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There needed to be a reliable way to send messages from R to Tika and back. The vast majority of time was spent on documenting the code, the introductory vignette, and continuous testing to integrate new code. If I stopped maintaining rtika, others could use their knowledge of the same standards to take over. They were helping create a maintainable package by following certain standards.
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The reviewers used a transparent onboarding process (see: and ) and taught about good documentation and coding style. I never distributed a package before on repositories such as CRAN or Github, and the rOpenSci group was the right place to learn how. The rOpenSci organization was ready to help. This was too good not to share, but I was apprehensive about maintaining a package over many years. The reduced time effort processing the entire batch led me to think about the broader applications of Tika. I estimate that starting Tika, loading the Java parsers each time, loading the file list from R, and reading the files back into an R object took a few extra seconds. Library ( 'rtika' ) timing % tika_text ( threads = 1 ) ) # average time elapsed *per document* parsed for rtika: timing / 2000 #> elapsed #> 0.006245įor this batch, the efficiency compared favorably to antiword, even with the overhead of loading Tika. Some documents parsed with the antiword package: They had been stored as ‘large object data’ in a database, and given the generic. The files did not have a helpful file extension. This package came together when parsing Word documents in a governmental archive. Now, Tika is the back-end of the rtika package. Tika began as part of Apache Nutch in 2005, and then became its own project in 2007 and a shared module in Lucene, Jackrabbit, Mahout, and Solr 1. Automatically producing information from semi-structured documents is a deceptively complex process that involves tacit knowledge of how document formats have changed over time, the gray areas of their specifications, and dealing with inconsistencies in metadata. Tika began as a common back-end for search engines and to reduce duplicated time and effort. I am blown away by the thousands of hours spent on the included parsers, such as Apache PDFBox, Apache Poi, and others 3. Microsoft Office document formats (Word, PowerPoint, Excel, etc.).It currently handles text or metadata extraction from over one thousand digital formats: The Tika auto-detect parser finds the content type of a file and processes it with an appropriate parser. Apache Tika is a common library to lower that complexity. The complexity of parsing can vary a lot. Parsing files is a common concern for many communities, including journalism 2, government, business, and academia. As the Babel fish allowed a person to understand Vogon poetry, Tika allows an analyst to extract text and objects from Microsoft Word. Although Tika does not yet translate natural language, it starts to tame the tower of babel of digital document formats. The Babel fish translates any natural language to any other. The Apache Tika parser is like the Babel fish in Douglas Adam’s book, “The Hitchhikers' Guide to the Galaxy” 1.