Data Set Information:

在普适计算中，交互用户无法获得关于彼此可信度的信息。因此，不公平的用户可以恶意地对待他人。所提出的解决方案能够通过监控每个用户在网络上交互期间的行为来评估每个用户的可信度。这些行为由包含重要参数的元组表示。基于这些元组，该体系结构结合了一些基于人工智能的技术来实现一个决策系统。

The tuples are as follows:
eij =

Attribute Information:

1) CT {CT_range_1, CT_range_2, CT_range_3, CT_range_4}
2) CU {CU_range_1, CU_range_2, CU_range_3, CU_range_4}
3) LT {LT_range_1, LT_range_2, LT_range_3, LT_range_4}
4) TC {sport, game, ECommerce, holiday}
5) TS {trustworthy, untrustworthy}

The numerical attributes (CT, CU, LT) was discretized.
Several of the papers listed below contain detailed descriptions of how these attributes were discretized.

Relevant Papers:

G. Da€?Angelo, S. Rampone, F. Palmieri, a€?Developing a Trust Model for Pervasive Computing based on Apriori Association Rules Learning and Bayesian Classificationa€?, SOCO a€“ Soft Computing Journal, Vol.21, n.21, pp. 6297-6315, 2017.  DOI: 10.1007/s00500-016-2183-1

Citation Request:

If you intend to use this dataset on your research, please cite the following works:
1. G. Da€?Angelo, S. Rampone, F. Palmieri, a€?Developing a Trust Model for Pervasive Computing based on Apriori Association Rules Learning and Bayesian Classificationa€?, SOCO a€“ Soft Computing Journal, Vol.21, n.21, pp. 6297-6315, 2017.  DOI: 10.1007/s00500-016-2183-1
2. G. D'Angelo, S. Rampone and F. Palmieri, 'An Artificial Intelligence-based Trust Model for Pervasive Computing,' 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), Krakow, 2015, pp. 701-706. DOI: 10.1109/3PGCIC.2015.94