

Kno.e.sis Publications

No abstract provided.

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On Using Synthetic Social Media Stimuli In An Emergency Preparedness Functional Exercise, Andrew Hampton, Shreyansh Bhatt, Gary Alan Smith, Jeremy S. Brunn, Hemant Purohit, Valerie L. Shalin, John M. Flach, Amit P. Sheth Feb 2015

On Using Synthetic Social Media Stimuli In An Emergency Preparedness Functional Exercise, Andrew Hampton, Shreyansh Bhatt, Gary Alan Smith, Jeremy S. Brunn, Hemant Purohit, Valerie L. Shalin, John M. Flach, Amit P. Sheth

Kno.e.sis Publications

This paper details the creation and use of a massive (over 32,000 messages) artificially constructed 'Twitter' microblog stream for a regional emergency preparedness functional exercise. By combining microblog conversion, manual production, and a control set, we created a web based information stream providing valid, misleading, and irrelevant information to public information officers (PIOs) representing hospitals, fire departments, the local Red Cross, and city and county government officials. PIOs searched, monitored, and (through conventional channels) verified potentially actionable information that could then be redistributed through a personalized screen name. Our case study of a key PIO reveals several capabilities that social …

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Direct Optimization For Classification With Boosting, Shaodan Zhai Jan 2015

Direct Optimization For Classification With Boosting, Shaodan Zhai

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Boosting, as one of the state-of-the-art classification approaches, is widely used in the industry for a broad range of problems. The existing boosting methods often formulate classification tasks as a convex optimization problem by using surrogates of performance measures. While the convex surrogates are computationally efficient to globally optimize, they are sensitive to outliers and inconsistent under some conditions. On the other hand, boosting's success can be ascribed to maximizing the margins, but few boosting approaches are designed to directly maximize the margin. In this research, we design novel boosting algorithms that directly optimize non-convex performance measures, including the empirical …

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Contrast Pattern Aided Regression And Classification, Vahid Taslimitehrani Jan 2015

Contrast Pattern Aided Regression And Classification, Vahid Taslimitehrani

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Regression and classification techniques play an essential role in many data mining tasks and have broad applications. However, most of the state-of-the-art regression and classification techniques are often unable to adequately model the interactions among predictor variables in highly heterogeneous datasets. New techniques that can effectively model such complex and heterogeneous structures are needed to significantly improve prediction accuracy. In this dissertation, we propose a novel type of accurate and interpretable regression and classification models, named as Pattern Aided Regression (PXR) and Pattern Aided Classification (PXC) respectively. Both PXR and PXC rely on identifying regions in the data space where …

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Efficient Training Of Small Kernel Convolutional Neural Networks Using Fast Fourier Transform, Tyler Highlander Jan 2015

Efficient Training Of Small Kernel Convolutional Neural Networks Using Fast Fourier Transform, Tyler Highlander

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Convolutional neural networks (CNNs) are currently state-of-the-art for various classification tasks, but are computationally expensive. Propagating through the convolutional layers is very slow, as each kernel in each layer must sequentially calculate many inner products for a single forward and backward propagation which equates to O(N^2 n^2) per kernel per layer where the inputs are N x N arrays and the kernels are n x n arrays. Convolution can be efficiently performed as a Hadamard product in the frequency domain. The bottleneck is the transformation which has a cost of O(N^2 log_2 N) using the fast Fourier transform (FFT). However, …

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Feature Extraction Using Dimensionality Reduction Techniques: Capturing The Human Perspective, Ashley B. Coleman Jan 2015

Feature Extraction Using Dimensionality Reduction Techniques: Capturing The Human Perspective, Ashley B. Coleman

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The purpose of this paper is to determine if any of the four commonly used dimensionality reduction techniques are reliable at extracting the same features that humans perceive as distinguishable features. The four dimensionality reduction techniques that were used in this experiment were Principal Component Analysis (PCA), Multi-Dimensional Scaling (MDS), Isomap and Kernel Principal Component Analysis (KPCA). These four techniques were applied to a dataset of images that consist of five infrared military vehicles. Out of the four techniques three out of the five resulting dimensions of PCA matched a human feature. One out of five dimensions of MDS matched …

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Gender-Based Violence In 140 Characters Or Fewer: A #Bigdata Case Study Of Twitter, Hemant Purohit, Tanvi Banerjee, Andrew Hampton, Valerie L. Shalin, Nayanesh Bhandutia, Amit P. Sheth Jan 2015

Gender-Based Violence In 140 Characters Or Fewer: A #Bigdata Case Study Of Twitter, Hemant Purohit, Tanvi Banerjee, Andrew Hampton, Valerie L. Shalin, Nayanesh Bhandutia, Amit P. Sheth

Kno.e.sis Publications

Public institutions are increasingly reliant on data from social media sites to measure public attitude and provide timely public engagement. Such reliance includes the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine big (social) data consisting of nearly fourteen million tweets collected from Twitter over a period of ten months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. We demonstrate the utility of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events …

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A Workload Balanced Mapreduce Framework On Gpu Platforms, Yue Zhang Jan 2015

A Workload Balanced Mapreduce Framework On Gpu Platforms, Yue Zhang

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The MapReduce framework is a programming model proposed by Google to process large datasets. It is an efficient framework that can be used in many areas, such as social network, scientific research, electronic business, etc. Hence, more and more MapReduce frameworks are implemented on different platforms, including Phoenix (based on multicore CPU), MapCG (based on GPU), and StreamMR (based on GPU). However, these MapReduce frameworks have limitations, and they cannot handle the collision problem in the map phase, and the unbalanced workload problems in the reduce phase. To improve the performance of the MapReduce framework on GPGPUs, in this thesis, …

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Knowledge Enabled Approach To Predict The Location Of Twitter Users, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan Jan 2015

Knowledge Enabled Approach To Predict The Location Of Twitter Users, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan

Kno.e.sis Publications

Knowledge bases have been used to improve performance in applications ranging from web search and event detection to entity recognition and disambiguation. More recently, knowledge bases have been used to analyze social data. A key challenge in social data analysis has been the identification of the geographic location of online users in a social network such as Twitter. Existing approaches to predict the location of users, based on their tweets, rely solely on social media features or probabilistic language models. These approaches are supervised and require large training dataset of geo-tagged tweets to build their models. As most Twitter users …

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Mining Behavior Of Citizen Sensor Communities To Improve Cooperation With Organizational Actors, Hemant Purohit Jan 2015

Mining Behavior Of Citizen Sensor Communities To Improve Cooperation With Organizational Actors, Hemant Purohit

Kno.e.sis Publications

Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including ‘ …

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Value Oriented Big Data Processing With Applications, Krishnaprasad Thirunarayan Jan 2015

Value Oriented Big Data Processing With Applications, Krishnaprasad Thirunarayan

Kno.e.sis Publications

We discuss the nature of Big Data and address the role of semantics in analyzing and processing Big Data that arises in the context of Physical-Cyber-Social Systems. To handle Volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision- making. To handle Variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle Velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and …

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Extracting City Traffic Events From Social Streams, Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit P. Sheth Jan 2015

Extracting City Traffic Events From Social Streams, Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit P. Sheth

Kno.e.sis Publications

Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present …

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Understanding Social Effects In Online Networks, Huda Alhazmi, Swapna S. Gokhale, Derek Doran Jan 2015

Understanding Social Effects In Online Networks, Huda Alhazmi, Swapna S. Gokhale, Derek Doran

Kno.e.sis Publications

Understanding the motives behind people’s interactions online can offer sound bases to predict how a social network may evolve and also support a host of applications. We hypothesize that three offline social factors, namely, stature, relationship strength, and egocentricity may also play an important role in driving users’ interactions online. Therefore, we study the influence of these three social factors in online interactions by analyzing the transitivity in triads or three-way relationships among users. Analyzing transitivity through the lens of triad census for four popular social networks, namely, Facebook, Twitter, YouTube and Slashdot, we find that: (i) users’ interactions are …

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Semantic Gateway As A Service Architecture For Iot Interoperability, Pratikkumar Desai, Amit P. Sheth, Pramod Anantharam Jan 2015

Semantic Gateway As A Service Architecture For Iot Interoperability, Pratikkumar Desai, Amit P. Sheth, Pramod Anantharam

Kno.e.sis Publications

The Internet of Things (IoT) is set to occupy a substantial component of future Internet. The IoT connects sensors and devices that record physical observations to applications and services of the Internet. As a successor to technologies such as RFID and Wireless Sensor Networks (WSN), the IoT has stumbled into vertical silos of proprietary systems, providing little or no interoperability with similar systems. As the IoT represents future state of the Internet, an intelligent and scalable architecture is required to provide connectivity between these silos, enabling discovery of physical sensors and interpretation of messages between things. This paper proposes a …

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Knowledge-Driven Personalized Contextual Mhealth Service For Asthma Management In Children, Pramod Anantharam, Tanvi Banerjee, Amit P. Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan Jan 2015

Knowledge-Driven Personalized Contextual Mhealth Service For Asthma Management In Children, Pramod Anantharam, Tanvi Banerjee, Amit P. Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan

Kno.e.sis Publications

Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data …

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Using Ehrs For Heart Failure Therapy Recommendation Using Multidimensional Patient Similarity Analytics, Maryam Panahiazar, Vahid Taslimitehrani, Naveen L. Pereira, Jyotishman Pathak Jan 2015

Using Ehrs For Heart Failure Therapy Recommendation Using Multidimensional Patient Similarity Analytics, Maryam Panahiazar, Vahid Taslimitehrani, Naveen L. Pereira, Jyotishman Pathak

Kno.e.sis Publications

Electronic Health Records (EHRs) contain a wealth of information about an individual patient’s diagnosis, treatment and health outcomes. This information can be leveraged effectively to identify patients who are similar to each for disease diagnosis and prognosis. In recent years, several machine learning methods 1 have been proposed to assessing patient similarity, although the techniques have primarily focused on the use of patient diagnoses data from EHRs for the learning task. In this study, we develop a multidimensional patient similarity assessment technique that leverages multiple types of information from the EHR and predicts a medication plan for each new patient …

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Whole-Lake Primary Production Calculator, Colin D. Leong Jan 2015

Whole-Lake Primary Production Calculator, Colin D. Leong

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This work describes an implementation of a model for estimation of both benthic and phytoplanktonic primary production in lakes. The web application makes use of interpolation techniques to allow estimates of primary production using values for photosynthesis/irradiance parameters at only 5 depths. These estimates compare favorably in accuracy with estimates using values listed at over one hundred depths. Validation of the implementation was done by comparison with primary production results from the Northern Temperate Lakes Long Term Ecological Research database.

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Features For Ranking Tweets Based On Credibility And Newsworthiness, Jacob W. Ross Jan 2015

Features For Ranking Tweets Based On Credibility And Newsworthiness, Jacob W. Ross

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We create a robust and general feature set for learning to rank algorithms that rank tweets based on credibility and newsworthiness. In previous works, it has been demonstrated that when the training and testing data are from two distinct time periods, the ranker performs poorly. We improve upon previous work by creating a feature set that does not over fit a particular year or set of topics. This is critical given how people utilize social media changes as time progresses, and the topics discussed vary. In addition, we are constantly gaining new tweet data. Thus, it is important to be …

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