International Journal of Business Intelligence and Data Mining http://www.inderscience.com/browse/index.php?journalID=143&year=2011&vol=6&issue=4 Inderscience Publishers Ltd en-uk International Journal of Business Intelligence and Data Mining 1743-8187 1743-8195 © 2011 Inderscience Publishers Ltd editor@inderscience.com https://www.inderscience.com/images/files/coverImgs/ijbidm_scoverijbidm.jpg http://www.inderscience.com/browse/index.php?journalID=143&year=2011&vol=6&issue=4 http://www.inderscience.com/link.php?id=44996 This paper analyses the impact of biological intelligence on the problem of selecting the optimal solution in Web service composition. Thus, we propose two selection methods, one inspired by the behaviour of bees searching for food and another one inspired by the behaviour of cuckoos searching for the nests where to lay eggs. The methods use a composition graph to search for the optimal solution. The quality of a composition is evaluated based on QoS and semantic quality. To comparatively analyse the proposed methods we implemented an experimental prototype and performed tests on a set of scenarios from trip planning. Bio-inspired methods for selecting the optimal web service composition: Bees or cuckoos intelligence?
Viorica Rozina Chifu; Cristina Bianca Pop; Ioan Salomie; Mihaela Dinsoreanu; Alexandru Nicolae Niculici; Dumitru Samuel Suia
International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 321 - 344
This paper analyses the impact of biological intelligence on the problem of selecting the optimal solution in Web service composition. Thus, we propose two selection methods, one inspired by the behaviour of bees searching for food and another one inspired by the behaviour of cuckoos searching for the nests where to lay eggs. The methods use a composition graph to search for the optimal solution. The quality of a composition is evaluated based on QoS and semantic quality. To comparatively analyse the proposed methods we implemented an experimental prototype and performed tests on a set of scenarios from trip planning.

]]> 10.1504/IJBIDM.2011.044996 International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 321 - 344 Viorica Rozina Chifu; Cristina Bianca Pop; Ioan Salomie; Mihaela Dinsoreanu; Alexandru Nicolae Niculici; Dumitru Samuel Suia Department of Computer Science, Technical University of Cluj-Napoca, 15 C, Daicoviciu Street, Cluj-Napoca 400020, Romania. ' Department of Computer Science, Technical University of Cluj-Napoca, 15 C, Daicoviciu Street, Cluj-Napoca 400020, Romania. ' Department of Computer Science, Technical University of Cluj-Napoca, 15 C, Daicoviciu Street, Cluj-Napoca 400020, Romania. ' Department of Computer Science, Technical University of Cluj-Napoca, 15 C, Daicoviciu Street, Cluj-Napoca 400020, Romania. ' Department of Computer Science, Technical University of Cluj-Napoca, 15 C, Daicoviciu Street, Cluj-Napoca 400020, Romania. ' Department of Computer Science, Technical University of Cluj-Napoca, 15 C, Daicoviciu Street, Cluj-Napoca 400020, Romania bio-inspired selection web services service composition biological intelligence bee colony optimisation cuckoo search multicriteria fitness function trip planning travel arrangements. 2012-01-17T23:20:50-05:00 6 4 321 344 2012-01-17T23:20:50-05:00 http://www.inderscience.com/link.php?id=44997 Nowadays, enterprises and their business processes are becoming more dynamic. Business Processes (BPs) need to be able to adapt to changes in open business systems environments. This paper presents a new approach for BP flexibility based on context environments and artefacts. This approach enables a better reusability and exibility of independent BP modules in a context-aware run-time environment. We present a context-aware process mining framework where contexts will be automatically captured from execution environments to maximise the exibility of BPs. Process mining techniques are used to extract information from BP run-time. The reasoning about those information using artefact is shown. Dynamic context-aware Business Process flexibility: an artefact-based approach using process mining
Mounira Zerari; Mahmoud Boufaida
International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 345 - 361
Nowadays, enterprises and their business processes are becoming more dynamic. Business Processes (BPs) need to be able to adapt to changes in open business systems environments. This paper presents a new approach for BP flexibility based on context environments and artefacts. This approach enables a better reusability and exibility of independent BP modules in a context-aware run-time environment. We present a context-aware process mining framework where contexts will be automatically captured from execution environments to maximise the exibility of BPs. Process mining techniques are used to extract information from BP run-time. The reasoning about those information using artefact is shown.

]]> 10.1504/IJBIDM.2011.044997 International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 345 - 361 Mounira Zerari; Mahmoud Boufaida LIRE Laboratory, Computer Science Department, Mentouri University of Constantine, 25000, Algeria. ' LIRE Laboratory, Computer Science Department, Mentouri University of Constantine, 25000, Algeria context-aware business processes artefacts process mining business rules process-aware information systems variables context business process flexibility open business systems. 2012-01-17T23:20:50-05:00 6 4 345 361 2012-01-17T23:20:50-05:00 http://www.inderscience.com/link.php?id=44976 Ant Colony Optimisation (ACO) algorithms use simple mutually cooperating agents (ants) to produce a robust and adaptive search system, which can be used for knowledge discovery. In this paper, a Support Vector Machine (SVM)-cAnt-Miner-based system for predicting the next-day's trend in stock markets is proposed. The trend predicted by the proposed system is then used to identify the appropriate time to buy and sell securities. Performance of the proposed system is evaluated against SVM-Ant-Miner, SVM-Ant-Miner2, Naïve-Bayes and an Artificial Neural Network (ANN)-based trend prediction system. The results indicate that the proposed system outperforms all the other techniques considered. Predicting stock market trends using hybrid ant-colony-based data mining algorithms: an empirical validation on the Bombay Stock Exchange
Binoy B. Nair; V.P. Mohandas; N.R. Sakthivel
International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 362 - 381
Ant Colony Optimisation (ACO) algorithms use simple mutually cooperating agents (ants) to produce a robust and adaptive search system, which can be used for knowledge discovery. In this paper, a Support Vector Machine (SVM)-cAnt-Miner-based system for predicting the next-day's trend in stock markets is proposed. The trend predicted by the proposed system is then used to identify the appropriate time to buy and sell securities. Performance of the proposed system is evaluated against SVM-Ant-Miner, SVM-Ant-Miner2, Naïve-Bayes and an Artificial Neural Network (ANN)-based trend prediction system. The results indicate that the proposed system outperforms all the other techniques considered.

]]> 10.1504/IJBIDM.2011.044976 International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 362 - 381 Binoy B. Nair; V.P. Mohandas; N.R. Sakthivel Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, P.O. Amrita Nagar, Ettimadai, Coimbatore, Tamil Nadu, 641112, India. ' Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, P.O. Amrita Nagar, Ettimadai, Coimbatore, Tamil Nadu, 641112, India. ' Department of Mechanical Engineering, Amrita Vishwa Vidyapeetham, P.O. Amrita Nagar, Ettimadai, Coimbatore, Tamil Nadu, 641112, India ACO ant colony optimisation data mining SVM support vector machines Ant-Miner Ant-Miner2 cAnt-Miner stock market trends technical indicators stock markets stock market predictions selling securities buying securities Bayes artificial neural networks ANNs. 2012-01-17T23:20:50-05:00 6 4 362 381 2012-01-17T23:20:50-05:00 http://www.inderscience.com/link.php?id=44977 A scheme is developed that clusters services based on outcome attributes deemed important by customers. The algorithm that determined clusters used empirical field research data from 164 different services. The services are modelled as binary vectors. They are analysed using a clustering method based on the Ward algorithm. The analysis reveals six distinct clusters by customer desiderata. Additionally, the medoid is a natural representative of each cluster. The clusters are quite distinct from previously considered groupings based on process characteristics, and offer new insights into their common features. Implications for service innovation, strategic planning, and staffing are discussed. Classifying services by attributes important to customers
Venkat Venkateswaran; John Maleyeff
International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 382 - 401
A scheme is developed that clusters services based on outcome attributes deemed important by customers. The algorithm that determined clusters used empirical field research data from 164 different services. The services are modelled as binary vectors. They are analysed using a clustering method based on the Ward algorithm. The analysis reveals six distinct clusters by customer desiderata. Additionally, the medoid is a natural representative of each cluster. The clusters are quite distinct from previously considered groupings based on process characteristics, and offer new insights into their common features. Implications for service innovation, strategic planning, and staffing are discussed.

]]> 10.1504/IJBIDM.2011.044977 International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 382 - 401 Venkat Venkateswaran; John Maleyeff Department of Engineering and Science, Rensselaer Polytechnic Institute, 275 Windsor Street, Hartford, CT 06120, USA. ' Lally School of Management and Technology, Rensselaer Polytechnic Institute, 275 Windsor Street, Hartford, CT 06120, USA service management service classification cluster analysis data mining services science service clusters customer requirements customer desires service innovation strategic planning staffing. 2012-01-17T23:20:50-05:00 6 4 382 401 2012-01-17T23:20:50-05:00 http://www.inderscience.com/link.php?id=44978 Subgroup Analysis (SA) is a helpful technique in the context of randomised experiments and in observational studies. With reference to program evaluation, it helps in determining whether and how treatment effects vary across subgroups induced by baseline covariates. However, the choice of the optimal number of subgroups is often ambiguous and causes concern. Here, SA is conducted using the cluster-based approach introduced in D'Attoma and Camillo (2011) and the usage of the Information Complexity Criterion to select the optimal number of groups is proposed. A simulation study and a real case have been illustrated to show such promising approach. An optimal cluster-based approach for Subgroup Analysis using Information Complexity Criterion
Ida D'Attoma; Caterina Liberati
International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 402 - 425
Subgroup Analysis (SA) is a helpful technique in the context of randomised experiments and in observational studies. With reference to program evaluation, it helps in determining whether and how treatment effects vary across subgroups induced by baseline covariates. However, the choice of the optimal number of subgroups is often ambiguous and causes concern. Here, SA is conducted using the cluster-based approach introduced in D'Attoma and Camillo (2011) and the usage of the Information Complexity Criterion to select the optimal number of groups is proposed. A simulation study and a real case have been illustrated to show such promising approach.

]]> 10.1504/IJBIDM.2011.044978 International Journal of Business Intelligence and Data Mining, Vol. 6, No. 4 (2011) pp. 402 - 425 Ida D'Attoma; Caterina Liberati Statistics Department, Universitá di Bologna, Via Belle Arti n 41, 40121 Bologna, Italy. ' Economics Department, Universitá degli Studi Milano-Bicocca, P.zza Ateneo Nuovo n 1, 20126 Milano, Italy heterogeneous treatment effects subgroup analysis information complexity criterion observational studies simulation clustering. 2012-01-17T23:20:50-05:00 6 4 402 425 2012-01-17T23:20:50-05:00