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Large Scale Search, Discovery and Analytics in Action
 

Large Scale Search, Discovery and Analytics in Action

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  • The bar is raised: when we first started Lucid, the problems were all around standing up Lucene or Solr or dealing with performance issues, now the large majority of them are around taking search to the next level: better relevance, personalization, recommendations, etc., i.e. how to have better relevance
  • How do you gain insight?The Search boxis the UI for data these daysFeedback improvements into system for usersExtract key metrics for business understanding
  • Make into images?
  • All about ad hoc and bulk storage and computationAll about the analytics that drive your computationGlue to make it all work together – data where it needs to be when it needs to be thereAll are examples of ways to do this. There are actually a fair number of viable alternatives for all of these pieces, all in open sourceI tend to stick to Apache and “commercial” friendly licenses, where possible
  • Analytics:Discovery:– Recommendations, trends, related searches
  • Authoritative store: managing across, consistency, etc.Analysis should be done where it most makes sense given the location of the data and the type of analysis being doneHadoop and HBase stuff are all pretty straightforward
  • Log and navigation: clicks, search trails, etc.Data cleanliness: Never viewed docs that are related to other documents
  • Big Picture: too often devs are stuck in the weeds

Large Scale Search, Discovery and Analytics in Action Large Scale Search, Discovery and Analytics in Action Presentation Transcript

  • Large Scale Search, Discovery and Analysis in Action Grant Ingersoll Chief Scientist September 18, 2012Confidential © Copyright 2012
  • Search is Dead, Long Live Search• Good keyword search is a commodity and easy to get up and running Documents• The Bar is Raised • Relevance is (always will be?) hard Content User• Holistic view of the data Relationships Interaction AND the users is critical• Search, Discovery and Analytics are the key to unlocking this view of users Access and data Confidential and Proprietary © 2012 LucidWorks
  • Topics• Background and needs• Architecture• Road Ahead• SDA In Action • Components • Challenges and Lessons Learned• Wrap UpConfidential and Proprietary© 2012 LucidWorks
  • Confidential © Copyright 2012
  • Sample Use Cases • Claims processing and analysis, including fraud analysis • Large scale content acquisition and access for: • Defense, intelligence and pharmaceutical applications • Views of data surrounding natural disasters and other tragedies for research, archiving and therapeutic purposes • Analysis of Website and social media interactions • Access and processing of genetic information for improved medical treatments • Log processing and fraud detection in telecommunications Confidential and Proprietary5 © 2012 LucidWorks
  • In Focus: Personalized Medicine Alignment and other Genetic analysis Variations Patient DNA Standard Therapies Alternative Therapies Search and Faceting Confidential and Proprietary6 © 2012 LucidWorks
  • In Focus: Log Processing in Telecommunications • Each year, large sums of money are lost due to fraudulent calls and poor service • Logs are usually semi-structured and contain vital information about errors and fraud • Deeper batch analytics can provide insight into patterns across vast amounts of data • Search of call and network information (via logs) is critical to providing deeper analysis and understanding of these errors and fraudulent activities Confidential and Proprietary7 © 2012 LucidWorks
  • Confidential © Copyright 2012
  • Confidential © Copyright 2012
  • Confidential © Copyright 2012
  • Confidential © Copyright 2012
  • Confidential © Copyright 2012
  • Computation and Storage LucidWorks Hadoop HBase Search/Solr• Document Index • Stores Logs, • Metric Storage Raw files,• Faceting intermediate • User files, etc. Histories/Profile • WebHDFS• SolrCloud makes sharding • Document easy • Small files are Storage an unnatural actChallenges • Who is the authoritative store? • Real time vs. Batch • Where should analysis be done? Confidential and Proprietary © 2012 LucidWorks
  • Search In Practice• Three primary concerns • Performance/Scaling • Relevance • Operations: monitoring, failover, etc.• Business typically cares more about relevance• Devs care more about performance at first…Confidential and Proprietary© 2012 LucidWorks
  • Search: Relevance• Always Be Testing • Experiment management is critical • Top X + sampling • Click Logs• Track Everything! • Queries • Clicks • Displayed Documents • Mouse/Scroll tracking?• Phrases are your friendsConfidential and Proprietary© 2012 LucidWorks
  • Discovery Components Serendipity Organization Data Quality• Trends • Importance • Document factor• Topics • Clustering Distributions• Recommendations • Classification • Length• Related Items • Named Entities • Boosts• More Like This • Time Factors • Duplicates• Did you mean? • Faceting• Stat. Interesting PhrasesChallenges• Many of these are intense calculations or iterative• Many are subjective and require a lot of experimentationConfidential and Proprietary© 2012 LucidWorks
  • Discovery with Mahout• Mahout’s 3 “C”s provide tools for helping across many aspects of discovery • Collaborative Filtering • Classification • Clustering• Also: • Collocations (Statistically Interesting Phrases) • Singular Value Decomposition (SVD) • Others• Challenges: • High cost to iterative machine learning algorithms • Mahout is very command line oriented • Some areas less matureConfidential and Proprietary© 2012 LucidWorks
  • Aside: Experiment Management• Plan for running experiments from the beginning across Search and Discovery components • Your engine should help!• Types of Experiments to consider • Indexing/Analysis • Query parsing • Scoring formulas • Machine Learning Models • Recommendations, many more• Make it easy to do A/B testing across all experiments and compare and contrast the resultsConfidential and Proprietary© 2012 LucidWorks
  • Analytics in Practice• Many of the components discussed provide analytical features • Leverage existing tools: R, etc.• Simple Counts: • Facets • Term and Document frequencies • Clicks• Search and Discovery example metrics • Relevance measures like Mean Reciprocal Rank • Histograms/Drilldowns around Number of Results • Log and navigation analysis• Data cleanliness analysis is helpful for finding potential issues in contentConfidential and Proprietary© 2012 LucidWorks
  • Wrap• Search, Discovery and Analytics, when combined into a single, coherent system provides powerful insight into both your content and your users• LucidWorks has combined many of these things into LucidWorks Big Data • http://www.lucidworks.com/products/lucidworks-big-data• Design for the big picture when building search-based applicationsConfidential and Proprietary© 2012 LucidWorks
  • Discussion and Resources • Questions? • http://www.lucidworks.com • grant@lucidworks.com • @gsingers Confidential and Proprietary21 © 2012 LucidWorks