Proceedings of the 8th International Conference on Intellectual ...

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Knowledge-Based Construction Element Database Marko Anzelak, Gert Morak and Wolfgang Ebner Industriestiftungsinstitut eBusiness(biztec), Klagenfurt, Austria marko@biztec.org gmorak@biztec.org wolfgang.ebner@biztec.org Abstract: Technical drawings contain a wealth <strong>of</strong> implicit knowledge, which is subsequently ei<strong>the</strong>r not used by production plants, or only in a limited manner. Fur<strong>the</strong>rmore, even though companies specialised in a particular field <strong>of</strong>ten use similar constructions to fulfil <strong>the</strong> same purpose, <strong>the</strong>y tend to start from scratch each time. To ensure that drawings can be utilised by more persons than merely <strong>the</strong> original design engineer, <strong>the</strong> descriptions would have to be very precise, in order to capture <strong>the</strong> ideas <strong>of</strong> <strong>the</strong> designer and allow for <strong>the</strong> subsequent comprehension by o<strong>the</strong>rs. This would allow ano<strong>the</strong>r designer to rapidly understand <strong>the</strong> idea and introduce any required adaptations. In reality, projects that are commonly used as templates are own projects, <strong>of</strong>ten limited to those from recent years. Older projects and drawings are frequently disregarded or simply not found. In order to utilize this wasted knowledge and potential, a knowledge database was designed, taking into account <strong>the</strong> following aspects: Construction elements and component groups must be indexed and described using meaningful meta information. The construction elements are managed in an object tree, which takes into account a variety <strong>of</strong> search parameters. Construction elements are categorised by function, type, size or properties and can be searched for accordingly. The meta data <strong>of</strong> <strong>the</strong> drawing are automatically transferred and assigned by means <strong>of</strong> a construction element recognition algorithm. In <strong>the</strong> best case, <strong>the</strong> user is merely required to check <strong>the</strong> data. The database can be accessed through a web client and is <strong>the</strong>refore location-independent. The additional benefit provided by <strong>the</strong> system, besides saving time and effort, is <strong>the</strong> increased communication between design engineers. When re-using a drawing, a designer is encouraged to communicate directly with <strong>the</strong> original designer, thus generating practical knowledge. Ideally, errors and problems, which occurred when <strong>the</strong> drawing was first created, will also have been documented. However, this is a cultural issue, which is difficult to regulate. In our case <strong>the</strong> problem is solved by entering into a one-to-one discussion <strong>of</strong> errors and deficiencies experienced during <strong>the</strong> design stage (Gentsch 1999). Keywords: knowledge management project, construction database, technical knowledge in drawings, knowledge management system 1. Introduction and motivation In all areas <strong>of</strong> knowledge management, and thus also in <strong>the</strong> case <strong>of</strong> technical drawings, <strong>the</strong>re is a problem <strong>of</strong> structured storage. The data quality <strong>of</strong> any given system depends to a large extent upon <strong>the</strong> precision and volume <strong>of</strong> <strong>the</strong> entered meta data. For technical draughtsmen it is <strong>of</strong>ten especially difficult to create a “table <strong>of</strong> content” for <strong>the</strong>ir drawing, or to find suitable keywords, which reflect <strong>the</strong> content or <strong>the</strong> function <strong>of</strong> <strong>the</strong> designed element. In addition, technical drawings are required to feature o<strong>the</strong>r information, such as <strong>the</strong> current revision status, <strong>the</strong> list <strong>of</strong> parts, etc. These special requirements are usually fulfilled by bespoke drawing management programs. However, most <strong>of</strong> <strong>the</strong>se do not <strong>of</strong>fer sufficient functionality to meet <strong>the</strong> needs <strong>of</strong> a knowledge management system. These formal requirements are subsequently joined by <strong>the</strong> requirements <strong>of</strong> <strong>the</strong> design engineers. Their main priority is <strong>the</strong> ability to re-use an existing drawing. In order to avoid breaching copyright or patent rights, <strong>the</strong> database is restricted to individual companies. However, one company may employ several design engineers at different locations. In view <strong>of</strong> <strong>the</strong> technical implementation it is <strong>the</strong>refore necessary to ensure that decentralised access to <strong>the</strong> database is possible. Fur<strong>the</strong>rmore, design engineers need to be able to access all associated documents quickly. During <strong>the</strong> process <strong>of</strong> creating a drawing many different documents are relevant, ranging from general guidelines for <strong>the</strong> design to specific instructions and regulations. In most cases, <strong>the</strong>se documents are well managed. However, <strong>the</strong>re are instances when ideas, which occur to a design engineer during <strong>the</strong> drawing process, are recorded but are not stored in a structured manner, and <strong>the</strong>refore cannot be included in <strong>the</strong> relevant guidelines. Drawing archives, where drawings are stored in <strong>the</strong>ir paper form are only searched in very urgent cases. If <strong>the</strong> archived drawing has already been scanned and is available in an electronic format, <strong>the</strong>n <strong>the</strong> resulting electronic files are more likely to be searched. Here, <strong>the</strong> quality <strong>of</strong> <strong>the</strong> scans and <strong>the</strong> incorrect labelling <strong>of</strong> <strong>the</strong> file is a big issue. These activities are usually carried out by interns, although it would be more helpful if a pr<strong>of</strong>essional viewed <strong>the</strong> archive, and attributed <strong>the</strong> relevant file name at <strong>the</strong> time <strong>of</strong> scanning, ideally adding keywords and <strong>the</strong> correct assignment at <strong>the</strong> same time. The technical drawing below (Figure 1) provides an example <strong>of</strong> a drawing, which does not provide much useful information without additional data. 42
Figure 1: Transmission gear Marko Anzelak et al. This has given rise to three different approaches. A design engineer decides – in accordance with his or her personal standard <strong>of</strong> knowledge – which approach to select. Variation 1: The new design is so simple that a time-consuming search for a template is not worthwhile. On average, <strong>the</strong> completion <strong>of</strong> such a drawing takes less than 30 minutes. Variation 2: The planned design is simple, but <strong>the</strong> designer has a suitable template in mind. A search is initiated, but if it takes longer than 10 - 15 minutes, <strong>the</strong> search is discontinued and variation 1 is selected. 43
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Proceedings <stron
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Contents Paper Title Author(s) Page
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Paper Title Author(s) Page No. Main
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Paper Title Author(s) Page No. Know
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Preface These proceedings represent
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TU Dortmund, Germany, ); Wing Bun L
Page 13 and 14: William Halal is Prof</stro
Page 15 and 16: Evgeny Blagov has obtained a specia
Page 17 and 18: science and mathem
Page 19 and 20: George Majdalany is a PhD candidate
Page 21 and 22: Joyce Nesamani Simson is a PhD cand
Page 23 and 24: Manpower Discipline Based on Knowle
Page 25 and 26: Peyman Akhavan et al. brains, knowl
Page 27 and 28: Peyman Akhavan et al. showed that o
Page 29 and 30: Peyman Akhavan et al. appropriate l
Page 31 and 32: Peyman Akhavan et al. sources for c
Page 33 and 34: Attitudes Towards Changes and Organ
Page 35 and 36: Ruth Alas et al. During the
Page 37 and 38: Professional inter
Page 39 and 40: Ruth Alas et al. Table 4: Correlati
Page 41 and 42: Research was supported by ETF Grant
Page 43 and 44: Rivadávia Correa Drummond de Alvar
Page 51 and 52: 5. Conclusions Rivadávia Correa Dr
Page 55 and 56: Toward a Meta-Synthe</stron
Page 57 and 58: Xiaomi An et al 2.3 Reasons for dev
Page 59 and 60: Xiaomi An et al PTC works closely w
Page 61 and 62: Xiaomi An et al The first is an adm
Page 63: Xiaomi An et al policy reflections
Page 67 and 68: Marko Anzelak et al. knowledge basi
Page 69 and 70: Marko Anzelak et al. 72% recognise
Page 71 and 72: Marko Anzelak et al. virtually all
Page 73 and 74: Gabriel Prananingrum Aryanindita an
Page 79 and 80: Rita Babiliūtė and Kęstutis Kri
Page 87 and 88: Knowledge Management, Cultural Know
Page 89 and 90: Leonard Barnett and Edward Carter 2
Page 91 and 92: Leonard Barnett and Edward Carter p
Page 93 and 94: Leonard Barnett and Edward Carter p
Page 95 and 96: Leonard Barnett and Edward Carter d
Page 97 and 98: Impact of Knowledg
Page 99 and 100: Bob Barrett shorter product life-cy
Page 101 and 102: Bob Barrett 6. Concept of</
Page 103 and 104: Bob Barrett Brown, J.S. and Duguid
Page 105 and 106: Aurilla Aurelie Bechina and Eli Hus
Page 113 and 114: Lorne Booker et al. Knowledge is us
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Figure 1: Business Valuation Knowle
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3.4 Practitioner knowledge market L
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4. Implications Lorne Booker et al.
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Strategic Thinking Dimensions in Em
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Constantin Bratianu et al. shows <s
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Constantin Bratianu et al. Academy
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Constantin Bratianu et al. cases wi
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Constantin Bratianu et al. Pinker,
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Delio Ignacio Castañeda and Sandra
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Quality in Teacher Education: A Sys
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Rajendra Chetty The mechanism <stro
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Academic leadership Rajendra Chetty
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Rajendra Chetty There must be a re-
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Developing Personal Knowledge Manag
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Thirathep Chonmait
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Thirathep Chonmait
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The Relationship Between Knowledge
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Samuel Kai Wah Chu et al. A knowled
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Samuel Kai Wah Chu et al. negative
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Samuel Kai Wah Chu et al. Questionn
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Samuel Kai Wah Chu et al. few done
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Value Creation Through Collaborativ
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Ridha Derrouiche et al. level for e
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Ridha Derrouiche et al. best, <stro
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Ridha Derrouiche et al. experience
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Ridha Derrouiche et al. Of course,
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Organizational Learning and Transfo
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Irra Chrisyanti Dewi was carried ou
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Irra Chrisyanti Dewi Table 2: Valid
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Irra Chrisyanti Dewi Library is exp
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Knowledge Acquisition and Sharing:
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Ikechukwu Diugwu articulated and st
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Ikechukwu Diugwu establishing setti
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Ikechukwu Diugwu Mentzer, J. T., Mi
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Eva Eckenhofer cou
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Eva Eckenhofer The
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Eva Eckenhofer The
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Eva Eckenhofer Bur
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1.2 Intellectual capital and innova
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3.2.2 Reliability Irene Y.H. Fan an
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Figure 4: latent variable coefficie
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Irene Y.H. Fan and Rongbin W.B. Lee
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Irene Y.H. Fan and Rongbin W.B. Lee
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2.1 Appropriation Ronny Gey From a
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Ronny Gey Table 1: Characterization
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Ronny Gey The developers feel that
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Ronny Gey A first limitation we wer
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Kenneth Grant part of</stro
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Kenneth Grant The diffusion was stu
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Kenneth Grant A detailed computer-s
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Kenneth Grant did not come to <stro
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Kenneth Grant of m
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Michel Grundstein although hundred
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Michel Grundstein Figure 1: DITEK p
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Michel Grundstein 4.1.1 Knowledge i
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Michel Grundstein share. However <s
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6. Conclusions and perspectives Mic
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Intellectual Property Valuation Dec
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Hezlin Harris et al. (Burrone and S
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Hezlin Harris et al. The need for s
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Acknowledgements Hezlin Harris et a
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Managing an Assessment Process <str
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Table 1: List of N
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Shaheed Hartley Table 3: OEQ respon
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Shaheed Hartley It is very difficul
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A Community Framework for Knowledge
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Igor Hawryszkiewycz between communi
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Figure 4: Modeling knowledge subsys
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Igor Hawryszkiewycz Prahalad, C.K.,
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Eli Hustad and Aurilla Aurelie Bech
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Does Knowledge Stickiness Affect a
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Nurul Indarti knowledge (Nelson and
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3. Research methods 3.1 Research in
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Coded knowledge Nurul Indarti The f
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Nurul Indarti Arrow, K. J. (1969).
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Organizational Learning in Pr<stron
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Brigita Janiunaite et al. learn and
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Brigita Janiunaite et al. given dif
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Brigita Janiunaite et al. empowered
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Brigita Janiunaite et al. A statist
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Brigita Janiunaite et al. learning
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Balancing the Ince
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Giedrius Jucevičius safeguard and
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Giedrius Jucevičius nature <strong
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Giedrius Jucevičius motivating fac
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Giedrius Jucevičius Ghosh, T. (200
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Palmira Juceviciene and Vyda Mozuri
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Robertas Jucevicius relations shoul
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Robertas Jucevicius do not know whe
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Robertas Jucevicius and innovation
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Robertas Jucevicius Lawton Smith, H
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Annukka Jyrämä et al. knowledge c
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Annukka Jyrämä et al. Respondents
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Annukka Jyrämä et al. various cit
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Annukka Jyrämä et al. example hea
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Managing Information Overload - Tea
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Harri Ketamo conceptual structure a
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Harri Ketamo limited to YouTube, Fl
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Harri Ketamo Figure 8: Visualizatio
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Farmers’ Knowledge Management in
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2.1 Farmer knowledge Le Nguyen Doan
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Le Nguyen Doan Khoi days needed for
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Le Nguyen Doan Khoi dosage. Moreove
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Le Nguyen Doan Khoi use homemade in
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Developing Human Capital through Ne
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Eric Kong organisation as individua
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Eric Kong As described by Tosey and
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Eric Kong Craft, A. (2001) "Neuro-l
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Sophie Le Bellu and Benoit Le Blanc
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Results Sophie Le Bellu and Benoit
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Pornpen Lertthasanawong and Nopasit
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2. Literature review Ya-Hui Ling 2.
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Ya-Hui Ling action of</stro
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Intellectual Capital Intellectual C
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Ya-Hui Ling Hitt, M.A., Hoskisson,
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Maksim Maksimovic et al paper prese
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Maksim Maksimovic et al 4. Lean kno
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Maksim Maksimovic et al of<
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Maurizio Massaro and Andrea Moro <s
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Maurizio Massaro and Andrea Moro Pr
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Maurizio Massaro and Andrea Moro hi
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Maurizio Massaro and Andrea Moro Si
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Helen Mitchell and Dennis Viehland
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Figure 1: KMS Framework for collabo
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Mohd Zali Mohd Nor et al. Ano<stron
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Mohd Zali Mohd Nor et al. Table 4:
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Mohd Zali Mohd Nor et al. KQ8 provi
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Maria Cristina Morariu economy and
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Table 1: Literature review summary
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Maria Cristina Morariu ATO = Turnov
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4.2 Correlation analysis Maria Cris
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4.3 Regression analysis Maria Crist
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Table 13: Regression analysis_M6 Mo
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The Arrival of <st
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Bertrand Pauget and Ahmed Dammak Mo
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Bertrand Pauget and Ahmed Dammak no
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Bertrand Pauget and Ahmed Dammak th
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Bertrand Pauget and Ahmed Dammak Sa
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Wannapa Pipattanawong et al. cannot
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Wannapa Pipattanawong et al. 3.2 Do
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Wannapa Pipattanawong et al. Moreov
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The Dark Side of S
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Siwarit Pongsakornrungsilp vans, ro
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Siwarit Pongsakornrungsilp Kreng Ja
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Siwarit Pongsakornrungsilp Lave, J.
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