Due to the increased preservation efforts, large amounts of past Web data have been stored in Web archives and other archival repositories. Utilizing this data can offer certain benefits to users, for example, it can facilitate page understanding. In this paper, we propose a system for interactive exploration of page histories. We demonstrate an application called Page History Explorer (PHE) for summarizing and visualizing histories of Web pages. PHE portrays the overview of page evolution, characterizes its typical content over time and lets users observe page histories from different viewpoints. In addition, it enables flexible comparison of histories of different pages.

Recently, machine learning approaches and user movement history analysis on mobile devices have attracted much attention. Generally, we need to apply text data into the word embedding tool for acquiring word vectors as the preprocessing of machine learning approaches. However, it is difficult for mobile devices to afford the huge cost of high-dimensional vector calculation. Thus, a low-cost user behavior and user movement history analysis approach should be considered. To address this issue, firstly, we convert the zip code and street house number into vectors instead of textual address information to reduce the cost of spatial vector calculation. Secondly, we propose a low-cost high-performance semantic and physical distance (real distance) calculation method that applied zip-code-based vectors. Finally, to verify the validity of our proposed method, we utilize the US zip code data to calculate both semantic and physical distances and compare their results with the previous method. The experimental results showed that our proposed method could significantly improve the performance of distance calculation and effectively control the cost to a low level.