METHOD AND SYSTEM FOR EFFICIENT STORAGE AND RETRIEVAL OF ANALYTICS DATA

• US 20110225288A1
• Filed: 03/12/2010
• Published: 09/15/2011
• Est. Priority Date: 03/12/2010
• Status: Abandoned Application

• Current Assignee: WebTrends Incorporated (WebTrends Holding, Inc.)
• Original Assignee: WebTrends Incorporated (WebTrends Holding, Inc.)
• Inventors: EASTERDAY, JOHN L., DALAL, MUKESH
• Application Number:
  US12/723,527
• Publication Number:
  US 20110225288A1
• Time in Patent Office: Days
• US Class Current: 709/224
• CPC Class Codes: G06F 16/217 Database tuning G06F16/2282 takes precedence; database performance monitoring G06F11/3409
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

A method and system for efficient storage and retrieval of current and historical analytics data. The method includes reading current event data and historical event data associated with a visitor from an analytics data store and producing one or more metrics based on the current or historical event data. Delta data is generated using the current and historical event data. The delta data is then combined with previously aggregated data to produce new aggregated data. A system includes an analytics data store. The analytics data store includes a plurality of analytics data store entities arranged chronologically in time. Each analytics data store entity includes a plurality of sub bands of data. Each sub band of data is associated with configurable data blocks. The analytics data store entities also include meta data portions for increasing the efficiency of storage and retrieval of information to and from the analytics data store entities.

Claims

1. A method for storing web traffic analytics data, comprising:

reading current event data and historical event data associated with a visitor from an analytics data store;

producing one or more metrics based on at least the current event data;

generating first delta data associated with the one or more metrics using the current and historical event data; and

storing the first delta data as aggregated data.

2. The method of claim 1, further comprising:

generating second delta data associated with the one or more metrics using the current and historical event data; and

combining the second delta data with the previously aggregated data to produce new aggregated data.

3. The method of claim 2, wherein generating the second delta data associated with the one or more metrics further comprises generating a negative metric.

4. The method of claim 3, further comprising removing a portion of the previously aggregated data by combining the negative metric with the portion of the previously aggregated data.

5. The method of claim 2, wherein reading the event data and generating the first and second delta data are performed by an analytics processor.

6. The method of claim 2, wherein combining the second delta data further comprises a report generator combining the second delta data with the previously aggregated data to produce the new aggregated data.

7. The method of claim 2, wherein reading the event data, producing the one or more metrics, generating the delta data, and combining the delta data, are repeatedly performed over a period of time.

8. The method of claim 7, wherein the new aggregated data includes an accumulation of reportable data over the period of time.

9. The method of claim 8, further comprising storing changes in the event data to the new aggregated data in lieu of every occurrence of an event.

10. The method of claim 2, wherein generating the first and second delta data further comprises reviewing the historical event data and comparing the current event data to the historical event data.

11. The method of claim 2, wherein the new aggregated data includes one or more unique visitor counts.

12. The method of claim 1, wherein producing the one or more metrics further comprises producing the one or more metrics based on the current event data and the historical event data.

13. The method of claim 1, wherein storing further comprises storing the first delta data as the aggregated data when the aggregated data does not previously exist, the method further comprising combining the first delta data with the aggregated data to produce new aggregated data when the aggregated data previously exists.

14. The method of claim 1, wherein the one or more metrics include a visitor-level dimension.

15. The method of claim 1, wherein the one or more metrics include a web page dimension.

16. The method of claim 1, wherein the one or more metrics include at least one of a geographic dimension, a time dimension, and a product dimension.

17. A system for efficient storage and retrieval of analytics data, comprising:

an analytics data store including a plurality of analytics data store entities arranged chronologically in time, each analytics data store entity including;

a plurality of sub bands of data, each sub band of data being associated with a plurality of configurable data blocks; and

a meta data portion having offset pointers, each offset pointer being associated with a corresponding one of the plurality of configurable data blocks.

18. The system of claim 17, wherein:

each of the data blocks includes a plurality of visitor data groupings;

each visitor data grouping is associated with one of a plurality of visitors; and

each visitor data grouping includes event data arranged chronologically in time.

19. The system of claim 18, wherein the meta data portion having offset pointers is accessible to determine which of the configurable data blocks are to be read for a given subset of the plurality of visitor data groupings.

20. The system of claim 17, wherein each offset pointer is configured to identify a location of a corresponding one of the plurality of data blocks.

21. The system of claim 17, wherein the meta data portion comprises a first meta data portion, the system further comprising a second meta data portion including a visitor information map.

22. The system of claim 21, wherein the visitor information map includes a mapping of each of a plurality of visitor identifications to a corresponding one of the data blocks.

23. The system of claim 22, wherein the second meta data portion further comprises most recent event times associated with the plurality of visitor identifications.

24. The system of claim 23, further comprising one or more analytics processors that are configured to obtain a list of visitors with activity beyond a time point based on the most recent event times associated with the plurality of visitor identifications.

25. The system of claim 22, wherein the second meta data portion further comprises an update time for detecting changes within event data between processing cycles for each of the plurality of visitor identifications.

26. The system of claim 17, wherein the size of each data block is configurable.

27. The system of claim 17, wherein each of the plurality of sub bands is associated with a range of partition keys.

28. The system of claim 27, wherein each of the partition keys includes a hash of a visitor identification.

29. The system of claim 17, wherein each of the analytics data store entities corresponds to an analytics data store file.

30. The system of claim 29, wherein each analytics data store file includes data associated with a discrete time bucket.

31. The system of claim 30, wherein each analytics data store file includes event data for each of a plurality of visitors experiencing event activity within the discrete time bucket.

32. The system of claim 31, wherein for a given visitor, the event data includes historical event data for said given visitor for all time back to a configurable history limit, and includes current event data for said given visitor within the discrete time bucket.

33. The system of claim 17, further comprising:

one or more analytics generators to generate the plurality of analytics data store entities and to store the data according to the plurality of sub bands; and

one or more analytics processors to read the data from the plurality of sub bands of the analytics data store entities.

34. The system of claim 33, wherein the one or more analytics generators are configured to read historical data from at least one of the analytics data store entities, and to replicate the historical data to at least one new analytics data store entity.

35. The system of claim 34, wherein the new analytics data store entity includes a complete history of event data for each of a plurality of visitors back to a configurable history limit.

36. The system of claim 35, wherein the one or more analytics processors are configured to produce one or more visitor-level metrics using at least some of the complete history of event data for each of the plurality of visitors.

37. The system of claim 34, wherein the at least one new analytics data store entity is readable and writeable, and previously generated analytics data store entities are readable.

38. The system of claim 17, further comprising:

a first local machine to cache a first portion of the plurality of analytics data store entities; and

a second local machine to cache a second portion of the plurality of analytics data store entities.

39. The system of claim 38, wherein:

the first local machine includes a first analytics generator to generate a first new analytics data store entity;

the second local machine includes a second analytics generator to generate a second new analytics data store entity; and

the first and second local machines are configured to copy the first and second new analytics data store entities, respectively, to the analytics data store.

40. An article comprising a storage-readable medium having associated data that, when executed by a machine, results in a machine:

reading current event data and historical event data associated with a visitor from an analytics data store;

producing one or more metrics based on at least the current event data;

generating first delta data associated with the one or more metrics using the current and historical event data; and

storing the first delta data as aggregated data.

41. The article of claim 40, further comprising:

generating second delta data associated with the one or more metrics using the current and historical event data; and

combining the second delta data with the previously aggregated data to produce new aggregated data.

42. The article of claim 41, wherein generating the second delta data associated with the one or more metrics further comprises generating a negative metric.

43. The article of claim 42, further comprising removing a portion of the previously aggregated data by combining the negative metric with the portion of the previously aggregated data.

44. The method of claim 41, wherein generating the first and second delta data further comprises reviewing the historical event data and comparing the current event data to the historical event data.