Abstract: Techniques for training an entity resolution model are presented. The techniques include inputting raw training data into the entity resolution model. The training data includes historical transaction data including a plurality of transactions. A label dictionary is generated by performing natural language processing (NLP) on the training data. The NLP includes scanning text of each transaction, extracting one or more entities from the text, and storing the label dictionary in a database. The label dictionary includes the extracted entities. Tagged data is generated from the training data using the label dictionary. Vocabulary training is performed on the training data, including tokenizing the text of each transaction and converting the tokenized text into a transformer model specific format. The entity resolution model is then trained using a transformer model, which uses the tokenized text and the tagged data. The trained entity resolution model is then stored in a database.

Abstract: Techniques for automatically designing an application-requirement based configuration for a distributed computing system are provided. One or more application-level templates are obtained, wherein the one or more templates are representative of one or more requirements associated with one or more applications. Using the one or more application-level templates, one or more logical flows are created. The one or more logical flows are then used to generate one or more system-level configuration plans of the distributed computing system.

Abstract: A method for validating that a networked system of modules conforms to at least one interoperability rule pertinent to interoperability of at least two of the modules includes the steps of obtaining the at least one interoperability rule, the at least one rule being encoded in computer-operable format; obtaining configuration information pertinent to at least one of the modules, the configuration information being encoded in computer-operable format; and comparing the at least one interoperability rule to the configuration information to validate whether the system conforms to the at least one interoperability rule

Abstract: Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels.

Abstract: Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels.

Abstract: A policy-based resource management system provides an infrastructure that uses high-level definitions of data-types along with desired policy rules to manage legacy data. In particular, relevant legacy data is identified as well as the characteristics of storage devices where the legacy data resides. The storage management system determines what if any of the legacy data is stored in a manner that is non-compliant with the policy rules and corrects any violations. It generates corrective action plans, and then uses existing data migration software to migrate the data. Accordingly, the legacy data may be managed in a policy-based manner without requiring typical storage management software.

Abstract: A multi-plan generation system generates a multi-plan for a multi-layer storage area network. The system generates a predetermined number of plans while generating and retaining globally optimum plans. The system dynamically calculates and orders these plans according to optimization criteria and user requirements. The system comprises a planner for each of the layers in a storage area network such as a host planner, a storage planner, a network planner, a zone planner, etc. Each of the planners applies a variance optimization, a preview optimization, and a detail optimization and interacts with a policy repository to obtain policy guidance, enabling the system to prune the set of plans. A control module invokes the planners in a predetermined order.

Abstract: Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels.

Abstract: Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels.

Abstract: Techniques for automatically designing an application-requirement based configuration for a distributed computing system are provided. One or more application-level templates are obtained, wherein the one or more templates are representative of one or more requirements associated with one or more applications. Using the one or more application-level templates, one or more logical flows are created. The one or more logical flows are then used to generate one or more system-level configuration plans of the distributed computing system.

Abstract: A method for validating that a networked system of modules conforms to at least one interoperability rule pertinent to interoperability of at least two of the modules includes the steps of obtaining the at least one interoperability rule, the at least one rule being encoded in computer-operable format; obtaining configuration information pertinent to at least one of the modules, the configuration information being encoded in computer-operable format; and comparing the at least one interoperability rule to the configuration information to validate whether the system conforms to the at least one interoperability rule

Abstract: Mechanisms for searching XML repositories for semantically related schemas from a variety of structured metadata sources, including web services, XSD documents and relational tables, in databases and Internet applications. A search is formulated as a problem of computing a maximum matching in pairwise bipartite graphs formed from query and repository schemas. The edges of such a bipartite graph capture the semantic similarity between corresponding attributes of the schema based on their name and type semantics. Tight upper and lower bounds are also derived on the maximum matching that can be used for fast ranking of matchings whilst still maintaining specified levels of precision and recall. Schema indexing is performed by ‘attribute hashing’, in which matching schemas of a database are found by indexing using query attributes, performing lower bound computations for maximum matching and recording peaks in the resulting histogram of hits.

Abstract: A multi-plan generation system generates a multi-plan for a multi-layer storage area network. The system generates a predetermined number of plans while generating and retaining globally optimum plans. The system dynamically calculates and orders these plans according to optimization criteria and user requirements. The system comprises a planner for each of the layers in a storage area network such as a host planner, a storage planner, a network planner, a zone planner, etc. Each of the planners applies a variance optimization, a preview optimization, and a detail optimization and interacts with a policy repository to obtain policy guidance, enabling the system to prune the set of plans. A control module invokes the planners in a predetermined order.

Abstract: A policy-based resource management system provides an infrastructure that uses high-level definitions of data-types along with desired policy rules to manage legacy data. In particular, relevant legacy data is identified as well as the characteristics of storage devices where the legacy data resides. The storage management system determines what if any of the legacy data is stored in a manner that is non-compliant with the policy rules and corrects any violations. It generates corrective action plans, and then uses existing data migration software to migrate the data. Accordingly, the legacy data may be managed in a policy-based manner without requiring typical storage management software.