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Analysis and monitoring of system logs such as transaction logs and access logs is important for various objectives in cluding trend discovery, update effort determination, and malicious behavior monitoring. However, it is not always an easy task because these logs may be massive, consist ing of millions of records containing tens of variables, and therefore it may be difficult or time-consuming to discover significant knowledge. This paper presents a visual analyt ics tool which enables us to effectively observe system logs. The tool recommends variables that can reveal interesting discoveries and provides feature-based filtering that selects meaningful items from the visualization results.

We introduce a method that can measure the degree of regularity or irregularity of the behavior for enhancing the performance of location-based services (LBSs) such as check-in. It is still challenging for LBSs to determine the places to recommend that best suits the user's needs. Our aim is to identify the user's status (regular or irregular) of each check-in. Most previous studies approached this problem by acquiring usual locations (e.g., home or office) or assessing check-in frequency. We propose more effective measure by using a multinomial-distribution-based method that considers the periodic check-ins of the user on various time-scales. Our method can accurately identify irregular check-ins even in usual locations and we find that the users tend to continue irregular check-ins in a certain range of time.

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