Abstract: Systems, products, and methods are disclosed for improving the accuracy of predictions. Possible values of an output variable can be generated based on past values and possible values of input variables and a model. Multiple scenarios can be run, each of which may vary in many factors, such as the model used and the input variables used. Results from multiple scenarios can be presented to a user. Prediction accuracy can be improved through selection of one or more desirable scenarios.

Abstract: Techniques to search for data elements in a distributed computing environment are described. An apparatus may comprise a processor and a memory unit communicatively coupled to the processor. The memory unit may store a correlation module that when executed by the processor is operative to determine a target rank position at a target percentile rank within a total data set. The correlation module may determine a target data item at the target rank position for the total data set using candidate data items at candidate rank positions for each of multiple sorted data subsets of the total data set, and correlation values associated with each of the candidate data items. Other embodiments are described and claimed.

Abstract: Systems and methods are provided for a grid computing system that performs analytical calculations on data stored in a distributed file system. A grid-enabled software component at a control node is configured to invoke the distributed file system software at the control node to provide block locations for a plurality of the data blocks; determine a configuration of data blocks to read at the plurality of worker nodes; instruct the grid-enabled software components at the plurality of worker nodes to retrieve locally stored data, perform an analytical calculation on the retrieved data, and send the results of the analytical calculation on the retrieved data to the grid-enabled software component at the control node; and assemble the results of the analytical calculations performed by the grid-enabled software components from the plurality of worker nodes.

Abstract: Computer-implemented systems and methods are provided for implementing a scenario analysis manager that performs multiple scenarios based upon time series data that is representative of transactional data are provided. A system and method provides candidate predictive models for a first scenario for selection where the set of candidate predictive models includes an identification of variables associated with a model. Model selection data is received from a scenario analysis manager where a selected model is configured to predict a future value of a first variable based on values of a second variable. Time series data is received representative of past transaction activity of the first variable and the second variable, and data representative of a future value of the second variable is also received. The future value of the first variable is determined using the selected model, the time-series data and the future value of the second variable.

Abstract: Techniques to search for data elements in a distributed computing environment are described. An apparatus may comprise a processor and a memory unit communicatively coupled to the processor. The memory unit may store a correlation module that when executed by the processor is operative to determine a target rank position at a target percentile rank within a total data set. The correlation module may determine a target data item at the target rank position for the total data set using candidate data items at candidate rank positions for each of multiple sorted data subsets of the total data set, and correlation values associated with each of the candidate data items. Other embodiments are described and claimed.

Abstract: Computer-implemented systems and methods are provided for implementing a scenario analysis manager that performs multiple scenarios based upon time series data that is representative of transactional data are provided. A system and method provides candidate predictive models for a first scenario for selection where the set of candidate predictive models includes an identification of variables associated with a model. Model selection data is received from a scenario analysis manager where a selected model is configured to predict a future value of a first variable based on values of a second variable. Time series data is received representative of past transaction activity of the first variable and the second variable, and data representative of a future value of the second variable is also received. The future value of the first variable is determined using the selected model, the time-series data and the future value of the second variable.