TY - GEN
T1 - A framework to analyze social tagging and unstructured data
AU - Al-Thuhli, Amjed
AU - Al-Badawi, Mohammed
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - The involvement of human interactions with business processes through Enterprise Social Networks improves organizations performance. However, Enterprise Social Networks consist of massive amount of data in form of structure and unstructured data. Therefore, finding valuable information from these types of data is a challenging issue. Nevertheless, with the annotation that are available in form of social tagging, some challenges have been resolved. In this paper, we investigate the problem of using social tagging in order to socialize organization business processes. Specifically, we present a framework to analyze social tagging and unstructured data that are generated by users to recommend tasks and activities of any type of business processes based on hybrid method of clustering and text classification. The framework uses k-means algorithm to cluster tags datasets and term frequency-inverse document frequency to weight user's documents. The experiment results performed on a real case study shows the efficiency of the framework after validates its accuracy.
AB - The involvement of human interactions with business processes through Enterprise Social Networks improves organizations performance. However, Enterprise Social Networks consist of massive amount of data in form of structure and unstructured data. Therefore, finding valuable information from these types of data is a challenging issue. Nevertheless, with the annotation that are available in form of social tagging, some challenges have been resolved. In this paper, we investigate the problem of using social tagging in order to socialize organization business processes. Specifically, we present a framework to analyze social tagging and unstructured data that are generated by users to recommend tasks and activities of any type of business processes based on hybrid method of clustering and text classification. The framework uses k-means algorithm to cluster tags datasets and term frequency-inverse document frequency to weight user's documents. The experiment results performed on a real case study shows the efficiency of the framework after validates its accuracy.
KW - Business processes
KW - Clustering
KW - Enterprise social networks
KW - TF-IDF
KW - Text mining
KW - Unstructured data
UR - http://www.scopus.com/inward/record.url?scp=85085581241&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085581241&partnerID=8YFLogxK
U2 - 10.1109/ICICT50521.2020.00016
DO - 10.1109/ICICT50521.2020.00016
M3 - Conference contribution
AN - SCOPUS:85085581241
T3 - Proceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020
SP - 46
EP - 53
BT - Proceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Information and Computer Technologies, ICICT 2020
Y2 - 9 March 2020 through 12 March 2020
ER -