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Using Variation in Weighting Criteria and String Size Matching on Hybrid Model Schema Matching

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@article{IJASEIT6650,
   author = {Edhy Sutanta and Erna Kumalasari Nurnawati and Rosalia Arum Kumalasanti},
   title = {Using Variation in Weighting Criteria and String Size Matching on Hybrid Model Schema Matching},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {1},
   year = {2021},
   pages = {326--333},
   keywords = {criteria; effectiveness; hybrid schema matching; string size variation; weight variation},
   abstract = {

Schema matching plays a vital role in the information integration process from heterogeneous databases. Generally, the process of schema matching is to receive input, which are two databases (one as the source and another as a target), to match similarity attributes, and generate output in the form of mapping the similarity of the attribute pairs that are declared suitable. Furthermore, the user will assess these attribute pairs to determine whether the results obtained are correct or still need to be revised. Our previous study developed a model and software prototype of hybrid schema matching using a combination of constraint-based method and instance-based method. In this study, the model improved by adding new features. This paper discusses the increasing effectiveness of adding the features to customize the weight of matching criteria and string sizes matching. The hybrid model's best effectiveness is obtained when the weight of instance is 0.286, the type is 0.238, width is 0.190, nullable is 0.143, unique is 0.095, and the domain is 0.048. The matching process using a bigger string size increases the model effectiveness with the highest precision of 97.66 when the string size interval is between (length-100) and (length+100). The best combination of weight and string size variation obtains 97.66% precision, a 99.90% recall, and an f-measure of 98.74%.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6650},    doi = {10.18517/ijaseit.11.1.6650} }

EndNote

%A Sutanta, Edhy
%A Nurnawati, Erna Kumalasari
%A Kumalasanti, Rosalia Arum
%D 2021
%T Using Variation in Weighting Criteria and String Size Matching on Hybrid Model Schema Matching
%B 2021
%9 criteria; effectiveness; hybrid schema matching; string size variation; weight variation
%! Using Variation in Weighting Criteria and String Size Matching on Hybrid Model Schema Matching
%K criteria; effectiveness; hybrid schema matching; string size variation; weight variation
%X 

Schema matching plays a vital role in the information integration process from heterogeneous databases. Generally, the process of schema matching is to receive input, which are two databases (one as the source and another as a target), to match similarity attributes, and generate output in the form of mapping the similarity of the attribute pairs that are declared suitable. Furthermore, the user will assess these attribute pairs to determine whether the results obtained are correct or still need to be revised. Our previous study developed a model and software prototype of hybrid schema matching using a combination of constraint-based method and instance-based method. In this study, the model improved by adding new features. This paper discusses the increasing effectiveness of adding the features to customize the weight of matching criteria and string sizes matching. The hybrid model's best effectiveness is obtained when the weight of instance is 0.286, the type is 0.238, width is 0.190, nullable is 0.143, unique is 0.095, and the domain is 0.048. The matching process using a bigger string size increases the model effectiveness with the highest precision of 97.66 when the string size interval is between (length-100) and (length+100). The best combination of weight and string size variation obtains 97.66% precision, a 99.90% recall, and an f-measure of 98.74%.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6650 %R doi:10.18517/ijaseit.11.1.6650 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 1 %@ 2088-5334

IEEE

Edhy Sutanta,Erna Kumalasari Nurnawati and Rosalia Arum Kumalasanti,"Using Variation in Weighting Criteria and String Size Matching on Hybrid Model Schema Matching," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, pp. 326-333, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.1.6650.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Sutanta, Edhy
AU  - Nurnawati, Erna Kumalasari
AU  - Kumalasanti, Rosalia Arum
PY  - 2021
TI  - Using Variation in Weighting Criteria and String Size Matching on Hybrid Model Schema Matching
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 1
Y2  - 2021
SP  - 326
EP  - 333
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - criteria; effectiveness; hybrid schema matching; string size variation; weight variation
N2  - 

Schema matching plays a vital role in the information integration process from heterogeneous databases. Generally, the process of schema matching is to receive input, which are two databases (one as the source and another as a target), to match similarity attributes, and generate output in the form of mapping the similarity of the attribute pairs that are declared suitable. Furthermore, the user will assess these attribute pairs to determine whether the results obtained are correct or still need to be revised. Our previous study developed a model and software prototype of hybrid schema matching using a combination of constraint-based method and instance-based method. In this study, the model improved by adding new features. This paper discusses the increasing effectiveness of adding the features to customize the weight of matching criteria and string sizes matching. The hybrid model's best effectiveness is obtained when the weight of instance is 0.286, the type is 0.238, width is 0.190, nullable is 0.143, unique is 0.095, and the domain is 0.048. The matching process using a bigger string size increases the model effectiveness with the highest precision of 97.66 when the string size interval is between (length-100) and (length+100). The best combination of weight and string size variation obtains 97.66% precision, a 99.90% recall, and an f-measure of 98.74%.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6650 DO - 10.18517/ijaseit.11.1.6650

RefWorks

RT Journal Article
ID 6650
A1 Sutanta, Edhy
A1 Nurnawati, Erna Kumalasari
A1 Kumalasanti, Rosalia Arum
T1 Using Variation in Weighting Criteria and String Size Matching on Hybrid Model Schema Matching
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 1
YR 2021
SP 326
OP 333
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 criteria; effectiveness; hybrid schema matching; string size variation; weight variation
AB 

Schema matching plays a vital role in the information integration process from heterogeneous databases. Generally, the process of schema matching is to receive input, which are two databases (one as the source and another as a target), to match similarity attributes, and generate output in the form of mapping the similarity of the attribute pairs that are declared suitable. Furthermore, the user will assess these attribute pairs to determine whether the results obtained are correct or still need to be revised. Our previous study developed a model and software prototype of hybrid schema matching using a combination of constraint-based method and instance-based method. In this study, the model improved by adding new features. This paper discusses the increasing effectiveness of adding the features to customize the weight of matching criteria and string sizes matching. The hybrid model's best effectiveness is obtained when the weight of instance is 0.286, the type is 0.238, width is 0.190, nullable is 0.143, unique is 0.095, and the domain is 0.048. The matching process using a bigger string size increases the model effectiveness with the highest precision of 97.66 when the string size interval is between (length-100) and (length+100). The best combination of weight and string size variation obtains 97.66% precision, a 99.90% recall, and an f-measure of 98.74%.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6650 DO - 10.18517/ijaseit.11.1.6650