Proceedings of the 12th European Conference on Knowledge ...

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Xiaoyan Bai, David White and David Sundaram Figure 3: Contextual adaptive visualization environment framework 5. Implementation To validate <strong>the</strong> concepts, models and framework <strong>of</strong> CAVE, we implemented a vertical prototypical system against <strong>the</strong> CAVE framework through utilizing a set <strong>of</strong> Micros<strong>of</strong>t technologies such as Bing map, WPF, ADO.NET entity framework, and SQL Server. To help with demonstrating <strong>the</strong> support <strong>of</strong> <strong>the</strong> prototype, we introduce two cases, that is, Napoleon’s army march to Russia, and child statistics. The former case resides more in <strong>the</strong> domain <strong>of</strong> historical data management while <strong>the</strong> latter is mainly about statistical data analysis. In <strong>the</strong> Napoleon’s march case, we focus on exploring <strong>the</strong> relationships between army size reduction and its potential causing factors such as temperature, speed, location altitude, enemy size, available resources at each location, etc. In <strong>the</strong> child statistics case, we concentrate on discovering patterns that exist among a variety <strong>of</strong> education related indicators in different countries, e.g. primary school completion rate, expenditure per student, and literacy rate <strong>of</strong> adult. Both cases require visualizing spatial temporal multi-dimensional data. When <strong>the</strong> problem situation changes from one case to ano<strong>the</strong>r, <strong>the</strong> CAVE prototype allows knowledge workers to create different problem and visualization scenarios for different cases. For visualizing <strong>the</strong> invasion and retreat related information <strong>of</strong> Napoleon’s main troop, in 1869 Charles Joseph Minard published a map to portray <strong>the</strong> defeat <strong>of</strong> Napoleon’s army in Russia (Tufte, 2006). Building on top <strong>of</strong> Minard’s work, we created an integrated problem-visualization scenario (Figure 4) to illustrate how <strong>the</strong> army size (indicated by <strong>the</strong> width <strong>of</strong> <strong>the</strong> route band) diminishes as <strong>the</strong> temperature and moving speed vary along <strong>the</strong> route in an animated fashion (Figure 5). In contrast, <strong>the</strong> problem-visualization scenario (Figure 6) we created for <strong>the</strong> child statistics case presents <strong>the</strong> trends <strong>of</strong> multiple education indicators in a static way (Figure 7). Figure 4: An integrated problem-visualization scenario for Napoleon’s march case 62
Xiaoyan Bai, David White and David Sundaram Figure 5: An animated visualization for exploring causes for Napoleon’s army death Figure 6: An integrated problem-visualization scenario for child statistics case Figure 7: A static visualization for aiding <strong>the</strong> pattern discovery <strong>of</strong> child statistical data 6. Conclusion Visualisations are integral for <strong>the</strong> creation, transfer and sharing <strong>of</strong> knowledge. Knowledge visualization context is complex and dynamic in nature. Such complexity is caused by <strong>the</strong> extensive diverse and changing factors involved in <strong>the</strong> problem and stakeholder contexts. The dynamic and changing problem and knowledge worker contexts <strong>of</strong>ten lead to changing visualization requirements 63
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Proceedings <stron
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Contents Title Author(s) Picture <s
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Title Author(s) Picture of<
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Volume Two The Influence of
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The Symbolic Innovation of<
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Biographies of <st
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Jan Pawlowski works as Prof
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in academia. He holds the</
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Biomedical Engineering Research Cen
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Mathematics and Ph
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Thi Hai Hang Nguyen is a lecturer a
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Dan Savescu graduate TCM Faculty, p
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Teaches Business Computing and Stat
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Abdallah Al-Shawabkeh, Alexander K<
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Table 1: Inn-KM approach contents 2
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Knowledge Dynamics and Organisation
Page 38 and 39: Eckhard Ammann We distinguish three
Page 40 and 41: Eckhard Ammann quality dimension ar
Page 42 and 43: Eckhard Ammann The second cycle, Cy
Page 44 and 45: Figure 7: Supervised learning-by-do
Page 46 and 47: Identifying and Ranking the
Page 48 and 49: Manouchehr Ansari et al users <stro
Page 50 and 51: 2.5 leadership and strategy Manouch
Page 52 and 53: Manouchehr Ansari et al conducted s
Page 54 and 55: Serious Games in the</stron
Page 56 and 57: Albena Antonova and Anandasivakumar
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Page 72 and 73: Seyed Esmaeil Asgharpour and Gholam
Page 74 and 75: Attitude of Senior
Page 78 and 79: 4.5 Comparative Advantage Seyed Esm
Page 82 and 83: Contextual Adaptive Visualization E
Page 84 and 85: Xiaoyan Bai, David White and David
Page 92 and 93: Simona-Clara Bârsan, Mihaela-Georg
Page 94 and 95: 2.4 Rights transfer Simona-Clara B
Page 100 and 101: A Framework for the</strong
Page 102 and 103: Leila Beig et al Additionally, Wei
Page 104 and 105: Leila Beig et al The proposed KM au
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Page 108 and 109: Leila Beig et al Figure 1: Learning
Page 110 and 111: Leila Beig et al Figure 3 visually
Page 112 and 113: Leila Beig et al based on collectiv
Page 114 and 115: Experiential Knowledge Creation Pro
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Page 118 and 119: Didiosky Benítez et al meanings ac
Page 120 and 121: Didiosky Benítez et al In previous
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Page 126 and 127: 4. Results and discussion Pavel Bog
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Ettore Bolisani, Francesca Gambarot
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Table 2: Synthesis
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Ettore Bolisani, Marco Paiola and E
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Strategies for Increasing Knowledge
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Constantin Bratianu, Adriana Agapie
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Klaus Bredl, Amrei Groß and Jane F
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Camelia Burja and Vasile Burja Appr
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∀ , i ≥ 0 v u i where: ui repre
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Camelia Burja and Vasile Burja Tabl
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Camelia Burja and Vasile Burja info
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Camelia Burja and Vasile Burja Jack
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Adriana Schiopoiu Burlea With respe
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Adriana Schiopoiu Burlea 3. ESF rol
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Adriana Schiopoiu Burlea In <strong
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3.2 Discussions Adriana Schiopoiu B
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Adriana Schiopoiu Burlea Nonaka, I.
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Simon Cadez and Vlado Dimovski with
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Simon Cadez and Vlado Dimovski scho
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Simon Cadez and Vlado Dimovski It i
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Why Should I Share my new ideas? Cu
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Francesco Calza, Rossella Canestrin
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José Manuel Cardenas and Mauro Spi
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Vincenzo Cavaliere and Daria Sarti
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National Knowledge Management Strat
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Behiye Çavuşoğlu and Mustafa Sag
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Implementing a work-life balance cu
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Juan-Gabriel Cegarra-Navarro, Mª E
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The Influence of R
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Huei-Fang Chen and Yi-Wen Lin outco
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3.2.4 Knowledge-sharing Huei-Fang C
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Huei-Fang Chen and Yi-Wen Lin that
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Huei-Fang Chen and Yi-Wen Lin Hair,
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2.1.2 Social capital Marguerite Cro
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Marguerite Cronk 2.3 Social capital
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Marguerite Cronk Figure 4: The rela
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An Exploratory Study of</st
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Françoise de Viron, Thomas Lederer
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4.1.2 The researchers Françoise de
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The Application of
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Nasser Easa and Robin Fincham 3. Th
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Nasser Easa and Robin Fincham knowl
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Nasser Easa and Robin Fincham <stro
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Network Management as a way to Mana
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Eva Eckenhofer An
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Eva Eckenhofer par
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Initiation Configuration Interessem
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Eva Eckenhofer Eve
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The Essence of Kno
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Emmanuel Innocents Edoun and Valden
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4. Case study and analysis Emmanuel
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2. The PUS-project Anandasivakumar
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Anandasivakumar Ekambaram and Agnar
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Jamal El-Den and Xin Zhou This rese
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Jamal El-Den and Xin Zhou 5. After
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Jamal El-Den and Xin Zhou includes
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Jamal El-Den and Xin Zhou Boyce, C.
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Tiit Elenurm development visions. C
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Tiit Elenurm companies the<
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Tiit Elenurm their
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References Tiit Elenurm Accorsi, F.
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Isaac Enakimio and Abdallah Al-Shaw
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A proposed Framework for Discoverin
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2.3 Knowledge mapping Mohamad Ali F
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Mohamad Ali Feyz, Babak Akhgar and
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Mohamad Ali Feyz, Babak Akhgar and
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Knowledge Workers: A Typology Frame
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Jiří Franek and Eva Grublova crea
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Jiří Franek and Eva Grublova Know
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Jiří Franek and Eva Grublova memb
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Middle Managers’ Maturity <strong
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Zoltán Gaál et al investigated m
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Zoltán Gaál et al Table 2: Total
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Zoltán Gaál et al characterised b
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Zoltán Gaál et al Matzler, K., Re
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Charles Gagné et al This paper pre
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Charles Gagné et al attributable t
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Charles Gagné et al intermediary d
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Adoption of Knowle
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1.1.2 SMEs and KMS Tendayi Gondo an
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Tendayi Gondo and Edmore Kori ambit
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Tendayi Gondo and Edmore Kori <stro
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Tendayi Gondo and Edmore Kori demon
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The Chain Value Process and Knowled
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Manel González-Piñero et al Captu
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4. What a biofeedb
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Manel González-Piñero et al The p
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References Manel González-Piñero
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Nebojsa Graca and Ana Lucija Gojako
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creating new, added, essential qual
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Norbert Gronau et al Those processe
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Norbert Gronau et al systematic mod
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Instance Schema Figure 4: Cyclic tr
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Norbert Gronau et al 4.2 Feasibilit
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Receive suggestions Compare suggest
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The Not-Invented-Here Syndrome in A
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David Grosse Kathoefer and Jens Lek
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Opinion of colleag
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Appendix David Grosse Kathoefer and
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Individual Level Influencers on Tac
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Claire Gubbins et al that such know
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Claire Gubbins et al 3.2 Individual
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Claire Gubbins et al “my thing wi
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Claire Gubbins et al Cabrera, A., C
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Oscar Guerra and Janeth Rojas Accor
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Oscar Guerra and Janeth Rojas Figur
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Why does knowledge transfer process
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3.7 Monitoring and control Oscar Gu
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Understanding Personal Knowledge De
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Markus Haag and Yanqing Duan In ord
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Markus Haag and Yanqing Duan 4.2 In
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Markus Haag and Yanqing Duan have a
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Chaordic Knowledge Management - Shi
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Frank Habermann, Jörg Fehlinger an
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Intellectual Capital in Universitie
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Meliha Handzic and Kursad Ozlen rep
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Table 3: Opinions about Human Capit
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Meliha Handzic and Kursad Ozlen How
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Developing a Knowledge Strategy Usi
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Harold Harlow knowledge to create c
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3.1 TKI Regression results Harold H
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Harold Harlow of <
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Understanding the
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3.1 Acquire Ciara Heavin and Freder
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Table 3: Distribution of</s
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Ciara Heavin and Frederic Adam Trav
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Ciara Heavin and Frederic Adam Tabl
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Ciara Heavin and Frederic Adam prov
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Key Knowledge Sharing Points: Explo
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Tore Hoel and Jan Pawlowski identif
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Tore Hoel and Jan Pawlowski The arc
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4.2.1 Discussion - Case 2 Tore Hoel
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Using Web 2.0 Technologies to Suppo
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Marta Infante Abreu, Florian Mat<st
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Pamela Chidiogo Izunwanne 2. Knowle
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Pamela Chidiogo Izunwanne use <stro
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Pamela Chidiogo Izunwanne 5.3 Organ
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Pamela Chidiogo Izunwanne Davenport
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Thomas Janke room for communication
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Thomas Janke 4. Reference implement
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Thomas Janke The rule states that w
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Thomas Janke Softw
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Towards a Detailed View on
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Vincent de Jong and Remko Helms Fig
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4.1 Case study setup Vincent de Jon
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Vincent de Jong and Remko Helms The
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Vincent de Jong and Remko Helms mak
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Vincent de Jong and Remko Helms Man
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Claudia Jooß et al at RWTH Aachen
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Claudia Jooß et al Regarding <stro
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Claudia Jooß et al learning (cf. B
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Expert Knowledge: Does it Help or H
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Selvi Kannan are people who have ex
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Selvi Kannan Amabile (1983) states
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Selvi Kannan American Task Force, R
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Integration of Kno
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Eva-Maria Kern et al Figure 1: Rela
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Eva-Maria Kern et al identification
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Eva-Maria Kern et al be gat
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Eva-Maria Kern et al For visualizin
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Knowledge Management Practices (KMP
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Knowledge Management Across <strong
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Aino Kianto, Tatiana Andreeva and X
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5. Conclusions Aino Kianto, Tatiana
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Facilitating Knowledge Sharing in V
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Andrea Kő, Péter Fehér and Krisz
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Esther Lage et al
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Monique Lortie and Lise Desmarais e
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Monique Lortie and Lise Desmarais 3
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Monique Lortie and Lise Desmarais I
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Monique Lortie and Lise Desmarais D
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Anca Mândruleanu teams, networks a
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Anca Mândruleanu organization and
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Simone Manfredi, Domenico Celenza a
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Modelling Knowledge Sharing Into a
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2 masseurs ; Virginia Maracine et a
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2.1.1 SNA - the co
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Virginia Maracine et al mapping <st
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Virginia Maracine et al Which are
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Objective 1 - Mapping of</s
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From Knowledge Acquisition to Knowl
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Peter Marshall and Damian Gordon el
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Peter Marshall and Damian Gordon kn
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2.3.4 Types of Kno
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Peter Marshall and Damian Gordon Ma
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