1 CS 430: Information Discovery Lecture 23 Non-Textual Materials

2 Course Administration

3 Examples ContentAttribute mapslatitude and longitude photographdate and place bird songs and imagesbird song softwarealgorithm data setsurvey characteristics videosubject matter

4 Surrogates Surrogates for searching Catalog records Finding aids Classification schemes Surrogates for browsing Summaries (thumbnails, titles, skims, etc.)

5 Catalog Records for Non-Textual Materials Metadata standards, such as Dublin Core and MARC, can be used to create a textual catalog record of non-textual items. Text-based searching methods can be used to search these catalog records.

6 Example 1: Photographs Photographs in the Library of Congress's American Memory collections In American Memory, each photograph is described by a MARC record. The photographs are grouped into collections, e.g., The Northern Great Plains, : Photographs from the Fred Hultstrand and F.A. Pazandak Photograph Collections Information discovery is by searching the catalog records or browsing the collections.

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10 Photographs: Cataloguing Difficulties Automatic Image recognition methods are very primitive Manual Photographic collections can be very large Many photographs may show the same subject Photographs have little or no internal metadata (no title page) The subject of a photograph may not be known (Who are the people in a picture? What is the location?)

11 Photographs: Difficulties for Users Searching Often difficult to narrow the selection down by searching -- browsing is required Criteria may be different from those in catalog (e.g., graphical characteristics) Browsing Offline. Handling many photographs is tedious. Photographs can be damaged by repeated handling Online. Viewing many images can be tedious. Screen quality may be inadequate.

12 Collection-Level Metadata Collection-level metadata is used to describe a group of items. For example, one record might describe all the images in a photographic collection. Note: There are proposals to add collection-level metadata records to Dublin Core. However, a collection is not a document-like object.

13 Collection-Level Metadata

14 Finding Aids and the EAD Finding aid A list, inventory, index or other textual document created by an archive, library or museum to describe holdings. May provide fuller information than is normally contained within a catalog record or be less specific. Does not necessarily have a detailed record for every item. The Encoded Archival Description (EAD) A format (XML DTD) used to encode electronic versions of finding aids. Heavily structured -- much of the information is derived from hierarchical relationships.

15 Multimedia 2: Geospatial Information Example: Alexandria Digital Library at the University of California, Santa Barbara Funded by the NSF Digital Libraries Initiative from Collections include any data referenced by a geographical footprint. terrestrial maps, aerial and satellite photographs, astronomical maps, databases, related textual information Program of research with practical implementation at the university's map library

16 Alexandria User Interface

17 Computer Systems and User Interfaces Computer systems Digitized maps and geospatial information -- large files Wavelets provide multi-level decomposition of image -> first level is a small coarse image -> extra levels provide greater detail User interfaces Small size of computer displays Slow performance of Internet in delivering large files -> retain state throughout a session

18 Alexandria: Information Discovery Metadata for information discovery Coverage: geographical area covered, such as the city of Santa Barbara or the Pacific Ocean. Scope: varieties of information, such as topographical features, political boundaries, or population density. Latitude and longitude provide basic metadata for maps and for geographical features.

19 Gazetteer Gazetteer: database and a set of procedures that translate representations of geospatial references: place names, geographic features, coordinates postal codes, census tracts Search engine tailored to peculiarities of searching for place names. Research is making steady progress at feature extraction, using automatic programs to identify objects in aerial photographs or printed maps -- topic for long-term research.

20 Special Purpose Systems Many non-textual collections have developed special purpose methods for organizing materials and for information discovery.

21 Example 3: Mathematical Software Netlib A digital library that of mathematical software (Jack Dongarra and Eric Grosse). Exchange of software in numerical analysis, especially for supercomputers with vector or parallel architectures. Organization of material assumes that users are mathematicians and scientists who will incorporate the software into their own computer programs. The collections are arranged in a hierarchy. The editors use their knowledge of the specific field to decide the method of organization.

22 GAMS: Guide to Available Mathematical Software

23 Direct Searching of Content Sometimes it is possible to match a query against the content of a digital object. The effectiveness varies from field to field. Examples Images -- crude characteristics of color, texture, shape, etc. Music -- optical recognition of score Bird song -- spectral analysis of sounds Fingerprints

24 Image Retrieval: Blobworld

25 Data Mining Extraction of information from online data. Not a topic of this course.