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Big Data Content Organization, Discovery, and Management

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Access Innovations, Inc.

Access Innovations, Inc. outlines the significance of big data in various sectors, emphasizing its economic value and the necessity for advanced data management techniques. The document discusses government initiatives focused on enhancing big data R&D to foster innovation in fields like education, health, and security. It also highlights challenges in integrating big data with existing systems and the importance of metadata, taxonomies, and structured vocabularies for effective data organization and retrieval.

TechnologyEducation
Related topics:
Data Visualization Techniques • Information Management • Information Retrieval
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Access Innovations, Inc.. 12/11/2013
Marjorie M.K. Hlava President Access Innovations, Inc. Mhlava@accessinn.com 12/11/2013 Access Innovations, Inc.. Big Data Content Organization, Discovery, and Management
Access Innovations, Inc.. • Big Data • New Government Initiative • Content Organization • Discovery (Search) • Management • Skills we bring • Examples of what we can do 12/11/2013 Outline
Access Innovations, Inc.. • Data is the new oil – we have to learn how to mine it! Qatar – European Commission Report • $ 7 trillion economic value in 7 US sectors alone • $90 B annually in sensitive devices • An insurance firm with 5 terabytes of data on share drives pays $1.5 m per year • New McKinsey 4th factor of production • Land, Labor, Capital, + Data 12/11/2013 Why do we care about Big Data?
Access Innovations, Inc.. 12/11/2013 The Data Deluge – Wired 16.07
Access Innovations, Inc.. • Google, eBay, LinkedIn, and Facebook were built around Big Data from the beginning. • No need to reconcile or integrate Big Data with more traditional sources of data and the analytics performed upon them • No merging Big Data technologies with their traditional IT infrastructures • Big Data could stand alone, Big Data analytics could be the only focus of analytics • Big Data technology architectures could be the only architecture. 12/11/2013 Big Data Born
Access Innovations, Inc.. • Large, well-established orgs • Must be integrated with everything else that’s going on in the company. • Analytics on Big Data have to coexist with analytics on other types of data. • Hadoop clusters have to do their work alongside IBM mainframes. • Data scientists must somehow get along and work jointly with mere quantitative analysts. 12/11/2013 Integrating Big Data
Access Innovations, Inc.. • Big Data is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big Data sizes are a constantly moving target currently ranging from a few dozen terabytes to many petabytes of data in a single data set. – Wikipedia, May 2011 • “Unstructured” • Terabytes, petabytes, zettabytes • Streaming 12/11/2013 What is Big Data?
Access Innovations, Inc.. 12/11/2013 New kind of science?
Access Innovations, Inc.. • Volume, Velocity, Variety • Ability to deal overwhelmed • More about methods than data • Location aware data • Life streaming • Insurance claims • Hubble telescope • CERN Collections • Flight data 12/11/2013 New Special Collections
Access Innovations, Inc.. 12/11/2013 www.researchobject.org
Access Innovations, Inc.. • Means untagged or unformatted • PDF • Word files • File shares • News feeds • News Data feeds • Images 12/11/2013 Unstructured data
Access Innovations, Inc.. • All data has some structure and more structure possibilities • PDF properties • Word file properties • File structures 12/11/2013 Bit of a misnomer
Access Innovations, Inc.. • PDF - property tables • Word files – property tables • Files shares – implied structure in file names • News feeds – headers have metadata • Hubbell telescope feeds • Images 12/11/2013 Property tables
Access Innovations, Inc.. 12/11/2013 PDF Properties
Access Innovations, Inc.. 12/11/2013 File structures
12/11/2013 • XML tagged data Access Innovations, Inc.. Structured data
Access Innovations, Inc.. • Data infrastructure challenges • “taking diverse and heterogeneous data sets and making them more homogeneous and usable” • An opportunity? • All that data – what can it tell us? • Privacy • Copyright • Neurological impact • Data collection methods 12/11/2013 What are the problems?
Access Innovations, Inc.. The Big Data Senior Steering Group (BDSSG) was formed to identify current Big Data research and development activities across the Federal government, offer opportunities for coordination, and identify what the goal of a national initiative in this area would look like. Subsequently, in March 2012, The White House Big Data R&D Initiative was launched and the BDSSG continues to work in four main areas to facilitate and further the goals of the Initiative. 12/11/2013 New Government Initiative
Access Innovations, Inc.. • Fast-growing volume of digital data of digital data • Advance state-of-the-art core technologies needed to collect, store, preserve, manage, analyze, and share huge quantities of data. • Harness these technologies to accelerate the pace of discovery in science and engineering, strengthen our national security, and transform teaching and learning; and • Expand the workforce needed to develop and use Big Data technologies. 12/11/2013 The National Big Data R&D Initiative
•Big Data •Data analytics; •Educate and expand the Big Data workforce •Improve key outcomes in economic growth, job creation, education, health, energy, sustainability, public safety, advanced manufacturing, science and engineering, and global development Access Innovations, Inc.. •Advance supporting technologies 12/11/2013 Data to Knowledge to Action
Access Innovations, Inc.. • January 22, 2013 - Data on Data: Presenting Stakeholder Alignment Data on the Cyberinfrastructure for Earth System Science • Presentation and discussion with Professor Joel Cutcher-Gershenfeld. Professor CutcherGershenfeld presented information on the NSF EarthCube initiative including stakeholder survey data (approximately 850 responses). 12/11/2013 Data on Data
Access Innovations, Inc.. 12/11/2013 Who is involved?
• • • • • • INTERAGENCY WORKING GROUPS Cyber Security and Information Assurance High End Computing COMMUNITY OF PRACTICE (CoP) · Faster Administration of Science and Technology Education and Research COORDINATING GROUPS Human Computer Interaction and Information Management High Confidence Software and Systems Large Scale Networking Software Design and Productivity Social, Economic, and Workforce Implications of IT Access Innovations, Inc.. • • • • • 12/11/2013 Groups breakdown
Access Innovations, Inc.. • Big Data • Cyber Physical Systems • Cyber Security and Information Assurance Research and Development • Health Information Technology Research and Development • Wireless Spectrum Research and Development • SUBGROUP • Health Information Technology Innovation and Development Environments Subgroup • TEAMS • Joint Engineering Team • Middleware and Grid Interagency Coordinating Team 12/11/2013 SENIOR STEERING GROUPS (SSGs)
• • • • Local Cloud Remote Streaming • Undifferentiated • Unstructured • Needs organization • Type of database structure • RDBMS • Object oriented Access Innovations, Inc.. • Data on machines 12/11/2013 Content Organization
Access Innovations, Inc.. 12/11/2013 RDBMS Connection Taxonomy term table
Public repositories Commercial data sources Agency data sources Search engine Library catalogs Locally held documents Search engine Search engine Search engine Search engine spiders Filtered content Search engine Meta-Search Tool TAXONOMY Web portal 12/11/2013 INTERNET (public) Access Innovations, Inc.. Data feeds
Structure unstructured data Create metadata Where to put it? Store metadata with the records • HTML header • Properties tables • XML files • Store metadata in a separate file • • • • • • Database Metadata repository Search system File structure SharePoint application Web interface Access Innovations, Inc.. • • • • 12/11/2013 Metadata options
• Uncontrolled list • Name authority file • Synonym set / ring • Controlled vocabulary • Taxonomy • Thesaurus • Ontology Highly complex - $$$$ • Topic Map LOTS OF OVERLAP! • Semantic Network 12/11/2013 Not complex - $ Access Innovations, Inc.. Information retrieval starts with a knowledge organization system
Synonyms Taxonomy Thesaurus INCREASING COMPLEXITY / RICHNESS Ambiguity control Synonym control Ambiguity control Ambiguity cont’l Synonym control Synonym cont’l Hierarchical rel’s Hierarchical rel’s Associative rel’s 12/11/2013 List of words Access Innovations, Inc.. Structure of controlled vocabularies
TAXONOMY ONTOLOGY • See also (SA) • Non-Preferred Term (NP) • Used for (UF), See (S) • Scope Note (SN) • History (H) THESAURUS *a.k.a. subject term, heading, node, index term, keyword, category, descriptor, class 12/11/2013 • Main Term (MT) * • Top Term (TT) • Broader Terms (BT) • Narrower Terms (NT) • Narrower Term Instance • Related Terms (RT) Access Innovations, Inc.. Taxonomy / thesaurus
12/11/2013 Thesaurus Term Record view Access Innovations, Inc.. Taxonomy view
Reference: “Taxonomies: the vital tool of information architecture”, www.tfpl.com. Courtesy of Lillian Gassie, Naval Postgraduate School, Monterey, CA Access Innovations, Inc.. A taxonomy aspires to be: • a correlation of the different functional, regional and (possibly) national languages used by a community of practice • a support mechanism for navigation • a support tool for search engines and knowledge maps • an authority for tagging documents and other information objects • a knowledge base in its own right 12/11/2013 Taxonomies in Context
Access Innovations, Inc.. • Link the taxonomy and indexing • Always in sync with the industry • Keep up to date with terminology • Automatically index the old data • Filter newsfeeds • Search using the taxonomy • File using the taxonomy • Spell check using the taxonomy • Link to translation system • Catalog using the taxonomy 12/11/2013 Where to use a taxonomy
Courtesy of Lillian Gassie, Naval Postgraduate School, Monterey, CA “Taxonomies are better created by professional indexers or librarians than by domain experts.” 12/11/2013 • Improves organization & structure • Facilitates navigation • Facilitates knowledge discovery • Reduces effort • Saves time Access Innovations, Inc.. Value of a Taxonomy
• knowledge management system • search retrieval system • portal interface Access Innovations, Inc.. • A well formed taxonomy is based on a thesaurus • Provides a flexible platform for many views of the taxonomy • Allows fast deployment • Is the basis for a good 12/11/2013 Taxonomies
Database Management Establish rules for term use System Suggest indexing terms Thesaurus tool Indexing tool Validate terms Add terms and rules Change terms and rules Delete terms and rules Access Innovations, Inc.. Search thesaurus Validate term entry Block invalid terms Record candidates 12/11/2013 A quick look behind the scenes
12/11/2013 Thesaurus Term Record view Access Innovations, Inc.. Taxonomy view
Suggested taxonomy descriptors Access Innovations, Inc.. 12/11/2013
Access Innovations, Inc.. • Create the metadata structure • Gather the locations of the records • Index in place? • Point to the data? • Add metadata to the record? • Store metadata in separate “table”? • Use in full text? • Use in search? • Link databases and data sets with APIs 12/11/2013 Workflow order
• Was: Card catalog or OPACs to books on shelf • Now: EBSCO host to Mendeley, Zotero, ResearchGate, Academic.edu, etc. • Use an API or web call • Web calls • Call to another web platform • APIs, written handshakes • Z39.50 is one standard for libraries Access Innovations, Inc.. • Link data cross platforms • Not federated search • Examples 12/11/2013 APIs and Web Calls
• Free text / full text • Fielded / Faceted • Parts of Search • Presentation layer • Caching • Inverted index Access Innovations, Inc.. • Search 12/11/2013 Discovery (Search)
• Autonomy / Verity • Bayesian – • FAST • Lucene • Faceted • Endeca • Boolean • Dialog • Ranking Algorithms • Google Access Innovations, Inc.. • Keyword – 12/11/2013 Kinds of search
Access Innovations, Inc.. • Query language • Search technology • Inverted index • Ranking algorithms • Other enhancements 12/11/2013 Parts of search
FAST ESP and Data Harmony 12/11/2013 Architecture Core Architectural Components MANAGEMENT API Custom Applications Content Push FILTER SERVER CUSTOM CONNECTOR MAIstro Agent DB Data Harmony Governance API Query Alerts Vertical Applications Portals Results NavTree & MAI Query LOTUS NOTES CONNECTOR Email, Groupware Index DB Pipeline QUERY PROCESSOR Databases DATABASE CONNECTOR Pipeline DOCUMENT PROCESSOR FILE TRAVERSER CONTENT API Files, Documents SEARCH SERVER QUERY API WEB CRAWLER Web Content Custom Front-Ends Mobile Devices Access Innovations, Inc.. Administrator’s Dashboard
Access Innovations, Inc.. 12/11/2013 SLA search Interpret search word “competencies” as taxonomy term Professional competencies
12/11/2013 Access Innovations, Inc.. Search “taxonomy” in XML descriptor field returns all documents in that category  27 Search in original metadata  1 Solution: Include descriptors with metadata!
Access Innovations, Inc.. • Data management layers • Curation • Preservation • Archiving • Storage • Choose tools wisely • Data mashups 12/11/2013 Management
• Sorting for production • Level of effort to get to 85% • Integration into the workflow is efficient Access Innovations, Inc.. • Reach 85% precision to launch for productivity assisted • Reach 85% for filtering or categorization 12/11/2013 Go – No Go
Access Innovations, Inc.. • Information infrastructure • Information dynamics • Tensions on data • Design elements • How and when to set the data framework • Identifiers (taxonomies) Glue to hold data together 12/11/2013 Learn to understand the parts
Access Innovations, Inc.. • Core cross disciplines • Combo of humans and machines • What is open • What needs a gatekeeper • Store locally • Store in cloud • Some things people do better 12/11/2013 Services
• Satisfice = satisfaction + suffice • How good is good enough? • How much error can you put up with? • Hits, misses, noise Access Innovations, Inc.. • 15 – 20% irrelevant returns / noise • Amount of work needed to achieve 85% level • How good is good enough? 12/11/2013 Benchmarks
• 1.5 synonyms per terms • 7500 terms total • Assume 85% accuracy • Use assisted for indexing • Use automatically for filtering • Assume $55 per hour for staff • Assume 10000 records for test batch Access Innovations, Inc.. • Assume – 5000 term thesaurus 12/11/2013 ROI Calculations
• • • • • • • • • Preferred and non-preferred terms Find records – could be programmatic Need to collect and review about 60 to get 20 appropriate ones Review records – ensure they are correct 3 minutes average per final selected record = 1 hour per term entry 1 minute to review a record (20 resulting records) 7500 terms at one hour per term = 7500 hours Estimated at 7500 hours $55 / hour = $412,500 Access Innovations, Inc.. • Collect 20 items per thesaurus entry 12/11/2013 Co-occurrence - Training sets
• • • • Average rule takes 5 minutes (Actually MUCH faster using a rule building tool) 5 x 1500 = 7500 minutes 125 hours x $55 = $6875 Access Innovations, Inc.. • 80% of rules built automatically • 7500 x .8 = 6000 • 20% require complex rules 12/11/2013 Rulebuilding - rules
Access Innovations, Inc.. • Cost of auto system • Cost of getting system ready • Ongoing maintenance • Increased efficiency • Increased quality of retrieval • Cost of legacy system maintenance 12/11/2013 ROI - Segments
• Cost of auto system- $500,000 (could be less, but the one used for this study cost this amount) • Cost of getting system ready 12/11/2013 ROI – Co-occurrence • Estimated at 2 weeks programming $100 / hour = $8000 • Training sets • Estimated at 7500 hours $55 / hour = $412,500 • Possible need to re-run training set several times • Ongoing maintenance • Estimated at 15% of purchase price for license = $75,000 • Quarterly retraining to keep up with new terms – • Training sets for new terms 50 terms per quarter = 200 x $55=$11,000 • • • • Increased efficiency Expected accuracy at 60% Increased quality of retrieval Cost of legacy system maintenance Access Innovations, Inc.. • Programming support and integration
• Cost of auto system- $60,000 • Cost of getting system ready 12/11/2013 ROI – Rules system • Programming support and integration • Rule building • Estimated at 125 hours at $55 / hour = $6850 • Possible need to re run training set several times • Ongoing maintenance • Estimated at 15% of purchase price for license = $9000 • Rule building for new terms, 50 terms per quarter • 200 terms x .8 = 160 automatic • 40 at 5 minutes per term = 200 minutes /60 = 3.33 hours x $55 = $183 • • • • Increased efficiency Expected accuracy at 60% Increased quality of retrieval Cost of legacy system maintenance Access Innovations, Inc.. • Estimated at 2 weeks programming at $100 / hour = $8000
• Year one 12/11/2013 ROI - Rules • Years thereafter • $9000 + $183 = $9183 • 85% accuracy Access Innovations, Inc.. • $60,000 + $8,000 + $6,850 = $74,850
• Year one 12/11/2013 ROI – Co-occurrence • Years thereafter • $75000 + $11,000 = $86,000 • 60% accuracy Access Innovations, Inc.. • $500,000 + $8,000 + $412,500 = $920,500
• Controlled – use rules • New terms - use inference Access Innovations, Inc.. • Novelty detection requires inference techniques • Is needed to discover new terms • Controlled vocabulary / thesaurus can be more controlled and accurate approach • Combination of the two would be best 12/11/2013 Summary on ROI
Client Data Full Text Tag and Create metadata Put in data base with tags Build Search inverted index Automatic Summarization Search Presentation Layer HTML, PDF, Data Feeds, etc. Machine Aided Indexer (M.A.I.™) Inline Tagging Client Client Taxonomy taxonomy Metadata and Entity Extractor Thesaurus Master Create user interface Access Innovations, Inc.. Gather source data 12/11/2013 Fully integrated with MOSS The Workflow Database Repository Search Software Increases accuracy Browse by Subject Auto-completion Broader Terms Narrower Terms Related Terms
Access Innovations, Inc.. 12/11/2013 Inline Tagging Shows the exact point where the concept is mentioned Mouse-over to view the term record Statistical summary, showing the number of times each term is mentioned in the article
Automatic Summarization Full text, HTML, PDF, data feeds Inline Tagging Metadata Extractor Apply terms Rules Base Thesaurus Master Client Taxonomy Data Repository Search Software User Interface Web Portal Access Innovations, Inc.. 12/11/2013 Semantic Process
Raw Full text data feeds Printed source materials Data Crawls on data sources Source data XIS Creation XIS repository Load to Search Taxonomy terms MAI Concept Extractor MAI Rule Base Taxonomy Thesaurus Master Add metadata Clean and enhance data Search Harmony Display Search Search data Access Innovations, Inc.. SQL for ecommerce 12/11/2013 Database Plus Search Workflow
Creating an Inverted File Index 1 Define key terminology 2 Thesaurus tools • Features • Functions 3 Costs • Thesaurus construction • Thesaurus tools 4 Why & when? Access Innovations, Inc.. Outline of Presentation 12/11/2013 Sample DOCUMENT
& 1 2 3 4 construction costs define features functions key of outline presentation terminology thesaurus tools when why 12/11/2013 Access Innovations, Inc.. Simple inverted file index The terms from the “outline”
& - Stop 1 - Stop 2 - Stop 3 - Stop 4 - Stop construction - L7, P2, SH costs - L6, P1, H define - L2, P1, H features - L4, P1, SH functions - L5, P1, SH 12/11/2013 key - L2, P2, H of - Stop outline - L1, P1, T presentation - L1, P3, T terminology - L2, P3, H thesaurus - (1) - L3, P1, H (2) - L7, P1, SH (3) - L8, P1, SH tools - (1) - L3, P2, H (2) - L8, P2, SH when - L9, P3, H why - L9, P1, H Access Innovations, Inc.. Complex inverted file index Placement location
12/11/2013 Complex Search Farm Query Search Harmony Presentation Layer Cleanup, etc. Federators Deploy Hub Repository XIS (cache) Cache Builders Source Data Access Innovations, Inc.. Query Servers Index Builders
• That is what librarians usually look at. • What are the options coming? • How can we encourage useful changes? 12/11/2013 Access Innovations, Inc.. What happens at the search presentation layer?
Access Innovations, Inc.. 12/11/2013 Semantic search options
Access Innovations, Inc.. 12/11/2013
Access Innovations, Inc.. 12/11/2013
Journal Article on Topic A Grant Available for Researchers Working on Topic A Author Networks Social Networking Based on an image by Helen Atkins Upcoming Conference on Topic A Job Posting for Expert on Topic A Podcast Interview with Researcher Working on Topic A Access Innovations, Inc.. Other Journal Articles on Topic A CME Activity on Topic A 12/11/2013 Data linked and served by metadata
Alcohol, Folate, Methionine, and Risk of Incident Breast Cancer in the American Cancer Society Cancer Prevention Study II Nutrition Cohort Heather Spencer Feigelson1, Carolyn R. Jonas, Andreas S. Robertson, Marjorie L. McCullough, Michael J. Thun and Eugenia E. Calle Department of Epidemiology and Surveillance Research, American Cancer Society, National Home Office, Atlanta, Georgia 30329-4251 Recent studies suggest that the increased risk of breast cancer associated with alcohol consumption may be reduced by adequate folate intake. We examined this question among 66,561 postmenopausal women in the American Cancer Society Cancer Prevention Study II Nutrition Cohort. Think Tank Report Related Working Groups •Finance Related Think Tank Report Content •Charter •Molecular Epidemiology Webcasts Related Awards Related Webcasts •AACR-GlaxoSmithKline Clinical Cancer Research Scholar Awards •ACS Award •Weinstein Distinguished Lecture Related Press Releases 12/11/2013 •How What and How Much We Eat (And Drink) Affects Our Risk of Cancer •Novel COX-2 Combination Treatment May Reduce Colon Cancer Risk Combination Regimen of COX-2 Inhibitor and Fish Oil Causes Cell Death •COX-2 Levels Are Elevated in Smokers Related AACR Workshops and Conferences •Frontiers in Cancer Prevention Research •Continuing Medical Education (CME) •Molecular Targets and Cancer Therapeutics Related Meeting Abstracts Access Innovations, Inc.. Cancer Epidemiology Biomarkers & Prevention Vol. 12, 161-164, February 2003 © 2003 American Association for Cancer Research Short Communications •Association between dietary folate intake, alcohol intake, and methylenetetrahydrofolate reductase C677T and A1298C polymorphisms and subsequent breast •Folate, folate cofactor, and alcohol intakes and risk for colorectal adenoma •Dietary folate intake and risk of prostate cancer in a large prospective cohort study Related Education Book Content Oral Contraceptives, Postmenopausal Hormones, and Breast Cancer Physical Activity and Cancer Hormonal Interventions: From Adjuvant Therapy to Breast Cancer Prevention After Helen Atkins
Access Innovations, Inc.. 12/11/2013 All data up-posted to the top level
This is a radial graph of “plosthes”. The number of records for which each index term occurs is reflected by circle sizes. Access Innovations, Inc.. 12/11/2013 Visualize your tagged data
Access Innovations, Inc.. 12/11/2013 Semantic Hierarchy browsable tree
Access Innovations, Inc.. 12/11/2013 Related Terms semantic web
Access Innovations, Inc.. 12/11/2013 Synonyms search foundation
Access Innovations, Inc.. 12/11/2013 Load to a visualization program such as Prefuse
• Migration patterns • YouTube • Citizen Science Access Innovations, Inc.. • Drawing together information from several sources • Overlay on additional surfaces – like maps • Fun distribution of data • Cornell School of Ornithology 12/11/2013 Data Mashups
• ISI does it with references • AIP’s UniPHY does it using semantics 12/11/2013 Scientific social networking basedciting who like on metadata • Idea has been here - Who is • good metadata and descriptors See the authors connections Map who is working in the field and where Access Innovations, Inc.. • Expand your options using
Access Innovations, Inc.. 12/11/2013 Authors at a Place
• With interpretive layer – computer assisted • Links to data • Open data / supplemental data • Too much is not enough! Access Innovations, Inc.. • Document systems replaced • New infrastructure • Institutional repositories not good long term for Big Data • Need to operate at scale • Integrated, ecosystem to infrastructure • Replace customized human mediated 12/11/2013 Future?
• Go down hall and ask • Big Data • Too many people to ask • Lose provenance • Hard to differentiate the layers Access Innovations, Inc.. • Not by size of collection • By the services they offer • More services more competitive • Spreadsheet science 12/11/2013 Differentiation
People, data usage Preservation and discovery Publishers’ content, not format Visualization of Big Data sets Reproducibility of research Shared knowledge Open peer review • • • • • Researcher.org Galaxy zoo Zooniverse 900,000 citizen scientists Wikipedia Access Innovations, Inc.. • • • • • • • 12/11/2013 New ways of doing things
Access Innovations, Inc.. 12/11/2013
• Weeding • Redefining collections • Collection development • Skills to apply to Big Data • Vocabulary development • Search expertise • Reference ability • What to throw away • What to keep for discoverability • Need metadata to preserve • Better discoverability • Easier preservation • Keep the data provenance Access Innovations, Inc.. • Librarians 12/11/2013 Skills we bring
Access Innovations, Inc.. • Big Data – What is it? • New Government Initiative • Content organization • Discovery (Search) • Management • Skills we bring • Examples of what we can do 12/11/2013 We covered
• Semantic Enrichment Services Provider • We change search to find! • Creator of Data Harmony tools • More than 600 taxonomies created • More than 2000 engagements • Financed by Sweat, Persistence, and Good Cash Flow Management • Accurate, on time, under budget! Access Innovations, Inc.. • Access Innovations – 12/11/2013 Who are we?
• Marjorie Hlava • mhlava@accessinn.com • +1-505-998-0800 • Access Innovations/Data Harmony/Access Integrity • Taxodiary.com (blog) • Taxobank.org (reference tool for taxonomists) 12/11/2013 Thank you Access Innovations, Inc.. Slides at www.accessinn.com/presentations

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Big Data Content Organization, Discovery, and Management

  • 2. Marjorie M.K. Hlava President Access Innovations, Inc. Mhlava@accessinn.com 12/11/2013 Access Innovations, Inc.. Big Data Content Organization, Discovery, and Management
  • 3. Access Innovations, Inc.. • Big Data • New Government Initiative • Content Organization • Discovery (Search) • Management • Skills we bring • Examples of what we can do 12/11/2013 Outline
  • 4. Access Innovations, Inc.. • Data is the new oil – we have to learn how to mine it! Qatar – European Commission Report • $ 7 trillion economic value in 7 US sectors alone • $90 B annually in sensitive devices • An insurance firm with 5 terabytes of data on share drives pays $1.5 m per year • New McKinsey 4th factor of production • Land, Labor, Capital, + Data 12/11/2013 Why do we care about Big Data?
  • 5. Access Innovations, Inc.. 12/11/2013 The Data Deluge – Wired 16.07
  • 6. Access Innovations, Inc.. • Google, eBay, LinkedIn, and Facebook were built around Big Data from the beginning. • No need to reconcile or integrate Big Data with more traditional sources of data and the analytics performed upon them • No merging Big Data technologies with their traditional IT infrastructures • Big Data could stand alone, Big Data analytics could be the only focus of analytics • Big Data technology architectures could be the only architecture. 12/11/2013 Big Data Born
  • 7. Access Innovations, Inc.. • Large, well-established orgs • Must be integrated with everything else that’s going on in the company. • Analytics on Big Data have to coexist with analytics on other types of data. • Hadoop clusters have to do their work alongside IBM mainframes. • Data scientists must somehow get along and work jointly with mere quantitative analysts. 12/11/2013 Integrating Big Data
  • 8. Access Innovations, Inc.. • Big Data is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big Data sizes are a constantly moving target currently ranging from a few dozen terabytes to many petabytes of data in a single data set. – Wikipedia, May 2011 • “Unstructured” • Terabytes, petabytes, zettabytes • Streaming 12/11/2013 What is Big Data?
  • 10. Access Innovations, Inc.. • Volume, Velocity, Variety • Ability to deal overwhelmed • More about methods than data • Location aware data • Life streaming • Insurance claims • Hubble telescope • CERN Collections • Flight data 12/11/2013 New Special Collections
  • 12. Access Innovations, Inc.. • Means untagged or unformatted • PDF • Word files • File shares • News feeds • News Data feeds • Images 12/11/2013 Unstructured data
  • 13. Access Innovations, Inc.. • All data has some structure and more structure possibilities • PDF properties • Word file properties • File structures 12/11/2013 Bit of a misnomer
  • 14. Access Innovations, Inc.. • PDF - property tables • Word files – property tables • Files shares – implied structure in file names • News feeds – headers have metadata • Hubbell telescope feeds • Images 12/11/2013 Property tables
  • 17. 12/11/2013 • XML tagged data Access Innovations, Inc.. Structured data
  • 18. Access Innovations, Inc.. • Data infrastructure challenges • “taking diverse and heterogeneous data sets and making them more homogeneous and usable” • An opportunity? • All that data – what can it tell us? • Privacy • Copyright • Neurological impact • Data collection methods 12/11/2013 What are the problems?
  • 19. Access Innovations, Inc.. The Big Data Senior Steering Group (BDSSG) was formed to identify current Big Data research and development activities across the Federal government, offer opportunities for coordination, and identify what the goal of a national initiative in this area would look like. Subsequently, in March 2012, The White House Big Data R&D Initiative was launched and the BDSSG continues to work in four main areas to facilitate and further the goals of the Initiative. 12/11/2013 New Government Initiative
  • 20. Access Innovations, Inc.. • Fast-growing volume of digital data of digital data • Advance state-of-the-art core technologies needed to collect, store, preserve, manage, analyze, and share huge quantities of data. • Harness these technologies to accelerate the pace of discovery in science and engineering, strengthen our national security, and transform teaching and learning; and • Expand the workforce needed to develop and use Big Data technologies. 12/11/2013 The National Big Data R&D Initiative
  • 21. •Big Data •Data analytics; •Educate and expand the Big Data workforce •Improve key outcomes in economic growth, job creation, education, health, energy, sustainability, public safety, advanced manufacturing, science and engineering, and global development Access Innovations, Inc.. •Advance supporting technologies 12/11/2013 Data to Knowledge to Action
  • 22. Access Innovations, Inc.. • January 22, 2013 - Data on Data: Presenting Stakeholder Alignment Data on the Cyberinfrastructure for Earth System Science • Presentation and discussion with Professor Joel Cutcher-Gershenfeld. Professor CutcherGershenfeld presented information on the NSF EarthCube initiative including stakeholder survey data (approximately 850 responses). 12/11/2013 Data on Data
  • 24. • • • • • • INTERAGENCY WORKING GROUPS Cyber Security and Information Assurance High End Computing COMMUNITY OF PRACTICE (CoP) · Faster Administration of Science and Technology Education and Research COORDINATING GROUPS Human Computer Interaction and Information Management High Confidence Software and Systems Large Scale Networking Software Design and Productivity Social, Economic, and Workforce Implications of IT Access Innovations, Inc.. • • • • • 12/11/2013 Groups breakdown
  • 25. Access Innovations, Inc.. • Big Data • Cyber Physical Systems • Cyber Security and Information Assurance Research and Development • Health Information Technology Research and Development • Wireless Spectrum Research and Development • SUBGROUP • Health Information Technology Innovation and Development Environments Subgroup • TEAMS • Joint Engineering Team • Middleware and Grid Interagency Coordinating Team 12/11/2013 SENIOR STEERING GROUPS (SSGs)
  • 26. • • • • Local Cloud Remote Streaming • Undifferentiated • Unstructured • Needs organization • Type of database structure • RDBMS • Object oriented Access Innovations, Inc.. • Data on machines 12/11/2013 Content Organization
  • 27. Access Innovations, Inc.. 12/11/2013 RDBMS Connection Taxonomy term table
  • 28. Public repositories Commercial data sources Agency data sources Search engine Library catalogs Locally held documents Search engine Search engine Search engine Search engine spiders Filtered content Search engine Meta-Search Tool TAXONOMY Web portal 12/11/2013 INTERNET (public) Access Innovations, Inc.. Data feeds
  • 29. Structure unstructured data Create metadata Where to put it? Store metadata with the records • HTML header • Properties tables • XML files • Store metadata in a separate file • • • • • • Database Metadata repository Search system File structure SharePoint application Web interface Access Innovations, Inc.. • • • • 12/11/2013 Metadata options
  • 30. • Uncontrolled list • Name authority file • Synonym set / ring • Controlled vocabulary • Taxonomy • Thesaurus • Ontology Highly complex - $$$$ • Topic Map LOTS OF OVERLAP! • Semantic Network 12/11/2013 Not complex - $ Access Innovations, Inc.. Information retrieval starts with a knowledge organization system
  • 31. Synonyms Taxonomy Thesaurus INCREASING COMPLEXITY / RICHNESS Ambiguity control Synonym control Ambiguity control Ambiguity cont’l Synonym control Synonym cont’l Hierarchical rel’s Hierarchical rel’s Associative rel’s 12/11/2013 List of words Access Innovations, Inc.. Structure of controlled vocabularies
  • 32. TAXONOMY ONTOLOGY • See also (SA) • Non-Preferred Term (NP) • Used for (UF), See (S) • Scope Note (SN) • History (H) THESAURUS *a.k.a. subject term, heading, node, index term, keyword, category, descriptor, class 12/11/2013 • Main Term (MT) * • Top Term (TT) • Broader Terms (BT) • Narrower Terms (NT) • Narrower Term Instance • Related Terms (RT) Access Innovations, Inc.. Taxonomy / thesaurus
  • 34. Reference: “Taxonomies: the vital tool of information architecture”, www.tfpl.com. Courtesy of Lillian Gassie, Naval Postgraduate School, Monterey, CA Access Innovations, Inc.. A taxonomy aspires to be: • a correlation of the different functional, regional and (possibly) national languages used by a community of practice • a support mechanism for navigation • a support tool for search engines and knowledge maps • an authority for tagging documents and other information objects • a knowledge base in its own right 12/11/2013 Taxonomies in Context
  • 35. Access Innovations, Inc.. • Link the taxonomy and indexing • Always in sync with the industry • Keep up to date with terminology • Automatically index the old data • Filter newsfeeds • Search using the taxonomy • File using the taxonomy • Spell check using the taxonomy • Link to translation system • Catalog using the taxonomy 12/11/2013 Where to use a taxonomy
  • 36. Courtesy of Lillian Gassie, Naval Postgraduate School, Monterey, CA “Taxonomies are better created by professional indexers or librarians than by domain experts.” 12/11/2013 • Improves organization & structure • Facilitates navigation • Facilitates knowledge discovery • Reduces effort • Saves time Access Innovations, Inc.. Value of a Taxonomy
  • 37. • knowledge management system • search retrieval system • portal interface Access Innovations, Inc.. • A well formed taxonomy is based on a thesaurus • Provides a flexible platform for many views of the taxonomy • Allows fast deployment • Is the basis for a good 12/11/2013 Taxonomies
  • 38. Database Management Establish rules for term use System Suggest indexing terms Thesaurus tool Indexing tool Validate terms Add terms and rules Change terms and rules Delete terms and rules Access Innovations, Inc.. Search thesaurus Validate term entry Block invalid terms Record candidates 12/11/2013 A quick look behind the scenes
  • 40. Suggested taxonomy descriptors Access Innovations, Inc.. 12/11/2013
  • 41. Access Innovations, Inc.. • Create the metadata structure • Gather the locations of the records • Index in place? • Point to the data? • Add metadata to the record? • Store metadata in separate “table”? • Use in full text? • Use in search? • Link databases and data sets with APIs 12/11/2013 Workflow order
  • 42. • Was: Card catalog or OPACs to books on shelf • Now: EBSCO host to Mendeley, Zotero, ResearchGate, Academic.edu, etc. • Use an API or web call • Web calls • Call to another web platform • APIs, written handshakes • Z39.50 is one standard for libraries Access Innovations, Inc.. • Link data cross platforms • Not federated search • Examples 12/11/2013 APIs and Web Calls
  • 43. • Free text / full text • Fielded / Faceted • Parts of Search • Presentation layer • Caching • Inverted index Access Innovations, Inc.. • Search 12/11/2013 Discovery (Search)
  • 44. • Autonomy / Verity • Bayesian – • FAST • Lucene • Faceted • Endeca • Boolean • Dialog • Ranking Algorithms • Google Access Innovations, Inc.. • Keyword – 12/11/2013 Kinds of search
  • 45. Access Innovations, Inc.. • Query language • Search technology • Inverted index • Ranking algorithms • Other enhancements 12/11/2013 Parts of search
  • 46. FAST ESP and Data Harmony 12/11/2013 Architecture Core Architectural Components MANAGEMENT API Custom Applications Content Push FILTER SERVER CUSTOM CONNECTOR MAIstro Agent DB Data Harmony Governance API Query Alerts Vertical Applications Portals Results NavTree & MAI Query LOTUS NOTES CONNECTOR Email, Groupware Index DB Pipeline QUERY PROCESSOR Databases DATABASE CONNECTOR Pipeline DOCUMENT PROCESSOR FILE TRAVERSER CONTENT API Files, Documents SEARCH SERVER QUERY API WEB CRAWLER Web Content Custom Front-Ends Mobile Devices Access Innovations, Inc.. Administrator’s Dashboard
  • 47. Access Innovations, Inc.. 12/11/2013 SLA search Interpret search word “competencies” as taxonomy term Professional competencies
  • 48. 12/11/2013 Access Innovations, Inc.. Search “taxonomy” in XML descriptor field returns all documents in that category  27 Search in original metadata  1 Solution: Include descriptors with metadata!
  • 49. Access Innovations, Inc.. • Data management layers • Curation • Preservation • Archiving • Storage • Choose tools wisely • Data mashups 12/11/2013 Management
  • 50. • Sorting for production • Level of effort to get to 85% • Integration into the workflow is efficient Access Innovations, Inc.. • Reach 85% precision to launch for productivity assisted • Reach 85% for filtering or categorization 12/11/2013 Go – No Go
  • 51. Access Innovations, Inc.. • Information infrastructure • Information dynamics • Tensions on data • Design elements • How and when to set the data framework • Identifiers (taxonomies) Glue to hold data together 12/11/2013 Learn to understand the parts
  • 52. Access Innovations, Inc.. • Core cross disciplines • Combo of humans and machines • What is open • What needs a gatekeeper • Store locally • Store in cloud • Some things people do better 12/11/2013 Services
  • 53. • Satisfice = satisfaction + suffice • How good is good enough? • How much error can you put up with? • Hits, misses, noise Access Innovations, Inc.. • 15 – 20% irrelevant returns / noise • Amount of work needed to achieve 85% level • How good is good enough? 12/11/2013 Benchmarks
  • 54. • 1.5 synonyms per terms • 7500 terms total • Assume 85% accuracy • Use assisted for indexing • Use automatically for filtering • Assume $55 per hour for staff • Assume 10000 records for test batch Access Innovations, Inc.. • Assume – 5000 term thesaurus 12/11/2013 ROI Calculations
  • 55. • • • • • • • • • Preferred and non-preferred terms Find records – could be programmatic Need to collect and review about 60 to get 20 appropriate ones Review records – ensure they are correct 3 minutes average per final selected record = 1 hour per term entry 1 minute to review a record (20 resulting records) 7500 terms at one hour per term = 7500 hours Estimated at 7500 hours $55 / hour = $412,500 Access Innovations, Inc.. • Collect 20 items per thesaurus entry 12/11/2013 Co-occurrence - Training sets
  • 56. • • • • Average rule takes 5 minutes (Actually MUCH faster using a rule building tool) 5 x 1500 = 7500 minutes 125 hours x $55 = $6875 Access Innovations, Inc.. • 80% of rules built automatically • 7500 x .8 = 6000 • 20% require complex rules 12/11/2013 Rulebuilding - rules
  • 57. Access Innovations, Inc.. • Cost of auto system • Cost of getting system ready • Ongoing maintenance • Increased efficiency • Increased quality of retrieval • Cost of legacy system maintenance 12/11/2013 ROI - Segments
  • 58. • Cost of auto system- $500,000 (could be less, but the one used for this study cost this amount) • Cost of getting system ready 12/11/2013 ROI – Co-occurrence • Estimated at 2 weeks programming $100 / hour = $8000 • Training sets • Estimated at 7500 hours $55 / hour = $412,500 • Possible need to re-run training set several times • Ongoing maintenance • Estimated at 15% of purchase price for license = $75,000 • Quarterly retraining to keep up with new terms – • Training sets for new terms 50 terms per quarter = 200 x $55=$11,000 • • • • Increased efficiency Expected accuracy at 60% Increased quality of retrieval Cost of legacy system maintenance Access Innovations, Inc.. • Programming support and integration
  • 59. • Cost of auto system- $60,000 • Cost of getting system ready 12/11/2013 ROI – Rules system • Programming support and integration • Rule building • Estimated at 125 hours at $55 / hour = $6850 • Possible need to re run training set several times • Ongoing maintenance • Estimated at 15% of purchase price for license = $9000 • Rule building for new terms, 50 terms per quarter • 200 terms x .8 = 160 automatic • 40 at 5 minutes per term = 200 minutes /60 = 3.33 hours x $55 = $183 • • • • Increased efficiency Expected accuracy at 60% Increased quality of retrieval Cost of legacy system maintenance Access Innovations, Inc.. • Estimated at 2 weeks programming at $100 / hour = $8000
  • 60. • Year one 12/11/2013 ROI - Rules • Years thereafter • $9000 + $183 = $9183 • 85% accuracy Access Innovations, Inc.. • $60,000 + $8,000 + $6,850 = $74,850
  • 61. • Year one 12/11/2013 ROI – Co-occurrence • Years thereafter • $75000 + $11,000 = $86,000 • 60% accuracy Access Innovations, Inc.. • $500,000 + $8,000 + $412,500 = $920,500
  • 62. • Controlled – use rules • New terms - use inference Access Innovations, Inc.. • Novelty detection requires inference techniques • Is needed to discover new terms • Controlled vocabulary / thesaurus can be more controlled and accurate approach • Combination of the two would be best 12/11/2013 Summary on ROI
  • 63. Client Data Full Text Tag and Create metadata Put in data base with tags Build Search inverted index Automatic Summarization Search Presentation Layer HTML, PDF, Data Feeds, etc. Machine Aided Indexer (M.A.I.™) Inline Tagging Client Client Taxonomy taxonomy Metadata and Entity Extractor Thesaurus Master Create user interface Access Innovations, Inc.. Gather source data 12/11/2013 Fully integrated with MOSS The Workflow Database Repository Search Software Increases accuracy Browse by Subject Auto-completion Broader Terms Narrower Terms Related Terms
  • 64. Access Innovations, Inc.. 12/11/2013 Inline Tagging Shows the exact point where the concept is mentioned Mouse-over to view the term record Statistical summary, showing the number of times each term is mentioned in the article
  • 65. Automatic Summarization Full text, HTML, PDF, data feeds Inline Tagging Metadata Extractor Apply terms Rules Base Thesaurus Master Client Taxonomy Data Repository Search Software User Interface Web Portal Access Innovations, Inc.. 12/11/2013 Semantic Process
  • 66. Raw Full text data feeds Printed source materials Data Crawls on data sources Source data XIS Creation XIS repository Load to Search Taxonomy terms MAI Concept Extractor MAI Rule Base Taxonomy Thesaurus Master Add metadata Clean and enhance data Search Harmony Display Search Search data Access Innovations, Inc.. SQL for ecommerce 12/11/2013 Database Plus Search Workflow
  • 67. Creating an Inverted File Index 1 Define key terminology 2 Thesaurus tools • Features • Functions 3 Costs • Thesaurus construction • Thesaurus tools 4 Why & when? Access Innovations, Inc.. Outline of Presentation 12/11/2013 Sample DOCUMENT
  • 69. & - Stop 1 - Stop 2 - Stop 3 - Stop 4 - Stop construction - L7, P2, SH costs - L6, P1, H define - L2, P1, H features - L4, P1, SH functions - L5, P1, SH 12/11/2013 key - L2, P2, H of - Stop outline - L1, P1, T presentation - L1, P3, T terminology - L2, P3, H thesaurus - (1) - L3, P1, H (2) - L7, P1, SH (3) - L8, P1, SH tools - (1) - L3, P2, H (2) - L8, P2, SH when - L9, P3, H why - L9, P1, H Access Innovations, Inc.. Complex inverted file index Placement location
  • 70. 12/11/2013 Complex Search Farm Query Search Harmony Presentation Layer Cleanup, etc. Federators Deploy Hub Repository XIS (cache) Cache Builders Source Data Access Innovations, Inc.. Query Servers Index Builders
  • 71. • That is what librarians usually look at. • What are the options coming? • How can we encourage useful changes? 12/11/2013 Access Innovations, Inc.. What happens at the search presentation layer?
  • 75. Journal Article on Topic A Grant Available for Researchers Working on Topic A Author Networks Social Networking Based on an image by Helen Atkins Upcoming Conference on Topic A Job Posting for Expert on Topic A Podcast Interview with Researcher Working on Topic A Access Innovations, Inc.. Other Journal Articles on Topic A CME Activity on Topic A 12/11/2013 Data linked and served by metadata
  • 76. Alcohol, Folate, Methionine, and Risk of Incident Breast Cancer in the American Cancer Society Cancer Prevention Study II Nutrition Cohort Heather Spencer Feigelson1, Carolyn R. Jonas, Andreas S. Robertson, Marjorie L. McCullough, Michael J. Thun and Eugenia E. Calle Department of Epidemiology and Surveillance Research, American Cancer Society, National Home Office, Atlanta, Georgia 30329-4251 Recent studies suggest that the increased risk of breast cancer associated with alcohol consumption may be reduced by adequate folate intake. We examined this question among 66,561 postmenopausal women in the American Cancer Society Cancer Prevention Study II Nutrition Cohort. Think Tank Report Related Working Groups •Finance Related Think Tank Report Content •Charter •Molecular Epidemiology Webcasts Related Awards Related Webcasts •AACR-GlaxoSmithKline Clinical Cancer Research Scholar Awards •ACS Award •Weinstein Distinguished Lecture Related Press Releases 12/11/2013 •How What and How Much We Eat (And Drink) Affects Our Risk of Cancer •Novel COX-2 Combination Treatment May Reduce Colon Cancer Risk Combination Regimen of COX-2 Inhibitor and Fish Oil Causes Cell Death •COX-2 Levels Are Elevated in Smokers Related AACR Workshops and Conferences •Frontiers in Cancer Prevention Research •Continuing Medical Education (CME) •Molecular Targets and Cancer Therapeutics Related Meeting Abstracts Access Innovations, Inc.. Cancer Epidemiology Biomarkers & Prevention Vol. 12, 161-164, February 2003 © 2003 American Association for Cancer Research Short Communications •Association between dietary folate intake, alcohol intake, and methylenetetrahydrofolate reductase C677T and A1298C polymorphisms and subsequent breast •Folate, folate cofactor, and alcohol intakes and risk for colorectal adenoma •Dietary folate intake and risk of prostate cancer in a large prospective cohort study Related Education Book Content Oral Contraceptives, Postmenopausal Hormones, and Breast Cancer Physical Activity and Cancer Hormonal Interventions: From Adjuvant Therapy to Breast Cancer Prevention After Helen Atkins
  • 77. Access Innovations, Inc.. 12/11/2013 All data up-posted to the top level
  • 78. This is a radial graph of “plosthes”. The number of records for which each index term occurs is reflected by circle sizes. Access Innovations, Inc.. 12/11/2013 Visualize your tagged data
  • 82. Access Innovations, Inc.. 12/11/2013 Load to a visualization program such as Prefuse
  • 83. • Migration patterns • YouTube • Citizen Science Access Innovations, Inc.. • Drawing together information from several sources • Overlay on additional surfaces – like maps • Fun distribution of data • Cornell School of Ornithology 12/11/2013 Data Mashups
  • 84. • ISI does it with references • AIP’s UniPHY does it using semantics 12/11/2013 Scientific social networking basedciting who like on metadata • Idea has been here - Who is • good metadata and descriptors See the authors connections Map who is working in the field and where Access Innovations, Inc.. • Expand your options using
  • 86. • With interpretive layer – computer assisted • Links to data • Open data / supplemental data • Too much is not enough! Access Innovations, Inc.. • Document systems replaced • New infrastructure • Institutional repositories not good long term for Big Data • Need to operate at scale • Integrated, ecosystem to infrastructure • Replace customized human mediated 12/11/2013 Future?
  • 87. • Go down hall and ask • Big Data • Too many people to ask • Lose provenance • Hard to differentiate the layers Access Innovations, Inc.. • Not by size of collection • By the services they offer • More services more competitive • Spreadsheet science 12/11/2013 Differentiation
  • 88. People, data usage Preservation and discovery Publishers’ content, not format Visualization of Big Data sets Reproducibility of research Shared knowledge Open peer review • • • • • Researcher.org Galaxy zoo Zooniverse 900,000 citizen scientists Wikipedia Access Innovations, Inc.. • • • • • • • 12/11/2013 New ways of doing things
  • 90. • Weeding • Redefining collections • Collection development • Skills to apply to Big Data • Vocabulary development • Search expertise • Reference ability • What to throw away • What to keep for discoverability • Need metadata to preserve • Better discoverability • Easier preservation • Keep the data provenance Access Innovations, Inc.. • Librarians 12/11/2013 Skills we bring
  • 91. Access Innovations, Inc.. • Big Data – What is it? • New Government Initiative • Content organization • Discovery (Search) • Management • Skills we bring • Examples of what we can do 12/11/2013 We covered
  • 92. • Semantic Enrichment Services Provider • We change search to find! • Creator of Data Harmony tools • More than 600 taxonomies created • More than 2000 engagements • Financed by Sweat, Persistence, and Good Cash Flow Management • Accurate, on time, under budget! Access Innovations, Inc.. • Access Innovations – 12/11/2013 Who are we?
  • 93. • Marjorie Hlava • mhlava@accessinn.com • +1-505-998-0800 • Access Innovations/Data Harmony/Access Integrity • Taxodiary.com (blog) • Taxobank.org (reference tool for taxonomists) 12/11/2013 Thank you Access Innovations, Inc.. Slides at www.accessinn.com/presentations

Editor's Notes

  • #76: Thanks to Helen Atkins of AACR for this illustration.The real power of this is that the links can all go in all directions, so we take advantage of having the user’s attention regardless of how they step into our “web”