Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center based on running processes in the data center that communicate with one another. For each computing node, running processes are identified using natural language processing (NLP) by: iteratively refining a rule set that enables processing of surveillance information from the data center into an initial map of systems and applications in the data center, and extracting known process entities according to predetermined rules from the rule set. A visual dependency representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center based on running processes in the data center that communicate with one another. For each computing node, running processes are identified using natural language processing (NLP) by: iteratively refining a rule set that enables processing of surveillance information from the data center into an initial map of systems and applications in the data center, and extracting known process entities according to predetermined rules from the rule set. A visual dependency representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center. For each computing node, running processes are identified using natural language processing (NLP) by: extracting known process entities according to predetermined rules; extracting unknown process entities by: grouping process logs that share process entities and identifying hints in parameters and directory paths; receiving annotations to the hints to identify an application a process is running; and creating a new rule based on the annotations and propagating the new rule to other process logs. A visual representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center. For each computing node, running processes are identified using natural language processing (NLP) by: extracting known process entities according to predetermined rules; extracting unknown process entities by: grouping process logs that share process entities and identifying hints in parameters and directory paths; receiving annotations to the hints to identify an application a process is running; and creating a new rule based on the annotations and propagating the new rule to other process logs. A visual representation of the computing nodes and the processes running on the computing nodes is generated.

Abstract: A computer-implemented method according to one embodiment includes receiving a request to perform an action associated with a virtual monetary account, applying one or more rules associated with the virtual monetary account to one or more characteristics of the request, and conditionally implementing the request, based on results of the applying.

Abstract: A method for creating a semantically aggregated index in an indexer-agnostic index building system includes: extracting documents from a data source, each document including a data object; distributing the documents to a plurality of processing nodes within the system; for each node: indexing the data objects for each document into fields using semantic rules; and grouping indexed data objects for related fields by: classifying the documents into logical groups based on the semantic rules; and creating a searchable index shard for related logical groups.

Abstract: A computer program product for an indexer-agnostic index building system includes a computer readable storage medium to store a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to perform operations for creating a semantically aggregated index. The operations include: extracting documents from a data source, wherein each document includes a data object; distributing the documents to a plurality of processing nodes within the system; for each node: indexing the data objects for each document into fields using semantic rules; and grouping indexed data objects for related fields by: classifying the documents into logical groups based on the semantic rules; and creating a searchable index shard for related logical groups.

Abstract: Embodiments of the invention relate to building a distributed reverse semantic index. In one general embodiment a plurality of documents are received with each document having at least one defined rule and or semantic. The documents are distributed among a plurality of nodes of a system. The documents are processed in a generally parallel fashion. Processing the documents includes processing text data of each of the document and breaking each document into fields to index the text data to create index data by deferring how to categorize the text data based upon the defined rule and or semantics. The indexed data is combined back together to create an indexer-agnostic semantic index including a plurality of the semantic index shards and to semantically classify the documents based on the index shards into groups based on document type to create the distributed reverse semantic index.

Abstract: A method for creating a semantically aggregated index in an indexer-agnostic index building system includes: extracting documents from a data source, each document including a data object; distributing the documents to a plurality of processing nodes within the system; for each node: indexing the data objects for each document into fields using semantic rules; and grouping indexed data objects for related fields by: classifying the documents into logical groups based on the semantic rules; and creating a searchable index shard for related logical groups.

Abstract: Embodiments of the invention relate to building a distributed reverse semantic index. In one general embodiment a plurality of documents are received with each document having at least one defined rule and or semantic. The documents are distributed among a plurality of nodes of a system. The documents are processed in a generally parallel fashion. Processing the documents includes processing text data of each of the document and breaking each document into fields to index the text data to create index data by deferring how to categorize the text data based upon the defined rule and or semantics. The indexed data is combined back together to create an indexer-agnostic semantic index including a plurality of the semantic index shards and to semantically classify the documents based on the index shards into groups based on document type to create the distributed reverse semantic index.

Abstract: A computer program product for an indexer-agnostic index building system includes a computer readable storage medium to store a computer readable program, wherein the computer readable program, when executed on a computer, causes the computer to perform operations for creating a semantically aggregated index. The operations include: extracting documents from a data source, wherein each document includes a data object; distributing the documents to a plurality of processing nodes within the system; for each node: indexing the data objects for each document into fields using semantic rules; and grouping indexed data objects for related fields by: classifying the documents into logical groups based on the semantic rules; and creating a searchable index shard for related logical groups.