DATA ACCELERATION

• US 20160373476A1
• Filed: 06/16/2016
• Published: 12/22/2016
• Est. Priority Date: 06/17/2015
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: DELL'ANNO, Vincent, PARTHASARATHY, Sonali, DUKATZ, Carl M., PURI, Colin Anil, SHETTERLEY, Nathan, WENDT, Michael, KURTH, Scott
• Granted Patent: US 10,192,051 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/254 Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
• CPC Class Codes: G06F 21/552 involving long-term monitoring or reporting
• CPC Class Codes: G06F 21/554 involving event detection and direct action
• CPC Class Codes: G06F 2221/034 Test or assess a computer or a system
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 63/1425 Traffic logging, e.g. anomaly detection

Abstract

According to an example, data acceleration may include receiving indications of levels of capabilities respectively needed for data movement, data processing, and data interactivity, and/or operational parameters associated with the data movement, the data processing, and the data interactivity. Data acceleration may further include determining, based on an analysis of the received indications and/or the operational parameters, specifications for the data movement to include streaming and/or batch, the data processing to include a big data platform, complex event processing, and/or an appliance, and the data interactivity to include an in-memory database (IMDB) and/or a distributed cache. Further, data acceleration may include generating, based on the determined specifications, a data acceleration architectural layout to meet the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity, and/or the operational parameters associated with the data movement, the data processing, and the data interactivity.

Claims

1. A data acceleration apparatus comprising:

a processor; and

a memory storing machine readable instructions that when executed by the processor cause the processor to;

receive indications of at least one of levels of capabilities respectively needed for data movement, data processing, and data interactivity, and operational parameters associated with the data movement, the data processing, and the data interactivity;

determine, based on an analysis of the received indications of the at least one of the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity, and the operational parameters associated with the data movement, the data processing, and the data interactivity, specifications for the

data movement to include at least one of streaming and batch,

data processing to include at least one of a big data platform, complex event processing, and an appliance, and

data interactivity to include at least one of an in-memory database (IMDB) and a distributed cache; and

generate, based on the determined specifications, a data acceleration architectural layout to meet the at least one of the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity, and the operational parameters associated with the data movement, the data processing, and the data interactivity.

2. The data acceleration apparatus according to claim 1, wherein the data movement includes transport of data from a first location to a second location by using the data acceleration architectural layout.

3. The data acceleration apparatus according to claim 1, wherein the data processing includes

extraction of actionable insights from data, and

implementation of computer clusters to increase a processing rate of the data.

4. The data acceleration apparatus according to claim 1, wherein the data interactivity includes analyzing data by using at least one of the IMDB and the distributed cache.

5. The data acceleration apparatus according to claim 1, wherein the big data platform includes a big data core including a distributed data storage.

6. The data acceleration apparatus according to claim 1, wherein the CEP includes tracking and processing streams of event data from multiple sources to infer and identify patterns in the event data.

7. The data acceleration apparatus according to claim 1, wherein the IMDB includes database management that uses the memory for data storage.

8. The data acceleration apparatus according to claim 1, wherein the distributed cache includes cache clusters that are maintained in the memory to provide access to frequently accessed data.

9. The data acceleration apparatus according to claim 1, wherein the appliance includes a prepackaged unit of hardware, and software, wherein the hardware includes at least one of a server, the memory, a storage, and input/output channels, wherein the software includes an operating system, a database management system, and an administrative management software, and wherein the hardware includes customized silicon.

10. The data acceleration apparatus according to claim 1, wherein the machine readable instructions to generate, based on the determined specifications, the data acceleration architectural layout to meet the at least one of the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity, and the operational parameters associated with the data movement, the data processing, and the data interactivity further comprise machine readable instructions to cause the processor to:

generate, based on the determined specifications, the data acceleration architectural layout from fourteen architectural layouts to meet the at least one of the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity, and the operational parameters associated with the data movement, the data processing, and the data interactivity.

11. The data acceleration apparatus according to claim 10, wherein the machine readable instructions to generate, based on the determined specifications, the data acceleration architectural layout to meet the at least one of the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity, and the operational parameters associated with the data movement, the data processing, and the data interactivity further comprise machine readable instructions to cause the processor to:

generate, based on the determined specifications, the data acceleration architectural layout from the fourteen architectural layouts that include the appliance, the big data platform and the appliance, the streaming to the appliance, the big data platform, the streaming to the big data platform, the big data platform and in-memory analytics, the streaming to the big data platform and the in-memory analytics, the big data platform with a query processor, the streaming to the big data platform and the query processor, the distributed cache, the big data platform to the distributed cache, the IMDB, the big data platform and the IMDB, and the streaming to the IMDB, to meet the at least one of the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity, and the operational parameters associated with the data movement, the data processing, and the data interactivity.

12. A method for data acceleration, the method comprising:

receiving, by at least one processor, indications of levels of capabilities respectively needed for data movement, data processing, and data interactivity;

determining, by the at least one processor, based on an analysis of the received indications of the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity, specifications for the

data movement from streaming and batch,

data processing from a big data platform, complex event processing, and an appliance, and

data interactivity from an in-memory database (IMDB) and a distributed cache; and

generating, by the at least one processor, based on the determined specifications, a data acceleration architectural layout to meet the levels of capabilities respectively needed for the data movement, the data processing, and the data interactivity.

13. The method of claim 12, further comprising:

using, for the data movement, the data acceleration architectural layout to transport data from a first location to a second location.

14. The method of claim 12, further comprising:

extracting, for the data processing, actionable insights from data; and

implementing, for the data processing, computer clusters to increase a processing rate of the data.

15. The method of claim 12, further comprising:

analyzing, for the data interactivity, data by using at least one of the IMDB and the distributed cache.

16. A non-transitory computer readable medium having stored thereon machine readable instructions for data acceleration, the machine readable instructions, when executed, cause a processor to:

receive indications of operational parameters associated with data movement, data processing, and data interactivity;

determine, based on an analysis of the received indications of the operational parameters associated with the data movement, the data processing, and the data interactivity, specifications for the

data movement to include at least one of streaming and batch,

data processing to include at least one of a big data platform, complex event processing, and an appliance, and

data interactivity to include at least one of an in-memory database (IMDB) and a distributed cache;

generate, based on the determined specifications, a data acceleration architectural layout to meet the operational parameters associated with the data movement, the data processing, and the data interactivity;

access data that is to be analyzed for an anomaly;

determine, by using the data acceleration architectural layout, whether the data includes the anomaly; and

in response to a determination that the data includes the anomaly, control a device associated with the data.

17. The non-transitory computer readable medium of claim 16, wherein the big data platform includes a big data core including a distributed data storage, and wherein the CEP includes tracking and processing streams of event data from multiple sources to infer and identify patterns in the event data.

18. The non-transitory computer readable medium of claim 16, wherein the IMDB includes database management that uses memory for data storage, and wherein the distributed cache includes cache clusters that are maintained in the memory to provide access to frequently accessed data.

19. The non-transitory computer readable medium of claim 16, wherein the appliance includes a prepackaged unit of hardware, and software, wherein the hardware includes at least one of a server, memory, a storage, and input/output channels, wherein the software includes an operating system, a database management system, and an administrative management software, and wherein the hardware includes customized silicon.

20. The non-transitory computer readable medium of claim 16, wherein the machine readable instructions to generate, based on the determined specifications, the data acceleration architectural layout to meet the operational parameters associated with the data movement, the data processing, and the data interactivity, when executed, further cause the processor to:

generate, based on the determined specifications, the data acceleration architectural layout from architectural layouts that include the appliance, the big data platform and the appliance, the streaming to the appliance, the big data platform, the streaming to the big data platform, the big data platform and in-memory analytics, the streaming to the big data platform and the in-memory analytics, the big data platform with a query processor, the streaming to the big data platform and the query processor, the distributed cache, the big data platform to the distributed cache, the IMDB, the big data platform and the IMDB, and the streaming to the IMDB, to meet the operational parameters associated with the data movement, the data processing, and the data interactivity.