Ontology based Big Data Analytics for the Benchmarking Hospitality Industry

• Main Authors: Ruei-Yang Lai, 賴瑞陽
• Other Authors: Chuan-Jun Su
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/86094624228790627954

碩士 === 元智大學 === 工業工程與管理學系 === 105 === Hospitality industry is a data rich industry that captures huge volumes of data of various types including arrival time, the frequency of use of public facilities, meals diet, customer service, social network comments, etc. with high velocity. These data encapsulate useful information regarding every phase of the customer journey. The effective use of analytics can improve dramatically how business is run in terms of delivering memorable and personalized guest experiences, while maximizing revenue and profits. The challenge of Big Data doesn't just stem from the volume and velocity of the data sets themselves, but also from the variety challenge posed by gaining big data insight in the context of an industry. For example, how to score the performance of hotels based on a variety of semantic data sources. Ontology is a formal representation of knowledge as a hierarchy of concepts within a domain, using a shared vocabulary to denote the types, properties and interrelationships of those concepts. An ontology driven Big Data analytics has potential in providing a pragmatic framework to address the semantic challenges presented by Big Data sets. This research aims to develop an ontology driven Big Data Sematic Analytic Platform which enables hotels to improve their overall performance in furnishing better customer experiences by infusing analytics through every phase of the guest journey. The platform allows the business in hospitality industry not only to capture and store the influx of semantic data effectively but also to evaluate its key performance factors by using ontology organized international standard knowledge-based such as U.S. AAA Diamond evaluation systems.