Abstract: In one aspect, a computerized method for explicit and implicit cloud-resource data extraction with a multi-cloud governance platform comprising: providing a plurality of cloud resources; automatically obtaining a cloud resource data for each cloud resource in the plurality of cloud resources; defining a set of Cloud Operations, Security, Cost, Access, and Resource (OSCAR) cloud resources information for a plurality of explicit relationships and a plurality of implicit relationships of each cloud resource of the plurality of cloud resource; defining a plurality of implicit relationships that are derived from a plurality of connections of each cloud resource; obtaining a billing information of each cloud resource; obtaining a third-party data for the cloud resource; obtaining and associating a plurality of relevant standards, policies and regulations for each cloud resource; obtaining one or more relevant regions and other geographical data relevant to each cloud resource; and generating a wholistic view of eac

Abstract: In one aspect, a method of a hierarchical multi-cloud key value tagging governance comprising: providing a hierarchical multi-cloud key value tagging governance engine; and with the hierarchical multi-cloud key value tagging governance engine: providing a plurality of tag values, wherein each tag is specified to have a value from a list of values, defines a set of baselines, organizing the set of baselines in a hierarchal manner, supporting a plurality of methods for matching the tag values, and specifying a plurality of conditions for each tag.

Abstract: In one aspect, a computerized method for heuristic-based classification of software discovered connected devices, comprising: implementing a discovery process on a set of devices and obtain a set of attributes of each device; identify a set of heuristics, wherein the set of heuristics combines an attribute from each discovered asset of the set of devices with an associated weight; comparing the attribute from each asset and the device catalog and using the weight to determine a partial match score; using the set of heuristics to reduce a search space used to identify the candidates for a friendly name of each device; and calculating a dot product of the weight of each of the heuristics and using the dot product to identify a set of candidate devices in the device catalog.

Abstract: In one aspect, a computerized method for federated data procurement using probabilistic information matching via domain specific heuristics. The method includes implementing procurement of the data from a plurality of online data sources. Each online data source comprises a plurality of measures. The method includes matching and validating the data. The method includes associating a plurality of weights with the plurality of set of domain specific heuristics that are optimized on an ongoing basis as newer data sources are identified. The method includes detecting that new information is collected and adding a plurality of additional heuristics to the domain specific heuristic frameworks.