Systems, methods, and related techniques and apparatus containing instructions which when executed by one or more computing devices for determining dataset rankings by determining, from the lineage order requirement, one or more first lineage level datasets from the collection of datasets, generating one or more first lineage level asset ranks respectively for each one of the one or more first lineage level datasets, determining at least one second lineage level dataset having an outflow to the one or more first lineage level datasets, and generating a first dataset rank for the at least one second lineage level dataset as a first function of the outflow and at least one of the one or more first lineage level asset ranks.

A method and system for detecting relevant potential participating entities across different databases. A method includes retrieving transaction data related to potential participating entities by resolving each of the plurality of potential participating entities between a dataset including transaction data and a dataset indicating the plurality of potential participating entities, wherein resolving the plurality of potential participating entities further includes applying resolution rules requiring matching a plurality of features between respective instances of the potential participating entity in the transaction data and in the dataset indicating the plurality of potential participating entities; determining a plurality of relevance scores based on the retrieved transaction data and entity characteristics of a subject entity, wherein each relevance score represents a relevance of the subject entity with respect to a respective potential participating entity; and identifying, based on the plurality of relevance scores, at least one relevant potential participating entity.

A data analysis system is proposed for providing fine-grained low latency access to high volume input data from possibly multiple heterogeneous input data sources. The input data is parsed, optionally transformed, indexed, and stored in a horizontally-scalable key-value data repository where it may be accessed using low latency searches. The input data may be compressed into blocks before being stored to minimize storage requirements. The results of searches present input data in its original form. The input data may include access logs, call data records (CDRs), e-mail messages, etc. The system allows a data analyst to efficiently identify information of interest in a very large dynamic data set up to multiple petabytes in size. Once information of interest has been identified, that subset of the large data set can be imported into a dedicated or specialized data analysis system for an additional in-depth investigation and contextual analysis.

A data analysis system is proposed for providing fine-grained low latency access to high volume input data from possibly multiple heterogeneous input data sources. The input data is parsed, optionally transformed, indexed, and stored in a horizontally-scalable key-value data repository where it may be accessed using low latency searches. The input data may be compressed into blocks before being stored to minimize storage requirements. The results of searches present input data in its original form. The input data may include access logs, call data records (CDRs), e-mail messages, etc. The system allows a data analyst to efficiently identify information of interest in a very large dynamic data set up to multiple petabytes in size. Once information of interest has been identified, that subset of the large data set can be imported into a dedicated or specialized data analysis system for an additional in-depth investigation and contextual analysis.

A data analysis system is proposed for providing fine-grained low latency access to high volume input data from possibly multiple heterogeneous input data sources. The input data is parsed, optionally transformed, indexed, and stored in a horizontally-scalable key-value data repository where it may be accessed using low latency searches. The input data may be compressed into blocks before being stored to minimize storage requirements. The results of searches present input data in its original form. The input data may include access logs, call data records (CDRs), e-mail messages, etc. The system allows a data analyst to efficiently identify information of interest in a very large dynamic data set up to multiple petabytes in size. Once information of interest has been identified, that subset of the large data set can be imported into a dedicated or specialized data analysis system for an additional in-depth investigation and contextual analysis.

The Automata Processor Workbench (AP Workbench) is an application for creating and editing designs of AP networks (e.g., one or more portions of the state machine engine, one or more portions of the FSM lattice, or the like) based on, for example, an Automata Network Markup Language (ANML). For instance, the application may include a tangible, non-transitory computer-readable medium configured to store instructions executable by a processor of an electronic device, wherein the instructions include instructions to represent an automata network as a graph.

Approaches for querying a telemetry dataset are described. The telemetry dataset may include telemetry data that may have been generated by a plurality of data endpoints of a physical asset within a facility. The querying of the telemetry dataset may be performed by initially using a semantic dataset. In an example, initially, the semantic dataset is queried by executing a first query comprising a query tag to identify a first data endpoint from amongst the plurality of data endpoints. Thereafter, a reference unique identifier which is linked with the first data endpoint is obtained to query the telemetry dataset by executing a subsequent query comprising the reference unique identifier. As a result of execution of the subsequent query, the telemetry data pertaining to the first data endpoint is retrieved.

Approaches for querying a telemetry dataset are described. The telemetry dataset may include telemetry data that may have been generated by a plurality of data endpoints of a physical asset within a facility. The querying of the telemetry dataset may be performed by initially using a semantic dataset. In an example, initially, the semantic dataset is queried by executing a first query comprising a query tag to identify a first data endpoint from amongst the plurality of data endpoints. Thereafter, a reference unique identifier which is linked with the first data endpoint is obtained to query the telemetry dataset by executing a subsequent query comprising the reference unique identifier. As a result of execution of the subsequent query, the telemetry data pertaining to the first data endpoint is retrieved.

Embodiments perform configuration checking between data types of table fields, in order to determine mismatches therebetween. A configuration check request including a parameter identifying a system is received. A first data type is retrieved based upon the parameter. The first data type is compared with a second, different retrieved data type to determine a mismatch. In some embodiments the first data type and the second data type may be retrieved from different systems, with the mismatch revealing inter-system inconsistency. According to certain embodiments, the first data type and the second data type may be retrieved from a same system, with the mismatch revealing intra-system inconsistency. A configuration check report is generated from the mismatch and communicated to a user, for use in proactively correcting inconsistency. Embodiments may also retrieve values of the data types, as part of value help functionality.

A method and system for detecting relevant potential participating entities across different databases. A method includes retrieving transaction data related to potential participating entities by resolving each of the plurality of potential participating entities between a dataset including transaction data and a dataset indicating the plurality of potential participating entities, wherein resolving the plurality of potential participating entities further includes applying resolution rules requiring matching a plurality of features between respective instances of the potential participating entity in the transaction data and in the dataset indicating the plurality of potential participating entities; determining a plurality of relevance scores based on the retrieved transaction data and entity characteristics of a subject entity, wherein each relevance score represents a relevance of the subject entity with respect to a respective potential participating entity; and identifying, based on the plurality of relevance scores, at least one relevant potential participating entity.