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	This ontology document is licensed under the Creative Commons
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/473/Taming-Wild-Big-Data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/472/Many-Facets-of-Energy-Disaggregation"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1210/Security-Compliance-for-Smart-Manufacturing-using-Knowledgegraph-based-Digital-Twin"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1157/Leveraging-semantic-context-to-establish-access-controls-for-secure-cloud-based-electronic-health-records"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1156/Privacy-Preserving-Data-Sharing-in-Agriculture-Enforcing-Policy-Rules-for-Secure-and-Confidential-Data-Synthesis"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1066/CDFMR-A-Distributed-Statistical-Analysis-of-Stock-Market-Data-using-MapReduce-with-Cumulative-Distribution-Function"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/473/Taming-Wild-Big-Data">
  <title><![CDATA[Taming Wild Big Data]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/473/Taming-Wild-Big-Data</link>
  <description><![CDATA[In this week's Ebiquity meeting, Jennifer Sleeman will talk about "Taming Wild Big Data".

Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is...]]></description>
  <dc:date>2014-11-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/472/Many-Facets-of-Energy-Disaggregation">
  <title><![CDATA[Many Facets of Energy Disaggregation]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/472/Many-Facets-of-Energy-Disaggregation</link>
  <description><![CDATA[In this week's Ebiquity meeting, PhD student Nilavra Phatak from the UMBC Information Systems Department will discuss his work on "Many Facets of Energy Disaggregation".

The objective of energy disaggregation is to get the appliance-wise energy consumption from the whole home energy signal. The disaggregated energy consumption provides a better insight into the electrical usage and helps the consumers to modify usage in order to save money and energy. The application of energy disaggregati...]]></description>
  <dc:date>2014-10-29</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/468/Semantics-for-Big-Data-Security-and-Privacy">
  <title><![CDATA[Semantics for Big Data (,) Security and Privacy]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/468/Semantics-for-Big-Data-Security-and-Privacy</link>
  <description><![CDATA[A short talk at the NSF Workshop on Big Data Security and Privacy, held 16-17 September 2014 at the University of Texas at Dallas Richardson, TX.]]></description>
  <dc:date>2014-09-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/465/PROB-A-tool-for-Tracking-Provenance-and-Reproducibility-of-Big-Data-Experiments">
  <title><![CDATA[PROB:  A tool for Tracking Provenance and Reproducibility of Big Data Experiments]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/465/PROB-A-tool-for-Tracking-Provenance-and-Reproducibility-of-Big-Data-Experiments</link>
  <description><![CDATA[Reproducibility of computations and data provenance are very important goals to achieve in order to improve the quality of one's research.  Unfortunately, despite some efforts made in the past, it is still very hard to reproduce computational experiments with high degree of certainty.  The Big Data phenomenon in recent years makes this goal even harder to achieve.  In this work, we propose a tool that aids researchers to improve reproducibility of their experiments through automated keeping o...]]></description>
  <dc:date>2014-02-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/446/Predicting-Chronic-Diseases-with-Machine-Learning">
  <title><![CDATA[Predicting Chronic Diseases with Machine Learning]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/446/Predicting-Chronic-Diseases-with-Machine-Learning</link>
  <description><![CDATA[In recent years we saw an explosion of cheap genetic tests, which lead to the emergence of personalized medicine.  Personalized medicine is defined as practice of medicine that is tailored to specifics of individual patient.  My work addresses the problem of attempting to predict individual’s predisposition towards certain chronic diseases based on the individual’s genetic makeup.  The benefits of such work allow for more selective administration of invasive tests such as biopsies, which ...]]></description>
  <dc:date>2013-03-05</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/429/Correlation-Aware-Optimizations-for-Analytic-Databases">
  <title><![CDATA[Correlation Aware Optimizations for Analytic Databases]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/429/Correlation-Aware-Optimizations-for-Analytic-Databases</link>
  <description><![CDATA[Recent years have seen that the analysis of large data-sets is crucially important in a wide range of business, governmental, and scientific applications. For example, research projects in astronomy need to analyze petabytes of image data taken from telescopes. Providing a fast and scalable analytical data management system for such users has become increasingly important.
The major bottlenecks for analytics on such big data are disk- and network-I/O. Because the data is too large to fit in ...]]></description>
  <dc:date>2012-03-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/345/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text">
  <title><![CDATA[Coarse and Fine Grained Sentiment Analysis of Online Text]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/345/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text</link>
  <description><![CDATA[Sentiment analysis - the automated extraction of expressions of
positive and negative attitudes from text - has received a great
amount of attention over the last ten years. Over the same
period, via the widespread growth in the use of what we have come
to call social media, there has been an explosion in the amount
of publically available user generated text on the Web. This text
has the potential of providing a source of real time, time tagged
sentiments from people all over the glob...]]></description>
  <dc:date>2010-05-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/getnews/html/id/47/Ebiquity-team-wins-the-Best-Paper-Award-at-the-IEEE-BigDataSecurity-2022-Conference">
  <title><![CDATA[Ebiquity team wins the Best Paper Award at the IEEE BigDataSecurity 2022 Conference]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/47/Ebiquity-team-wins-the-Best-Paper-Award-at-the-IEEE-BigDataSecurity-2022-Conference</link>
  <description><![CDATA[The paper "Semantically Rich Access Control in Cloud EHR Systems Based on MA-ABE" authored by Sharad Dixit, Karuna Pande Joshi, SeungGeol Choi, and Lavanya Elluri won the Best Paper Award at the IEEE Big Data Security on the Cloud 2022 held in May 6-8 at Jinan, China.]]></description>
  <dc:date>2022-05-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/99/Policy-Compliant-Integration-of-Linked-Data">
  <title><![CDATA[Policy Compliant Integration of Linked Data]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/99/Policy-Compliant-Integration-of-Linked-Data</link>
  <description><![CDATA[Tim Finin and Anupam Joshi have received a $400,000 research award from the NSF Secure and Trustworthy  Cyberspace (SaTC) program  for a three year project to investigate how to better manage security and privacy constraints while querying semantically annotated linked data sources.  The project, Policy Compliant Integration of Linked Data, is a collaboration with researchers at M.I.T. and the University of Texas at Dallas.

The ubiquity of computing technology and the Internet have created...]]></description>
  <dc:date>2012-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1210/Security-Compliance-for-Smart-Manufacturing-using-Knowledgegraph-based-Digital-Twin">
  <title><![CDATA[Security Compliance for Smart Manufacturing using Knowledgegraph based Digital Twin]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1210/Security-Compliance-for-Smart-Manufacturing-using-Knowledgegraph-based-Digital-Twin</link>
  <description><![CDATA[The combination of Information Technology (IT) and Operational Technology (OT) in smart manufacturing, driven by smart factory innovations and Internet of Things (IoT) devices, generates vast, diverse, and rapidly evolving Big Data, which in turn increases cybersecurity and compliance issues. Adherence to security standards, such as NIST SP 800-171, which requires rigorous access control and audit reporting, is currently obstructed by the resource-intensive and error-prone aspects of manual e...]]></description>
  <dc:date>2025-12-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1157/Leveraging-semantic-context-to-establish-access-controls-for-secure-cloud-based-electronic-health-records">
  <title><![CDATA[Leveraging semantic context to establish access controls for secure cloud-based electronic health records]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1157/Leveraging-semantic-context-to-establish-access-controls-for-secure-cloud-based-electronic-health-records</link>
  <description><![CDATA[With the continuous growth of cloud-based Electronic Health Record (EHR) systems and medical data, medical organizations are particularly concerned about storing patient data to provide fast services while adhering to privacy and security concerns. Existing EHR systems often face challenges in handling heterogeneous data and maintaining good performance with data growth. These systems mostly use relational databases or partially store data in a knowledge graph, making it challenging to handle...]]></description>
  <dc:date>2024-04-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1156/Privacy-Preserving-Data-Sharing-in-Agriculture-Enforcing-Policy-Rules-for-Secure-and-Confidential-Data-Synthesis">
  <title><![CDATA[Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1156/Privacy-Preserving-Data-Sharing-in-Agriculture-Enforcing-Policy-Rules-for-Secure-and-Confidential-Data-Synthesis</link>
  <description><![CDATA[Big Data empowers the farming community with the information needed to optimize resource usage, increase productivity, and enhance the sustainability of agricultural practices. The use of Big Data in farming requires the collection and analysis of data from various sources such as sensors, satellites, and farmer surveys. While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as th...]]></description>
  <dc:date>2023-12-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1066/CDFMR-A-Distributed-Statistical-Analysis-of-Stock-Market-Data-using-MapReduce-with-Cumulative-Distribution-Function">
  <title><![CDATA[CDFMR: A Distributed Statistical Analysis of Stock Market Data using MapReduce with Cumulative Distribution Function]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1066/CDFMR-A-Distributed-Statistical-Analysis-of-Stock-Market-Data-using-MapReduce-with-Cumulative-Distribution-Function</link>
  <description><![CDATA[The stock market generates massive data daily on
top of a deluge of historical data. Investors and traders look to
stock market data analysis for assurance in their investments, a
prime indicator of our global economy. This has led to immense
popularity in the topic, and consequently, much research has been
done on stock market predictions and future trends. However,
due to the relatively slow electronic trading systems and order
processing times, the velocity of data, the variety of d...]]></description>
  <dc:date>2023-07-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1050/Drug-Abuse-Ontology-to-Harness-Web-Based-Data-for-Substance-Use-Epidemiology-Research-Ontology-Development-Study">
  <title><![CDATA[Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1050/Drug-Abuse-Ontology-to-Harness-Web-Based-Data-for-Substance-Use-Epidemiology-Research-Ontology-Development-Study</link>
  <description><![CDATA[Background: Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends.

Methods: The domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The 101 method includes determining the domain and scope of ontol...]]></description>
  <dc:date>2022-12-23</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1010/Trusted-Compliance-Enforcement-Framework-for-Sharing-Health-Big-Data">
  <title><![CDATA[Trusted Compliance Enforcement Framework for Sharing Health Big Data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1010/Trusted-Compliance-Enforcement-Framework-for-Sharing-Health-Big-Data</link>
  <description><![CDATA[COVID pandemic management via contact tracing and vaccine distribution has resulted in a large volume and high velocity of Health-related data being collected and exchanged among various healthcare providers, regulatory and government agencies, and people. This unprecedented sharing of sensitive health-related Big Data has raised technical challenges of ensuring robust data exchange while adhering to security and privacy regulations. We have developed a semantically rich and trusted Complianc...]]></description>
  <dc:date>2021-12-15</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/992/A-Policy-Driven-Approach-to-Secure-Extraction-of-COVID-19-Data-From-Research-Papers">
  <title><![CDATA[A Policy-Driven Approach to Secure Extraction of COVID-19 Data From Research Papers]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/992/A-Policy-Driven-Approach-to-Secure-Extraction-of-COVID-19-Data-From-Research-Papers</link>
  <description><![CDATA[The entire scientific and academic community has been mobilized to gain a better understanding of the COVID-19 disease and its impact on humanity. Most research related to COVID-19 needs to analyze large amounts of data in very little time. This urgency has made Big Data Analysis, and related questions around the privacy and security of the data, an extremely important part of research in the COVID-19 era. The White House OSTP has, for example, released a large dataset of papers related to CO...]]></description>
  <dc:date>2021-08-12</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/966/Ontology-driven-AI-and-Access-Control-Systems-for-Smart-Fisheries">
  <title><![CDATA[Ontology driven AI and Access Control Systems for Smart Fisheries]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/966/Ontology-driven-AI-and-Access-Control-Systems-for-Smart-Fisheries</link>
  <description><![CDATA[Increasing number of internet connected devices has paved a path for smarter ecosystems in various sectors such as agriculture, aquaculture, manufacturing, healthcare, etc. Especially, integrating technologies like big data, artificial intelligence (AI), blockchain, etc. with internet connected devices has increased efficiency and productivity. Therefore, fishery farmers have started adopting smart fisheries technologies to better manage their fish farms. Despite their technological advanceme...]]></description>
  <dc:date>2021-01-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/961/Measuring-Semantic-Similarity-across-EU-GDPR-Regulation-and-Cloud-Privacy-Policies">
  <title><![CDATA[Measuring Semantic Similarity across EU GDPR Regulation and Cloud Privacy Policies]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/961/Measuring-Semantic-Similarity-across-EU-GDPR-Regulation-and-Cloud-Privacy-Policies</link>
  <description><![CDATA[Data protection authorities formulate policies and rules which the service providers have to comply with to ensure security and privacy when they perform Big Data analytics using users Personally Identifiable Information (PII). The knowledge contained in the data regulations and organizational privacy policies are typically maintained as short unstructured text in HTML or PDF formats. Hence it is an open challenge to determine the specific regulation rules that are being addressed by a provid...]]></description>
  <dc:date>2020-12-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/962/Cloud-based-Encrypted-EHR-System-with-Semantically-Rich-Access-Control-and-Searchable-Encryption">
  <title><![CDATA[Cloud-based Encrypted EHR System with Semantically Rich Access Control and Searchable Encryption]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/962/Cloud-based-Encrypted-EHR-System-with-Semantically-Rich-Access-Control-and-Searchable-Encryption</link>
  <description><![CDATA[Cloud-based electronic health records (EHR) systems provide important security controls by encrypting patient data. However, these records cannot be queried without decrypting the entire record. This incurs a huge amount of burden in network bandwidth and the client-side computation. As the volume of cloud-based EHRs reaches Big Data levels, it is essential to search over these encrypted patient records without decrypting them to ensure that the medical caregivers can efficiently access the E...]]></description>
  <dc:date>2020-12-13</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text">
  <title><![CDATA[Coarse and Fine Grained Sentiment Analysis of Online Text]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/298/Coarse-and-Fine-Grained-Sentiment-Analysis-of-Online-Text</link>
  <description><![CDATA[Sentiment analysis - the automated extraction of expressions of positive and negative attitudes from text - has received a great amount of attention over the last ten years. Over the same period, via the widespread growth in the use of what we have come to call social media, there has been an explosion in the amount of publically available user generated text on the Web. This text has the potential of providing a source of real time, time tagged sentiments from people all over the globe.

T...]]></description>
  <dc:date>2010-05-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/370/Ontology-for-Data-Privacy-Policy">
  <title><![CDATA[Ontology for Data Privacy Policy]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/370/Ontology-for-Data-Privacy-Policy</link>
  <description><![CDATA[We have developed a detailed ontology, using semantic web language OWL, to define the range of information that should be included in the Privacy Policy documents. This ontology can be used by user communities to determine their privacy policies for using applications in the E-Commerce, Big Data and Cloud Computing domain.]]></description>
  <dc:date>2016-10-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/363/Semantics-for-big-data-security-and-privacy">
  <title><![CDATA[Semantics for big data security and privacy]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/363/Semantics-for-big-data-security-and-privacy</link>
  <dc:date>2014-09-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/374/Structural-Metadata-from-ArXiv-Articles">
  <title><![CDATA[Structural Metadata from ArXiv Articles]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/374/Structural-Metadata-from-ArXiv-Articles</link>
  <description><![CDATA[{
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "name": "Structural Metadata from ArXiv Articles",
  "version": "1.0",
  "license": "https://creativecommons.org/licenses/by-sa/4.0/",
  "description": "The dataset contains metadata encoded in JSON and extracted from more than one million arXiv articles that were put online before the end of 2016. The metadata includes the arXiv id, category names, title, author names, abstract, link to article, publication date and table ...]]></description>
  <dc:date>2017-09-01</dc:date>
 </item>
</rdf:RDF>
