Scientific data curation and processing with Apache Tika

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Presentation on Tika by Chris Mattmann in the Lucene track of ApacheConNA 2010.

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Scientific data curation and processing with Apache Tika

  1. 1. Scientific data curation and processing with Apache Tika Chris A. Mattmann Senior Computer Scientist, NASA Jet Propulsion Laboratory Adjunct Assistant Professor, Univ. of Southern California Member, Apache Software Foundation
  2. 2. Roadmap • 1st part of the talk – Why Tika? – What is Tika? – What are the current versions of Tika? – What can it do? • 2nd part of the talk – NASA Earth Science Data Systems – Data System Needs and Requirements – How does Tika help?
  3. 3. And you are? • Apache Member involved in – Tika (VP,PMC), Nutch (PMC), Incubator (PMC), OODT (Mentor), SIS (Mentor), Lucy (Mentor) and Gora (Champion) • Architect/Developer at NASA JPL in Pasadena, CA • Software Architecture/Engineeri ng Prof at USC
  4. 4. The Information Landscape
  5. 5. Proliferation of content types available • By some accounts, 16K to 51K content types* • What to do with content types? – Parse them • How? • Extract their text and structure – Index their metadata • In an indexing technology like Lucene, Solr, or in Google Appliance – Identify what language they belong to • Ngrams *http://filext.com/
  6. 6. Importance of content types
  7. 7. Importance of content type detection
  8. 8. Search Engine Architecture
  9. 9. Goals • Identify and classify file types – MIME detection • Glob pattern – *.txt – *.pdf • URL – http://…pdf – ftp://myfile.txt • Magic bytes • Combination of the above means • Classification means reaction can be targeted
  10. 10. is… • A content analysis and detection toolkit • A set of Java APIs providing MIME type detection, language identification, integration of various parsing libraries • A rich Metadata API for representing different Metadata models • A command line interface to the underlying Java code • A GUI interface to the Java code
  11. 11. Tika’s (Brief) History • Original idea for Tika came from Chris Mattmann and Jerome Charron in 2006 • Proposed as Lucene sub-project – Others interested, didn’t gain much traction • Went the Incubator route in 2007 when Jukka Zitting found that there was a need for Tika capabilities in Apache Jackrabbit – A Content Management System • Graduated from the Incubator to Lucene sub- project in 2008 • Graduated to Apache TLP in April 2010 • Over 90 issues shipping in latest release (0.8)
  12. 12. Community • Mailing lists – User: 153 peeps – Dev: 114 peeps • Committers/PMC – 10 peeps – Probably 5-6 active • Releases – 7 releases so far – Working on 0.8 Credit: svnsearch.org
  13. 13. Getting started rapidly…like now! • Download Tika from: – http://tika.apache.org/download.html • Grab tika-app-0.7.jar • alias tika “java –jar tika-app-0.7.jar” • tika < somefile.doc > extracted- text.xhtml • tika –m < somefile.doc > extracted.met • Works on Windows too (alias only on UNIX)
  14. 14. Detecting MIME types from Java • String type = Tika.detect(…) – java.io.InputStream – java.io.File – java.net.URL – java.lang.String
  15. 15. Adding new MIME types • Got XML? • Based on freedesktop.org spec (loosely)
  16. 16. Many custom applications and tools • You need this: to read this:
  17. 17. Third-party parsing libraries • Most of the custom applications come with software libraries and tools to read/write these files – Rather than re-invent the wheel, figure out a way to take advantage of them • Parsing text and structure is a difficult problem – Not all libraries parse text in equivalent manners – Some are faster than others – Some are more reliable than others
  18. 18. Parsing • String content = Tika.parseToString(…) – InputStream – File – URL
  19. 19. Streaming Parsing • Reader reader = Tika.parse(…) – InputStream – File – URL
  20. 20. Extraction of Metadata • Important to follow common Metadata models – Dublin Core – any electronic resource – XMP – also general like Dublin Core – Word Metadata – specific to .doc, .ppt, etc. – EXIF – image related • Lots of standards and models out there – The use and extraction of common models allows for content intercomparison – All standardize mechanisms for searching – You always know for X file type that field Y is there and of type String or Int or Date
  21. 21. Cancer Research Example
  22. 22. Cancer Research Example Attributes Relationships
  23. 23. Metadata • Metadata met = new Metadata(); //Dubiln Core met.set(Metadata.FORMAT, “text/html”); //multi-valued met.set(Metadata.FORMAT, “text/plain”); System.out.println( met.getValues(Metadata.FORMAT)); • Other met models supported (HTTP Headers, Word, Creative Commons, Climate Forcast, etc.) – New in Tika 0.8! run: tika --list-met-models
  24. 24. Methods for language identification • N-grams – Method of detecting next character or set of characters in a sequence – Useful in determine whether small snippets of text come from a particular language, or character set • Non-computational approaches – Tagging – Looking for common words or characters
  25. 25. Language Detection • LanguageIdentifier lang = new LanguageIdentifier(new LanguageProfile( FileUtils.readFileToString(new File(filename)))); • System.out.println(lang.getLanguage()); • Uses Ngram analysis included with Tika – Originating from Nutch – Can be improved
  26. 26. Running Tika in GUI form • tika --gui <html xmlns:html=“…”> <body> … </body> </html>
  27. 27. Integrating Tika into your App • Maven • Ant • Eclipse • It’s just a set of jars – tika-core – tika-parsers – tika-app – tika-bundle tika-core tika-parsers tika- app tika- bundle
  28. 28. Some really great stuff in 0.8 • Container aware detection and MIME improvements • “Drop in” Parsers – Compressed RTF / TNEF / LZFU parsing available via external plugin at Github • New Parsers – RSS – Scientific files: NetCDF, HDF
  29. 29. Improvements to Tika • Adding more parsers for content types – Omnigraffle? • Expanding ability to handle random access file parsing – Scientific data file formats, some work on this • Improving language and charset detection
  30. 30. Part 2 Science Data Systems at NASA
  31. 31. NASA Ground Data Systems Credit: D. Woollard
  32. 32. Context • NASA develops science data processing systems for multiple earth science missions • These systems convert the instrument telemetry delivered to earth from space into useful data for scientific research • Typical characteristics – Remote sensing instruments that orbit the Earth multiple times daily – Data are acquired constantly – Complex algorithms convert instrument measurements to geophysical quantities
  33. 33. The Square Kilometer Array • 1 sq. km of antennas • Never-before seen resolution looking into the sky • 700 TB – Per second!
  34. 34. NASA DESDynI Mission • 16 TB/day • Geographically distributed • 10s of 1000s of jobs per day • Tier 1 Earth Science Decadal Mission
  35. 35. Some Considerations • Scale – Data throughput rates – # of data types – # of metadata types – # of users to send the data to • Federation – Must leave the data where it is – Socio/Economic/Political • Heterogeneity – Technology, data formats, skills!
  36. 36. Apache OODT • We’ve got some components to deal with these issues
  37. 37. How are we building these systems now? -Allow for push/pull of data over arbitrary protocols - Ingestion builds std catalog and archive -Deliver product metadata to search, portal or GIS -Plug in arbitrary met extractors
  38. 38. How are we building these systems now? -Separation of file management from workflow management -Allow for heterogeneous computing resources -Easily integrate PGEs -Leverages same ingestion crawler
  39. 39. What does this have to do with Tika? Metadata Ext: TIKA! Metadata Ext: TIKA! MIME identification: TIKA! MIME identification: TIKA!
  40. 40. What does this have to do with Tika? Metadata Ext: TIKA! MIME identification: TIKA! MIME identification: TIKA!
  41. 41. Science Data File Formats • Hierarchical Data Format (HDF) – http://www.hdfgroup.org – Versions 4 and 5 – Lots of NASA data is in 4, newer NASA data in 5 – Encapsulates • Observation (Scalars, Vectors, Matrices, NxMxZ…) • Metadata (Summary info, date/time ranges, spatial ranges) – Custom readers/writers/APIs in many languages • C/C++, Python, Java
  42. 42. Science Data File Formats • network Common Data Form (netCDF) – www.unidata.ucar.edu/software/netcdf/ – Versions 3 and 4 – Heavily used in DOE, NOAA, etc. – Encapsulates • Observation (Scalars, Vectors, Matrices, NxMxZ…) • Metadata (Summary info, date/time ranges, spatial ranges) – Custom readers/writers/APIs in many languages • C/C++, Python, Java – Not Hierarchical representation: all flat
  43. 43. So how does it work? • Ingestion – Science data files, ancillary information from other missions, etc., arrive in NetCDF or HDF format – Need to extract their met, catalog and archive them, etc. • Can now use Tika to do this! TIKA-399 and TIKA- 400 added this capability into the Apache trunk • Processing – Processors (PGEs) generate NetCDF and HDF, must extract met, catalog and archive
  44. 44. Tool support • Entire stacks of tools written around these formats – OPeNDAP, LAS, readers, writers, custom NASA mission toolkits – OGC • WMS, WCS, etc. – Unique, one of a kind software build around these data file formats • Apache can contribute strongly in this area!
  45. 45. Besides processing science files • …Tika also helps with • MIME identification – Useful in remote file acquisition – Useful in classification (catalog/archive) of existing content – Useful in crawling (see my Nutch talk) • Language identification – Can be useful when data is coming from around the world, but need to quickly identify whether or not we can process it
  46. 46. Big Goal • More closely link OODT and Tika – Add new parser to Tika – Easily get OODT met extractor based on it • Contribute back some features still baking in OODT – Configuration aspects of parsing – File types and extensions for science data files • Spatial – Some work done in my CS572 class on spatial parser for Tika – would be great to integrate with Tika, OODT, SIS, and Solr
  47. 47. NASA Geo Challenges • Sometimes the data isn’t annotated with lat and lon – How to discover this? • Even when the data is annotated with spatial information, computation of e.g., bounding box around the poles is difficult • Efficiency and speed are difficult since data is at scale
  48. 48. Alright, I’ll shut up now • Any questions? • THANK YOU! – mattmann@apache.org – @chrismattmann on Twitter
  49. 49. Acknowledgements • Some Tika material inspired by Jukka Zitting’s talks – http://www.slideshare.net/jukka/text-and- metadata-extraction-with-apache-tika – http://www.slideshare.net/jukka/text-and- metadata-extraction-with-apache-tika- 4427630 • NASA Jet Propulsion Laboratory – OODT Team
  50. 50. Book • Jukka and I are writing a book on Tika – Working on Chapters 8 and 9 of 15 • Early Access available through MEAP program • http://manning.com/mattmann/

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