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A system, method and computer program product for developing an entity context frame or situation summary before using said context frame/situation summary to develop an index, perform a context search and return prioritized results. The search results may comprise a plurality of health related data

A system, method and computer program product for developing an entity context frame or situation summary before using said context frame/situation summary to develop an index, perform a context search and return prioritized results. The search results may comprise a plurality of health related data where said health related data comprises a plurality of microbiome data.

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1. A non-transitory computer-readable medium having computer-executable instructions stored thereon that, if executed by a computing device, cause the computing device to perform operations comprising: obtaining a subject entity definition of a subject entity, a node depth criteria and an impact cut

1. A non-transitory computer-readable medium having computer-executable instructions stored thereon that, if executed by a computing device, cause the computing device to perform operations comprising: obtaining a subject entity definition of a subject entity, a node depth criteria and an impact cutoff criteria;aggregating and preparing a plurality of data items that include data related to the subject entity for processing, wherein the data comprises at least one entity function, one or more entity function measures and a creation date for each of the plurality of data items;storing the aggregated plurality of data items in one or more context layers by a component of context;developing a subject entity situation summary by analyzing the subject entity related data, wherein the subject entity situation summary comprises a linear or nonlinear regression model of each of the one or more entity function measures, a relevance for each of the measures and one or more of the context layers;using the subject entity situation summary, the node depth criteria and the impact cutoff criteria to identify components of context to include in a composite index;creating a composite index for the data associated with the identified components of context, wherein the composite index comprises a column for the creation dates of the plurality of data items, a column for each of the identified components of context and a ranking for each of the plurality of data items of the composite index;receiving a search request; andproviding a plurality of search results in response to the search request, wherein the plurality of search results are prioritized using a weight comprised of a mathematical combination of an index position ranking and a ranking provided by a relevance measure. 2. The non-transitory computer-readable medium of claim 1, wherein the plurality of search results comprise a plurality of health related data, wherein the health related data comprises a plurality of microbiome data. 3. The non-transitory computer-readable medium of claim 1, wherein the subject entity is selected from the group consisting of: team, group, department, division, company, organization or multi-entity organization. 4. The non-transitory computer-readable medium of claim 1, wherein the subject entity situation summary comprises a context frame, and wherein the operations further comprise: developing a prioritized context frame that comprises of the identified components of context that meet the impact cutoff and node depth criteria specified by the user; andproviding one or more applications that use the prioritized context frame to adapt to and manage a performance situation for the subject entity, wherein the one or more applications are selected from the group consisting of: benefit plan analysis, customization, database, display, exchange, forecast, metric development, optimization, planning, profile development, review, rule development, summary, sustainability forecast and wellness program optimization. 5. The non-transitory computer-readable medium of claim 1, wherein the search request is received from a browser. 6. The non-transitory computer-readable medium of claim 1, wherein the relevance measure is selected from the group consisting of: cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores, and wherein reinforcement learning determines which relevance measure is selected. 7. The non-transitory computer-readable medium of claim 1, wherein the search request comprises one or more keywords or a question, wherein the search request is received from a natural language interface or an anticipated need for data automatically initiates the search request. 8. The non-transitory computer-readable medium of claim 1, wherein the one or more context layers are stored in a database that automatically captures and incorporates any changes in a performance situation of the subject entity. 9. The non-transitory computer-readable medium of claim 1, wherein the computing device comprises at least one processor in a computer, at least one processor in a mobile access device or a combination thereof. 10. The non-transitory computer-readable medium of claim 1, wherein the linear or nonlinear regression model is developed using automated learning, and wherein the automated learning comprises: completing a multi-stage process, wherein each stage of the multi-stage process comprises an automated selection of an output from a plurality of outputs produced by a plurality of modeling algorithms after processing at least part of the data, wherein linearity of the linear or nonlinear regression model is determined by learning from the data, and wherein the plurality of modeling algorithms are selected from the group consisting of: neural network; classification and regression tree; generalized autoregressive conditional heteroskedasticity; projection pursuit regression; generalized additive model; linear regression, path analysis; Bayesian; multivariate adaptive regression spline and support vector method. 11. A method, comprising: using a computer and a mobile access device to complete processing comprising:obtaining a subject entity definition of a subject entity, a node depth criteria and an impact cutoff criteria;aggregating and preparing a plurality of data items that include data related to the subject entity for processing, wherein the data comprises at least one entity function, one or more entity function measures and a creation date for each of the plurality of data items;storing the aggregated plurality of data items in one or more context layers by a component of context;developing a subject entity situation summary by analyzing the subject entity related data, wherein the summary comprises a linear or nonlinear regression model of each of the one or more entity function measures, a relevance for each of the measures and one or more of the context layers;using the subject entity situation summary, the node depth criteria and the impact cutoff criteria to identify components of context to include in a composite index;creating a composite index for the data associated with the identified components of context, wherein the composite index comprises a column for the creation dates of the plurality of data items, a column for each of the identified components of context and a ranking for each of the plurality of data items of the composite index;receiving a search request; andproviding a plurality of search results in response to the search request, wherein the plurality of search results are prioritized using a weight comprised of a mathematical combination of an index position ranking and a ranking provided by a relevance measure, wherein the subject entity physically exists, and wherein the subject entity situation summary supports a graphical display of a relative contribution of one or more drivers to the one or more entity function measures. 12. The method of claim 11, wherein the plurality of search results comprise a plurality of health related data, wherein the health related data comprises a plurality of microbiome data. 13. The method of claim 11, wherein the one or more context layers are selected from the group consisting of: Physical, Tactical, Organization, Social Environment and combinations thereof when the subject entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization and has a single financial or a single non-financial function, and wherein the one or more context layers are selected from the group consisting of: Element, Environment, Resource, Reference Frame, Relationship, Transaction and combinations thereof when the subject entity is selected from the group consisting of team, group, department, division, company, organization or multi-entity organization and has two or more functions and when the subject entity is not a member of the group consisting of team, group, department, division, company, organization or multi-entity organization. 14. The method of claim 11, wherein the subject entity situation summary comprises a context frame and wherein the method further comprises: developing a prioritized context frame that comprises the identified components of context that meet the impact cutoff and node depth criteria; andproviding one or more applications that use the prioritized context frame to adapt to and manage a performance situation for the subject entity, wherein the one or more applications are selected from the group consisting of: benefit plan analysis, customization, database, display, exchange, forecast, metric development, optimization, planning, profile development, review, rule development, summary, sustainability forecast and wellness program optimization. 15. The method of claim 11, wherein the search request is received from a browser. 16. The method of claim 11, wherein the relevance measure is selected from the group consisting of: cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores, and wherein reinforcement learning determines which relevance measure is selected. 17. The method of claim 11, wherein the search request comprises one or more keywords or a question, and wherein the search request is received from a natural language interface or an anticipated need for data automatically initiates the search request. 18. The method of claim 11, wherein the one or more context layers are stored in a database that automatically captures and incorporates any changes in a performance situation of the subject entity. 19. A system, comprising: a computing device and a storage device having computer-executable instructions stored therein which, if executed by the computing device, cause the computing device to perform operations comprising:obtaining a subject entity definition of a subject entity, a node depth criteria and an impact cutoff criteria;aggregating and preparing a plurality of data items that include data related to the subject entity for processing, wherein the data comprises at least one entity function, one or more entity function measures and a creation date for each of the plurality of data items;storing the aggregated plurality of data items in one or more context layers by a component of context;developing a subject entity situation summary by analyzing the subject entity related data, wherein the subject entity situation summary comprises a linear or nonlinear regression model of each of the one or more entity function measures, a relevance for each of the measures and one or more of the context layers;using the subject entity situation summary, the node depth criteria and the impact cutoff criteria to identify components of context to include in a composite index;creating a composite index for the data associated with the identified components of context, wherein the composite index comprises a column for the creation dates of the plurality of data items, a column for each of the identified components of context and a ranking for each of the plurality of data items of the composite index;receiving a search request from a mobile access device, andproviding a plurality of search results in response to the search request, wherein the plurality of search results are prioritized using a weight comprised of a mathematical combination of an index position ranking and a ranking provided by a relevance measure, and wherein at least part of the data and the search request are obtained from a mobile device. 20. The system of claim 19, wherein the linear or nonlinear regression model is developed using automated learning, and wherein the automated learning comprises: completing a multi-stage process, wherein each stage of the multi-stage process comprises an automated selection of an output from a plurality of outputs produced by a plurality of modeling algorithms after processing at least part of the data, wherein linearity of the linear or nonlinear regression model is determined by learning from the data, and wherein the plurality of modeling algorithms are selected from the group consisting of: neural network; classification and regression tree; generalized autoregressive conditional heteroskedasticity; projection pursuit regression; generalized additive model; linear regression, path analysis; Bayesian; multivariate adaptive regression spline and support vector method. 21. The system of claim 19, wherein the plurality of search results comprise a plurality of health related data, and wherein the health related data comprises a plurality of microbiome data. 22. The system of claim 19, wherein the subject entity is selected from the group consisting of: team, group, department, division, company, organization or multi-entity organization. 23. The system of claim 19, wherein the subject entity situation summary comprises a context frame, and wherein the operations further comprise: developing a prioritized context frame that comprises the identified components of context that meet the impact cutoff and node depth criteria; andproviding one or more applications that use the prioritized context frame to adapt to and manage a performance situation for the subject entity, wherein the one or more applications are selected from the group consisting of: benefit plan analysis, customization, database, display, exchange, forecast, metric development, optimization, planning, profile development, review, rule development, summary, sustainability forecast and wellness program optimization. 24. The system of claim 19, wherein the search request is received from a browser. 25. The system of claim 19, wherein the relevance measure is selected from the group consisting of: cover density rankings, vector space model measurements, okapi similarity measurements, three level relevance scores and hypertext induced topic selection algorithm scores, and wherein reinforcement learning determines which relevance measure is selected. 26. The system of claim 19, wherein the search request comprises one or more keywords or a question, and wherein the search request is received from a natural language interface or an anticipated need for data automatically initiates the search request. 27. The system of claim 19, wherein the one or more context layers are stored in a database that automatically captures and incorporates any changes in a performance situation of the subject entity. 28. The system of claim 19, wherein the computing device includes at least one processor in a mobile access device or a combination thereof.