Abstract: Cash demand for a cash dispensing device is forecasted for individual days, weeks and multi-day load periods. In one embodiment, an expected value of withdrawal for any particular period, as well as an upper limit on expected withdrawals for a given confidence level, are computed. The technique facilitates the creation of a daily and multi-day withdrawal forecast for a given ATM. The forecasts are also used to compute an optimum load amount for a given load period (the period between loading operations) for a given ATM, subject to given parameters (such as courier cost, interest rate, transit time, desired confidence level, cost of run-out, and the like) and given constraints (bundle size, maximum capacity, possible delivery days, and the like). The forecasts are also used to determine an optimal load period length and schedule.

Abstract: Cash demand for a cash dispensing device is forecasted for individual days, weeks and multi-day load periods. In one embodiment, an expected value of withdrawal for any particular period, as well as an upper limit on expected withdrawals for a given confidence level, are computed. The technique facilitates the creation of a daily and multi-day withdrawal forecast for a given ATM. The forecasts are also used to compute an optimum load amount for a given load period (the period between loading operations) for a given ATM, subject to given parameters (such as courier cost, interest rate, transit time, desired confidence level, cost of run-out, and the like) and given constraints (bundle size, maximum capacity, possible delivery days, and the like). The forecasts are also used to determine an optimal load period length and schedule.

Abstract: A method of detecting an intrusion into (or an anomaly in a behavior of) a target software system begins by instrumenting the target software system to generate behavior data representing a current observation or observation aggregate. The method then determines whether the current observation or observation aggregate warrants a second level examination. If a result of executing the first level detection algorithm indicates that the current observation or observation aggregate warrants a second level examination, the method continues by processing the current observation or observation aggregate through at least one or more second level detection algorithms to provide a second, more definite, fine grain indication of a possible intrusion.

Abstract: A method of and apparatus for forecasting cash demand for an automated teller machine (ATM) or any other cash dispensing devices for individual days, weeks and multi-day load periods. In one embodiment, an expected value of withdrawal for any particular period, as well as an upper limit on expected withdrawals for a given confidence level, are computed. The invention facilitates the creation of a daily and multi-day withdrawal forecast for a given ATM. The forecasts are also used to compute an optimum load amount for a given load period (the period between loading operations) for a given ATM, subject to given parameters (such as courier cost, interest rate, transit time, desired confidence level, cost of run-out, and the like) and given constraints (bundle size, maximum capacity, possible delivery days, and the like). The forecasts are also used to determine an optimal load period length and schedule.

Abstract: A target software system is instrumented to generate behavior data representing a current observation or observation aggregate. A method then determines whether the current observation or observation aggregate warrants a second level examination; preferably, this determination is made by processing the current observation or observation aggregate through a first level detection algorithm that provides a provisional indication of a possible intrusion. If executing the first level detection algorithm indicates that the current observation or observation aggregate warrants a second level examination, the method continues by processing the current observation or observation aggregate through at least one second level detection algorithms to provide a more definite, fine grain indication of a possible intrusion.

Abstract: A method of and apparatus for forecasting cash demand for an automated teller machine (ATM) or any other cash dispensing devices for individual days, weeks and multi-day load periods. In one embodiment, an expected value of withdrawal for any particular period, as well as an upper limit on expected withdrawals for a given confidence level, are computed. The invention facilitates the creation of a daily and multi-day withdrawal forecast for a given ATM. The forecasts are also used to compute an optimum load amount for a given load period (the period between loading operations) for a given ATM, subject to given parameters (such as courier cost, interest rate, transit time, desired confidence level, cost of run-out, and the like) and given constraints (bundle size, maximum capacity, possible delivery days, and the like). The forecasts are also used to determine an optimal load period length and schedule.

Abstract: A method of and apparatus for forecasting cash demand for an automated teller machine (ATM) or any other cash dispensing devices for individual days, weeks and multi-day load periods. In one embodiment, an expected value of withdrawal for any particular period, as well as an upper limit on expected withdrawals for a given confidence level, are computed. The invention facilitates the creation of a daily and multi-day withdrawal forecast for a given ATM. The forecasts are also used to compute an optimum load amount for a given load period (the period between loading operations) for a given ATM, subject to given parameters (such as courier cost, interest rate, transit time, desired confidence level, cost of run-out, and the like) and given constraints (bundle size, maximum capacity, possible delivery days, and the like). The forecasts are also used to determine an optimal load period length and schedule.

Abstract: A novel method of and system and procedures for more accurately measuring the resource usage of UNIX processes by sampling methods involving appropriate corrections for the resource usage of the terminated processes and analyzing UNIX process data along with subsystem data such as RDBMSs, allowing system administrators and mangers to get a much better picture of who is using the resources on the system and thus perform a better job at performance analysis and capacity planning, the technique also enabling and reducing the error in the process measurements collected by sampling of the resources usage measured by the operating system and correlating the measurements taken by subsystems with the measurements taken by the operating system.