BieColl - Bielefeld eCollections

Kienle, Andrea

Schlösser, Christian

Schlieker-Steens, Philipp

Prof. Dr. Andrea Kienle, Christian Schlösser, Philipp Schlieker-Steens 
University of Applied Sciences and Arts Dortmund, Emil-Figge-Str. 42, 44227 Dortmund 
(Germany), 
Email: andrea.kienle@fh-dortmund.de, christian.schloesser@fh-dortmund.de, philipp.schlieker-
steens@fh-dortmund.de 
Experiences from eye tracking chat-
communication using the INKA-SUITE 
Prof. Dr. Andrea Kienle, Christian Schlösser, Philipp Schlieker-Steens 
1 Introduction 
Eye tracking is a common way for analyzing the usability of applications (e.g., 
Duchowski, 2007), stationary single user systems (e.g., Shneiderman & Plaisant, 
2010) and websites (e.g., Nielsen & Loranger, 2006). Existing eye tracking sys-
tems are limited to static content like websites. In these applications the content is 
placed at the same position for all participants and can be easily compared. If this 
area of interest (AOI) is not static, but changes its size, shape or position, the eval-
uation becomes more complex and requires an amount of manual analysis. This is 
especially the case in cooperative applications like chats. The difficulty is, that 
you cannot predict the time of change, because it is caused by user input (e.g., new 
chat message, scrolling) - the dynamics of the AOI cannot be foretold. Existing 
eye tracking software is therefore not suitable in situations of changing AOIs. 
Therefore a new approach is needed to overcome these problems. 
The paper is structured by introducing you to the INKA-SUITE itself, followed by 
the description of a first experiment with its results. At the end there is an outlook. 
2 INKA-SUITE 
The INKA-SUITE is a platform that integrates the application and the analysis of 
eye tracking dynamic AOIs (e.g., Kienle et al., 2013). The base of the INKA-
SUITE is the connectivity to Tobii eye trackers (http://www.tobii.com). With a di-
rect connection between the eye tracker gaze data stream and GUI, the INKA-
SUITE identifies AOIs at runtime, regardless of size, shape and position. Every 
gaze data record is complemented by the underlying GUI element, identified by 
names, ID’s and/or references to other database tables such as chat messages or 
users. A subsequent manual work is eliminated and the evaluation can be started 
immediately. This profound cross-linking between analyses related functions and 
user software cannot be adapted by existing eye tracking analysis software. 
DOI: 10.2390/biecoll-saga2013_4
In Pfeiffer and Essig (2013): Proceedings of the First International Workshop on Solutions for Automatic Gaze-Data Analysis 2013 
(SAGA 2013). DOI: 10.2390/biecoll-saga2013_0
Experiences from eye tracking chat-communication using the INKA-SUITE 
The components of the INKA-SUITE are: 
(a) A server to control and manage clients as well as providing tem-
plates for the clients. Using templates enables researchers to conduct 
A/B tests. 
(b) A client application for user interaction, which is connected to an 
eye tracker and presents the chosen template. 
(c) An analysis component for managing projects and analyzing the 
collected data. Because fixations are represented by GUI elements, 
further information, such as fixation length and underlying AOI, can 
be retrieved through tooltips. 
Fig. 1: INKA-SUITE Timeline (left) and Replay (right) as part of the analysis component 
The analysis component processes the recorded data and is divided into three 
parts: 
(a) In the statistic tool, general information about the chat session and 
also about each user is presented. For example these are duration of 
chat, keystrokes per minute, time to first message and messages per 
minute. 
(b) Within the timeline tool, the entire chat session is presented on a 
timeline for each user (see Figure 1, left). This ensures the compara-
bility between users. Shortcuts, keystrokes, chat messages and fixa-
tions of the users are listed separately for each user. Using annota-
tions, important areas can be marked. The displayed output is fully 
dynamic and can be adapted to the current problem. The presentation 
of the collected data in form of the timeline, is oriented at the table 
structure of Beißwenger (2007) and the presentation of fixations by 
Stellmach et al. (2010). 
(c) The replay tool shows a replay of the chat from the user’s point of 
view (see Figure 1, right). This tool is chronology replaying the chat, 
similar to a screen recording, but with options to select and deselect 
the data that is output at runtime, also showing fixations and sac-
cades as a scanpath (e.g., Mealha et al., 2012). 
  Prof. Dr. Andrea Kienle, Christian Schlösser, Philipp Schlieker-Steens 
3 Experiment 
To test the INKA-SUITE, an experiment in cooperation with the Institute of Ger-
man language and literature of the TU Dortmund was accomplished. In this study 
the usability of the INKA-SUITE should be tested at first. Secondly questions 
from the linguists with respect to the influence of different user interfaces in chats 
for the structure of discussions should be answered.  
Fig. 2: Two templates: Standard Chat (left) and Talk (right) 
By using the templating feature two templates were created (see Figure 2). One 
template (Figure 2 left) represents a Standard-Chat-Environment (SCE) (e.g., 
Beißwenger, 2003) with an input field and a chat protocol. Template two (Figure 2 
right) is designed as a so called Talk layout similar to the Unix Talk. It contains 
the same features as the SCE but in addition a synchronous user list, which shows 
all connected chat partners and a live view from their text input fields. 
Tab.  1: Study organization 
The table above offers an overview about the experiment organization. In the 
study, 26 participants were recorded while chatting in two different scenarios. To 
solve the problems given by the scenarios, the participants have to exchange their 
information which differs to each other.  
The using of the INKA-SUITE while collecting and analyzing the data provides 
no problems. All ten chat-communications were completely recorded and supply a 
wide database for the analysis by the linguists. 
Day 1 - Chat Groups of two Groups of three 
Movie jury - scenario 2 1 
Murderer - scenario 2 
Day 2 - Talk Groups of two Groups of three 
Movie jury - scenario 2 1 
Murderer - scenario 2 
Experiences from eye tracking chat-communication using the INKA-SUITE 
Fig. 3: Study result – eye tracking inaccuracy 
The study revealed that the inaccuracy of the eye trackers is a problem (see Figure 
3). Some gaze plots do not land on the AOI which is fixated. Therefore a manual 
effort is needed in some cases. A solution could be repositioning and larger scal-
ing of the AOIs. The analysis by the linguists is still ongoing. 
4 Outlook 
The INKA-SUITE is basis for further research like automatic pattern recognition 
(e.g., revisions), contextualized communication and extension of simultaneous 
chats. A planned project called Chat++, enabled by real-time AOI identification, 
could therefore support features like reading awareness, eye tracking-based refer-
encing and activity and context-awareness. 
References 
1. Beißwenger, M. (2003): Sprachhandlungskoordination im Chat. In: Zeitschrift für german-
istische Linguistik 31 (2), S. 198–231.
2. Beißwenger, M. (2007): Sprachhandlungskoordination in der Chat-Kommunikation: Walter
de Gruyter (26).
3. Duchowski, Andrew (2007): Eye Tracking Methodology. Theory and Practice. Second.
London: Springer-Verlag London Limited. Online verfügbar unter
http://dx.doi.org/10.1007/978-1-84628-609-4.
4. Kienle, A.; Schlieker-Steens, P.; Schlösser C. (2013): INKA-SUITE: An Integrated Test-
Environment for Analyzing Chat Communication. In: CSCL 2013 - Conference Proceedings
Volume II, Conference Proceedings Volume II, S. 285–286.
5. Mealha, O.; Veloso, A.; Almeida, S.; Rodrigues, R.; Roque, L.; Marques, R.; Manteigueiro,
C. (2012): Eye tracking data representation and visualization: on information and communi-
cation studies at CETAC. MEDIA.
6. Nielsen, Jakob; Loranger, Hoa (2006): Prioritizing web usability: New Riders.
7. Shneiderman, Ben; Plaisant, Catherine (2010): Designing the user interface. Strategies for
effective human-computer interaction. 5. Aufl. Boston: Addison-Wesley.
8. Stellmach, Sophie; Nacke, Lennart; Dachselt, Raimund (2010): Advanced gaze visualiza-
tions for three-dimensional virtual environments. In: Proceedings of the 2010 symposium on
eye-tracking research & Applications. ACM, S. 109–112.


English

Experiences from eye tracking chat-communication using the INKA-SUITE

Schindler, Kirsten

Texte beurteilen - Feedback geben. Kompetenzen für Lehramtsstudierende

Peil, Vitali

PUB Linked Data Hub

Meißner, Martin

Pfeiffer, Jella

Pfeiffer, Thies

Jella Pfeiffer, Chair of Information Systems and Business Administration 
    Johannes Gutenberg-University Mainz, e-mail: jella.pfeiffer@uni-mainz.de 
Thies Pfeiffer, e-mail: Thies.Pfeiffer@uni-bielefeld.de 
    Cognitive Interaction Technology Center of Excellence, Bielefeld University 
Martin Meißner, e-mail: mmeissner@wiwi.uni-bielefeld.de 
  Chair of Business Administration and Marketing, Bielefeld University 
Mobile Eyetracking for Decision Analysis at the 
Point-of-Sale: Requirements from the 
Perspectives of Marketing Research and 
Human-Computer Interaction 
Martin Meißner, Jella Pfeiffer, and Thies Pfeiffer 
1 Introduction 
In a typical grocery-shopping trip consumers are overwhelmed not only by the 
number of products and brands in the store, but also by other possible distractions 
like advertisements, other consumers or smartphones. In this environment, atten-
tion is the key source for investigating the decision processes of customers. Recent 
mobile eyetracking systems have opened the gate to a better understanding of in-
store attention. We present perspectives from the two disciplines marketing re-
search and human-computer interaction and refine methodical and technological 
requirements for attention analysis at the point-of-sale (POS). 
2 The Marketing-Research Perspective 
For those who want to sell their products to the potential customers, knowledge 
about the customers’ decision processes is crucial. Most of the research findings 
cited below come from experiments which have been set up in lab-like situations. 
For marketing researchers, however, experiments in more realistic decision envi-
ronments are important to solve the following central research questions. 
How can we bring more attention to our product in the store? A couple of stud-
ies have been conducted to determine whether attention to products in a store is 
top-down (endogenous), which means that consumers direct their attention to 
products according to their preferences, or rather bottom-up (exogenous), which 
means that preferences are being formed based on attention (Theeuwes, 2010; Or-
quin and Mueller Loose, 2013). Identifying in which decision contexts attention is 
driven by endogenous and exogenous factors has been recognized as an important 
research topic (see, e.g. Atalay et al., 2012). Research has shown that visual sali-
ency is an exogenous factor that makes products more likely to be chosen (Mi-
losavljevic et al., 2012). That makes tools which assess the saliency of products in  
competitive settings a valuable resource for marketing practitioners. Centrality is a 
second important exogenous factor. Recent findings suggest that central options in 
DOI: 10.2390/biecoll-saga2013_3
In Pfeiffer and Essig (2013): Proceedings of the First International Workshop on Solutions for Automatic Gaze-Data Analysis 2013 
(SAGA 2013). DOI: 10.2390/biecoll-saga2013_0
Mobile Eyetracking for Decision Analysis at the Point-of-Sale  
a supermarket shelf receive an increased amount of attention which also increases 
the likelihood that a central-positioned product is being chosen (Atalay et al., 
2012). 
What can we learn from the attentional processes of consumers about their de-
cision processes? Research has shown that the finally chosen product receives 
more attention than the non-chosen products (Pieters and Warlop, 1999). Building 
on these findings, Shimojo et al. (2003) gave evidence that the increased attention 
on the finally chosen product results from the fact that attention shifts to this op-
tion in the very last seconds of the decision process. This effect has been called a 
gaze cascade effect and has been replicated in a couple of different decision envi-
ronments (see, e.g., Shi et al., 2013), but not at the POS.  
Researchers have also developed metrics to divide the attentional process into 
different stages. Russo and Leclerc (1994), for example, suggest using refixations 
to define three stages of the decision process which they named the orientation, 
evaluation and verification stage. An important aim of research in this field there-
fore is to develop ways to define decision stages and associated information needs. 
How do previous attentional processes influence later attentional processes in 
POS-decisions? Most of the previously described studies have been tested in sin-
gle decisions. Grocery-shopping trips, however, most of the times include dozens 
of purchase decisions which may influence one another. The influence of atten-
tional processes in earlier decisions on later ones has been investigated in only a 
small number of studies (see, e.g., Janiszewski et al., 2013). 
3 The Human-Computer-Interaction Perspective 
Imagine consumers wearing an attentive mobile interactive cognitive assistant 
(AMICA), e.g. realized as “intelligent goggles”, at the point-of-sale that gives ad-
vice based on the context-sensitive data collected by an integrated eyetracking de-
vice. The AMICA will detect the interaction context by localization and computer 
vision techniques and activate apps tailored to the specific situation, here a shop-
ping decision. Instead of a command-based interface, the attentive system moni-
tors the ongoing cognitive processes of the wearer in particular by observing eye 
movements and establishing a semantic link between the fixations and the objects 
of the environment. Just as an observant sales-person, the AMICA will stay in the 
background, monitoring the ongoing decision process and only provide help when 
it detects uncertainty in the user’s gaze patterns, or if it is directly asked to do so. 
Regarding general behavior models of such decision processes, HCI perspec-
tive and Marketing Research meet in their interest in mobile eyetracking systems. 
For example, findings on decision stages or the gaze cascade effect (see Section 2) 
allow predicting the stage of the information acquisition process. In each of the 
stages, consumers have different information needs. Early in the decision process, 
explanation-facilities about the use of the recommender system help building trust 
Meißner, Pfeiffer, and Pfeiffer 
and lead to a higher chance of adoption (Wang and Benbasat, 2005; Wang and 
Benbasat, 2007). Furthermore, consumers might need additional information 
about the product attributes being relevant in the purchase decision depending on 
the consumers’ product knowledge. In the next stage, promising products must be 
identified by the system which the consumer will compare in more detail along 
several product attributes in the later stages (Häubl and Trifts, 2000).  
However, an AMICA needs to identify the context and the objects of interest in 
real-time, to detect products and attributes of interest. The detection needs to be 
highly robust and be able to cope with dynamic environments, because errors will 
not average out with an increased number of trials. It also requires a high level of 
personalization, both to adapt to the peculiarities of the individual user’s cognitive 
processes and to the user’s preferences to provide sound recommendations.  
4 Requirement Analysis 
Based on the research questions outlined above, we identify the following infor-
mation and technical requirements for mobile eyetracking at the POS: 
It is not surprising that knowledge about typical features such as fixations and 
dwell-times are a common requirement. Transitions between fixations are equally 
important and thus saccadic distances and saccadic speed between fixations pro-
vide highly supplemental information. In all cases, the semantic link between overt 
visual attention and the object of interest is of uttermost importance. Information 
about visible product categories and individual products in focus as well as prod-
uct features such as price, brand name and specific features, e.g., nutritional in-
formation are required. Finally, the choice of the customer, as the outcome of a 
decision process, needs to be detected, too. 
Marketing Research is in particular interested in the topological distribution of 
attention, e.g. over a shelf, to answer questions regarding the saliency of products 
in a shelf or shelf set-up and centrality of a product for product placement. These 
aspects are not so important for HCI, but robustness of the systems could never-
theless benefit if topological information is available. As Marketing Research is 
interested in aggregating and visualizing gaze data from many customers, a rela-
tively stable topology of the products and a large number of respondents for statis-
tical tests are required.  
HCI, in contrast, focusses on the individual and the interaction does not stop at 
the shelf: Typically customers take products out of the shelf to inspect them, to 
haptically test them, and to carry them around. Approaches that support dynamic 
and noisy scenarios are thus a must in the envisioned HCI context. Low latencies 
for the classification of gaze data and the fixated objects of interest are important 
to support a timely reaction of the system. This is challenged by the requirement 
of a robust classification process to allow for a smooth human-computer interac-
tion without many interactive reparations and backtracking operations.  
Mobile Eyetracking for Decision Analysis at the Point-of-Sale  
5 Summary 
We have approached the analysis of the decision processes at the POS from 
two perspectives. By tendency, Marketing Research is often associated with the 
interests of salesperson, but this is not necessarily the case. While Marketing Re-
search has a stronger focus on the description of behavior in general, HCI focusses 
stronger on individual preferences. In the end, both approaches may cross-
fertilize, as one benefits from the methods and results aimed at by the other disci-
pline, e.g. HCI could benefit from an initial general behavior model that can be 
personalized over time and Marketing Research could benefit from unobtrusive 
and flexible methods for measuring decision processes as they happen at the POS 
and not in clean laboratory environments. The technology developed in the HCI 
part (AMICA) is applicable to other domains, as long as the domain specific as-
pects of the cognitive models and the computer vision parts are adapted. 
ACKNOWLEDGMENTS: This project is funded by the Internal University Research Funding 
of the Johannes Gutenberg-Universität Mainz and by the Bielefelder Nachwuchsfonds of Biele-
feld University. 
References 
1. Atalay AS, Onur Budur H, Rasolofoarison D (2012). Shining in the Center: Central Gaze
Cascade Effect on Product Choice. Journal of Consumer Research 39(4):848-866.
2. Häubl G, Trifts V (2000). Consumer Decision Making in Online Shopping Environments:
The Effects of Interactive Decision Aids. Marketing Science 19(1):4–21.
3. Janiszewski C, Kuo A, Tavassoli NT (2013). The Influence of Selective Attention and Inat-
tention to Products on Subsequent Choice. Journal of Consumer Research, in print.
4. Milosavljevic M, Navalpakkam V, Koch C, Rangel A (2012). Relative Visual Saliency Dif-
ferences Induce Sizable Bias in Consumer Choice. Journal of Consumer Psychology
22(1):67-74.
5. Orquin, JL, Mueller Loose S (2013). Attention and Choice: A Review on Eye Movements in
Decision Making. Acta Psychologica 144 (1):190-206
6. Pieters, R, Warlop L (1999). Visual Attention during Brand Choice: The Impact of Time
Pressure and Task Motivation. International Journal of Research in Marketing 16 (1):1-16
7. Russo, JE, Leclerc F (1994). An Eye-fixation Analysis of Choice Processes for Consumer
Nondurables. Journal of Consumer Research 21 (2):274-290
8. Shi W, Wedel M, Pieters R (2013). Information Acquisition during Online Decision-Making:
A Model-Based Exploration Using Eye-Tracking Data. Management Science in print.
9. Shimojo S, Simion C, Shimojo E, Scheier C (2003). Gaze Bias Both Reflects and Influences
Preference. Nature Neuroscience 6(12):1317-1322.
10. Theeuwes J (2010). Top-down and Bottom-up Control of Visual Selection. Acta Psychologi-
ca 135(2):77-99.
11. Wang W, Benbasat I (2005). Trust in and adoption of online recommendation agents. Journal
of the Association for Information Systems 6(3):72–101.
12. Wang W, Benbasat I (2007). Recommendation Agents for Electronic Commerce: Effects of
Explanation Facilities on Trusting Beliefs. Journal of Management Information Systems
23(4):217–246.


Mobile Eyetracking for Decision Analysis at the Point-of-Sale: Requirements from the Perspectives of Marketing Research and Human-Computer Interaction

Albayati, Faiza

Facts and Challenges facing Iraqi University Libraries

Summann, Friedrich

Pietsch, Christian

Katalog.plus -  OPAC 2.13
Friedrich Summann – Christian Pietsch
Universitätsbibliothek Bielefeld, Jan. 2013
Einführung
Als Nachfolgesystem bzw. Weiterentwicklung für den seit 2009 eingesetzten neuen 
Bibliothekskatalog (als  Web 2.0  – Anwendung)   soll  in  Kürze eine neue Version 
(OPAC  2.13)  in  den  Produktionsbetrieb  überführt  werden.  Diese  Lösung  basiert 
weiterhin im Kern auf dem Sourcecode der UB Heidelberg, der mit einigen lokalen 
Bielefelder  Ergänzungen  und  einer  neuen  Version  der  UB  Heidelberg  gemischt 
wurde. Der Arbeitstitel für die neue Version ist dabei „Katalog.plus“.
In unterschiedlichen Kontexten sind Teile der neuen OPAC-Lösung bereits aktiv. Die 
seit  ein  paar  Wochen  produkive  Mobil-Version  und  die  Integration  der  Ebsco 
Discovery  Services  im  Katalog  basieren  bereits  auf  aktualisierten  und  ergänzten 
Versionen  der Katalogsoftware.
Technische Lösung Katalog.plus
Wie bisher besteht die Anwendung aus einem Bündel von Perl-Skripten und einer 
Kombination  aus  Lucene-Suchmaschinenindex  und  einer  relationalen  MySQL-
Datenbank.
Wesentliche Änderungen und Ergänzungen im Technik-Bereich sind:
• Ausweitung der Schnittstellen-Aufrufe via REST
• Datenformatanalyse basierend auf JSON-Analyse statt XML
• Templating-Engine für Frontend-Ausgabe
• Nutzung der EDS-API Version 2.1
• Nutzung der erweiterten BASE-API 1.2
• Ausweitung der Ajax-basierten Aufrufe (für Trefferzahlen, Drilldown)
Katalog.plus erweitert den Nachweisfokus, indem über den traditionellen Katalog-
Bereich Printbestände hinaus ergänzend erschlossen wird:
Print-Nachweise - Bibliothekskatalog
EBooks                               -         HBZ-Nachweise inkl. Zuspielungen in den 
Katalog
Artikel-Nachweise  - EBSCO EDS Discovery Service 
Online-Dokumente  - BASE
Weitere Nachweisinstrumente - Links zu Fachdatenbanken
      
Zahlenmäßig bewegen sich die Nachweise in den folgenden Größenordnungen:
• 2,2 Mill. Print-Bände (Katalog)
• EBooks (Katalog)
• 62146 Elektron. Zeitschriften (EZB)
• 500 Mill. Nachweise (Artikel, Monographien, Sammelwerke inkl. Volltexten via 
EDS)
• 41 Mill. Wiss. Dokumente (BASE, Stand Jan. 2013)
• Fach-Datenbanken (DBIS)
Die  Kataloglösung  integriert  diverse  externe  Systeme  über  unterschiedliche 
Schnittstellen,  die  in  der  neuen Version im Umfang durchaus noch zugenommen 
haben. Dabei wird im Hintergrund (meist per AJAX) das Target-System befragt, das 
Ergebnis analysiert und in die Nutzeranzeige eingebettet.
Im einzelnen sind das  
• SIAS-Ausleihsystem 
• EZB
• EDS API
• BASE API
• DBIS
• SFX
• [CSL Engine] (in Vorbereitung)
Konkrete Schwerpunkte der lokalen Softwareentwicklung waren die Verbesserung 
der EDS- und BASE-Integration,  basierend auf neuen erweiterten Fassungen der 
API-Schnittstellen. Statt eines reinen Linkübergangs zu diesen externen Systemen 
ist es jetzt möglich, Suche und Anzeigefunktionalität in einer integrierten, homogenen 
Suchumgebung  umzuschalten,  realisiert  durch  eine  sogenannte  Reiterlösung. 
Zusätzlich (Details in [2]) werden zur Benutzeranfrage die zugehörigen Fachgebiete 
und  eine  nach  Priorität  sortierte  Liste  von  verfügbaren  Fachdatenbanken 
ausgegeben.
Eine generelle Überarbeitung fand im Bereich Endnutzer-Schnittstelle  statt,  wobei 
durch  Umstellung  auf  eine  Standard-Templating-Engine,  die  Funktionen 
Metadatenanzeige, Drilldown, Navigationsleiste und Datenformate deutlich flexibler 
für die Konfiguration unabhängig von der Programmierung gestaltet werden konnten.
Grundsätzlich  wird  versucht,  mit  der  Farbgebung  die  Teilsuchumgebung  zu 
visualisieren, um dem Endnutzer Navigation und Orientierung zu erleichtern. Dabei 
wird  die jeweilige Farbe durch die verschiedenen Schritte  der Benutzeroberfläche 
beibehalten, um die aktuelle Position des Teilsystems zu verdeutlichen.
Eine Übersicht der integrierten Teile zeigt die folgende Abbildung 1.
Abbildung 1: Integrierte Suchumgebung
Normalisierung
Die drei  Nachweisdatenbanken Bibliothekskatalog,  EDS und BASE unterscheiden 
sich  in  etlichen  Punkten  strukturell  voneinander.  Die  besondere  Herausforderung 
einer  integrativen  Nutzung  liegt  darin,  diese Strukturen zu  harmonisieren und  zu 
normalisieren und damit in der Endnutzersicht als Einheit benutzbar zu machen.
Dazu müssen insbesondere die folgenden Punkte betrachtet werden:
• Query-Syntax
• Kurztitelanzeige
• Einzelsatzanzeige
• Drilldown-Funktion
• Sortierung
Als konkretes Beispiel soll zur Veranschaulichung die Normalisierung beim Drilldown 
dienen. Dabei gibt unterschiedliche Felder für die sogenannte Facetten angeboten 
werden (bei BASE und EDS gibt es Datenlieferanten, nicht aber beim Katalog) und 
daneben  bei  oberflächlich  gleichem  Feldtyp  (z.B.  Sprache  bei  allen  drei 
Teilsystemen)  unterschiedliche  Wertebereich  oder  auch  bei  Dokument-  und 
Publikationstypen). Die folgende Abbildung illustriert die unterschiedlichen Strukturen 
und Inhalte der drei Zielsysteme.
Abbildung 2: Drilldown-Normalisierung
Links
Testsystem Katalogplus der UB Bielefeld:
[http://katalogplus.ub.uni-bielefeld.de/]
HEIDI-Katalog der UB Heidelberg
[http://www.ub.uni-heidelberg.de/helios/kataloge/heidi.htm]
Publikationen
[1] Teil 2 des Kolloquiumsvortrags: 
Pietsch: Christian: Datenbank-Empfehlungsdienst
[http://www.ub.uni-bielefeld.de/~cpietsch/talks/2012-12-13-opac-
stand_und_perspektiven/#1]
[2] 
Summann, Friedrich: OPAC reloaded. Bielefeld 2011
 (http://pub.uni-bielefeld.de/publication/2444271) 


German

Katalog.plus – OPAC 2.13

https://www.ub.uni-bielefeld.de/library/databases/eds.ht

:Fortbildungstermin: Discovery Services: Entwicklung und erste Ergebnisse

Höppner, Michael

:Fortbildungstermin: Die Universitätsbibliothek Bielefeld, die Universität und die (Fach)Welt.

Köhler, Doris

Knorn, Barbara

RFID - Einführung und Praxis an der UB

SCOAP³ und arXiv: OA-Modelle der Zukunft?

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