A method and system for ascertaining warranty issues associated with transportation products is provided. Transportation products are manufactured at a factory, and as the products are made, historical information concerning significant events related to the manufacturing process may be stored. As the transportation product is delivered to a retail outlet, other significant event information may be stored. Locations are determined within a region for warranty claims related to the servicing of selected transportation products. Product history information for the selected transportation products may be retrieved from one or more databases. Data mining algorithms may be employed to generate input data for forming a set of spatial relationships. The locations of products within the region are then associated with the product history information to form a set of spatial relationships. Product history information may include such information as purchaser address information, information related to manufacture of the selected transportation products, information related to transportation of the selected transportation products from a manufacturing site to a retail outlet, etc.
CROSS REFERENCE TO RELATED APPLICATIONS
The present invention is related to the following applications entitled "METHOD AND SYSTEM FOR INTEGRATING SPATIAL ANALYSIS AND DATA MINING ANALYSIS TO ASCERTAIN FAVORABLE POSITIONING OF PRODUCTS IN A RETAIL ENVIRONMENT", U.S. application Ser. No. 09/400,583; and "METHOD AND SYSTEM FOR INTEGRATING SPATIAL ANALYSIS AND DATA MINING ANALYSIS TO ASCERTAIN RELATIONSHIPS BETWEEN COLLECTED SAMPLES AND GEOLOGY WITH REMOTELY SENSED DATA", U.S. application Ser. No. 09/400,776; all of which are filed even date hereof, assigned to the same assignee, and incorporated herein by reference.
A method for analyzing vehicle information. The method utilizes a multi-dimensional relational database or a data cube and On Line Analytical Processing technology to integrate information which is acquired from various different sources and allows all of the data to be analyzed in an efficient, concise and unambiguous manner.
A computing system and method for selecting parameters for a data mining modeling algorithm. The computing system comprises a computer readable medium and computing devices electrically coupled through an interface apparatus. A data mining modeling algorithm and test data are stored on the computer readable medium. Each of the computing devices comprises a data subset from the a plurality of data subsets. The data mining modeling algorithm is distributed simultaneously using a selected technique to each of the computing devices. An associated parameter setting for each data mining modeling algorithm in each of the computing devices is adjusted simultaneously. Each associated parameter setting comprises a different parameter setting. Each data mining modeling algorithm comprising the associated parameter setting is run simultaneously to generate an associated data mining model on each of the computing devices. A data mining modeling algorithm comprising a best parameter setting is determined.
An automated employee selection system can use a variety of techniques to provide information for assisting in selection of employees. For example, pre-hire and post-hire information can be collected electronically and used to build an artificial-intelligence based model. The model can then be used to predict a desired job performance criterion (e.g., tenure, number of accidents, sales level, or the like) for new applicants. A wide variety of features can be supported, such as electronic reporting. Pre-hire information identified as ineffective can be removed from a collected pre-hire information. For example, ineffective questions can be identified and removed from a job application. New items can be added and their effectiveness tested. As a result, a system can exhibit adaptive learning and maintain or increase effectiveness even under changing conditions.
An automated employee selection system can use a variety of techniques to provide information for assisting in selection of employees. For example, pre-hire and post-hire information can be collected electronically and used to build an artificial-intelligence based model. The model can then be used to predict a desired job performance criterion (e.g., tenure, number of accidents, sales level, or the like) for new applicants. A wide variety of features can be supported, such as electronic reporting. Pre-hire information identified as ineffective can be removed from a collected pre-hire information. For example, ineffective questions can be identified and removed from a job application. New items can be added and their effectiveness tested. As a result, a system can exhibit adaptive learning and maintain or increase effectiveness even under changing conditions.
An automated employee selection system can use a variety of techniques to provide information for assisting in selection of employees. For example, pre-hire and post-hire information can be collected electronically and used to build an artificial-intelligence based model. The model can then be used to predict a desired job performance criterion (e.g., tenure, number of accidents, sales level, or the like) for new applicants. A wide variety of features can be supported, such as electronic reporting. Pre-hire information identified as ineffective can be removed from a collected pre-hire information. For example, ineffective questions can be identified and removed from a job application. New items can be added and their effectiveness tested. As a result, a system can exhibit adaptive learning and maintain or increase effectiveness even under changing conditions.
