Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Barrad, S.; Gagnon, S.; Valverde, R.
An analytics architecture for procurement Article de journal
Dans: International Journal of Information Technologies and Systems Approach, vol. 13, no 2, p. 73–98, 2020, ISSN: 1935570X (ISSN), (Publisher: IGI Global).
Résumé | Liens | BibTeX | Étiquettes: Big data, Business process management, Complex event processing, Complex event processing (CEP), Computer science, Cost reduction, Digital transformation, Emerging technologies, Enterprise Architecture, Information technology, Machine learning, Predictive analytics, Procurement, Procurement organizations, Proposed architectures, Rules based systems, Skill shortage, Supply chain management, Technology limitations
@article{barrad_analytics_2020,
title = {An analytics architecture for procurement},
author = {S. Barrad and S. Gagnon and R. Valverde},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083561161&doi=10.4018%2fIJITSA.2020070104&partnerID=40&md5=79790ea0afaa8174f59e639d7c9ce917},
doi = {10.4018/IJITSA.2020070104},
issn = {1935570X (ISSN)},
year = {2020},
date = {2020-01-01},
journal = {International Journal of Information Technologies and Systems Approach},
volume = {13},
number = {2},
pages = {73–98},
abstract = {Procurement transformation and pure cost reduction are no longer a novelty in today's modern business world. Procurement, as a core business function, plays a key role given its ability to generate value for the firm. From maximizing supplier value to minimizing contract leakage, challenges seldomly lack in this department. In fact, both resource and skill shortages and technology limitations are typically "top-of-mind" for Procurement Executives. Many research articles around the concept of cost reduction however, limited literature has been published in the areas of Artificial Intelligence, analytics and Rules-Based Systems and their specific application in Procurement. This article proposes a new enterprise architecture, leveraging emerging technologies to guide procurement organizations in their digital transformation. Our intent is to discuss how analytics, business rules and complex event processing (CEP) can be explored and adapted to the world of procurement to help reduce costs. This article concludes by suggesting an approach to implement the proposed architecture. Copyright © 2020, IGI Global.},
note = {Publisher: IGI Global},
keywords = {Big data, Business process management, Complex event processing, Complex event processing (CEP), Computer science, Cost reduction, Digital transformation, Emerging technologies, Enterprise Architecture, Information technology, Machine learning, Predictive analytics, Procurement, Procurement organizations, Proposed architectures, Rules based systems, Skill shortage, Supply chain management, Technology limitations},
pubstate = {published},
tppubtype = {article}
}
Procurement transformation and pure cost reduction are no longer a novelty in today's modern business world. Procurement, as a core business function, plays a key role given its ability to generate value for the firm. From maximizing supplier value to minimizing contract leakage, challenges seldomly lack in this department. In fact, both resource and skill shortages and technology limitations are typically "top-of-mind" for Procurement Executives. Many research articles around the concept of cost reduction however, limited literature has been published in the areas of Artificial Intelligence, analytics and Rules-Based Systems and their specific application in Procurement. This article proposes a new enterprise architecture, leveraging emerging technologies to guide procurement organizations in their digital transformation. Our intent is to discuss how analytics, business rules and complex event processing (CEP) can be explored and adapted to the world of procurement to help reduce costs. This article concludes by suggesting an approach to implement the proposed architecture. Copyright © 2020, IGI Global.
2.
Davoust, A.; Esfandiari, B.
User participation and honesty in online rating systems: What a social network can do Article d'actes
Dans: AAAI Workshop - Technical Report, p. 477–483, AI Access Foundation, 2016, ISBN: 978-1-57735-759-9.
Résumé | Liens | BibTeX | Étiquettes: Aggregation techniques, Artificial intelligence, Behavioral research, Big data, Co-operative behaviors, Cognitive systems, Computer games, Computer programming, Computer systems programming, Data mining, Hybrid systems, Incentive structure, On-line communities, Online rating systems, Online systems, Population statistics, Prisoners' Dilemma, Rating, Social networking (online), User participation
@inproceedings{davoust_user_2016,
title = {User participation and honesty in online rating systems: What a social network can do},
author = {A. Davoust and B. Esfandiari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021919921&partnerID=40&md5=6a33a1ab6d3b6ddd037240f4f664b6fe},
isbn = {978-1-57735-759-9},
year = {2016},
date = {2016-01-01},
booktitle = {AAAI Workshop - Technical Report},
volume = {WS-16-01 - WS-16-15},
pages = {477–483},
publisher = {AI Access Foundation},
abstract = {An important problem with online communities in general, and online rating systems in particular, is uncooperative behavior: lack of user participation, dishonest contributions. This may be due to an incentive structure akin to a Prisoners' Dilemma (PD). We show that introducing an explicit social network to PD games fosters cooperative behavior, and use this insight to design a new aggregation technique for online rating systems. Using a dataset of ratings from Yelp, we show that our aggregation technique outperforms Yelp's proprietary filter, as well as baseline techniques from recommender systems. Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org).},
keywords = {Aggregation techniques, Artificial intelligence, Behavioral research, Big data, Co-operative behaviors, Cognitive systems, Computer games, Computer programming, Computer systems programming, Data mining, Hybrid systems, Incentive structure, On-line communities, Online rating systems, Online systems, Population statistics, Prisoners' Dilemma, Rating, Social networking (online), User participation},
pubstate = {published},
tppubtype = {inproceedings}
}
An important problem with online communities in general, and online rating systems in particular, is uncooperative behavior: lack of user participation, dishonest contributions. This may be due to an incentive structure akin to a Prisoners' Dilemma (PD). We show that introducing an explicit social network to PD games fosters cooperative behavior, and use this insight to design a new aggregation technique for online rating systems. Using a dataset of ratings from Yelp, we show that our aggregation technique outperforms Yelp's proprietary filter, as well as baseline techniques from recommender systems. Copyright © 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org).