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Digital Libraries From Information retrieval to search engines e-books, e-libraries & related topics hardware General background Driving forces – rapid evolution of : Computing power Memory Networking (internet) DB systems IR systems Hypertext (WWW) GUI/presentation tools (html)

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digital libraries
Digital Libraries
  • From Information retrieval to search engines
  • e-books, e-libraries & related topics

Introduction – Beeri/Feitelson

general background

hardware

General background

Driving forces – rapid evolution of:

  • Computing power
  • Memory
  • Networking (internet)
  • DB systems
  • IR systems
  • Hypertext (WWW)
  • GUI/presentation tools (html)

software

Introduction – Beeri/Feitelson

slide3
Consequences:

Transformation of existing applications

Generation of new applications

related to

data collection, organization, classification, access

Some examples:

Introduction – Beeri/Feitelson

slide4
(Classical) Libraries:
  • Automation of catalogs (old stuff)
  • On-line e-journals
  • Collections of born-digital materials

New:

  • Digitized collections (images, maps,..)
  • on-line archives
  • On-line, virtual museums

Introduction – Beeri/Feitelson

slide5
Digital libraries & bibliographic services:
  • ACM Digital Library acm-diglib

collection of all (full) papers from ACM journals

  • SIGMOD digital anthology anthology
  • DBLP dblp

collection of bibliographic information

  • Citeseer citeseer

citation and impact factor data

Introduction – Beeri/Feitelson

slide6
Portals, directories, search engines
  • Yahoo – a directory (manual labor)
  • Google – a search engine (fully automatic)

IR technology & hypertext structure

  • Amazon (& similar on-line sales companies)

(book/dvd/.. Portal/directory)

Introduction – Beeri/Feitelson

slide7
Data and information services:
  • Medline & US national library of medicine: nlmed
  • On-line encyclopedias:

Wikipedia, art encyclopedia

  • Lexis-nexis (and many like it)

Introduction – Beeri/Feitelson

slide8
Scientific data directories/repositories/portals:
  • Bioinformatics: -- over 500 db’s/data sources

bio-source

  • Astronomy – the world-wide telescope project wwt
  • Classical humanities – the Perseus digital library

perseus

Introduction – Beeri/Feitelson

slide9
Issues:
  • Heterogeneity  data transformation/integration
  • Dependence on experiments  scientific experiment meta-data
  • Huge volumes  fast, approximate, on-line stream processing

Introduction – Beeri/Feitelson

slide10
New kinds of services:
  • Query subscription on XML/data streams

niagara, niagaracq

    • news
    • Stocks
    • satellite data

Issues:

Fast streams

Millions and more queries

& subscribers

  • Needs ultra-fast
  • stream processing
  • Query evaluation/ data routing

Introduction – Beeri/Feitelson

slide11
A few more relevant buzzwords :
  • Customer profiling

targeted advertisment

  • Knowledge management

Collecting, managing, exploiting the know-how in large organizations

  • E-learning
  • E-publication

a End of examples

Introduction – Beeri/Feitelson

library ir basic concepts
Library/IR basic concepts

Classification: מיון

Axiom: unique position for a book

  • Unique call number מס' מיון

(+ secondary subjects)

  • hierarchical & universal classification system

Dewey (1876) LC (1900)

+ subjects  books

x Single main catalog

x Claim to universality

Introduction – Beeri/Feitelson

slide13
Metadata נתוני על

data describing a collection/item

Example: library bibliographic record for a book

See:

Dublin Core & its use in stanford

Introduction – Beeri/Feitelson

slide14
Operations in collection creation/maintenance :

Cataloging: קטלוג

create metadata record for an item

(many style/spelling … conventions used here)

Indexing: מיפתוח

identify key terms (in all text/some fields)

  • Controlled מבוקר -- uses a fixed vocabulary
  • Uncontrolled terms chosen by indexer

Abstraction: תקצור

create short description (of key ideas)

Introduction – Beeri/Feitelson

slide15
Products:
  • Bibliographic record db
  • Author/title catalog
  • Subject (header) catalog

(provides entry points in on-line catalogs)

Currently, operations performed manually

  • Expensive, slow, time-consuming
  • Require experts
  • Results are non-uniform (even with experts)

Automatic indexing & abstracting ---

research areas

Introduction – Beeri/Feitelson

slide16
Thesaurus: מילון נתונים, תיזאורוס

a vocabulary of standard terms/concepts

  • Hierarchical organization – every term has
    • BT – broader term, NT – narrower terms
  • Additional relationships
    • Synonyms, RT – related terms

Used for:

  • Indexing  standardization of index terms
  • Querying  standardization of query terms

(supposedly solves problem of non-uniform indexing)

Creation: complex, lengthy, community process

See also: ontology, KWIC (google them!)

Introduction – Beeri/Feitelson

technology theory background
Technology/theory background

Structured data – dbms

  • Schema (structure) describes data precisely
  • Queries & query language (based on structure)
  • Indices, query optimization

covered in DB course

Big challenge:

integration of multiple heterogeneous autonomous sources

Introduction – Beeri/Feitelson

slide18
Unstructured data – (free) text:

information retrieval איחזור מידע

Data = collection of texts

Query = set of terms (words)

Indices = inverted lists (words to locations)

Results = ranked answers

Issues/challenges:

  • Answers are imprecise, approximate
  • Difficult to evaluate answer goodness

Introduction – Beeri/Feitelson

slide19
Hypertext/www:

html, http, soap, ….

A browsing model

covered in bsdi course

Issues/ disadvantages:

  • No notion of query, just browsing
  • No structure on data
  • no data quality guarantees

Introduction – Beeri/Feitelson

slide20
Semi-structured data --- XML:

A standard for data exchange, also stored

covered in bsdi course

  • Self-describing data
  • Optional meta-data --- DTD/schemas
  • Validation tools
  • query language
  • Stream processing tools (under development)

Current move: extend to semantic web

Introduction – Beeri/Feitelson

slide21
Machine learning:

Used for automatic

  • Classification
  • Indexing
  • Abstracting
  • Clustering

Of free text/semi-structured data

covered in machine learning courses

End of technology survey

Introduction – Beeri/Feitelson

the course
The course
  • IR -- classical to Google
    • System architectures
    • Kinds of queries
    • Auxiliary data structures (indices) & efficient query processing
    • Compression, a bit of theory, uses in IR
    • Extensions to hypertext

(using link structure)

  • “Conceptual” topics
    • E-books
    • E-publishing

Introduction – Beeri/Feitelson

end of introduction
End of Introduction

Introduction – Beeri/Feitelson