enterprise search @epam

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Enterprise Search• Best Practices• Connector Framework• Relevancy overview

Sharepoint User Group2013, March 26, Minsk

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Information.epam.com

XXX.epam.com

knowledgebase.epam.com

HR file shares

Jira.epam.comYYY.epam.com

trainings.epam.com

Bla.bla.bla.epam.com

???????

EPAM has more than 100 systems

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Little homework

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We started POC in September 2012

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Available as search.epam.com in November 2012

• Sharepoint 2010• FAST Search for Sharepoint• Branded Search Center• Custom connectors• Fine-tuned relevance to reflect EPAM

landscape

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We become stronger every day…

• 550 000 searchable items

• 30+ content sources

• 400+ daily searches

• Exposed to internet

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… to help you search

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What we’ve learned

1. Deploy “painkiller” project as soon as possible2. Connect as much systems as possible (Cap O. speaking)

3. Analyze• Watch search logs• Connect external analytics• Speak with users

• Feedback forms sucks4. Tune relevancy

• hot-fix using bugs using best-bets5. Work with departments to adopt their content

• Basic SEO

Excellence in Software Engineering

Search Connectors in SP2010/2013

Search Connectors

Protocol Handers

File Share

SharePoint

WebSite

People

BCS

Lotus Notes Exchange Custom BCS

Database

WebService

.NET

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BCS Connectors in SP 2010/2013

Stereotyped Operations• Get IDs• Get By ID• Describe Security• Read Stream

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EPAM Data Import Framework

ISource

IDestination

IImporter

• Altassian Confluence• SVN• PMC

• SharePoint Library• File System

Workflow1. Source to build tree2. Destination to build tree3. Diff trees4. Destination to import diff (add, remove)

Tree DescribeTree()Node DownloadData(Node)

Tree DescribeTree()void Import(Tree)

Timer Job

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BCS vs DataImport Comparison

Data Import BCS

Effort to build the same + +Document Previews + -Indexing Speed + +/-Customizable + -Storage Space - +Unit Testing + +/-Incremental crawl + +/-

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RELEVANCY

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0. User submits query

1. Get candidates: all docs that match query

2. Predict relevancy• Query terms importance

• Proximity of query terms

• Hit location (mp) importance

• Freshness

• Clicks

• User rating

• …

Search is a two step process

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• Linear combination of features

• RankProfile

• Weights are configured via Powershell

• Easy to understand via RankLog

• Easy tuning

– Content Source

– Managed Property

Relevancy in FAST Search

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RankLog example (QueryLogger @codeplex)

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Relevancy in Sharepoint

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Relevancy in Sharepoint

Type InstanceBM25 BM25Static UrlDepthBucketedStatic InternalFileTypeBucketedStatic LanguageStatic ClickDistanceStatic QueryLogClicksStatic QueryLogSkipsStatic LastClicksStatic EventRateMinSpan - soft TitleMinSpan - soft TitleMinSpan - soft TitleMinSpan - soft Content

• Nonlinear combination of features. Two Neural Networks.• Ranking Model Schema described

• http://www.google.com/patents/US8296292• http://www.google.com/patents/US7840569

• Cmdlets to import/export• Default Ranking Model Features:

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• Google for “explain rank sharepoint”

• Parses RankDetail managed Property

ExplainRank page

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Ranking Model Tuning

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Approach described by Microsoft

– http://msdn.microsoft.com/en-us/library/bb499682(v=office.12).aspx

1. Collect Query Judgements

2. Use Machine Learning to train Neural Network

• namespace Microsoft.Office.Server.Search.RankerTuning

• Wait for tuning tool

Ranking Model Tuning

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Query Judgment framework

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• Authoritative Pages

• QueryRules

– Best Bets

– Understanding User Intent

• Synonyms (cmdlets)

• Entity Extractors

• Spelling Corrections

• Query Suggestions

• ManagedMetadata

• (!) Query Builder

Manual relevancy tuning in Sharepoint

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Manual relevancy tuning in Sharepoint

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Manual relevancy tuning in Sharepoint

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• http://sp2013searchtool.codeplex.com/

SP 2013 REST Query tool

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Solution Architect, Enterprise Search

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Alexey Kozhemiakin