The embodiments herein provide a method and process for storing and provisioning system catalogue(s) of metadata object relationships, from diverse and disparate physical data elements. The method comprises the steps of creating a logical enterprise data warehouse by assembling a plurality of nanomart storage structures; visually mashingup a plurality of interconnected data elements from the plurality of nanomart storage structures to produce a business analytics; and generating a neural metadata framework for providing a mathematical model to abstract and catalogue the plurality of data elements from the plurality of nanomart storage structures.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for storing and provisioning system catalogue(s) of metadata object relationships, from diverse and disparate physical data elements, the method comprises the steps of: creating a logical enterprise data warehouse by assembling a plurality of nanomart storage structures; visually mashingup a plurality of interconnected data elements from the plurality of nanomart storage structures to produce a business analytics; and generating a neural metadata framework for providing a mathematical model to abstract and catalogue the plurality of data elements from the plurality of nanomart storage structures. 2. The method according to claim 1, wherein the nanomart storage structure comprises a set of measures and a set of dimensions, and wherein the set of dimensions comprises dimension levels and dimension members. 3. The method according to claim 1, wherein the measures defines a measurable parameter for an entity. 4. The method according to claim 1, wherein the dimensions defines a sub-measurable parameter for the measures. 5. The method according to claim 1, wherein the dimension levels define a sub level in a dimension, and wherein the dimension level comprises a set of dimension members. 6. The method according to claim 1 further comprises the steps of: applying a plurality of algorithms for identifying an interconnected relationship of data elements; adopting a machine learning process to acquire a common relationship of from a plurality of the identified interconnected relationship of the data elements; performing a data mining to identify a statistical correlation between the data elements; and computing a measure of strength of interconnections between the data elements by a preset algorithm. 7. The method according to claim 1, wherein the neural metadata framework stores and provisions a neural metadata from a plurality of sources of discrete data elements, and wherein the discrete data elements are a plurality of unorganized data curated from a plurality of data stores that are arranged internal or external to a referenced organization or entity. 8. The method according to claim 1, wherein the data elements are abstracted and catalogued from a plurality of data sources distributed within a referenced organization or entity, and wherein the abstracted and catalogued data elements form a neural metadata bases on an inter-relationship or interconnection between the data elements. 9. The method according to claim 1, wherein the neural metadata is configured to select a desired and suitable discrete data elements stored within the nanomart storage structure. 10. The method according to claim 1, wherein the data elements are abstracted and catalogued using a mathematical model based on a business glossary, and wherein the business glossary comprises a plurality of representations of values and codes stored in the plurality of nanomart storage structures. 11. The method according to claim 1, wherein the neural metadata framework is architecture built with a plurality of artificial intelligence modules, and wherein the plurality of artificial intelligence modules defines relationships between the data elements for storing and provisioning the neural metadata. 12. The method according to claim 1, wherein the plurality of nanomart storage structures is a physical storage device of related and unrelated data derived from a plurality of files, technical metadata and direct upload of data through connectors from web based on a mathematical model using the business glossary. 13. The method according to claim 1, wherein the plurality of algorithms is applied to identify a type of interconnected relationship between the interconnected data elements, by estimating an inter-relationship of a construct, and wherein the inter-relationship of the construct is identified based on the stored explicit human inputs, and implicit derivations and calculations of the cross-correlations among the data elements in the plurality of nanomart storage structures. 14. The method according to claim 1, wherein the explicit human input comprises ordering of data elements together or using the data elements in the same context. 15. The method according to claim 1 wherein the neural metadata is stored and provisioned based on the estimated strength of the interconnections between the data elements. 16. The method according to claim 1, wherein the visual mashing up of the interconnected metadata elements is prepared and presented after recognizing the inter-relationships of the data elements from the plurality of nanomart storage structures, to produce a business analytics, and wherein the visual mashing up of the interconnected data elements from the plurality of nanomart storage structures is performed to creates a results of the business analytics in a plurality of forms, and wherein the plurality of forms include graphical representations, charts, and graphs. 17. The method according to claim 1, wherein the results of the business analytics and user picked analytical results of the interconnected data elements are stored, and wherein the plurality of machine learning algorithms of the neural metadata framework are applied to enrich a future process of business analytics based on the results of analysis stored over a period of time.