federated search webinar for sla (special libraries assoc.)
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September 9, 2009
Federated Search in a Disparate
Environment
PREPARED FOR:
SLA Webinar Series
Evidence-Based Practice in Libraries
2040 Corbett Rd
Monkton, Md 21111
(410.472.4631
* hmitchell5@gmail.com
Helen L. Mitchell Curtis
Principal, Enterprising Solutions
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Biography
Helen L. Mitchell Curtis – Principal, Enterprising Solutions
32+ years at FDA leading one of the largest enterprise search implementations among Civilian Federal Agencies
Develop enterprise-wide search strategies & solutions
Integrate search technologies across IT applications and disparate document repositories
Build governance, management and end user buy-in
Promote collaboration, standards, findability and improved organization of data and document assets
Passion – to help clients to reduce costs, improve quality and efficiency, reduce 'pain points' and achieve a positive search experience
Enterprising Solutions
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Polling Question
• What is Your Role? (select all that apply, if group participants)
• CIO, Executive Director
• Library Director (Corporate, Gov’t, Academia, Solo)
• Librarian/Information Management Professional
• IT Professional or Consultant
• Project/Product Manager
• Sales/Marketing/Communications
• End User (i.e., Scientist, Researcher, Engineering Professional)
• Federated Search Vendor
• Other
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Agenda
1. Terms Clarified
2. Types of Federated Search (FS)
3. FS Challenges & Benefits
4. FDA Case Study
5. FS Evaluation Criteria
6. Examples of FS Solutions
7. Live Federated Search Demo
8. Best Practices
9. Future Vision
10.Questions & Answers
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Clarify Terms
1. Definition by AIIM Market IQ
2. Definition by CMS Watch
3. A Federated Search Primer – Part II
4. Deep Web Technologies
5. Federated Search Rpt & Toolkit-Jill Hurst-Wahl
• Reliable and complete retrieval of content based on user need, i.e. everything relevant is recalled (recall) while simultaneously returning only that content relevant to the user’s focus (precision), thus eliminating the review of irrelevant content by the user.
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Findability
• Systems…within an organization…seeking information held internally…in a variety of formats and locations, including databases, document management systems, and other repositories.
2 Content is pre-indexed, simultaneously searched,
and displayed to authorized users.
Enterprise Search
(ES)
• The process of performing a simultaneous real-time search of multiple diverse and distributed sources from a single search page, with the federated search engine acting as intermediary.
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Federated
Search (FS)
• The set of web-sites and their documents that cannot be accessed via crawler-type search engines such as Google. Deep web content typically lives inside of databases, and is accessed through search forms.
4 It is also referred to as the Hidden or Invisible Web.
Deep Web
• SW written to access a content source that must know the URL of the source, how to send search commands, its search syntax, & how to process the search results returned from a source.
5Connector
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Polling Question
Information Accessibility (select all that apply)
1. I can easily find information to do my job
2. Less than 50% of our organization’s info is searchable online
3. More than 50% of our organization's info is searchable online
4. I reference less than 5 systems (info sources) in any given week
5. I reference 5 or more systems (info sources) in any given week
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Findability Issues
AIIM Market IQ Research on Findability (of 528 end users):
50% believe Findability in their organization is ―Worse to Much Worse‖ than their consumer-facing web sites
49% have no formal goal for Enterprise Findability within their organizations
49% ―Agreed or Strongly Agreed‖ that finding the information to do their job is difficult and time consuming
69% believe less than 50% of their organization's information is searchable online
36% reference five or more systems in any given week
Source: AIIM Market Intelligence, 2008
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Why Use Federated Search
To increase findability to better accomplish business objectives.
To issue a single query across multiple content sources through a common search interface.
When not feasible to re-index all of the content available from large public sites like PubMed.
To increase user awareness of all content sources such as deep web for scientific, technical and business content.
To eliminate using multiple database search protocols & passwords.
When don‘t have the rights to index the content (e.g. subscription sites).
Real-time search: for content constantly being updated & impractical to keep the data as timely as it needs to be.
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Federated Search Sources(examples)
Reason Corporate Academic Gov’t Public
Library
Subscription Databases X X X X
Internal or External Repositories X X
Library Catalog(s) X X X X
News X X
Digitized Material X X X
Blogs & Wikis X X X
Intranet/Internet Sites X X
Industry Specific Sources X
DB‘s available to customers X X
Historical Collections X
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Typical Non-Federated Search
Courtesy of MuseGlobal, Inc.
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Typical Federated Search
Courtesy of MuseGlobal, Inc.
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Federated „Master Index‟ Search
Index multiple data sources content into a single master index
Queries & results come from that one master index
Many Enterprise Search products integrate FS via ‗connectors‘ to accomplish this (ex., FAST, Autonomy, Endeca)
Source: New Idea Engineering, Inc.
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Federated „Data Silos‟ Search
Source: New Idea Engineering, Inc.
‗Search Federator‘ processes queries for each data source silo
Transforms search terms to match each content source requirements
Submits query to each of the sources simultaneously
Merges each source‘s results together - single look & feel
Maintains no indices of its own, relies on linked systems capabilities
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Surface vs. Deep Web Search
Popular search engines (Google, Yahoo…) ―crawl‖ surface web
FS can drill down to the deep web where specialized content (i.e., scientific and technical databases) reside
Deep Web FS Examples:www.completeplanet.com -70,000+ searchable DBs & specialty
search engines
www.science.gov- federates U.S. federal agency science info
http://imlsdcc.grainger.uiuc.edu/ -Institute of Museum & Library Services (IMLS) - Digital Collections
& Content w/descriptions of digital
resources developed by IMLS
grantees
Source: Juanico-Environmental Consultants, Ltd.
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Vertical Search Engine
Closely related to Deep Web – searches for a particular niche i.e., a specific industry, topic, type of content (e.g., scientific research, travel, movies, images, blogs)
Example: www.vetseek.info - is a search engine focusing on veterinary science and related topics
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Polling Question
Federated Search Solutions (select one)
1. We are currently conducting an evaluation to procure a Federated Search Product
2. We currently have a Federated Search Solution installed that satisfies our requirements
3. We have a Federated Search Solution by are considering replacing it or enhancing its capabilities & features
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Challenges
Authentication Showing each record‘s branding and copyright information
Licensed or subscription databases
True De-duplication Virtually impossible because DBs return 10-20 results at a
time
Vendors usually just de-dupe the first results set returned
Security Mapping user credentials and access rights to each
repository security model
Speed Limited by slowest search engine‘s performance
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Challenges (continued)
Lack of data standardization Each source has a unique access method & needs
translation
Metadata mapping between FSS and underlying systems
Access methods to sources may change Requires an interface rewrite or modification
Rules for error handling Ex. Query term not available—exclude the query, the
repository, or proceed without the term?
Ex. Timeouts or connection problem
Complex searches usually not available Fielded searches
Known Items, i.e. Article Name Best to directly search database
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Challenges (continued)
Relevancy scores Can‘t identify a single relevancy ranking model
Relevancy rankings for repository‘s results refers to its own
May be not be useful when comparing the results with those from another system
Access to content stored in a variety of places Results page may not let user obtain identified documents
This may involve a built-in viewer or invoking the owning product‘s interface.
Combining navigators from each result set i.e., faceted search, taxonomies and auto-generate
clusters
Selecting the right FS engine Depends on business goals, type of content sources –
structured vs. unstructured, licensed/subscriptions
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Benefits
• Single master index• Quicker response times
• No need to access original data sources
• Relevancy algorithms applied uniformly
• Dynamic navigators are available for all documents
• Time savings• Searches many sources at one time
• Combines results into a single results page
• Quality of results• Client selects the sources to search
• Minimum impact on the data silos • Only accessed when a user performs a query
• Eliminates increased load crawling/indexing the data source
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Benefits (continued)
• Improve productivity• Reduces number of searches executed to find relevant results
• Save, reuse, schedule, and share effective search queries
• Leverage security controls at queried source• Access repositories secured against crawls but can be accessed
by search queries
• Reduce costs• No additional capacity requirements for content index since its
not crawled by search server
• Most current content• Real time searches - as soon as the source is updated, the info is
available to the searcher on the very next query
• Increase awareness• Identify most relevant sources to search based on # of results
each source produced
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Enterprising Solutions FDA Case Study Success(Federated „Master Index‟ Search System)
ACTIONS RESULT
Started small with high ‘pain points’.
Increased productivity & popularity.
Modified business processes. Standardized nomenclature improved efficiencies.
Users across organizationcould find content in silos.
Produced more timely & QUALITYwork products.
Indexed structured & unstructured content with document level security.
Grew from 1 repository of 500 docs to 50 with 30 million docs. Accessed on ‘need to know’ basis.
Introduced standardized search web services into applications.
Reduced development time & costs. Increased mgmt & user acceptance. Integrated in more applications.
Increased user awareness with training, newsletters & meetings.
Used more & content added. Search requirements now captured at BEGINNING of project development.
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Evaluation Criteria Overview
Identify Goals
Create an Effective Search Strategy
Collect Business Requirements
Conduct needs assessment
Work Closely with User Community
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Evaluation Criteria Overview(continued)
Define Features and Functions Eliminate emotional decisions re: product,
company or others using the product
High Precision
Return content relevant to user‘s focus
High Recall Recall everything relevant to user‘s need
Thoroughly Research Products, Users & Product Reviewers
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Sample Evaluation Criteria
Rating Criteria Importance
(Rank 1-5)
Product #1
Score
(0-100)
Product #1
Weighted Score
(Rank x Score)
Product #2
Score
(0-100)
Product #2
Weighted Score
(Rank x Score)
Ease of Use 5 85 425 70 350
Ability to Customize UI 1 80 80 65 65
Speed 5 90 450 85 425
De-duplication 4 75 300 75 300
Clustering 4 85 340 80 320
Help Functionality 3 70 210 0 0
Alerts 4 90 360 50 200
# of Searchable Sources 3 90 270 80 240
Save Selections/Citations 2 85 170 0 0
Security 4 90 360 85 340
Product Cost 5 75 375 85 425
Vendor Credibility 4 95 380 85 340
Total Weighted Score 1010 3720 760 3005
-Courtesy of Federated Search Report & Tool Kit
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FSS Example(uses FAST ESP – Vertical Search)
Features of Interest
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FSS Example(uses MS & Vivisimo)
Features of Interest
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FSS Example(uses Deep Web Technologies)
Features of Interest
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FSS Example (uses Webfeat)
Features of Interest
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Digital Library FSS Examplehttp://www.calisphere.universityofcalifornia.edu/
Features of Interest
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Digital Library FSS Example http://www.calisphere.universityofcalifornia.edu
1 2
3
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FSS Example(LibraryFind® developed by Oregon State Univ Libraries)
Features of Interest
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Semantic Federated Search(prototype by Collexis & Deep Web Technologies)
SOURCES:
•PubMed
•NCI=Nat‘l Cancer Inst
•DTIC=Defense Tech. Info Ctr
•PMC=PubMed Central
•ScrDOEIB=DOE Info Bridge
•Eurekalert=Science News
THESAURI Used:
•MeSH
•DTIC=Defense Tech. Info Ctr
DeepWeb Technologies (a federated search provider) and
Collexis (a developer of semantic search & knowledge
discovery solutions) teamed up to deliver the world’s first
semantic federated search.
•How does semantic federated search work? •All results from your initial query are processed
through one or more thesauri. (i.e., MeSH & DTIC.)
•The system then returns terms that are found both in
the top results and in the thesauri.
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Collexis & Deep Web Technologies(Search Results – screenshot 1)
2429 hits
Semantic terms.
Unlike clustering, which
simply lumps together
words that are
frequently found near
each other, these terms
are being suggested
from an expert-
developed thesaurus
(taxonomy) in which
terms are meaningfully
& consistently
organized.
The longer the
blue bar, the
more semantic
evidence found
for that term.
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Collexis & Deep Web Technologies(Search Results – screenshot 2)
•Thesaurus-based search will
consistently suggest terms in
the same organized way.
•Clustering changes the way it
organizes suggestions with
every query.
• Clustering tends to be useful
for very broad, general or
unpredictable content.
•Clicking on term
“Mental Recall” from
prior screen added
term to search, reduced
relevant hits to 3; &
terms suggested are
organized.
*Thesaurus-based semantic search tends to be better
when you are working consistently in knowledge
domains, such as medicine, physics or electronics.
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Best Practices
Strategically plan how to deliver your mission and just DO IT!
Do proof of concept – demos can be deceiving
Establish common set of standards & governance model
Measure results by establishing key performance indicators
Leverage lessons learned to reduce project cycles, increase trust and empower communities
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Future Vision
• A simple, persistent box on a users‘ browser, cell, or entertainment screen that initiates a search based on what the user was doing, their previous keystrokes, & perhaps using historical data.
Personalized Search
• Number of results retrieved, Relevance Ranking, De-Duplication
Better Quality of Search Results
• Combine real-time searching with social networking tools, maps, etc.
Enterprise Mashups
• Know Web pages people display, what‘s on them & what apps are showing up on users' computers
Users build the index by their searching
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Future Vision (continued)
• Business users expect to access info behind company firewalls & from the larger web world using the same tools and consistency
Query analysis & predictive modeling on the fly
• Filter result sets dynamically for more relevant results
Improved Navigators, Facets, Clustering
• Automate analysis of database structures and cross-reference results. Ex.- Health site cross-references data from pharmaceutical companies with the latest findings from medical researchers
Web of Interconnected Data
• Enable extreme-scale knowledge discovery
Visualization Technologies
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Resources
1. Great resource for many Federated Search topics: www.federatedsearchblog.com – Author: Sol Lederman
2. Open Source & commercial search components & tools list: http://tinyurl.com/l3w8of
3. Federated Search Vendors: http://tinyurl.com/92s8qv
4. Deep Web Databases: http://tinyurl.com/yam3sw
5. Deep Web resources: http://www.internettutorials.net/deepweb.asp
6. Digital Image Resources on the Deep Web: http://tinyurl.com/46vcqp
7. Info on Vertical Search Engines: http://tinyurl.com/lpcufw
8. 50 Niche Search Engines: http://tinyurl.com/lukxwx
9. Library of Congress FS Portal Products/Vendors list: http://tinyurl.com/l6mdy8
10. Resources to Research & Mine the Deep Web: http://tinyurl.com/6g5768
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References
1) ―What‟s in a Name: Federated Search‖ – Miles Kehoe, New Idea Engineering, Inc,Vol. 4 No.4 8/07
2) “Federated Search Engine Article” - Online (Weston, Conn.) 28 no2 16-19 Mr/Ap 2004 (Reprint of article by Donna Fryer www.SearchitRight.com )
3) “Growing Up With Federated Search” - by Walt Warnick, OSTI
4) “Sophisticated Yet Simple - The Technology Behind OSTI's E-print Network: Part 3” – Walt Warnick, OSTI
5) “Vertical Search Engines & the Deep Web” - Laura B. Cohen http://www.internettutorials.net/
6) Blog: www.federatedsearchblog.com – by Sol Lederman
7) “Exploring a „Deep Web‟ that Google can‟t Grasp” - NYT 2-23-09 http://tinyurl.com/mvt42f
8) “Federated Search Primer, Part I-III” – by Sol Lederman
9) www.searchdoneright.com – by Vivisimo –Raoul – CEO & Cofounder
10) “Enterprise Search Grows Up‟”- Podcast from BizTalk
11) “Federation: Big Need, Still a Challenge” – Stephen Arnold, 4/25/08
12) “The Future of Federated Search or What Will the World Look Like in 10 Years” – Rich Turner
13) “Federated Search Report & Tool Kit” – Jill Hurst-Wahl, 10/08, © Free Pint Limited 2008
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QUESTIONS
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THANK YOU!
Helen L. Mitchell Curtis
Principal
Enterprising Solutions
hmitchell5@gmail.com
410-472-4631(w)410-259-7766(m)
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Enterprising Solutions
“Results Driven…Exceeding Expectations”
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