Variability-based model transformation: formal foundation and application
Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rule...
Ausführliche Beschreibung
Autor*in: |
Strüber, D. [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Schlagwörter: |
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Anmerkung: |
© British Computer Society 2017 |
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Übergeordnetes Werk: |
Enthalten in: Formal aspects of computing - London : Springer, 1989, 30(2017), 1 vom: 27. Nov., Seite 133-162 |
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Übergeordnetes Werk: |
volume:30 ; year:2017 ; number:1 ; day:27 ; month:11 ; pages:133-162 |
Links: |
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DOI / URN: |
10.1007/s00165-017-0441-3 |
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Katalog-ID: |
SPR001358359 |
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520 | |a Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, called variability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact. | ||
650 | 4 | |a Model transformation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Graph transformation |7 (dpeaa)DE-He213 | |
650 | 4 | |a Variability |7 (dpeaa)DE-He213 | |
650 | 4 | |a Category theory |7 (dpeaa)DE-He213 | |
700 | 1 | |a Rubin, J. |4 aut | |
700 | 1 | |a Arendt, T. |4 aut | |
700 | 1 | |a Chechik, M. |4 aut | |
700 | 1 | |a Taentzer, G. |4 aut | |
700 | 1 | |a Plöger, J. |4 aut | |
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10.1007/s00165-017-0441-3 doi (DE-627)SPR001358359 (SPR)s00165-017-0441-3-e DE-627 ger DE-627 rakwb eng Strüber, D. verfasserin (orcid)0000-0002-5969-3521 aut Variability-based model transformation: formal foundation and application 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © British Computer Society 2017 Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, called variability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact. Model transformation (dpeaa)DE-He213 Graph transformation (dpeaa)DE-He213 Variability (dpeaa)DE-He213 Category theory (dpeaa)DE-He213 Rubin, J. aut Arendt, T. aut Chechik, M. aut Taentzer, G. aut Plöger, J. aut Enthalten in Formal aspects of computing London : Springer, 1989 30(2017), 1 vom: 27. Nov., Seite 133-162 (DE-627)270126325 (DE-600)1476364-3 1433-299X nnns volume:30 year:2017 number:1 day:27 month:11 pages:133-162 https://dx.doi.org/10.1007/s00165-017-0441-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 30 2017 1 27 11 133-162 |
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10.1007/s00165-017-0441-3 doi (DE-627)SPR001358359 (SPR)s00165-017-0441-3-e DE-627 ger DE-627 rakwb eng Strüber, D. verfasserin (orcid)0000-0002-5969-3521 aut Variability-based model transformation: formal foundation and application 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © British Computer Society 2017 Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, called variability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact. Model transformation (dpeaa)DE-He213 Graph transformation (dpeaa)DE-He213 Variability (dpeaa)DE-He213 Category theory (dpeaa)DE-He213 Rubin, J. aut Arendt, T. aut Chechik, M. aut Taentzer, G. aut Plöger, J. aut Enthalten in Formal aspects of computing London : Springer, 1989 30(2017), 1 vom: 27. Nov., Seite 133-162 (DE-627)270126325 (DE-600)1476364-3 1433-299X nnns volume:30 year:2017 number:1 day:27 month:11 pages:133-162 https://dx.doi.org/10.1007/s00165-017-0441-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 30 2017 1 27 11 133-162 |
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10.1007/s00165-017-0441-3 doi (DE-627)SPR001358359 (SPR)s00165-017-0441-3-e DE-627 ger DE-627 rakwb eng Strüber, D. verfasserin (orcid)0000-0002-5969-3521 aut Variability-based model transformation: formal foundation and application 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © British Computer Society 2017 Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, called variability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact. Model transformation (dpeaa)DE-He213 Graph transformation (dpeaa)DE-He213 Variability (dpeaa)DE-He213 Category theory (dpeaa)DE-He213 Rubin, J. aut Arendt, T. aut Chechik, M. aut Taentzer, G. aut Plöger, J. aut Enthalten in Formal aspects of computing London : Springer, 1989 30(2017), 1 vom: 27. Nov., Seite 133-162 (DE-627)270126325 (DE-600)1476364-3 1433-299X nnns volume:30 year:2017 number:1 day:27 month:11 pages:133-162 https://dx.doi.org/10.1007/s00165-017-0441-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 30 2017 1 27 11 133-162 |
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Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, called variability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact. © British Computer Society 2017 |
abstractGer |
Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, called variability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact. © British Computer Society 2017 |
abstract_unstemmed |
Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, called variability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact. © British Computer Society 2017 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR001358359</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230330155112.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201001s2017 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00165-017-0441-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR001358359</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00165-017-0441-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Strüber, D.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-5969-3521</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Variability-based model transformation: formal foundation and application</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© British Computer Society 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Model transformation systems often contain transformation rules that are substantially similar to each other, causing maintenance issues and performance bottlenecks. To address these issues, we introduce variability-based model transformation. The key idea is to encode a set of similar rules into a compact representation, called variability-based rule. We provide an algorithm for applying such rules in an efficient manner. In addition, we introduce rule merging, a three-component mechanism for enabling the automatic creation of variability-based rules. Our rule application and merging mechanisms are supported by a novel formal framework, using category theory to provide precise definitions and to prove correctness. In two realistic application scenarios, the created variability-based rules enabled considerable speedups, while also allowing the overall specifications to become more compact.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model transformation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph transformation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variability</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Category theory</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rubin, J.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Arendt, T.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chechik, M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Taentzer, G.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Plöger, J.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Formal aspects of computing</subfield><subfield code="d">London : Springer, 1989</subfield><subfield code="g">30(2017), 1 vom: 27. 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