Rule-based spreadsheet data transformation from arbitrary to relational tables
•Spreadsheet data transformation can be considered as table understanding.•The two-layered table object model represents arbitrary spreadsheet tables.•Our rule-based language enables to express consecutive steps of table understanding.•Execution of the rules provides recovering implicit table semant...
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| Veröffentlicht in: | Information systems (Oxford) Jg. 71; S. 123 - 136 |
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01.11.2017
Elsevier Science Ltd |
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| ISSN: | 0306-4379, 1873-6076 |
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| Abstract | •Spreadsheet data transformation can be considered as table understanding.•The two-layered table object model represents arbitrary spreadsheet tables.•Our rule-based language enables to express consecutive steps of table understanding.•Execution of the rules provides recovering implicit table semantic (structure).•A rule-set allows converting arbitrary tables of the same genre into databases.
The paper discusses issues of rule-based data transformation from arbitrary spreadsheet tables to a canonical (relational) form. We present a novel table object model and rule-based language for table analysis and interpretation. The model is intended to represent a physical (cellular) and logical (semantic) structure of an arbitrary table in the transformation process. The language allows drawing up this process as consecutive steps of table understanding, i. e. recovering implicit semantics. Both are implemented in our tool for spreadsheet data canonicalization. The presented case study demonstrates the use of the tool for developing a task-specific rule-set to convert data from arbitrary tables of the same genre (government statistical websites) to flat file databases. The performance evaluation confirms the applicability of the implemented rule-set in accomplishing the stated objectives of the application. |
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| AbstractList | The paper discusses issues of rule-based data transformation from arbitrary spreadsheet tables to a canonical (relational) form. We present a novel table object model and rule-based language for table analysis and interpretation. The model is intended to represent a physical (cellular) and logical (semantic) structure of an arbitrary table in the transformation process. The language allows drawing up this process as consecutive steps of table understanding, i. e. recovering implicit semantics. Both are implemented in our tool for spreadsheet data canonicalization. The presented case study demonstrates the use of the tool for developing a task-specific rule-set to convert data from arbitrary tables of the same genre (government statistical websites) to flat file databases. The performance evaluation confirms the applicability of the implemented rule-set in accomplishing the stated objectives of the application. •Spreadsheet data transformation can be considered as table understanding.•The two-layered table object model represents arbitrary spreadsheet tables.•Our rule-based language enables to express consecutive steps of table understanding.•Execution of the rules provides recovering implicit table semantic (structure).•A rule-set allows converting arbitrary tables of the same genre into databases. The paper discusses issues of rule-based data transformation from arbitrary spreadsheet tables to a canonical (relational) form. We present a novel table object model and rule-based language for table analysis and interpretation. The model is intended to represent a physical (cellular) and logical (semantic) structure of an arbitrary table in the transformation process. The language allows drawing up this process as consecutive steps of table understanding, i. e. recovering implicit semantics. Both are implemented in our tool for spreadsheet data canonicalization. The presented case study demonstrates the use of the tool for developing a task-specific rule-set to convert data from arbitrary tables of the same genre (government statistical websites) to flat file databases. The performance evaluation confirms the applicability of the implemented rule-set in accomplishing the stated objectives of the application. |
| Author | Mikhailov, Andrey A. Shigarov, Alexey O. |
| Author_xml | – sequence: 1 givenname: Alexey O. surname: Shigarov fullname: Shigarov, Alexey O. email: shigarov@icc.ru, shigarov@gmail.com – sequence: 2 givenname: Andrey A. surname: Mikhailov fullname: Mikhailov, Andrey A. email: mikhailov@icc.ru |
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| Cites_doi | 10.1007/s10032-016-0259-1 10.1016/j.datak.2004.10.004 10.14778/2536274.2536276 10.14778/1453856.1453916 10.1007/978-3-642-34213-4_2 10.1016/j.ijhcs.2017.02.006 10.14778/2002938.2002939 10.1145/1993316.1993536 10.1007/978-3-642-04930-9_23 10.14778/1920841.1921005 10.1016/j.csi.2007.08.006 10.1007/978-3-540-88564-1_29 10.14778/2536258.2536271 10.1016/j.eswa.2014.08.045 10.1145/2536669.2536674 10.1007/978-3-319-32025-0_33 10.1016/j.jvlc.2006.06.001 10.1016/j.datak.2009.02.010 10.1145/2813885.2737952 10.1007/s10515-014-0167-x 10.1016/j.datak.2006.04.002 10.1007/s10032-005-0001-x 10.14778/2536336.2536343 10.1007/s11280-005-0360-8 10.1016/j.is.2016.10.010 10.1007/978-3-319-11964-9_31 10.1007/978-3-642-17749-1_13 10.1007/978-3-319-49004-5_11 10.1145/2240236.2240260 |
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| Snippet | •Spreadsheet data transformation can be considered as table understanding.•The two-layered table object model represents arbitrary spreadsheet tables.•Our... The paper discusses issues of rule-based data transformation from arbitrary spreadsheet tables to a canonical (relational) form. We present a novel table... |
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| SubjectTerms | Arbitrariness Case studies Cellular structure Data Information systems Language shift Performance evaluation Rule-based programming Semantics Spreadsheet data transformation Spreadsheets Table analysis Table interpretation Table model Table understanding Transformation Transformations Websites |
| Title | Rule-based spreadsheet data transformation from arbitrary to relational tables |
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