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Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
Video Analysis in Hadoop
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Video Analysis in Hadoop

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Our secure remote connectivity tool provides full video recording of all work our engineers perform on client systems. We have requirements to analyze the video log to detect suspicious activity, …

Our secure remote connectivity tool provides full video recording of all work our engineers perform on client systems. We have requirements to analyze the video log to detect suspicious activity, provide forensic and root cause analysis capabilities. Some of the obvious use cases include detection of credit card patterns or personally identifiable information (PII) as well as malicious activity like dropping database objects. We need to process hundreds of gigabytes per day representing thousands of hours of video. Our solution leverages a variety of Hadoop components to perform optical text recognition and indexing, keyboard and mouse movement analysis as well as integration with variety of other data sources such as our monitoring, documentation, ticketing and communication systems. We will present our complete architecture starting from multi-source data ingestion through data processing and analysis up to the end user interface, reporting and integration layer.

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  • Moore’s lawConsolidationVirtualizationEngineered systemsMulti-tenant databases like in 12cBusiness/IT convergence
  • Established 1997235 people and grew 50% in 2012Manages data infrastructure running Oracle, SQL Server, MySQL, Netezza, Hadoop and MongoDB plus UNIX Sysadmin and Oracle appsClients in diverse industries including Western Union, Virgin America Airlines, The New York Times, UPenn, Sunnybrook Hospital, Sonos, PPL, Australia Post
  • Echoprint – music identification
  • Transcript

    • 1. Video Analysis in Hadoop A Case Study Alex Gorbachev & Alan Gardner San Jose, CA June 2013 @alexgorbachev @alanctgardner
    • 2. @AlexGorbachev • CTO @ Pythian • Incubator of things • Database geek • Cloudera Champion of Big Data @AlanctGardner • Solutions Architect @ Pythian • Founder, Ottawa Drones • Polyglot Hacker • Part-time Data Scientist © 2013 Pythian
    • 3. Datafication Era © 2013 Pythian3 Tier 3 Data Insight from Big Data Value of Data Impact of an incident, whether it be data loss, security, human error, etc. Tier 2 Data Tier 1 Data Profit Loss LOVE YOUR DATA
    • 4. Who is Pythian? • 15 Years of Data infrastructure management consulting • 170+ Top brands • 6000+ databases under management • Over 200 DBA’s, in 26 countries • Top 5% of DBA work force • Oracle, SQL Server, MySQL, Netezza, Hadoop, MongoDB, IT Infrastructure © 2013 Pythian4
    • 5. Agenda • Introducing Adminiscope • The case for Video OCR • Video processing in Hadoop • Architecture • MapReduce workflow details • Solr Integration • Optimizing Hadoop cluster for OCR • Beyond text recognition and video processing © 2013 Pythian
    • 6. © 2013 Pythian6 Administration of information infrastructure has the same issue Trust but Verify in the physical world
    • 7. We wanted surveillance capabilities over administrative access to data infrastructure © 2013 Pythian
    • 8. Adminiscope architecture simplified © 2013 Pythian
    • 9. © 2013 Pythian9 Trust but Verify in the digital world
    • 10. Can’t we do it more efficiently and reliably in digital age? © 2013 Pythian
    • 11. © 2013 Pythian11 DEMO
    • 12. Hadoop as Data Reservoir © 2013 Pythian Adminiscope Internal Systems Ticketing & monitoring Knowledge base
    • 13. Hadoop as Data Reservoir © 2013 Pythian Adminiscope Internal Systems Ticketing & monitoring Knowledge base
    • 14. What is Run-Length Encoding? © 2013 Pythian t dog cat elephant
    • 15. Screen text processing options One page per frame • Store text of each frame in a stream • Large volume • Contextual analysis • Detect Personal Identifiable Information (PII) • Detect credit card patterns Run-Length Encoded • Store term appearance in a stream • Small volume • Termed search • Find when “DROP TABLE” was on the screen © 2013 Pythian
    • 16. Ingest Architecture Now © 2013 Pythian16 .bmp Encoder • Encoder writes directly to HDFS using libhdfs • Custom serialization format • Binary, compressed, splittable • Chosen over Avro for simplicity on the C side • Wrote custom InputFormat, RecordReader
    • 17. Flume Ingest Architecture © 2013 Pythian17 VideosourceArchive .bmp Encoder Support in Cloudera Search for binary files in the directory spooler and REST endpoint.
    • 18. © 2013 Pythian18 Video Processing Architecture
    • 19. OCR Mapper RLE .bmp
    • 20. © 2013 Pythian20 RLE and Secondary Sort
    • 21. Avro Serialization • Second MapReduce job to aggregate all terms per session • Separate from RLE for modularity and parallelism • Output records include a bag of words for indexing and a JSON representation for the web UI • Avro chosen for Cloudera Search support © 2013 Pythian21
    • 22. Morphlines • Part of Cloudera Development Kit, provides a quick way to transform data and index it in Solr • Common ETL operations are supplied, can be extended with user-defined function • Can be run as MapReduce, or in a low-latency configuration consuming Flume output © 2013 Pythian22
    • 23. Morphlines - Example morphlines : [ { id : morphline1 importCommands : [ "com.cloudera.**", "org.apache.solr.**" ] commands : [ # Some commands go here ] } ] © 2013 Pythian23
    • 24. Morphlines – Avro Commands readAvroContainer { readerSchemaFile : /path/to/json_schema.avsc } extractAvroPaths { flatten : false paths : { id : /session_id bag_of_words : /bag_of_words json_rle : /json_rle } © 2013 Pythian24
    • 25. Morphlines – Solr Commands sanitizeUnknownSolrFields { solrLocator : ${SOLR_LOCATOR} } loadSolr { solrLocator : ${SOLR_LOCATOR} } © 2013 Pythian25
    • 26. Optimizing task trackers for OCR • Nodes running OCR don’t utilize much memory, disk, network, so optimize: • Move OCR to a separate Hadoop cluster oriented on CPU or in the cloud • Schedule OCR MR jobs using task trackers on non- data-nodes • Move OCR outside of Hadoop • But then unable to do other types of processing that need combine multiple data-sources © 2013 Pythian
    • 27. • Full text search • Automatic recognition of text patterns – CC# – SSN – Suspicious activity ( DROP TABLE ) • Similar video sessions • Related tickets / knowledge base articles • Keystroke / mouse movement analysis • User working tired or under influence? © 2013 Pythian27 Adminiscope initial use cases
    • 28. Beyond Adminiscope Online video analytics Security camera analytics Beyond text Faces on the screen License plates Brain activity scans Other time series data audio geo-location data © 2013 Pythian28
    • 29. Thank you – Q&A To contact us gorbachev@pythian.com gardner@pythian.com 1-877-PYTHIAN @pythian @alexgorbachev @alanctgardner © 2013 Pythian29

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