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Articles 61 - 90 of 646
Full-Text Articles in OS and Networks
Domain Specific Document Retrieval Framework On Near Real-Time Social Health Data, Swapnil Soni
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 …
Analyzing The Social Media Footprint Of Street Gangs, Sanjaya Wijeratne, Derek Doran, Amit P. Sheth, Jack Dustin
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 …
Big Data And Smart Cities, Amit P. Sheth
Context-Driven Automatic Subgraph Creation For Literature-Based Discovery, Delroy H. Cameron, Ramakanth Kavuluru, Thomas Rindflesch, Amit P. Sheth, Krishnaprasad Thirunarayan, Olivier Bodenreider
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 …
Smart Data - How You And I Will Exploit Big Data For Personalized Digital Health And Many Other Activities, Amit P. Sheth
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.
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
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 …
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
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 …
Knowledge Enabled Approach To Predict The Location Of Twitter Users, Revathy Krishnamurthy, Pavan Kapanipathi, Amit P. Sheth, Krishnaprasad Thirunarayan
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 …
Mining Behavior Of Citizen Sensor Communities To Improve Cooperation With Organizational Actors, Hemant Purohit
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 ‘ …
Value Oriented Big Data Processing With Applications, Krishnaprasad Thirunarayan
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 …
Extracting City Traffic Events From Social Streams, Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, Amit P. Sheth
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 …
Understanding Social Effects In Online Networks, Huda Alhazmi, Swapna S. Gokhale, Derek Doran
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 …
Semantic Gateway As A Service Architecture For Iot Interoperability, Pratikkumar Desai, Amit P. Sheth, Pramod Anantharam
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 …
Knowledge-Driven Personalized Contextual Mhealth Service For Asthma Management In Children, Pramod Anantharam, Tanvi Banerjee, Amit P. Sheth, Krishnaprasad Thirunarayan, Surendra Marupudi, Vaikunth Sridharan
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 …
Using Ehrs For Heart Failure Therapy Recommendation Using Multidimensional Patient Similarity Analytics, Maryam Panahiazar, Vahid Taslimitehrani, Naveen L. Pereira, Jyotishman Pathak
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 …
Is The Smartphone Smart In Kathmandu?, Seth Bird
Is The Smartphone Smart In Kathmandu?, Seth Bird
Independent Study Project (ISP) Collection
This is the extensive study of the smartphone in the developing country of Nepal, specifically the Kathmandu valley. Throughout my research I conducted various interviews with businesses, Tibetan refugees, and Nepali millennials (18yrs-33yrs) with the goal of identifying how the smartphone is used and understood. I chose the Kathmandu valley as my main area of research because the usage of smartphones in rural Nepal is extremely limited, and the valley represents the economic hub where progressive thinking flourishes. As a main objective I sought to understand how, if at all, the smartphone is used differently between Nepal and America. All …
Anonymized Video Analysis Methods And Systems, Marjorie Skubic, James M. Keller, Fang Wang, Derek T. Anderson, Erik Stone, Robert H. Luke Iii, Tanvi Banerjee, Marilyn J. Rantz
Anonymized Video Analysis Methods And Systems, Marjorie Skubic, James M. Keller, Fang Wang, Derek T. Anderson, Erik Stone, Robert H. Luke Iii, Tanvi Banerjee, Marilyn J. Rantz
Kno.e.sis Publications
Methods and systems for anonymized video analysis are described. In one embodiment, a first silhouette image of a person in a living unit may be accessed. The first silhouette image may be based on a first video signal recorded by a first video camera. A second silhouette image of the person in the living unit may be accessed. The second silhouette image may be of a different view of the person than the first silhouette image. The second silhouette image may be based on a second video signal recorded by a second video camera. A three-dimensional model of the person …
Protecting Web Servers From Web Robot Traffic, Derek Doran
Protecting Web Servers From Web Robot Traffic, Derek Doran
Kno.e.sis Publications
No abstract provided.
Triad-Based Role Discovery For Large Social Systems, Derek Doran
Triad-Based Role Discovery For Large Social Systems, Derek Doran
Kno.e.sis Publications
The social role of a participant in a social system conceptualizes the circumstances under which she chooses to interact with others, making their discovery and analysis important for theoretical and practical purposes. In this paper, we propose a methodology to detect such roles by utilizing the conditional triad censuses of ego-networks. These censuses are a promising tool for social role extraction because they capture the degree to which basic social forces push upon a user to interact with others in a system. Clusters of triad censuses, inferred from network samples that preserve local structural properties, define the social roles. The …
Discovering Perceptions In Online Social Media: A Probabilistic Approach, Derek Doran, Swapna S. Gokhale, Aldo Dagnino
Discovering Perceptions In Online Social Media: A Probabilistic Approach, Derek Doran, Swapna S. Gokhale, Aldo Dagnino
Kno.e.sis Publications
People across the world habitually turn to online social media to share their experiences, thoughts, ideas, and opinions as they go about their daily lives. These posts collectively contain a wealth of insights into how masses perceive their surroundings. Therefore, extracting people’s perceptions from social media posts can provide valuable information about pertinent issues such as public transportation, emergency conditions, and even reactions to political actions or other activities. This paper proposes a novel approach to extract such perceptions from a corpus of social media posts originating from a given broad geographical region. The approach divides the broad region into …
Online Information Searching For Cardiovascular Diseases: An Analysis Of Mayo Clinic Search Query Logs, Ashutosh Sopan Jadhav, Amit P. Sheth, Jyotishman Pathak
Online Information Searching For Cardiovascular Diseases: An Analysis Of Mayo Clinic Search Query Logs, Ashutosh Sopan Jadhav, Amit P. Sheth, Jyotishman Pathak
Kno.e.sis Publications
Since the early 2000’s, Internet usage for health information searching has increased significantly. Studying search queries can help us to understand users “information need” and how do they formulate search queries (“expression of information need”). Although cardiovascular diseases (CVD) affect a large percentage of the population, few studies have investigated how and what users search for CVD. We address this knowledge gap in the community by analyzing a large corpus of 10 million CVD related search queries from MayoClinic.com. Using UMLS MetaMap and UMLS semantic types/concepts, we developed a rule-based approach to categorize the queries into 14 health categories. We …
An Analysis Of Mayo Clinic Search Query Logs For Cardiovascular Diseases, Ashutosh Sopan Jadhav, Amit P. Sheth, Jyotishman Pathak
An Analysis Of Mayo Clinic Search Query Logs For Cardiovascular Diseases, Ashutosh Sopan Jadhav, Amit P. Sheth, Jyotishman Pathak
Kno.e.sis Publications
Increasingly, individuals are taking active participation in learning and managing their health by leveraging online resources. Understanding online health information searching behavior can help us to study what health topics users search for and how search queries are formulated. In this work, we analyzed 10 million cardiovascular diseases (CVD) related search queries from MayoClinic.com. We performed semantic analysis on the queries using UMLS MetaMap and analyzed structural and textual properties as well as linguistic characteristics of the queries.
Data Analytics For Power Utility Storm Planning, Lan Lin, Aldo Dagnino, Derek Doran, Swapna S. Gokhale
Data Analytics For Power Utility Storm Planning, Lan Lin, Aldo Dagnino, Derek Doran, Swapna S. Gokhale
Kno.e.sis Publications
As the world population grows, recent climatic changes seem to bring powerful storms to populated areas. The impact of these storms on utility services is devastating. Hurricane Sandy is a recent example of the enormous damages that storms can inflict on infrastructure, society, and the economy. Quick response to these emergencies represents a big challenge to electric power utilities. Traditionally utilities develop preparedness plans for storm emergency situations based on the experience of utility experts and with limited use of historical data. With the advent of the Smart Grid, utilities are incorporating automation and sensing technologies in their grids and …
A Keyword Sense Disambiguation Based Approach For Noise Filtering In Twitter, Sanjaya Wijeratne, Bahareh R. Heravi
A Keyword Sense Disambiguation Based Approach For Noise Filtering In Twitter, Sanjaya Wijeratne, Bahareh R. Heravi
Kno.e.sis Publications
In this paper, we describe an approach to filter out noisy data generated by keywords-based tweet filtering methods by performing Word Sense Disambiguation on those keywords used to collect tweets. We present the noise filtering problem as a binary classification problem and discuss our evaluation strategy which is to be carried out in future.
Rasp-Qs: Efficient And Confidential Query Services In The Cloud, Zohreh S. Alavi, Lu Zhou, James L. Powers, Keke Chen
Rasp-Qs: Efficient And Confidential Query Services In The Cloud, Zohreh S. Alavi, Lu Zhou, James L. Powers, Keke Chen
Kno.e.sis Publications
Hosting data query services in public clouds is an attractive solution for its great scalability and significant cost savings. However, data owners also have concerns on data privacy due to the lost control of the infrastructure. This demonstration shows a prototype for efficient and confidential range/kNN query services built on top of the random space perturbation (RASP) method. The RASP approach provides a privacy guarantee practical to the setting of cloudbased computing, while enabling much faster query processing compared to the encryption-based approach. This demonstration will allow users to more intuitively understand the technical merits of the RASP approach via …
Document Retrieval Using Predication Similarity, Kalpa Gunaratna
Document Retrieval Using Predication Similarity, Kalpa Gunaratna
Kno.e.sis Publications
Document retrieval has been an important research problem over many years in the information retrieval community. State-of-the-art techniques utilize various methods in matching documents to a given document including keywords, phrases, and annotations. In this paper, we propose a new approach for document retrieval that utilizes predications (subject-predicate-object triples) extracted from the documents. We represent documents as sets of predications. We measure the similarity between predications to compute the similarity between documents. Our approach utilizes the hierarchical information available in ontologies in computing concept-concept similarity, making the approach flexible. Predication-based document similarity is more precise and forms the basis for …
A Novel Web-Based Depth Video Rewind Approach Toward Fall Preventive Interventions In Hospitals, Moein Enayati, Tanvi Banerjee, Mihail Popescu, Marjorie Skubic, Marilyn J. Rantz
A Novel Web-Based Depth Video Rewind Approach Toward Fall Preventive Interventions In Hospitals, Moein Enayati, Tanvi Banerjee, Mihail Popescu, Marjorie Skubic, Marilyn J. Rantz
Kno.e.sis Publications
Falls in the hospital rooms are considered a huge burden on healthcare costs. They can lead to injuries, extended length of stay, and increase in cost for both the patients and the hospital. It can also lead to emotional trauma for the patients and their families [1]. Having Microsoft Kinects installed in the hospital rooms to capture and process every movement in the room, we deployed our previously developed fall-detection system to detect naturally occurring falls, generate a real-time fall alarm and broadcast it to hospital nurses for immediate intervention. These systems also store a processed and reduced version …
Assisting Coordination During Crisis: A Domain Ontology Based Approach To Infer Resource Needs From Tweets, Shreyansh Bhatt, Hemant Purohit, Andrew J. Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach
Assisting Coordination During Crisis: A Domain Ontology Based Approach To Infer Resource Needs From Tweets, Shreyansh Bhatt, Hemant Purohit, Andrew J. Hampton, Valerie L. Shalin, Amit P. Sheth, John Flach
Kno.e.sis Publications
Ubiquitous social media during crises provides citizen reports on the situation, needs and supplies. Previous research extracts resource needs directly from the text (e.g. "Power cut to Coney Island and Brighton beach" indicates a power need). This approach assumes that citizens derive and write about specific needs from their observations, properly specified for the emergency response system, an assumption that is not consistent with general conversational behavior. In our study, Twitter messages (tweets) from Hurricane Sandy in 2012 clearly indicate power blackouts, but not their probable implications (e.g. loss of power to hospital life support systems). We use a domain …
Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth
Active Learning With Efficient Feature Weighting Methods For Improving Data Quality And Classification Accuracy, Justin Martineau, Lu Chen, Doreen Cheng, Amit P. Sheth
Kno.e.sis Publications
Many machine learning datasets are noisy with a substantial number of mislabeled instances. This noise yields sub-optimal classification performance. In this paper we study a large, low quality annotated dataset, created quickly and cheaply using Amazon Mechanical Turk to crowdsource annotations. We describe computationally cheap feature weighting techniques and a novel non-linear distribution spreading algorithm that can be used to iteratively and interactively correcting mislabeled instances to significantly improve annotation quality at low cost. Eight different emotion extraction experiments on Twitter data demonstrate that our approach is just as effective as more computationally expensive techniques. Our techniques save a considerable …
Semantic Modelling Of Smart City Data, Stefan Bischof, Athanasios Karapantelakis, Cosmin-Septimiu Nechifor, Amit P. Sheth, Alessandra Mileo, Payam Barnaghi
Semantic Modelling Of Smart City Data, Stefan Bischof, Athanasios Karapantelakis, Cosmin-Septimiu Nechifor, Amit P. Sheth, Alessandra Mileo, Payam Barnaghi
Kno.e.sis Publications
Cities present an opportunity for rendering Web of Things-enabled services. According to the World Health Organization, population in cities will double by the middle of this century, while cities deal with increasingly pressing issues such as environmental sustainability, economic growth and citizen mobility. In this paper, we propose a discussion around the need for common semantic descriptions for smart city data to facilitate future services in "smart cities". We present examples of data that can be collected from cities, discuss issues around this data and put forward some preliminary thoughts for creating a semantic description model to describe and help …