Multi-criteria diagnosis of control knowledge for cartographic generalisation
► We propose a method to automatically evaluate the quality of control knowledge used for cartographic generalisation. ► Our method consists in analysing the system’s execution logs. ► Our method allows detecting defective elements of knowledge. ► Our method uses the ELECTRE TRI method for evaluatin...
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| Veröffentlicht in: | European journal of operational research 2012-03, Vol.217 (3), p.633-642 |
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| container_title | European journal of operational research |
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| creator | Taillandier, Patrick Taillandier, Franck |
| description | ► We propose a method to automatically evaluate the quality of control knowledge used for cartographic generalisation. ► Our method consists in analysing the system’s execution logs. ► Our method allows detecting defective elements of knowledge. ► Our method uses the ELECTRE TRI method for evaluating the knowledge global quality.
The development of interactive map websites increases the need of efficient automatic cartographic generalisation. The generalisation process, which aims at decreasing the level of details of geographic data in order to produce a map at a given scale, is extremely complex. A classical method for automating the generalisation process consists in using a heuristic tree-search strategy. This type of strategy requires having high quality control knowledge (heuristics) to guide the search for the optimal solution. Unfortunately, this control knowledge is rarely perfect and its evaluation is often difficult. Yet, this evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to offer an automatic method for evaluating the quality of control knowledge for cartographic generalisation based on a heuristic tree-search strategy. Our diagnosis method consists in analysing the system’s execution logs, and in using a multi-criteria analysis method for evaluating the knowledge global quality. We present an industrial application as a case study using this method for building block generalisation and this experiment shows promising results. |
| doi_str_mv | 10.1016/j.ejor.2011.10.004 |
| format | Article |
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The development of interactive map websites increases the need of efficient automatic cartographic generalisation. The generalisation process, which aims at decreasing the level of details of geographic data in order to produce a map at a given scale, is extremely complex. A classical method for automating the generalisation process consists in using a heuristic tree-search strategy. This type of strategy requires having high quality control knowledge (heuristics) to guide the search for the optimal solution. Unfortunately, this control knowledge is rarely perfect and its evaluation is often difficult. Yet, this evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to offer an automatic method for evaluating the quality of control knowledge for cartographic generalisation based on a heuristic tree-search strategy. Our diagnosis method consists in analysing the system’s execution logs, and in using a multi-criteria analysis method for evaluating the knowledge global quality. We present an industrial application as a case study using this method for building block generalisation and this experiment shows promising results.</description><identifier>ISSN: 0377-2217</identifier><identifier>EISSN: 1872-6860</identifier><identifier>DOI: 10.1016/j.ejor.2011.10.004</identifier><identifier>CODEN: EJORDT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>(S) Knowledge-based systems ; (S) Multiple criteria analysis ; Applications ; Applied sciences ; Cartographic generalisation ; Cartography ; Computer Science ; Computer science; control theory; systems ; Computer systems and distributed systems. User interface ; Control knowledge quality diagnosis ; Exact sciences and technology ; Heuristic ; Heuristic tree-search strategy ; Knowledge ; Knowledge management ; Mathematics ; Multiple criteria decision making ; Operations Research ; Probability and statistics ; Quality control ; Reliability, life testing, quality control ; Sciences and techniques of general use ; Software ; Statistics ; Studies</subject><ispartof>European journal of operational research, 2012-03, Vol.217 (3), p.633-642</ispartof><rights>2011 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Elsevier Sequoia S.A. Mar 16, 2012</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c489t-575adf1fab762bcc7da83969dfd66ecfdbb5a8e5a396575b78a7d732dc2c6e483</citedby><cites>FETCH-LOGICAL-c489t-575adf1fab762bcc7da83969dfd66ecfdbb5a8e5a396575b78a7d732dc2c6e483</cites><orcidid>0000-0002-2558-2153 ; 0000-0001-5278-2145 ; 0000-0003-2939-4827</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejor.2011.10.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,780,784,885,3548,27923,27924,45994</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=25668405$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-00688354$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Taillandier, Patrick</creatorcontrib><creatorcontrib>Taillandier, Franck</creatorcontrib><title>Multi-criteria diagnosis of control knowledge for cartographic generalisation</title><title>European journal of operational research</title><description>► We propose a method to automatically evaluate the quality of control knowledge used for cartographic generalisation. ► Our method consists in analysing the system’s execution logs. ► Our method allows detecting defective elements of knowledge. ► Our method uses the ELECTRE TRI method for evaluating the knowledge global quality.
The development of interactive map websites increases the need of efficient automatic cartographic generalisation. The generalisation process, which aims at decreasing the level of details of geographic data in order to produce a map at a given scale, is extremely complex. A classical method for automating the generalisation process consists in using a heuristic tree-search strategy. This type of strategy requires having high quality control knowledge (heuristics) to guide the search for the optimal solution. Unfortunately, this control knowledge is rarely perfect and its evaluation is often difficult. Yet, this evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to offer an automatic method for evaluating the quality of control knowledge for cartographic generalisation based on a heuristic tree-search strategy. 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The development of interactive map websites increases the need of efficient automatic cartographic generalisation. The generalisation process, which aims at decreasing the level of details of geographic data in order to produce a map at a given scale, is extremely complex. A classical method for automating the generalisation process consists in using a heuristic tree-search strategy. This type of strategy requires having high quality control knowledge (heuristics) to guide the search for the optimal solution. Unfortunately, this control knowledge is rarely perfect and its evaluation is often difficult. Yet, this evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to offer an automatic method for evaluating the quality of control knowledge for cartographic generalisation based on a heuristic tree-search strategy. Our diagnosis method consists in analysing the system’s execution logs, and in using a multi-criteria analysis method for evaluating the knowledge global quality. We present an industrial application as a case study using this method for building block generalisation and this experiment shows promising results.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ejor.2011.10.004</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-2558-2153</orcidid><orcidid>https://orcid.org/0000-0001-5278-2145</orcidid><orcidid>https://orcid.org/0000-0003-2939-4827</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | European journal of operational research, 2012-03, Vol.217 (3), p.633-642 |
| issn | 0377-2217 1872-6860 |
| language | eng |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | (S) Knowledge-based systems (S) Multiple criteria analysis Applications Applied sciences Cartographic generalisation Cartography Computer Science Computer science control theory systems Computer systems and distributed systems. User interface Control knowledge quality diagnosis Exact sciences and technology Heuristic Heuristic tree-search strategy Knowledge Knowledge management Mathematics Multiple criteria decision making Operations Research Probability and statistics Quality control Reliability, life testing, quality control Sciences and techniques of general use Software Statistics Studies |
| title | Multi-criteria diagnosis of control knowledge for cartographic generalisation |
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