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Performance Analysis of Heuristic Miner and Genetics Algorithm in Process Cube: a Case Study

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@article{IJASEIT11544,
   author = {Rachmadita Andreswari and Ismail Syahputra and Muharman Lubis},
   title = {Performance Analysis of Heuristic Miner and Genetics Algorithm  in Process Cube: a Case Study},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {1},
   year = {2021},
   pages = {393--399},
   keywords = {Process mining; process cube; OLAP; heuristic miner algorithm; genetics algorithm.},
   abstract = {

Databases that are processed in the form of Online Analytical Processing (OLAP) can solve large query loads that cannot be resolved by transactional databases. OLAP systems are based on a multidimensional model commonly called a cube. In this study, OLAP techniques are applied in process mining, a method for bridging analysis based on business process models with database analysis. Like data mining, process mining produces process models by implementing the algorithms. This study implements the heuristic miner algorithm compared with genetic algorithms. The selection of these two algorithms is due to the characteristics to be able to model the event log correctly and can handle the control-flow. The capability in handling control-flow including the ability to detect hidden task, looping, duplicate task, detecting implicit/explicit concurrency, non-free-choice, the ability to mine and exploiting time, overcoming noise, and overcome incompleteness. The results of conformance checking on the heuristic miner algorithm for all data, fitness values, position, and structure are 1, 0.495, and 1, while the results of the genetic algorithm are 0.977, 0.706 and 1. Both algorithms have good ability in modeling processes and have high accuracy. The results of the F-score calculation on the heuristic miner algorithm for all data is 0.622, while the result in the genetic algorithm is 0.820. It indicates that genetic algorithms have better performance in modeling event logs based on process cube.

},    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=11544},    doi = {10.18517/ijaseit.11.1.11544} }

EndNote

%A Andreswari, Rachmadita
%A Syahputra, Ismail
%A Lubis, Muharman
%D 2021
%T Performance Analysis of Heuristic Miner and Genetics Algorithm  in Process Cube: a Case Study
%B 2021
%9 Process mining; process cube; OLAP; heuristic miner algorithm; genetics algorithm.
%! Performance Analysis of Heuristic Miner and Genetics Algorithm  in Process Cube: a Case Study
%K Process mining; process cube; OLAP; heuristic miner algorithm; genetics algorithm.
%X 

Databases that are processed in the form of Online Analytical Processing (OLAP) can solve large query loads that cannot be resolved by transactional databases. OLAP systems are based on a multidimensional model commonly called a cube. In this study, OLAP techniques are applied in process mining, a method for bridging analysis based on business process models with database analysis. Like data mining, process mining produces process models by implementing the algorithms. This study implements the heuristic miner algorithm compared with genetic algorithms. The selection of these two algorithms is due to the characteristics to be able to model the event log correctly and can handle the control-flow. The capability in handling control-flow including the ability to detect hidden task, looping, duplicate task, detecting implicit/explicit concurrency, non-free-choice, the ability to mine and exploiting time, overcoming noise, and overcome incompleteness. The results of conformance checking on the heuristic miner algorithm for all data, fitness values, position, and structure are 1, 0.495, and 1, while the results of the genetic algorithm are 0.977, 0.706 and 1. Both algorithms have good ability in modeling processes and have high accuracy. The results of the F-score calculation on the heuristic miner algorithm for all data is 0.622, while the result in the genetic algorithm is 0.820. It indicates that genetic algorithms have better performance in modeling event logs based on process cube.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11544 %R doi:10.18517/ijaseit.11.1.11544 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 1 %@ 2088-5334

IEEE

Rachmadita Andreswari,Ismail Syahputra and Muharman Lubis,"Performance Analysis of Heuristic Miner and Genetics Algorithm  in Process Cube: a Case Study," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, pp. 393-399, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.1.11544.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Andreswari, Rachmadita
AU  - Syahputra, Ismail
AU  - Lubis, Muharman
PY  - 2021
TI  - Performance Analysis of Heuristic Miner and Genetics Algorithm  in Process Cube: a Case Study
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 1
Y2  - 2021
SP  - 393
EP  - 399
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Process mining; process cube; OLAP; heuristic miner algorithm; genetics algorithm.
N2  - 

Databases that are processed in the form of Online Analytical Processing (OLAP) can solve large query loads that cannot be resolved by transactional databases. OLAP systems are based on a multidimensional model commonly called a cube. In this study, OLAP techniques are applied in process mining, a method for bridging analysis based on business process models with database analysis. Like data mining, process mining produces process models by implementing the algorithms. This study implements the heuristic miner algorithm compared with genetic algorithms. The selection of these two algorithms is due to the characteristics to be able to model the event log correctly and can handle the control-flow. The capability in handling control-flow including the ability to detect hidden task, looping, duplicate task, detecting implicit/explicit concurrency, non-free-choice, the ability to mine and exploiting time, overcoming noise, and overcome incompleteness. The results of conformance checking on the heuristic miner algorithm for all data, fitness values, position, and structure are 1, 0.495, and 1, while the results of the genetic algorithm are 0.977, 0.706 and 1. Both algorithms have good ability in modeling processes and have high accuracy. The results of the F-score calculation on the heuristic miner algorithm for all data is 0.622, while the result in the genetic algorithm is 0.820. It indicates that genetic algorithms have better performance in modeling event logs based on process cube.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11544 DO - 10.18517/ijaseit.11.1.11544

RefWorks

RT Journal Article
ID 11544
A1 Andreswari, Rachmadita
A1 Syahputra, Ismail
A1 Lubis, Muharman
T1 Performance Analysis of Heuristic Miner and Genetics Algorithm  in Process Cube: a Case Study
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 1
YR 2021
SP 393
OP 399
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Process mining; process cube; OLAP; heuristic miner algorithm; genetics algorithm.
AB 

Databases that are processed in the form of Online Analytical Processing (OLAP) can solve large query loads that cannot be resolved by transactional databases. OLAP systems are based on a multidimensional model commonly called a cube. In this study, OLAP techniques are applied in process mining, a method for bridging analysis based on business process models with database analysis. Like data mining, process mining produces process models by implementing the algorithms. This study implements the heuristic miner algorithm compared with genetic algorithms. The selection of these two algorithms is due to the characteristics to be able to model the event log correctly and can handle the control-flow. The capability in handling control-flow including the ability to detect hidden task, looping, duplicate task, detecting implicit/explicit concurrency, non-free-choice, the ability to mine and exploiting time, overcoming noise, and overcome incompleteness. The results of conformance checking on the heuristic miner algorithm for all data, fitness values, position, and structure are 1, 0.495, and 1, while the results of the genetic algorithm are 0.977, 0.706 and 1. Both algorithms have good ability in modeling processes and have high accuracy. The results of the F-score calculation on the heuristic miner algorithm for all data is 0.622, while the result in the genetic algorithm is 0.820. It indicates that genetic algorithms have better performance in modeling event logs based on process cube.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11544 DO - 10.18517/ijaseit.11.1.11544