Abstract: To efficiently retain data online, an instance of a data set may be generated. The instance may have a set of data fields with corresponding data values. The instance also may be searchable in response to a data selection request. Another instance may be generated, which also may have a set of data fields with corresponding data values, and which also may be searchable in response to a data selection request. The two instances may each use its own blueprint to normalize data, to perform searches and to return search results. Both of the instances may be stored online such that they are stored independently but are accessible jointly.

Abstract: Address information is analyzed and ranked to provide a relative indication of the reliability of a particular address. The system and method utilized, perform modeling of address data, which produces a model that can be applied to a particular address. A resulting score is generated for each address discovered relating to a particular individual or entity. Using these scores, multiple addresses can then be ranked, to determine which address is most likely to be accurate and reliable.

Abstract: To efficiently retain data online, an instance of a data set may be generated. The instance may have a set of data fields with corresponding data values. The instance also may be searchable in response to a data selection request. Another instance may be generated, which also may have a set of data fields with corresponding data values, and which also may be searchable in response to a data selection request. The two instances may each use its own blueprint to normalize data, to perform searches and to return search results. Both of the instances may be stored online such that they are stored independently but are accessible jointly.

Abstract: To efficiently retain data online, an instance of a data set may be generated. The instance may have a set of data fields with corresponding data values. The instance also may be searchable in response to a data selection request. Another instance may be generated, which also may have a set of data fields with corresponding data values, and which also may be searchable in response to a data selection request. The two instances may each use its own blueprint to normalize data, to perform searches and to return search results. Both of the instances may be stored online such that they are stored independently but are accessible jointly.

Abstract: To provide efficient and effective modeling of data set, the data set is initially separated into several subsets which can then be processed independently. The subsets themselves are chosen to have some internal commonality, thus providing effective independent tools where possible. This commonality may include correlation between variables or interaction amongst the variables in the subset. Once separated, each subset is independently modeled, creating a subset model having predictive qualities related to the data subset. Next, the subset models themselves are aggregated to generate a overall final model. This final model is predictive of outcomes based upon all data in the data set, thus providing a more robust stable model.

Abstract: The system and process used for modeling of data sets is improved by achieving one pass modeling which proactively anticipates issues with the model and deals with these issues prior to model formation. The anticipated issues include those involving offending variables, which are initially identified and eliminated so as to avoid any further contribution by those variables. Once offending variables are eliminated, the process then deals with variables having only minimal contributions. To create a simplified and more effective model, these minimal contributors are then eliminated before completion of the model.