Computing and Information Technology Interactive Digital Educational Library Technical Development Content Collection Edward Fox (director) John A. N. Lee Manuel Pérez-Quiñones Community Development John Impagliazzo Assessment Lillian Cassel Search Engines C. Lee Giles CSTC Deborah Knox

Searching CITIDEL searching, which is driven by the ESSEX search engine for relevance computation (fast, in-RAM processing with checkpoints), also provides a list of relevant categories within the classification schemes. Browsing and Searching with Filters Users are placed in chosen sub-communities. They can filter results based on these sub-communities. Also there is further customization. Alternatively, users may view all results. Users may set up multiple filters for simple or complex filtering based on many factors such as education level, role, resource type, language, source, and much more. This allows users to get exactly what they are or are not looking for in the digital library. At any time, users are free to disable these filters or see results excluded by them.

Enjoy in GrapeZone Derived from Carrot 2 project ( ex.xml?lang=en) Online Grape Cluster search results from CITIDEL Offline Grape Cluster a static collection

Cluster search results from CITIDEL

Cluster a member collection in CITIDEL The Computer Science Teaching Center (CSTC) NDLTD-Computing ACM Digital Library

Cluster CSTC

Cluster NDLTD-Computing

Cluster ACM

PIPE: Personalization by Partial Evaluation Interactions at existing web sites are predefined by the site designer Personalization is achieved by the designer’s anticipation of users’ expectations PIPE allows automatic personalization of a web site without designer anticipation –Recognized with the 2001 New Century Technology Council Innovation award

PIPE provides Mixed-Initiative Interaction Involves an extra specification window (e.g., a toolbar) system-initiated + user-initiated modes of interaction Traditional browser: the user merely clicks on available hyperlinks. PIPE window: the user can type in any information out-of-turn Can also mix-n-match

Features of PIPE applicable to many information system technologies web sites (even third-party) Digital Libraries (currently working on CITIDEL integration) voice-activated systems (e.g., pizza ordering, movie information, and flight reservation services) PIPE is available for licensing and is ready for commercialization, through VTIP PIPE has been featured in IEEE Internet Computing, IEEE IT Professional, and the Appian Web Personalization Report.

PIPE system architecture

CITIDEL + PIPE Adds Interaction Personalization to CITIDEL Automatically handles multi-modal conversion to Cell phone, PDA, Etc. Can be adopted to any digital data set, only requires XML file of content with hierarchy maintained.

Programming Team DL Project Logan Hanks, Mike Scarborough, Stafford Fuller, Problem Description: VT has multiple programming teams, and has sent a team to the ACM world finals every year for the past decade. Each week during the semester, the teams practice using a problem set from a past regional or international contest. Each practice generates multiple solutions for each problem. What is needed is a digital library to collect these solutions and serve as a reference.

Programming Team DL Project Deliverables: Problem statement and solution importer/archiver. Classification framework for problems and solutions. Search engine for the DL to locate problems and solutions by their relevance to a set of classes given as input. Web interface for browsing problems and solutions as well as accessing all of the above deliverables. Integration with CITIDEL. Requirements: Importing and classifying problem statements and solutions. Solutions should be classified based on what algorithms and methods they use and what problems they solve. Interface for browsing problem statements and their solutions. A search engine for finding problem statements or solutions based on their classifications.

The XML Log Standard for Digital Libraries: Analysis, Evolution, and Deployment Marcos André Gonçalves, Ganesh Panchanathan, Unnikrishnan Ravindranathan, Aaron Krowne, Edward A. Fox – Virginia Tech Filip Jagodzinski, Lillian Cassel – Villanova University

Evolution of the Log Tool Monolithical Log Tool DL Hierarchical Log Tool DL Socket (e.g., MARIAN) Componentized Log Tool DL C x XML Log Repository 1st Generation 2nd Generation Standard Protocol (XOAI) (e.g., CITIDEL) DL C y Next Generation DL C z DL

The XML Log Format Log SessionIdMachineInfo StatementTransactionTimestamp SessionInfoRegisterInfo StatementEventTimestamp Action SearchBrowse StoreSysInfoUpdate SearchBy QueryString CatalogCollection PresentationInfo StatusInfo Timeout

Evolution of the Log Format

Log Analysis Tools Standardization of log processing and analysis –Different DLs can be compared, monitored, and analyzed Support of clickstream analysis –Provides detailed information on user activity and overall user trends

XML Log Log Data Parser/ Error Checker Routine module usr T20:10: … low back pain … 5114 Step 1: Extract Log Data User Activity Files Query String StatsUser ID Stats Domain StatsError Stats Document ID Stats Step 3: Individual Modules Populate Intermediate Stat files, Increment Global Variables, etc. Step 4: At Conclusion of XML Log, different stats can be combined and/or used to get additional statisticss. Final Report/ Statistics module Step 2: Parse XML Log Data; Send Log Line to Appropriate Module Log Analysis Tools: Parsing the XML Log

Log Analysis Tools: Creating the Clickstream Stats Visualizer (GUI) Visualizations Use Activity File User 4532; 25 Logons, 22 Logoffs, 3 accesses from.edu, 2 hits from.gov, history:: Logon page [13 may 2003, 16:00] -> Browse page [13 may 2003, 16:02] -> search page [13 may 2003, 16:04] -> results page [13 may 2003, 16:04] -> view document 254 page [13 may 2003, 16:07] -> download page [13 may 2003, 16:10] -> logoff page [13 may 2003, 16:11] User ; 3 Logons, 0 Logoffs, 1 accesses from.mil, 2 hits from.com, history: Logon page [12 may 2003, 12:00] -> Browse page [12 may 2003, 12:02] -> logoff page [12 may 2003, 12:03] Etc. etc. etc Clickstream stat generator Clickstream stats Step 1: intermediate Statistics Files are Used as input Step 2: Clickstream Data is produced Step 3: GUI is used To produce usage Statistics, clickstream Stat visual aids, etc.