現今雲端運算、行動網際網路、虛擬化、物聯網與大數據已經成為大數據時代的前瞻技術，本論文實作 ELK Stack 處理 Call-Center 語音數據，ELK 是基於 Elastic Search (E)、LogStash (L)、Kibana (K) 三個系統來處理大量資料，目前傳統的資訊探索，資料擷取、知識呈現與理論技術難以滿足當下多樣化的需求。大數據的理念與理論已經成為了人所共知的科學常識，但是大數據搜索、擷取與視覺化和落地的專案實作尚有較大距離。在每個公司或是學校、甚至任何組織都可能建立 Call-Center(語音客服中心) 環境，將 Call-Center 環境所產生的語音數據導入 ELK Stack 中進行分析比較，進而提供使用者較為精準及可靠的撥打行為，輔助未來針對特定時段或是特定人物，有效的建議使用者提升完成效率。ELK Stack 分散式系統架構，Call-Center 中心所需要分析的語音資料，論文實驗目前結合了語音數據中心的年齡、活動資訊、參加意願與否、男女比例的分析，具有參考價值的數據欄位，藉由撥打接通率，撥打接通的時間點，進而能夠分析或是協助撥打者在進行電話訪問時，能夠提升顧客的熱忱與興趣，分析結果在正確的時間點，以更有效的數據提供給系統使用者與決策者。 Cloud computing, Internet, machine learning, Internet of things and Big datahas become a Big data era of technology this paper using ELK Stack deal withlarge data. ELK is based on Elasticsearch (E), Logstash (L), Kibana (K) threesystems to handle large amounts of data. In each company or school and evenany organization may establish a Call-Center (voice call center) environment. TheCall-Center environment generated by the voice data into the ELK Stack for analysisand comparison and thus provide users with more accurate and reliable dialingbehavior. Assist the future for a specific time period a specific person. The effectiverecommendation of the user to improve the completion of efficiency.ELKStack system with Call-Center needs analysis of voice data. The paper experimentcurrently combines the voice data center age, activity information, willingness tojoin or not. The ratio of men and women analysis with reference to the field ofdata. Such as dial-up rate and dial-up time and as to be able to analyze or assistcallers in raising telephone calls to enhance the enthusiasm and interest of customers.The analysis results are provided in a more effective manner at the righttime System users and decision makers.
