# US 9,882,949 B1 · Dynamic detection of data correlations based on realtime data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,882,949 B1 - Document ID: US9882949B1 - Status: Granted - Kind: B1 - Application number: 14/503,077 - Filed: 2014-09-30 - Granted: 2018-01-30 - Earliest claimed: 2014-06-20 - Assignees: Amazon Technologies, Inc. - Applicants: Amazon Technologies, Inc. - Inventors: Santosh Kalki; Swaminathan Sivasubramanian; Srinivasan Sundar Raghavan; Timothy Andrew Rath; Amol Devgan; Mukul Vijay Karnik - Web page: https://patentgrep.com/patent/US9882949B1 ## CPC - G06F 40/30: Semantic analysis - H04L 65/60: Network streaming of media packets - G06F 16/24: Querying - G06F 16/282: {Hierarchical databases, e.g. IMS, LDAP data stores or Lotus Notes} - G06F 16/283: {Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP} - G06N 20/00: Machine learning - H04L 43/18: Protocol analysers - H04L 67/52: specially adapted for the location of the user terminal - G06F 2216/03: Data mining - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A hosted analytics system may be integrated with transactional data systems and additional data sources such real-time systems and log files. A data processing pipeline may transform data on arrival for incorporation into an n-dimensional cube. Correlation between patterns of events in transactional data may be identified. Upon arrival, new data may be transformed and incorporated into the n-dimensional cube. Similarity between the new data and a previously identified correlation may be determined and flagged. ## Claims ### Claim 1 A system comprising: one or more memories having stored thereon computer readable instructions that, upon execution by a computing device, cause the system at least to: store a first data point associated with a first entity; receive information indicative of a second entity, wherein the second entity is identified based at least in part on applying a grammar to a subset of the data received from a real time stream of data; determine that the second entity is related to the first entity; identify a second data point associated with the second entity, the second data point corresponding to the first data point, wherein the first data point is input to a first analysis involving the first entity; and calculate a value for the second data point, according to a priority for performing a second analysis, the priority based at least in part on the identification of the second entity in the subset of the data received from the real time stream of data and the determination that the second entity is related to the first entity. ### Claim 2 (depends on 1) The system of claim 1, further comprising one or more memories having stored thereon computer-readable instructions that, upon execution by the computing device, cause the system at least to: cause a service, upon deployment, to supply operational data to the real time stream of data; and detect the second entity based at least in part on applying the grammar to the operational data received from the real time stream of data. ### Claim 3 (depends on 1) The system of claim 1, wherein the analysis comprises identifying a pattern of frequent additions to data points associated with the first or second entity. ### Claim 4 (depends on 1) The system of claim 1, further comprising one or more memories having stored thereon computer-readable instructions that, upon execution by the computing device, cause the system at least to: identify a deviation between a first inference based on the analysis and an additional inference based on an additional analysis of an additional entity belonging to a same class of entities as the first entity. ### Claim 5 (depends on 1) The system of claim 1, wherein the at least one attribute relating the first and second entities comprises a location. ### Claim 6 (depends on 5) The system of claim 5, further comprising one or more memories having stored thereon computer-readable instructions that, upon execution by the computing device, cause the system at least to: determine the priority for performing the second analysis based at least in part on the location. ### Claim 7 A non-transitory computer-readable storage medium having stored thereon instructions that, upon execution by one or more computing devices, cause the one or more computing devices at least to: identify a second entity based at least in part on applying a grammar to a subset of data received from a real time stream of data; determine that the second entity is related to a first entity; identify a data point associated with the second entity, the data point corresponding to another data point associated with the first entity, wherein the another data point is an input to a first analysis involving the first entity; and calculate a value for the data point, according to a priority for performing a second analysis, the priority based at least in part on the identification of the second entity in the subset of data received from the real time stream of data and the second entity being related to the first entity. ### Claim 8 (depends on 7) The non-transitory computer-readable storage medium of claim 7, comprising further instructions that, upon execution by the one or more computing devices, cause the one or more computing devices to at least: cause a second service, upon deployment, to supply operational data to the real time stream of data; and detect the second entity based at least in part on applying the grammar to the operational data received from the real time stream of data. ### Claim 9 (depends on 7) The non-transitory computer-readable storage medium of claim 7, comprising further instructions that, upon execution by the one or more computing devices, cause the one or more computing devices to at least: identify a deviation between a first inference based on the first analysis and an additional inference based on the second analysis, wherein the second entity belongs to a same class of entities as the first entity. ### Claim 10 (depends on 7) The non-transitory computer-readable storage medium of claim 7, wherein the at least one attribute relating the first and second entity comprises a location. ### Claim 11 (depends on 10) The non-transitory computer-readable storage medium of claim 10, comprising further instructions that, upon execution by the one or more computing devices, cause the one or more computing devices to at least: determine the priority for performing the second analysis based at least in part on the location. ### Claim 12 (depends on 7) The non-transitory computer-readable storage medium of claim 7, wherein determining that the second entity is related to the first entity comprises identifying a statistical correlation between an attribute of the first entity and an attribute of the second entity. ### Claim 13 (depends on 7) The non-transitory computer-readable storage medium of claim 7, wherein the second entity corresponds to an event. ### Claim 14 A computer-implemented method, comprising: identifying a second entity based at least in part on applying a grammar to a subset of data received from a stream of data; determining that the second entity is related to a first entity; identifying a data point associated with the second entity, the data point corresponding to another data point associated with the first entity, wherein the another data point is an input to a first analysis involving the first entity; and calculating a value for the data point, according to a priority for performing a second analysis, the priority based at least in part on the identifying of the second entity in the subset of data received from the stream of data and the determining that the second entity is related to the first entity. ### Claim 15 (depends on 14) The method of claim 14, wherein the first entity corresponds to a first computing node and the second entity corresponds to a second computing node. ### Claim 16 (depends on 14) The method of claim 14, further comprising: identifying a deviation between a first inference based on the first analysis and an additional inference based on the second analysis, wherein the second entity belongs to a same class of entities as the first entity. ### Claim 17 (depends on 14) The method of claim 14, wherein the at least one attribute relating the first and second entities comprises a location. ### Claim 18 (depends on 17) The method of claim 17 further comprising: determining the priority for performing the second analysis based at least in part on the location. ### Claim 19 (depends on 14) The method of claim 14, further comprising: setting the priority for analysis based at least in part on a deviation between a first inference based on the first analysis and an additional inference based on the second analysis, wherein the second entity belongs to a same class of entities as the first entity. ### Claim 20 (depends on 14) The computer-implemented method of claim 14, further comprising: determining that the second entity is related to the first entity by at least identifying a statistical correlation between an attribute of the first entity and an attribute of the second entity.