مشخصات مقاله | |
انتشار | مقاله سال 2018 |
تعداد صفحات مقاله انگلیسی | 35 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
منتشر شده در | نشریه الزویر |
نوع مقاله | ISI |
عنوان انگلیسی مقاله | Developing an integrated framework for using data mining techniques and ontology concepts for process improvement |
ترجمه عنوان مقاله | توسعه یک چارچوب یکپارچه برای تکنیک های داده کاوی و مفاهیم هستی شناسی برای بهبود فرآیند |
فرمت مقاله انگلیسی | |
رشته های مرتبط | مدیریت و مهندسی صنایع |
گرایش های مرتبط | مدیریت دانش و داده کاوی |
مجله | مجله سیستم ها و نرم افزارها – The Journal of Systems & Software |
دانشگاه | Department of Information Technology Management – Islamic Azad University – Iran |
کلمات کلیدی | داده کاوی، بهبود فرایند، هستی شناسی، طبقه بندی، خوشه بندی |
کلمات کلیدی انگلیسی | Data mining, Process improvement, Ontology, Classification, Clustering |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.jss.2017.11.019 |
کد محصول | E8297 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
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1. Introduction
Nowadays, the vast majority of large organizations possess hundreds of different business processes (BPs), which are typically poorly documented. Furthermore, the relationships between the different types of processes are not clearly specified (Houy et al., 2011). Rebuge and Ferreira (2012) presented characteristics of processes, including the following: the dynamic and changing nature, complexity, interdisciplinary nature of the processes; interactions between the processes in different departments; and the requirement for obtaining experience, knowledge, and expertise for implementing the processes. Moreover, they stated that the traditional analysis of processes was time consuming. In addition, creating a common understanding of the processes between employees was difficult. The other problems related to this research work are presented in Table 1. With respect to the above-mentioned problems, data mining (DM) techniques can extract valuable patterns hidden in the high volume of BPs for recommending PI suggestions. In this regard, a process ontology concept can be considered to share the patterns extracted from the application of DM in PI to gain process ontology benefits. Pivk et al. (2014) stated that applying ontology for implementing DM in BPs has benefits: (1) sharing a common understanding between people, (2) re-using the domain knowledge, (3) making explicit domain assumptions, (4) differentiating between domain and operational knowledge, and (5) analyzing the domain knowledge. This paper contributes an integrated three-part, five-stage framework for applying the DM approach for PI under a process ontology concept. Figure 1 exhibits the conceptual model of the proposed framework including three parts as follows: 1. process ontology, 2. DM, and 3. PI. Each part has a series of attributes and behaviors. The attributes explain the characters of these parts and describe what they are. The behaviors describe the activities that these parts can implement. |