Perspectives of information science in the digital age Tefko Saracevic, PhD Rutgers University USA

© Tefko Saracevic, Rutgers University2 Information science: “the science dealing with the efficient collection, storage, and retrieval of information” Webster

© Tefko Saracevic, Rutgers University3 Organization 1. Big picture – problems, solutions, social place 2. Underlying stuff – theories, phenomena 3. Structure – what is inside stuff 4. Systems stuff – information retrieval, relevance 5. People stuff – users, use, seeking, context 6. Alliances, competition – the OUCH stuff 7. Digital libraries – whose are they anyhow? 8. Conclusions – Will we have a field stuff?

© Tefko Saracevic, Rutgers University4 1.The big picture Problems addressed Bit of history: Vannevar Bush (1945): Problem: “... the massive task of making more accessible of a bewildering store of knowledge.” still with us & growing Basic problem of information science: Information explosion today: PLUS Communication explosion

© Tefko Saracevic, Rutgers University5 … solution Bush: “Memex... association of ideas... duplicate mental processes artificially.” Technological fix to problem Still with us: technological determinant tail that wags the dog

© Tefko Saracevic, Rutgers University6 Problems & solutions: SOCIAL CONTEXT Professional practice AND scientific inquiry related to: Effective communication of knowledge records - ‘literature’ - among humans in the context of social, organizational, & individual need for and use of information. “modeling the world of publications with a practical goal of being able to deliver their content to inquirers [users] on demand.” White & McCain Taking advantage of modern information technology

© Tefko Saracevic, Rutgers University7 Elaboration Knowledge records = texts, sounds, images, multimedia... literature in given domains content-bearing structures symbol manipulations are content neutral - infrastructural to inf. sc. Communication = human-computer-literature interface study of inf. science is the interface between people & literatures Inf. need, seeking, and use = reason d'être Effectiveness = relevance, utility

© Tefko Saracevic, Rutgers University8 General characteristics - leitmotifs ¶ Intedisciplinarity - relations with a number of fields ·Technological imperative - driving force, as in many modern fields ¸Information society - social context and role in evolution - shared with many fields

© Tefko Saracevic, Rutgers University9 2.Underlying stuff What is information? Intuitively well understood, but formally???? Several viewpoints, models Shannon: source-channel-destination grapes into wine Cognitive: changes in cognitive structures water into wine Social: context is the king whatever into wine to get drunk

© Tefko Saracevic, Rutgers University10 K(S) +  I = K(S +  S) (Brookes) Information [structured information] when operating on a knowledge structure produces an effect whereby the knowledge structure is changed Potential information added (Ingwersen) Actually, it states the problem – “unoperational” in information systems involves mental events only constructivists rejected it

© Tefko Saracevic, Rutgers University11 Information in inf science: Three senses (from narrowest to broadest) ¶Inf. in terms of decision involving little or no cognitive processing signals, bits, straightforward data - e.g.. inf. theory, economics ·Inf. involving cognitive processing & understanding understanding, matching texts ¸Inf. also as related to situation, task, problem-at- hand : USERS, USE For information science (incl. information retrieval): third, broadest interpretation

© Tefko Saracevic, Rutgers University12 The biggest problem MEASUREMENT

© Tefko Saracevic, Rutgers University13 3. Structure Specialties (White & McCain) In desc. order of author co-citation; (120 authors, 24 years): experimental retrieval citation analysis practical retrieval bibliometrics library systems, automation user studies and theory scientific communication OPAC’s general - other disciplines indexing theory communication theory

© Tefko Saracevic, Rutgers University14 Structure or oeuvres Two large sub-disciplines: Ê “Domain” cluster: analytical study of literatures, their structure, communication, social context, uses - Ë Retrieval cluster: human-literature interface: IR systems (largest); interaction; library systems, OPACs, user studies - within each sub-clusters, eras e.g.. Salton & post-Salton era Largely not connected some authors in both, migrating BUT: lacking integrating works, authors, texts - big payout

© Tefko Saracevic, Rutgers University15 Paradigm split in retrieval cluster Split from early 80’s to date ¶ System-centered algorithms, TREC continue traditional IR model · Human-(user)-centered cognitive, situational, user studies interaction models, some started in TREC Calls for user-centered approaches & evaluation But: most support for system work in the digital age support is for digital

© Tefko Saracevic, Rutgers University16 Human vs. system Human (user) side: often highly critical, even one-sided mantra of implications for design but does not deliver concretely System side: mostly ignores user side & studies ‘tell us what to do & we will’ Issue NOT H or S approach even less H vs. S but how can H AND S work together major challenge for the future

© Tefko Saracevic, Rutgers University17 4.Systems stuff Information Retrieval “ IR:... intellectual aspects of description of inf.,... search,... & systems, machines...” Calvin Mooers, 1951 How to provide users with useful information effectively? For that objective: 1. How to organize information intellectually? 2. How to specify the search & interaction intellectually? 3. What techniques & systems to use effectively?

© Tefko Saracevic, Rutgers University18 Streams in IR Res. & Dev. 1. Information science: Services, users, use; Human-computer interaction; Cognitive aspects 2. Computer science: Algorithms, techniques Systems aspects 3. Information industry: Products, services, Web Market aspects Problems:...relative isolation...inadequate cooperation, transfer

© Tefko Saracevic, Rutgers University19 IR successfully effected: Emergence & growth of the INFORMATION INDUSTRY Evolution of IS as a PROFESSION & SCIENCE Many APPLICATIONS in many fields including on the Web – search engines Improvements in HUMAN - COMPUTER INTERACTION Evolution of INTEDISCIPLINARITY IR has a long, proud history

© Tefko Saracevic, Rutgers University20 Broadening of IR OPACs (Online Public Access Catalogs) Natural language processing Summarization Metadata representations Text “understanding” Hypertext, hypermedia Multimedia - images, sounds... image IR, music IR Many human-computer interactions Web search engines

© Tefko Saracevic, Rutgers University21 5.People stuff Quite a few areas Professional services in organization – moving toward knowledge management, competitive intelligence in industry – vendors, aggregators, Internet, Research user & use studies interaction studies broadening to information seeking studies, social context, collaboration relevance studies social informatics

© Tefko Saracevic, Rutgers University22 User & use studies Oldest area covers many topics, methods, orientations many studies related to IR e.g. searching, multitasking, browsing, navigation Branching into Web use studies quantitative & qualitative studies emergence of webmetrics

© Tefko Saracevic, Rutgers University23 Interaction Traditional IR model concentrates on matching not user side & interaction Several interaction models suggested Ingwersen’s cognitive, Belkin’s episode, Saracevic’s stratified model hard to get experiments & confirmation Considered key to providing basis for better design understanding of use of systems Web interactions a major new area

© Tefko Saracevic, Rutgers University24 Relevance Effectiveness in IR = relevance thus, relevance became a key notion and a key headache A number of studies & reviews on: Nature: Framework, base? Manifestations: Contexts? Typologies? Behavior: Variables? Observations? Effects: Use? Evaluation?

© Tefko Saracevic, Rutgers University25 Manifestations (types) of relevance System or algorithmic relevance relation between query & objects (‘texts’) retrieved or failed to retrieve Topical or subject relevance Cognitive relevance or pertinence Situational relevance or utility relation between the situation, task or problem at hand & texts Motivational or affective relevance intent, goals, & motivation of user & “texts” Manifestations interact dynamically

© Tefko Saracevic, Rutgers University26 Information seeking Concentrates on broader context not only IR or interaction, people as they move in life & work Number of models provided e.g. Kuhlthau’s stages, Vakkari’s problem situation, task complexity Includes studies of ‘life in the round,’ making sense, information encountering, work life, information discovery Based on concept of social construction of information

© Tefko Saracevic, Rutgers University27 6.Alliances, competition Relations With a number of fields... Strongest: 1. Librarianship 2. Computer science

© Tefko Saracevic, Rutgers University28 Librarianship [Library is]... “contributing to the total communication system in society. Created to maximize the utility of graphic record for the benefits of society... it achieves that goal by working with the individual and through the individual it reaches society.” J.H.Shera, 1972

© Tefko Saracevic, Rutgers University29 Common grounds IS & librarianship share: Social role in information society Concern with effective utilization of graphic & other types of records Research problems related to a number of topics Transfer to & from information retrieval

© Tefko Saracevic, Rutgers University30 Differences IS & librarianship differ in: Selection & definition of many problems addressed Theoretical questions & framework Nature & degree of experimentation Tools and approaches used Nature & strength of interdisciplinary relations

© Tefko Saracevic, Rutgers University31 One field or two? Point of many debates Suggest: TWO fields in strong interdisciplinary relations Not a matter of “better” or “worse” - matters little common arguments between many fields Differences matter in: problem selection & definition agenda, paradigms theory, methodology practical solutions, systems Best example: IR & library automation

© Tefko Saracevic, Rutgers University32 Which? Librarianship. Information science Library and information science Information science Information sciences Information like in the “Information School”

© Tefko Saracevic, Rutgers University33 Computer science “systematic study of algorithmic processes that describe and transfer information.... The fundamental question in computing is: ‘What can be (efficiently) automated’.” Denning et al., 1989

© Tefko Saracevic, Rutgers University34 IS & computer science CS primarily about algorithms IS primarily about information and its users and use Not in competition, but complementary Growing number of computer scientists active in IS – particularly in IR and digital libraries Concentrating on advanced IR algorithms & techniques digital library infrastructure & various domains human computer interaction

© Tefko Saracevic, Rutgers University35 Human-computer interaction (HCI) “ Human computer interaction is a discipline concerned with the design, evaluation and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them.” ACM SIGCHI, 1993 Another interdisciplinary area computers sc., cognitive sc., ergonomics,...

© Tefko Saracevic, Rutgers University36 Interaction and IS Two streams: computer-human interaction human-computer interaction Modern IR is interactive BUT: difference between retrieval engine & retrieval interface Many studies on: machine aspects of interaction human variables in interaction Problem: little feedback between Interaction very hard to evaluate - few methods yet

© Tefko Saracevic, Rutgers University37 7.Digital libraries LARGE & growing area “Hot” area in R&D a number of large grants & projects in the US, European Union, & other countries but “DIGITAL” big & “libraries“ small “Hot” area in practice building digital collections, hybrid libraries, many projects throughout the world

© Tefko Saracevic, Rutgers University38 Technical problems Substantial - larger & more complex than anticipated: representing, storing & retrieving of library objects particularly if originally designed to be printed & then digitized operationally managing large collections - issues of scale dealing with diverse & distributed collections interoperability assuring preservation & persistence incorporating rights management

© Tefko Saracevic, Rutgers University39 Digital Library Initiatives in the US (DLI) Research consortia under National Science Foundation DLI 1: , 3 agencies, $24M, six large projects DLI 2: , 8 agencies, $60+M, 77 large & small projects in various categories ‘digital library’ not defined to cover many topics & stretch ideas not constrained by practice

© Tefko Saracevic, Rutgers University40 European Union DELOS Network of Excelence on Digital Libraries many projects throughout European Union heavily technological many meetings, workshops resembles DLIs in the US well funded, long range

© Tefko Saracevic, Rutgers University41 Research issues understanding objects in DL representing in many formats non-textual materials metadata, cataloging, indexing conversion, digitization organizing large collections managing collections, scaling preservation, archiving interoperability, standardization accessing, using,

© Tefko Saracevic, Rutgers University42 DL projects in practice Heavily oriented toward institutions Assoc of Res Libraries (ARL) database: 427 DL projects in 13 countries 374 in the US 51% in universities; 24% fed govmt; 9% hist societies; 6% regional … 84% are explicitly retrospective; 16% technological 1 listed from DLI (Illinois) no connection with DLI projects

© Tefko Saracevic, Rutgers University43 Agendas Most DL research agenda is set from top down from funding agencies to projects imprint of the computer science community's interest & vision Most DL practice agendas are set from bottom up from institutions, incl. many libraries imprint of institutional missions, interests & vision providing access to specialized materials and collections from an institution (s) that are otherwise not accessible covering in an integral way a domain with a range of sources

© Tefko Saracevic, Rutgers University44 Connection? DL research & DL practice presently are conducted mostly independent of each other, minimally informing each other, & having slight, or no connection Parallel universes with little connections & interaction

© Tefko Saracevic, Rutgers University45 8.Conclusions IS contributions IS effected handling of inf. in society Developed an organized body of knowledge & professional competencies Applied interdisciplinarity IR reached a mature stage IR penetrated many fields & human activities Stressed HUMAN in human-computer interaction

© Tefko Saracevic, Rutgers University46 Challenges Adjust to the growing & changing social & organizational role of inf. & related inf. infrastructure Play a positive role in globalization of information Respond to technological imperative in human terms Respond to changes from inf. to communication explosion - bringing own experiences to resolutions, particularly to the INTERNET Join competition with quality Join DIGITAL with LIBRARIES

© Tefko Saracevic, Rutgers University47 Juncture IS is at a critical juncture in its evolution Many fields, groups... moving into information big competition entrance of powerful players fight for stakes To be a major player IS needs to progress in its: research & development professional competencies educational efforts interdisciplinary relations Reexamination necessary

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© Tefko Saracevic, Rutgers University49