Abstract: A data validation service includes providing a user interface to a subscriber of the service via a computer device of the subscriber, receiving a data validation rule specified by the subscriber and an address of a database subject to the data validation, and generating a configuration file that includes the address of the database. The service also includes transmitting the configuration file and a thin client application to the computer device over a network, the thin client application configured to read the configuration file and pull data from the database. The service further includes receiving the data from the computer device via the network, performing the data validation by executing the data validation rule with respect to the data, and compiling results of the data validation and providing the results to the computer device.

Abstract: A data validation service includes providing a user interface to a subscriber of the service via a computer device of the subscriber, receiving, via the user interface, a data validation rule specified by the subscriber and an address of a database subject to the data validation, and generating a configuration file that includes the address of the database and an address of a location of executable code corresponding to the data validation rule. The data validation service also includes transmitting the configuration file and remote methods to the computer device over the network. The remote methods are configured to execute the data validation rule with respect to the data and compile results of the execution.

Abstract: A method, system and computer program product are disclosed for modeling the temporal behavior of clients to develop a predictive system to influence client relationships. In an embodiment, the method comprises establishing for each of a plurality of clients a temporal model for a given time period, and the temporal model identifies a plurality of factors as contributing to a specified relationship with the each client over the given time period. For each of a plurality of different stages of the given time period, the temporal model identifies one or more of these factors as contributing to the specified relationship with the each client. One of the clients is identified as a model client for another client, and the temporal model of this model client is used to predict one or more of the plurality of factors as contributing to a specified relationship with this another client at a specified time.

Abstract: A plurality of application profiles are obtained, for a plurality of applications. Each of the profiles specifies a list of resources, and requirements for each of the resources, associated with a corresponding one of the applications. Specification of a plurality of constraints associated with the applications is facilitated, as is obtaining a plurality of cost models associated with at least two different kinds of servers on which the applications are to run. A recommended server configuration is generated for running the applications, by formulating and solving a bin packing problem. Each of the at least two different kinds of servers is treated as a bin of a different size, based on its capacity, and has an acquisition cost associated therewith. The size is substantially equal to a corresponding one of the resource requirement as given by a corresponding one of the application profiles. Each of the applications is treated as an item, with an associated size, to be packed into the bins.

Abstract: To predict edges in a non-cumulative graph, based on a sequence of non-cumulative previous occurrences of the graph, the sequence of non-cumulative previous occurrences of the graph is obtained, and topological properties of each of the previous occurrences of the graph are extracted. Topological similarities between each of the previous occurrences of the graph are computed based on the topological properties. The edges of the graph are predicted by computing a score for each possible edge of the graph, based upon the topological similarities.

Abstract: Methods and arrangements for prioritizing customer service restoration, in the event of service failure or compromise such that any adverse effect of the service disruption on the customer is minimized, the perceived drop in quality of service, if any, is minimized and timely and efficient resource reallocation for service restoration is achieved. Input data relating to customer service protocols is assimilated. A service restoration order is implemented responsive to a service disruption and based on the assimilated input data. This implementing includes determining bufferable and non-bufferable services, postponing restoration of the bufferable services, and determining an order of priority of the non-bufferable services.

Abstract: A plurality of application profiles are obtained, for a plurality of applications. Each of the profiles specifies a list of resources, and requirements for each of the resources, associated with a corresponding one of the applications. Specification of a plurality of constraints associated with the applications is facilitated, as is obtaining a plurality of cost models associated with at least two different kinds of servers on which the applications are to run. A recommended server configuration is generated for running the applications, by formulating and solving a bin packing problem. Each of the at least two different kinds of servers is treated as a bin of a different size, based on its capacity, and has an acquisition cost associated therewith. The size is substantially equal to a corresponding one of the resource requirement as given by a corresponding one of the application profiles. Each of the applications is treated as an item, with an associated size, to be packed into the bins.

Abstract: A plurality of application profiles are obtained, for a plurality of applications. Each of the profiles specifies a list of resources, and requirements for each of the resources, associated with a corresponding one of the applications. Specification of a plurality of constraints associated with the applications is facilitated, as is obtaining a plurality of cost models associated with at least two different kinds of servers on which the applications are to run. A recommended server configuration is generated for running the applications, by formulating and solving a bin packing problem. Each of the at least two different kinds of servers is treated as a bin of a different size, based on its capacity, and has an acquisition cost associated therewith. The size is substantially equal to a corresponding one of the resource requirement as given by a corresponding one of the application profiles. Each of the applications is treated as an item, with an associated size, to be packed into the bins.

Abstract: At least a first future external event is obtained as input. At least one future effect of the at least one future external event is predicted. The at least first future external event is similar to at least a first past external event. Future utilization of at least one tangible resource is forecast by superimposing events-normalized forecasted data and the at least one predicted future effect of the at least one future event similar to the at least first past external event.

Abstract: Techniques for scheduling one or more services are provided. The techniques include obtaining customer information, obtaining one or more service characteristics for each of the one or more services, and using the customer information and the one or more service characteristics to schedule the one or more services.

Abstract: A plurality of application profiles are obtained, for a plurality of applications. Each of the profiles specifies a list of resources, and requirements for each of the resources, associated with a corresponding one of the applications. Specification of a plurality of constraints associated with the applications is facilitated, as is obtaining a plurality of cost models associated with at least two different kinds of servers on which the applications are to run. A recommended server configuration is generated for running the applications, by formulating and solving a bin packing problem. Each of the at least two different kinds of servers is treated as a bin of a different size, based on its capacity, and has an acquisition cost associated therewith. The size is substantially equal to a corresponding one of the resource requirement as given by a corresponding one of the application profiles. Each of the applications is treated as an item, with an associated size, to be packed into the bins.

Abstract: A method and system are disclosed for planning a workforce headcount for a given business process. The method comprises the steps of providing as inputs, i) productivity ramp-ups to model the level of experience and to measure the performance of both new hires and current employees, and ii) industry/market attrition rates for employees; and performing an evaluation, using said inputs, of at least one given management objective. On the basis of this evaluation, a future hiring and transition plan is provided for the given business process for a defined period of time. In the preferred embodiment of the invention, uncertainty is associated with one or more of the inputs, and the future hiring and transition plan is provided by using stochastic programming to model the uncertainty associated with at least one of said one or more of the inputs.

Abstract: To predict edges in a non-cumulative graph, based on a sequence of non-cumulative previous occurrences of the graph, the sequence of non-cumulative previous occurrences of the graph is obtained, and topological properties of each of the previous occurrences of the graph are extracted. Topological similarities between each of the previous occurrences of the graph are computed based on the topological properties. The edges of the graph are predicted by computing a score for each possible edge of the graph, based upon the topological similarities.

Abstract: To predict edges in a non-cumulative graph, based on a sequence of non-cumulative previous occurrences of the graph, the sequence of non-cumulative previous occurrences of the graph is obtained, and topological properties of each of the previous occurrences of the graph are extracted. Topological similarities between each of the previous occurrences of the graph are computed based on the topological properties. The edges of the graph are predicted by computing a score for each possible edge of the graph, based upon the topological similarities.