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	This ontology document is licensed under the Creative Commons
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=web+logs">
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  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=web+logs]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for web logs]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/496/The-Geolocation-of-Web-Logs-from-Textual-Clues"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/446/On-Using-a-Warehouse-to-Analyze-Web-Logs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/333/On-Mining-Web-Access-Logs"/>
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 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/496/The-Geolocation-of-Web-Logs-from-Textual-Clues">
  <title><![CDATA[The Geolocation of Web Logs from Textual Clues]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/496/The-Geolocation-of-Web-Logs-from-Textual-Clues</link>
  <description><![CDATA[Understanding the spatial distribution of people who author social media content is of growing interest for researchers and commerce. Blogging platforms depend on authors reporting their own location. However, not all authors report or reveal their location on their blog’s home page. Automated geolocation strategies using IP address and domain name are not adequate for determining an author’s location because most blogs are not self-hosted. In this paper we describe a method that uses the...]]></description>
  <dc:date>2009-08-29</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/446/On-Using-a-Warehouse-to-Analyze-Web-Logs">
  <title><![CDATA[On Using a Warehouse to Analyze Web Logs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/446/On-Using-a-Warehouse-to-Analyze-Web-Logs</link>
  <description><![CDATA[Analyzing Web Logs for usage and access trends can not only provide important information to web site developers and administrators, but also help in creating adaptive web sites. While there are many existing tools that generate fixed reports from web logs, they typically do not allow ad-hoc analysis queries. Moreover, such tools cannot discover hidden patterns of access embedded in the access logs. We describe a relational OLAP (ROLAP) approach for creating a web-log warehouse. This is popul...]]></description>
  <dc:date>2003-03-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/333/On-Mining-Web-Access-Logs">
  <title><![CDATA[On Mining Web Access Logs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/333/On-Mining-Web-Access-Logs</link>
  <description><![CDATA[The proliferation of information on the world wide web has made
the personalization of this information space a necessity. One
possible approach to web personalization is to mine typical user
profiles from the vast amount of historical data stored in access
logs. In the absence of any a priori knowledge, unsupervised
classification or clustering methods seem to be ideally suited to
analyze the semi-structured log data of user accesses. In this paper,
we define the notion of a “user s...]]></description>
  <dc:date>2000-05-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/445/Warehousing-and-Mining-Web-Logs">
  <title><![CDATA[Warehousing and Mining Web Logs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/445/Warehousing-and-Mining-Web-Logs</link>
  <description><![CDATA[Analyzing Web Logs for usage and access trends can not only provide important information to web site developers and administrators, but also help in creating adaptive web sites. While there are many existing tools that generate fixed reports from web logs, they typically do not allow ad-hoc analysis queries. Moreover, such tools cannot discover hidden patterns of access embedded in the access logs. We describe a relational OLAP (ROLAP) approach for creating a web-log warehouse. This is popul...]]></description>
  <dc:date>1999-11-02</dc:date>
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