隨著現在社會交通的便利及通訊的發達，觀光旅遊業也越來越熱門。許多想旅遊的人常常會透過網路來尋找有趣的景點，也有許多人透過網路分享出遊心得，這些心得文往往能吸引這些想去旅遊且從網路找資訊的人，甚至於將這些心得問的推薦景點納入出遊行程中，這些大都是零散的資訊，並未透過整合與統計，若是第一次出遊到未知地區的人們，可能無法快速地知道那個地區的熱門景點，在安排行程上就會花上許多時間。 目前有許多網路平台在整理不同區域的景點，但未提供推薦數，看的人只知道景點，而無法得知熱門程度，就如同找聚會的餐廳時，只知道許多家餐廳，但並不知道這家餐廳是否許多人推薦。本研究也是在解決這個問題，實作一個幫助規劃出遊行程，讓人們能了解各地的熱門景點的平台。這個研究結合計算大量旅遊訊息有效的方法，通過興趣點比較不同的算法解決這個問題，結果顯示Prefixspan 比aprioriall 在目前Hadoop 計算平台上更有效率。 The rapid development and popularity of transport technology encourages people to travel frequently. As many traveling experiences are been shared through the Internet, more people are searching interesting sites from different web sites. Traveling informations including itinerary and accomodations are scattered and it is not easy to grasp relevant information. Especially when people want information regarding to regions where they never been to before, questions such as the most popular attractions, and what are the restaurant most visited are not easy to answer. In order to answer the above questions, efficient methods in combining and calculating the large amount of traveling informations are needed. This thesis address the issue by comparing different algorithms in calculating point of interest. The result showed prefixspan is more efficient than aprioriall in a modern day Hadoop computation platform.
