A framework to analyze social tagging and unstructured data

Amjed Al-Thuhli, Mohammed Al-Badawi

نتاج البحث

ملخص

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.

اللغة الأصليةEnglish
عنوان منشور المضيفProceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020
ناشرInstitute of Electrical and Electronics Engineers Inc.
الصفحات46-53
عدد الصفحات8
رقم المعيار الدولي للكتب (الإلكتروني)9781728172835
المعرِّفات الرقمية للأشياء
حالة النشرPublished - مارس 2020
الحدث3rd International Conference on Information and Computer Technologies, ICICT 2020 - San Jose
المدة: مارس ٩ ٢٠٢٠مارس ١٢ ٢٠٢٠

سلسلة المنشورات

الاسمProceedings - 3rd International Conference on Information and Computer Technologies, ICICT 2020

Conference

Conference3rd International Conference on Information and Computer Technologies, ICICT 2020
الدولة/الإقليمUnited States
المدينةSan Jose
المدة٣/٩/٢٠٣/١٢/٢٠

ASJC Scopus subject areas

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