Abstract:
A computer-implemented method determines current beliefs and/or behaviors of a population, and forecasts the behaviors and/or beliefs which this population is likely to have in the future. The method involves selecting a subset of members from a target population, obtaining survey responses from the members in the subset, generating point estimates of at least one population parameter, generating confidence bounds for the point estimates, and conducting a trend analysis on the survey responses and the point estimates of the at least one population parameter. Future behavior, beliefs, or other attributes of the population is then determined based on the trend analysis.
Abstract:
A computer method improves the robustness of population parameters estimated using sample responses. Two different factors that may impact the accuracy of estimates are considered. The first factor is referred to as statistical outliers. By statistical outliers, what is meant is observations that fall “statistically outside” of the other remaining observations in the sample. The second factor considered is the impact of the weight assigned to each observation on the overall parameter estimate. More specifically, the fact that weights assigned to each respondent are typically estimated and thus not exact is addressed. Consequently, the weights do not unduly influence the value of the parameter estimates.
Abstract:
An approach is presented for identifying related problem tickets in an information technology (IT) environment. User interactions with a computer program are stored. The user interactions include inputs to the computer program to search for problem tickets issued in the IT environment that have the same characteristics. One or more user interaction patterns within the user interactions are recognized. A user interaction pattern of the one or more user interaction patterns is selected based on an evaluation of effectiveness of each of the one or more user interaction patterns. Based on the user interaction pattern, a rule is generated for determining which problem tickets in the IT environment share a common characteristic. The rule is applied to additional problem tickets issued in the IT environment to identify which of the additional problem tickets share the common characteristic.
Abstract:
Common sub-process patterns in a plurality of deployed process models may be discovered, and performance measures associated with the sub-process patterns may be computed based on runtime events of the deployed process models. Positive or negative performance patterns among sub-process patterns may be identified and used for creating new process models or improving existing process models.
Abstract:
An approach is presented for identifying related problem tickets in an information technology (IT) environment. User interactions with a computer program are stored. The user interactions include inputs to the computer program to search for problem tickets issued in the IT environment that have the same characteristics. One or more user interaction patterns within the user interactions are recognized. A user interaction pattern of the one or more user interaction patterns is selected based on an evaluation of effectiveness of each of the one or more user interaction patterns. Based on the user interaction pattern, a rule is generated for determining which problem tickets in the IT environment share a common characteristic. The rule is applied to additional problem tickets issued in the IT environment to identify which of the additional problem tickets share the common characteristic.
Abstract:
Common sub-process patterns in a plurality of deployed process models may be discovered, and performance measures associated with the sub-process patterns may be computed based on runtime events of the deployed process models. Positive or negative performance patterns among sub-process patterns may be identified and used for creating new process models or improving existing process models.
Abstract:
Business processes that may be affected by events, conditions or circumstances that were unforeseen or undefined at modeling time (referred to as unforeseen events) are modeled and/or executed. Responsive to an indication of such an event during process execution, a transfer is performed from the process, in which selected data is stored and the process is terminated. The selected data may then be used by a target process. The target process may be, for instance, a new version of the same process, the same process or a different process. The target process may or may not have existed at the time the process was deployed.
Abstract:
Techniques for dispatching one or more services requests to one or more agents are provided. The techniques include obtaining one or more attributes of each service request, obtaining one or more attributes of each agent, obtaining feedback from each of one or more agent queues, and using the one or more attributes of each service request, the one or more attributes of each agent and the feedback from each of the one or more agent queues to determine one or more suitable agents to receive a dispatch for each of the one or more service requests. Techniques are also provided for generating a database of one or more attributes of one or more service requests and one or more attributes of one or more agents.
Abstract:
An approach for validating a model is presented. Data from a system being modeled is collected. First and second models of the system are constructed from the collected data. Based on the first model, a first determination of an aspect of the system is determined. Based on the second model, a second determination of the aspect of the system is determined. A variation between the first and second determinations is determined. An input for resolving the variation is received and in response, a model of the system that reduces the variation is derived.
Abstract:
Techniques for dispatching one or more services requests to one or more agents are provided. The techniques include obtaining one or more attributes of each service request, obtaining one or more attributes of each agent, obtaining feedback from each of one or more agent queues, and using the one or more attributes of each service request, the one or more attributes of each agent and the feedback from each of the one or more agent queues to determine one or more suitable agents to receive a dispatch for each of the one or more service requests. Techniques are also provided for generating a database of one or more attributes of one or more service requests and one or more attributes of one or more agents.