Abstract: A method for providing a forecast includes providing a database storing data at a lowest level in a first dimension, calculating a first forecast at a level that is higher than the lowest level of a first dimension in the database, calculating a forecast for each category within the lowest level of the first dimension, aggregating a second forecast across a category at the lowest level of the first dimension based upon an aggregation of the calculated forecast for the category within the lowest level of the first dimension, determining a difference between the first forecast and the second forecast, and creating a dummy category including a new category at the lowest level of the first dimension.

Abstract: A method for providing a forecast includes providing a multi-dimensional database storing data at a lowest level in a first dimension, calculating a first forecast at a level that is higher than the lowest level of a first dimension in the database, calculating a forecast for each category within the lowest level of the first dimension, aggregating a second forecast across a category at the lowest level of the first dimension based upon an aggregation of the calculated forecast for the category within the lowest level of the first dimension, determining a difference between the first forecast and the second forecast, creating a dummy category including a new category at the lowest level of the first dimension, and storing the difference in the dummy category at the lowest level of the first dimension.

Abstract: A method for providing a forecast includes providing a multi-dimensional database storing data at a lowest level in a first dimension, calculating a first forecast at a level that is higher than the lowest level of a first dimension in the database, calculating a forecast for each category within the lowest level of the first dimension, aggregating a second forecast across a category at the lowest level of the first dimension based upon an aggregation of the calculated forecast for the category within the lowest level of the first dimension, determining a difference between the first forecast and the second forecast, creating a dummy category including a new category at the lowest level of the first dimension, and storing the difference in the dummy category at the lowest level of the first dimension.

Abstract: A method (and system) for providing a forecast, the method including providing a multi-dimensional database storing data at a lowest level in a first dimension, calculating a first forecast at a level that is higher than the lowest level of a first dimension in the database, calculating a forecast for each category within the lowest level of the first dimension, aggregating a second forecast across all categories at the lowest level of the first dimension based upon an aggregation of the calculated forecasts for each category within the lowest level of the first dimension, determining a difference between the first forecast and the second forecast, and storing the difference in a dummy category at the lowest level of the first dimension.

Abstract: In an approach to revenue forecasting, one or more computer processors retrieve revenue forecast data. The one or more computer processors process the revenue forecast data through one or more revenue forecast models. The one or more computer processors retrieve one or more weights associated with a performance of the one or more revenue forecast models, where each of the one or more weights corresponds to one of the one or more revenue forecast models. The one or more computer processors apply one of the one or more weights associated with the performance of the one or more revenue forecast models to each of a corresponding processed revenue forecast data. The one or more computer processors compute a weighted average revenue forecast.

Abstract: Embodiments include a data based methodology for projecting cumulative shipments of a product throughout the balance of its life cycle. Critical milestones are defines, and are employed to break a product life cycle into manageable segments. A double base line curve is employed, with a first baseline curve representing shipment of a predecessor product prior to announcement of the new product and a second baseline curve representing shipment of the predecessor product after announcement of the new product. The curves are used as a reference point with a product analysis to statistically forecast an end of life demand for the predecessor product.

Abstract: Embodiments include a data based methodology for projecting cumulative shipments of a product throughout the balance of its life cycle. Critical milestones are defines, and are employed to break a product life cycle into manageable segments. A double base line curve is employed, with a first baseline curve representing shipment of a predecessor product prior to announcement of the new product and a second baseline curve representing shipment of the predecessor product after announcement of the new product. The curves are used as a reference point with a product analysis to statistically forecast an end of life demand for the predecessor product.

Abstract: Techniques for monitoring and predicting environmental operating conditions in a data center are provided. In one aspect, a method for real-time, three-dimensional analysis of environmental operating conditions in a data center includes the following steps. High spatial resolution three-dimensional measurements of one or more environmental variables in the data center made at a time t1 are obtained. Real-time measurements of the environmental variables in the data center made at a time t2, wherein t2 is later in time than t1, are obtained. The high spatial resolution three-dimensional measurements are combined with the real-time measurements to derive a model for the environmental variables in the data center at the time t2. The model is used to predict three-dimensional distributions of the environmental variables in the data center at the time t2. A base model can be created and used to derive the model for the data center at the time t2.

Abstract: A method and structure for predicting traffic on a network, includes a receiver which receives data related to traffic on at least a portion of a network. A calculator calculates a traffic prediction for at least a part of the network, the traffic prediction being calculated by using a deviation from a historical traffic on the network.

Abstract: Products include autonomic logic to self-monitor one or more parameters associated with the products to provide product usage information. Data relating to the product usage information generated by the products is automatically disseminated to interested entities. The interested entities may analyze the provided data and automatically forward information relating to the provided data back towards the products. The interested entities may also effect changes to products based on the data.

Abstract: Techniques for monitoring and predicting environmental operating conditions in a data center are provided. In one aspect, a method for real-time, three-dimensional analysis of environmental operating conditions in a data center includes the following steps. High spatial resolution three-dimensional measurements of one or more environmental variables in the data center made at a time t1 are obtained. Real-time measurements of the environmental variables in the data center made at a time t2, wherein t2 is later in time than t1, are obtained. The high spatial resolution three-dimensional measurements are combined with the real-time measurements to derive a model for the environmental variables in the data center at the time t2. The model is used to predict three-dimensional distributions of the environmental variables in the data center at the time t2. A base model can be created and used to derive the model for the data center at the time t2.

Abstract: A method (and system) for providing a forecast, the method including providing a multi-dimensional database storing data at a lowest level in a first dimension, calculating a first forecast at a level that is higher than the lowest level of a first dimension in the database, calculating a forecast for each category within the lowest level of the first dimension, aggregating a second forecast across all categories at the lowest level of the first dimension based upon an aggregation of the calculated forecasts for each category within the lowest level of the first dimension, determining a difference between the first forecast and the second forecast, and storing the difference in a dummy category at the lowest level of the first dimension.

Abstract: A method and structure for predicting traffic on a network, includes a receiver which receives data related to traffic on at least a portion of a network. A calculator calculates a traffic prediction for at least a part of the network, the traffic prediction being calculated by using a deviation from a historical traffic on the network.

Abstract: An embodiment of the present invention proposes to describe an enterprise or company in terms of its structure and represent that structure in performing revenue forecasts for the enterprise or company. Mapping the company structure in a multi-dimensional matrix, for example, can represent that structure. The revenue forecasting method is novel in that forecasts for any level of the enterprise or company make use of data and previous forecasts for that and other elements of the structure. In this way, the method improves upon existing methods by leveraging information contained in some data on other data, and learning the relations between them.

Abstract: Storage media of a storage system are grouped into one or more groups based upon parameters associated with the storage media. Various parameters may be taken into consideration depending upon the criteria selected for the grouping. At least one grouping criterion is selected, and based on the grouping criterion, one or more parameters are measured. Then, using the measured data, a plurality of storage media are placed into one or more groups. The groups may then be used to form Redundant Array of Independent Disks (RAID) arrays employed in the storage system.

Abstract: Products include autonomic logic to self-monitor one or more parameters associated with the products to provide product usage information. Data relating to the product usage information generated by the products is automatically disseminated to interested entities. The interested entities may analyze the provided data and automatically forward information relating to the provided data back towards the products. The interested entities may also effect changes to products based on the data.

Abstract: Storage media of a storage system are grouped into one or more groups based upon parameters associated with the storage media. Various parameters may be taken into consideration depending upon the criteria selected for the grouping. At least one grouping criterion is selected, and based on the grouping criterion, one or more parameters are measured. Then, using the measured data, a plurality of storage media are placed into one or more groups. The groups may then be used to form Redundant Array of Independent Disks (RAID) arrays employed in the storage system.