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 <channel rdf:about="http://ebiquity.umbc.edu//tag/html/data mining/?t=data+mining">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tag/html/data mining/?t=data+mining]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for data mining]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/448/Privacy-and-Security-in-Online-Social-Media"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/376/COVER-Model-Pivot-Index-for-Flexible-Adaptable-and-Agile-Systems"/>
      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/330/Map-Reduce-for-Scientific-Applications"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure"/>
      <rdf:li resource="http://ebiquity.umbc.edu/getnews/html/id/39/From-Need-to-Know-to-Need-to-Share-"/>
      <rdf:li resource="http://ebiquity.umbc.edu/project/html/id/17/Web-Data-Mining-and-Personalization"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/743/Collaborative-data-mining-for-clinical-trial-analytics"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/718/Parallelizing-Natural-Language-Techniques-for-Knowledge-Extraction-from-Cloud-Service-Level-Agreements"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/787/Intellectual-knowledge-extraction-from-online-social-data"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/554/Clinical-Genomic-Analysis-for-Disease-Prediction"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/796/A-more-appropriate-Protein-Classification-using-Data-Mining"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1185/National-Science-Foundation-Symposium-on-Next-Generation-of-Data-Mining-and-Cyber-Enabled-Discovery-for-Innovation-NGDM-07-Final-Report"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/354/StreetSmart-Traffic-Discovering-and-Disseminating-Automobile-Congestion-Using-VANETs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/350/StreetSmart-Traffic-Discovering-and-Disseminating-Automobile-Congestion-Using-VANET-s"/>
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      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/273/Analysis-of-Data-Mining-Algorithms"/>
      <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/304/Managing-the-Assured-Information-Sharing-Lifecycle"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/448/Privacy-and-Security-in-Online-Social-Media">
  <title><![CDATA[Privacy and Security in Online Social Media]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/448/Privacy-and-Security-in-Online-Social-Media</link>
  <description><![CDATA[With increase in usage of the Internet, there has been an exponential increase in the use of online social media on the Internet. Websites like Facebook, Google+, YouTube, Orkut, Twitter and Flickr have changed the way Internet is being used. There is a dire need to investigate, study and characterize privacy and security on online social media from various perspectives (computational, cultural, psychological). Real world scalable systems need to be built to detect and defend security and pri...]]></description>
  <dc:date>2013-03-11</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/376/COVER-Model-Pivot-Index-for-Flexible-Adaptable-and-Agile-Systems">
  <title><![CDATA[COVER Model Pivot Index for Flexible, Adaptable, and Agile Systems]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/376/COVER-Model-Pivot-Index-for-Flexible-Adaptable-and-Agile-Systems</link>
  <description><![CDATA[To support corporate business’ competition on speed to market for product and service development, generically modeled data structures have been  used in the development of vertical application software systems, and in storing XML and RDF data for its flexibility, adaptability, and agility. However, generic data models require multiple self-joins on a single table with a large volume of data, causing slow performance for business intelligence (BI) applications. Conversely, traditional speci...]]></description>
  <dc:date>2010-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/330/Map-Reduce-for-Scientific-Applications">
  <title><![CDATA[Map Reduce for Scientific Applications]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/330/Map-Reduce-for-Scientific-Applications</link>
  <description><![CDATA[In this week's Ebiquity Lab meeting David Chapman will talk about Map Reduce for Scientific Applications
			
Abstract:
Map Reduce is a programming paradigm popularized by google for very large
set computations.  It is a meta algorithm generic enough to solve a large
number of problems.  However, unless care is taken, this generality can
easily come at the price of a significant performance drop.  Current
infrastructure, such as Apache Hadoop, offers a practical solution for
many data ...]]></description>
  <dc:date>2009-11-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/312/Privacy-Preserving-Distributed-Data-Mining-A-Multi-objective-Optimization-and-Algorithmic-Game-theoretic-Approach">
  <title><![CDATA[Privacy Preserving Distributed Data Mining: A Multi-objective Optimization and Algorithmic Game-theoretic Approach]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/312/Privacy-Preserving-Distributed-Data-Mining-A-Multi-objective-Optimization-and-Algorithmic-Game-theoretic-Approach</link>
  <description><![CDATA[Use of technology for data collection and analysis has seen an unprecedented growth in the last couple of decades. Individuals and organizations generate huge amount of data through everyday activities. This data is either centralized for pattern identification or mined in a distributed fashion for efficient knowledge discovery and collaborative computation. This, obviously, has raised serious concerns about privacy issues. The data mining community has responded to this challenge by deve...]]></description>
  <dc:date>2009-09-16</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks">
  <title><![CDATA[Probabilistic Approximate Algorithms for Distributed Data Mining in Peer-to-Peer Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks</link>
  <description><![CDATA[Peer-to-peer(P2P) computing is emerging as a new distributed computing 
paradigm for novel applications that involves exchange of information 
among  peers with little centralized coordination. Analyzing data 
distributed in P2P networks requires peer-to-peer data mining algorithms 
that can mine the data without data centralization. However, 
replicating  result of centralized data mining in an exact fashion is 
often communication-wise expensive. Approximate algorithms can be a 
real...]]></description>
  <dc:date>2008-04-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/237/Research-Challenges-In-Data-Mining">
  <title><![CDATA[Research Challenges In Data Mining]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/237/Research-Challenges-In-Data-Mining</link>
  <description><![CDATA[Research in data mining has led to advanced knowledge discovery
technologies and applications. In this talk, we will discuss some
emerging research issues for advanced technologies and
applications in data mining and discuss some recent progress in
this direction, including (1) exploration of the power of pattern
mining, (2) analysis of multidimensional, heterogeneous and
evolving information network, (3) mining of fast changing data
streams, (4) mining of moving object data, RFID data...]]></description>
  <dc:date>2008-04-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/223/A-Game-Theoretic-Framework-for-Distributed-Multi-Party-Privacy-Preserving-Data-Mining">
  <title><![CDATA[A Game Theoretic Framework for Distributed Multi-Party Privacy Preserving Data Mining]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/223/A-Game-Theoretic-Framework-for-Distributed-Multi-Party-Privacy-Preserving-Data-Mining</link>
  <description><![CDATA[Privacy protection is increasingly becoming an important issue in many
data mining applications, particularly in the area of security
and surveillance. However, privacy preserving data analysis is a
non-trivial problem because of many reasons. First of all, privacy
is a social concept. In most multi-party data mining scenarios
participants have varying interests, objectives and expectations
about data privacy. Enforcing a single model of privacy with strong
assumptions regarding the be...]]></description>
  <dc:date>2007-11-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure">
  <title><![CDATA[Empowering Scientific Discovery by Distributed Data Mining on the Grid Infrastructure]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/215/Empowering-Scientific-Discovery-by-Distributed-Data-Mining-on-the-Grid-Infrastructure</link>
  <description><![CDATA[The grid-based computing paradigm has attracted much attention in recent years. The sharing of distributed computing resources (such as software, hardware, data, sensors, etc) is an important aspect of grid computing. Computational Grids focus on methods for handling compute intensive tasks while Data Grids are geared toward data-intensive computing. Grid-based computing has been put to use in several scientific disciplines such as astronomy, engineering, climate studies, ecology, biology and...]]></description>
  <dc:date>2007-09-28</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/getnews/html/id/39/From-Need-to-Know-to-Need-to-Share-">
  <title><![CDATA[From 'Need to Know’ to ‘Need to Share’:]]></title>
  <link>http://ebiquity.umbc.edu/getnews/html/id/39/From-Need-to-Know-to-Need-to-Share-</link>
  <description><![CDATA[From 'Need to Know’ to ‘Need to Share’: UMBC to Lead Six Campus-Team to Turn 9-11 Commission Intel-Sharing Reforms into Technology System

$7.5-million, Five-Year DoD Grant Partners UMBC With Purdue, Michigan, Illinois, Others

CONTACT: Chip Rose, UMBC News, 
410-455-5793, 
crose@umbc.edu


A six-campus team of computer scientists led by UMBC has been awarded a $7.5 million, five-year grant from the Department of Defense to turn the 9-11 Commission’s recommendations for bette...]]></description>
  <dc:date>2008-04-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/project/html/id/17/Web-Data-Mining-and-Personalization">
  <title><![CDATA[Web/Data Mining and Personalization]]></title>
  <link>http://ebiquity.umbc.edu/project/html/id/17/Web-Data-Mining-and-Personalization</link>
  <description><![CDATA[The evolution of the Internet into the Global Information Infrastructure, coupled with the immense
    popularity of the Web, has also enabled the ordinary citizen to become not just a consumer of information, but also its
    disseminator. The Web, then, is becoming the apocryphal Vox Populi. Given that there is this vast and ever growing
    amount of information, how does the average user quickly find what s/he is looking for -- a task in which the present
    day search engines don'...]]></description>
  <dc:date>1999-09-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/743/Collaborative-data-mining-for-clinical-trial-analytics">
  <title><![CDATA[Collaborative data mining for clinical trial analytics]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/743/Collaborative-data-mining-for-clinical-trial-analytics</link>
  <description><![CDATA[his paper proposes a collaborative data mining technique to provide multi-level analysis from clinical trials data. Clinical trials for clinical research and drug development generate large amount of data. Due to dispersed nature of clinical trial data, it remains a challenge to harness this data for analytics. In this paper, we propose a novel method using master data management (MDM) for analyzing clinical trial data, scattered across multiple databases, through collaborative data mining. O...]]></description>
  <dc:date>2015-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/718/Parallelizing-Natural-Language-Techniques-for-Knowledge-Extraction-from-Cloud-Service-Level-Agreements">
  <title><![CDATA[Parallelizing Natural Language Techniques for Knowledge Extraction from Cloud Service Level Agreements]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/718/Parallelizing-Natural-Language-Techniques-for-Knowledge-Extraction-from-Cloud-Service-Level-Agreements</link>
  <description><![CDATA[To efficiently utilize their cloud based services, consumers have to continuously monitor and manage the Service Level Agreements (SLA) that define the service performance measures. Currently this is still a time and labor intensive process since the SLAs are primarily stored as text documents. We have significantly automated the process of extracting, managing and monitoring cloud SLAs using natural language processing techniques and Semantic Web technologies. In this paper we describe our p...]]></description>
  <dc:date>2015-10-19</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/788/Mining-social-data-to-extract-intellectual-knowledge">
  <title><![CDATA[Mining social data to extract intellectual knowledge]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/788/Mining-social-data-to-extract-intellectual-knowledge</link>
  <description><![CDATA[Social data mining is an interesting phe-nomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pat-tern recognition, social mapping, responsibility distri-bution and many other applications. This paper presents a systematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect dif...]]></description>
  <dc:date>2012-09-04</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/787/Intellectual-knowledge-extraction-from-online-social-data">
  <title><![CDATA[Intellectual knowledge extraction from online social data]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/787/Intellectual-knowledge-extraction-from-online-social-data</link>
  <description><![CDATA[Social data mining is an interesting phenomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pattern recognition, social mapping, responsibility distribution and many other applications. This paper presents a systematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect differ...]]></description>
  <dc:date>2012-05-18</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/554/Clinical-Genomic-Analysis-for-Disease-Prediction">
  <title><![CDATA[Clinical-Genomic Analysis for Disease Prediction]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/554/Clinical-Genomic-Analysis-for-Disease-Prediction</link>
  <description><![CDATA[Recent advances in genomic research have generated vast amounts of information that can help identify individuals who differ in their susceptibility to a particular disease or response to a specific treatment. This information may offer solutions for the treatment of complex chronic diseases that are influenced by a wide array of factors. This vast amount of information brings critical challenges in applying advanced technology to synthesize clinical-genomic patient data. Synthesizing this in...]]></description>
  <dc:date>2011-07-06</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/796/A-more-appropriate-Protein-Classification-using-Data-Mining">
  <title><![CDATA[A more appropriate Protein Classification using Data Mining]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/796/A-more-appropriate-Protein-Classification-using-Data-Mining</link>
  <description><![CDATA[Research in bioinformatics is a complex phenomenon as it overlaps two knowledge domains, namely, biological and computer sciences. This paper has tried to introduce an efficient data mining approach for classifying proteins into some useful groups by representing them in hierarchy tree structure. There are several techniques used to classify proteins but most of them had few drawbacks on their grouping. Among them the most efficient grouping technique is used by PSIMAP. Even though PSIMAP (Pr...]]></description>
  <dc:date>2010-11-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1185/National-Science-Foundation-Symposium-on-Next-Generation-of-Data-Mining-and-Cyber-Enabled-Discovery-for-Innovation-NGDM-07-Final-Report">
  <title><![CDATA[National Science Foundation Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM’07): Final Report]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1185/National-Science-Foundation-Symposium-on-Next-Generation-of-Data-Mining-and-Cyber-Enabled-Discovery-for-Innovation-NGDM-07-Final-Report</link>
  <description><![CDATA[In this report, we review the events of the National Science Foundation Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation, which was held in Baltimore from October 10 to October 12, 2007. We discuss the key research issues identified by the participants and offer a set of recommendations that evolved out of the presentations and discussions in the symposium.]]></description>
  <dc:date>2007-10-10</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/354/StreetSmart-Traffic-Discovering-and-Disseminating-Automobile-Congestion-Using-VANETs">
  <title><![CDATA[StreetSmart Traffic: Discovering and Disseminating Automobile Congestion Using VANETs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/354/StreetSmart-Traffic-Discovering-and-Disseminating-Automobile-Congestion-Using-VANETs</link>
  <description><![CDATA[Automobile traffic is a major problem in developed societies. We collectively waste huge amounts of time and resources traveling through traffic congestion. Drivers choose the route that they believe will be the fastest; however traffic congestion can significantly change the duration of a trip. Significant savings of fuel and time could be achieved if traffic congestion patterns could be effectively discovered and disseminated to drivers. We propose a system that uses a standard GPS driving ...]]></description>
  <dc:date>2007-04-22</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/350/StreetSmart-Traffic-Discovering-and-Disseminating-Automobile-Congestion-Using-VANET-s">
  <title><![CDATA[StreetSmart Traffic: Discovering and Disseminating Automobile Congestion Using VANET's]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/350/StreetSmart-Traffic-Discovering-and-Disseminating-Automobile-Congestion-Using-VANET-s</link>
  <description><![CDATA[Automobile traffic is a major problem in developed societies.  We collectively waste huge amounts of time and resources traveling through traffic congestion.  Drivers choose the route that they believe will be the fastest; however traffic congestion can significantly change the duration of a trip.  Drivers that know the location of areas of slow traffic can choose other, more efficient routes.  We could save significant amounts of time if traffic congestion patterns could be effectively disco...]]></description>
  <dc:date>2006-08-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/425/Discovering-Domain-Specific-Composite-Kernels">
  <title><![CDATA[Discovering Domain-Specific Composite Kernels]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/425/Discovering-Domain-Specific-Composite-Kernels</link>
  <description><![CDATA[Kernel-based data mining algorithms, such as Support Vector
Machines, project data into high-dimensional feature spaces,
wherein linear decision surfaces correspond to non-linear decision
surfaces in the original feature space. Choosing a kernel
amounts to choosing a high-dimensional feature space,
and is thus a crucial step in the data mining process. Despite
this fact, and as a result of the difficulty of establishing that a
function is a positive definite kernel, only a few standard...]]></description>
  <dc:date>2005-07-09</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/research/area/id/14/Data-Mining">
  <title><![CDATA[Data Mining]]></title>
  <link>http://ebiquity.umbc.edu/research/area/id/14/Data-Mining</link>
  <dc:date>2026-09-07</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/273/Analysis-of-Data-Mining-Algorithms">
  <title><![CDATA[Analysis of Data Mining Algorithms]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/273/Analysis-of-Data-Mining-Algorithms</link>
  <description><![CDATA[A report comparing the various algorithms used in Data Mining]]></description>
  <dc:date>1999-03-30</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/304/Managing-the-Assured-Information-Sharing-Lifecycle">
  <title><![CDATA[Managing the Assured Information Sharing Lifecycle]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/304/Managing-the-Assured-Information-Sharing-Lifecycle</link>
  <description><![CDATA[We live in the information age, a time when data and knowledge is plentiful and easily moved, processed and mined by machines. This has made it easier to discover knowledge and more efficiently manage our affairs but has raised concerns about information security, confidentiality, privacy and trust. Balancing these is particularly urgent today in organizations responsible for national defense, law enforcement, health care, emergency services and finance. The 9/11 Commission addressed this in ...]]></description>
  <dc:date>2010-07-27</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/243/Research-challenges-for-The-web-semantics-and-data-mining-">
  <title><![CDATA[Research challenges for "The web, semantics and data mining"]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/243/Research-challenges-for-The-web-semantics-and-data-mining-</link>
  <description><![CDATA[A presentation for a panel at the NSF Symposium on Next Generation of Data Moning and Cyber-Enabled Discovery and Innovation]]></description>
  <dc:date>2007-10-10</dc:date>
 </item>
</rdf:RDF>
