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The Once and Future History of Enterprise Search and Open Source,[object Object],Marc Krellenstein, CTOmarc@lucidimagination.com,[object Object]
Evolving challenges in full text search ,[object Object],Finding something in a lot of content (recall, scalability),[object Object],IBM/STAIRS vs. US gov/Basis, BRS, Dialog, Verity,[object Object],Lycos and Fast , Infoseek, Excite  AltaVista,[object Object],Centralized search  Distributed search (Fast, Google, Lucene/Solr),[object Object],Finding just the good stuff (precision),[object Object],SMART, Autonomy, Google, Lucene/Solr, authority scores, browsing/clustering/faceting,…,[object Object],Finding it fast (performance) ,[object Object],Fast, Google, Lucene/Solr,[object Object],Making it easy (simplicity),[object Object],Google, Lucene/Solr*,[object Object],Deploying good search everywhere (all of the above plus price, flexibility),[object Object],Lucene/Solr ,[object Object]
Google	,[object Object],Breakthrough in precision of Internet search,[object Object],Popularity algorithm hides the bad stuff,[object Object],Proved importance of understanding data & users,[object Object],Set expectations for accuracy of enterprise search,[object Object],Set a new standard for search performance,[object Object],Sub-second (or near),[object Object],Proved value of good adaptive spell-checking,[object Object],Demonstrated the power of distributed search for scale,[object Object],Reinforced the importance of simplicity and a single search box,[object Object],Proved the value of search,[object Object],Search needs to be everywhere,[object Object]
But Google is not like most enterprise search applications,[object Object],Google,[object Object],Most data is bad, many good enough answers…task is to screen out the bad,[object Object],Many privacy issues among users,[object Object],No security issues,[object Object],Many naïve users with little patience…speed is important,[object Object],Enterprise search,[object Object],Most or all the data may be good, often only one answer to a search need,[object Object],Many security issues,[object Object],Few or no privacy issues between users,[object Object],Naïve and sophisticated users motivated by an organizational purpose,[object Object],The best enterprise search tools will fit enterprise needs,[object Object]
Best practice recall and precision	 ,[object Object],Recall,[object Object],Percent of relevant documents (items) returned,[object Object],50 good answers in system, 25 returned = 50% recall,[object Object],Precision,[object Object],Percent of documents returned that are relevant,[object Object],100 returned, 25 are relevant = 25% precision,[object Object],Ideal is 100% recall and 100% precision: return all relevant documents and only those,[object Object],100% recall is easy – return all documents…but precision so low they can’t be found…precision harder,[object Object],Need adequate recall & enough precision for the task,[object Object],That will vary by application (data & users)…,[object Object],6,[object Object]
How to get good recall,[object Object],Collect, index and search all the data,[object Object],Check for missing or corrupt data,[object Object],Index everything – stop words not usually needed today,[object Object],Search everything…limit results by category AFTER the search (clustering/faceting),[object Object],Normalize the data,[object Object],Convert to lower case, strip/handle special characters, stemming, …,[object Object],Use spell-checking, synonyms to match users’ vocabulary with content,[object Object],Adaptive spell-checking, application-specific synonyms,[object Object],Light (or real) natural language processing for abstract concepts,[object Object],‘Recent documents on Asia’,[object Object]
How to get good precision,[object Object],Term frequency (TF) – more occurrences of query terms is better,[object Object],Inverse document frequency (IDF) – rarer query terms are more important,[object Object],Phrase boost – query terms near each other is better,[object Object],Field boost – where the query term is in doc matters (e.g., in ‘title’ better),[object Object],Length normalization – avoid penalizing short docs,[object Object],Recency – all things being equal, recent is better,[object Object],Authority – items linked to, clicked on or bought by others may be better,[object Object],Implicit and explicit relevance feedback, more-like-this – expand query (queries usually underdetermined…intent??) ,[object Object],Clustering/faceting – when above fail or intent is not specific,[object Object],Lots of data…Watson, Google Translate,[object Object],8,[object Object]
The emergence of open source Lucene/Solr,[object Object],Lucene,[object Object],Built in late 90’s by Doug Cutting…. Apache release 2001,[object Object],State of the art Java library for indexing and ranking…many ports since,[object Object],Contributed to open source to keep it going and reusable,[object Object],Wide acceptance by 2005, mostly by technology organizations, products,[object Object],Solr,[object Object],Build in 2005 by Yonik Seeley to meet CNET needs for quicker-to-build applications and faceting…had to be open source…Apache release 2006,[object Object],Lucene over HTTP, schema, cache management, replication,… and faceting,[object Object],Open source as a development model, not a religion,[object Object],4,000+ sites – Apple, Cisco, EMC, HP, IBM, LinkedIn, MySpace, Netflix, Salesforce, Twitter, Gov, Wikipedia…,[object Object]
Current Lucene/Solr: strengths ,[object Object],Best practice segmented index (like Google, Fast),[object Object],Scalability via SolrCloud distributed search  billions of documents,[object Object],Best practice, flexible ranking (term/field/doc boosts, function queries, custom scoring…),[object Object],Best overall query performance and complete query capabilities (unlimited Boolean operations, wildcards, find-similar, synonyms, spell-check…),[object Object],Multilingual, query filters, geo search, memory mapped indexes, near real-time search, advanced proximity operators…,[object Object],Rapid innovation,[object Object],Extensible architecture, complete control (open source),[object Object],No license fees (open source),[object Object],CORE TECHNOLOGY AS GOOD OR BETTER THAN ANY OTHER…AND OPEN SOURCE,[object Object]
Open source Lucene/Solr: weaknesses,[object Object],Those typical of open source,[object Object],No formal support,[object Object],Limited access to training, consulting,[object Object],Lack of stringent integrated QA,[object Object],Pace of development and open source environment too complex for some (e.g., what version should I download? What patches? GUI? ,[object Object],Others,[object Object],Lucene/Solr development has tended to focus on core capabilities, so missing certain features for enterprise search (e.g., connectors, security, alerts, advanced query operations),[object Object]
Addressing open source Lucene/Solr weaknesses,[object Object],Lucene/Solr Community,[object Object],Apache Lucene/Solr community has a wealth of information on web sites, wikis and mailing lists,[object Object],Community members usually respond quickly to questions,[object Object],Consultants,[object Object],May be especially helpful for systems integration or addressing gaps,[object Object],Commercialization ,[object Object],Companies commercializing open source  provide commercial support, certified versions, training and consulting…may fill in gaps or address ease of use,[object Object],Examples: Red Hat, MySQL ,Lucid Imagination,[object Object],Internal resources – usually in combination with one or more of the above,[object Object]
Product strengths of top commercial competitors,[object Object],Well established players tend to be full-featured,[object Object],Some organizations have focused on a particular application or domain (e.g., ecommerce, publishing, legal, help desk),[object Object],Some competitors have focused on appliance-like simplicity,[object Object]
Weaknesses of top commercial competitors,[object Object],Usually expensive, especially at scale,[object Object],Platform or portability limitations,[object Object],Limited transparency,[object Object],Limited flexibility, especially for other than intended application or domain,[object Object],Limited customization, especially for appliance-like products,[object Object],Sometimes limited scalability,[object Object],Technical debt and/or lack of rapid innovation,[object Object],Customers are dependent on the company’s continued business success,[object Object]
Current competitive landscape	,[object Object],For last 5 years commercial companies have felt increasing competition from Lucene/Solr because of the combination of its capability and price,[object Object],Very hard to justify multi-million dollar deals given Lucene/Solr,[object Object],Lucene/Solr sometimes wins on performance alone,[object Object],Some competitors have responded with diversification,[object Object],Re-invent themselves as a business intelligence or other kind of company,[object Object],Produce search derivative applications,[object Object],Focus on specific domains,[object Object],Some have been acquired,[object Object],But the need for good, affordable, flexible search remains,[object Object]
The competitive future,[object Object],Basic search has become commoditized and widespread…but,[object Object],Top commercial companies usually often have one or more key weaknesses,[object Object],Existing search is often mediocre and too expensive or difficult to maintain, grow or customize/enhance,[object Object],Producing best practice search is still hard (and search remains a hard problem…intent, context, NLP…),[object Object],Market strength and features of competitors will keep competitors going a while…but,[object Object],Very hard to justify high prices, especially for large applications,[object Object],Very hard to justify closed and proprietary technology ,[object Object],Lucene/Solr capabilities, performance, control, price and continued rapid innovation (and addressing weaknesses) will likely lead to its dominance,[object Object]
Resources,[object Object],Lucene in Action, Second Edition, by Michael McCandless, Erik Hatcher and Otis Gospodnetic. Manning, 2010.,[object Object],Solr 1.4 Enterprise Search Server, by David Smiley and Eric Pugh. Packt Publishing, 2009.  ,[object Object],Solr reference guide: http://www.lucidimagination.com/Downloads/LucidWorks-for-Solr/Reference-Guide,[object Object],17,[object Object]

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Keynote: The once and future history of enterprise search and open source - By Marc Krellenstein

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