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We present two major information examination strategies for diagnosing the reasons for system disappointments and for identifying system disappointments early. Syslogs contain log information created by the framework. We dissected syslogs what's more, prevailing with regards to distinguishing the reason for a system disappointment via consequently learning more than 100 million logs without requiring any past learning of log information. Investigation of the information of an interpersonal interaction benefit (in particular, Twitter) empowered us to recognize conceivable system disappointments by extricating system disappointment related tweets, which represent under 1% of all tweets, continuously and with high exactness.

A service-demand-forecasting method that uses multiple data sourcesfor improved accuracy is presented in this paperWe presented a advanced scenario simulation framework to analyze each customer service choice behavior and total service demand under an assumed condition. To estimate the demand on service, especiallyfor a new service, scenario simulation is executed based on stated preference data (SP). However, there is usually a considerable gap between an estimate based on SP and the actual outcome. Furthermore, the distribution of attributes of users collected in market surveys is not equal to that ofactual market users, and there is a bias between collected data and attributes ofusers in the market. In this paper, we propose an adjusting method that can re-estimate demands by analyzing the difference between actual and estimated results. We consider the problem ofchoosing an Internet access line service to evaluate the method.An application example shows that distribution differences between areas with respect to customer attributes strongly affect actual market shares.

This paper concerns early change detection problem in time series. For the early detection problem, we propose an application of Sequential Probability Ratio Test (SPRT) that has been mainly used in the field of quality control. The SPRT is used for testing a null hypothesis H 0 (e.g. the quality is under pre-specified limit 1%) against hypothesis H 1 (e.g. the quality is over pre-specified limit 1%). And it is defined as follows: At each stage of successive observations on data Z 1 ,Z 2 , …, the probability ratio λ i is computed.