<xml>
  <records>
    <record>
       <contributors>
          <authors>
             <author>Xu, K.</author>
          </authors>
       </contributors>
       <titles>
          <title>
             CAFlux: A New EPICS Channel Archiver System
          </title>
       </titles>
		 <publisher>JACoW Publishing</publisher>
       <pub-location>Geneva, Switzerland</pub-location>
		 <isbn>2226-0358</isbn>
		 <isbn>978-3-95450-209-7</isbn>
		 <electronic-resource-num>10.18429/JACoW-ICALEPCS2019-WEPHA164</electronic-resource-num>
		 <language>English</language>
		 <pages>1470-1472</pages>
       <pages>WEPHA164</pages>
       <keywords>
          <keyword>EPICS</keyword>
          <keyword>MMI</keyword>
          <keyword>database</keyword>
          <keyword>status</keyword>
          <keyword>interface</keyword>
       </keywords>
       <work-type>Contribution to a conference proceedings</work-type>
       <dates>
          <year>2020</year>
          <pub-dates>
             <date>2020-08</date>
          </pub-dates>
       </dates>
       <urls>
          <related-urls>
              <url>https://doi.org/10.18429/JACoW-ICALEPCS2019-WEPHA164</url>
              <url>https://jacow.org/icalepcs2019/papers/wepha164.pdf</url>
          </related-urls>
       </urls>
       <abstract>
          We post a new EPICS channel archiver system that is being developed at LANSCE of Los Alamos National Laboratory. Different from the legacy archiver system, this system is built on InfluxDB database and Plotly visualization toolkits. InfluxDB is an open­source time series database system and provides a SQL-like language for fast storage and retrieval of time series data. By replacing the old archiving engine and index file with InfluxDB, we have a more robust, compact and stable archiving server. On a client side, we intro­duce a new implementation combined with asynchronous programming and multithreaded programming. We also describe a web-based archiver configuration system that is associ­ated with our current IRMIS system. To visualize the data stored, we use the JavaScript Plotly graphing library, another open source toolkit for time series data, to build front­end pages. In addition, we also develop a viewer application with more functionality including basic data statistics and simple arithmetic for channel values. Finally, we propose some ideas to integrate more statistical analysis into this system.
       </abstract>
    </record>
  </records>
</xml>
