WINDOW MANAGEMENT FOR STREAM PROCESSING AND STREAM REASONING

• US 20160246845A1
• Filed: 10/15/2014
• Published: 08/25/2016
• Est. Priority Date: 10/22/2013
• Status: Abandoned Application

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: BANERJEE, Snehasis, MUKHERJEE, Debnath
• Application Number:
  US15/031,177
• Publication Number:
  US 20160246845A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/245 Query processing
• CPC Class Codes: G06F 16/24564 Applying rules; Deductive queries
• CPC Class Codes: G06F 16/24568 Data stream processing; Continuous queries
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

Disclosed is a system and method for providing data required for resolving a query. A data receiving module may receive a dataset captured by a plurality of sensors. A logic manager module may create a space for storing the dataset in a window of a plurality of windows. The logic manager module may further receive the query from a user. In one aspect, the query may either be registered query or an ad-hoc query. A window manager module may determine the data based upon the query. The window manager module may further resize the window when the query is the registered query. A learning module may select a maximum size window having a maximum size amongst the plurality of windows when the query is the ad-hoc query. A data facilitating module may provide the data in order to resolve the query.

Claims

1. A method for providing data required for resolving a query, the method comprising:

receiving a dataset captured by a plurality of sensors, wherein the dataset is received based on a pre-defined set of rules;

creating a space for storing the dataset in a window, of a plurality of windows, of the database by removing pre-stored data from the window, wherein the pre-stored data is removed on arrival of the dataset, and wherein the pre-stored data is stored in a repository post removal from the window;

receiving the query from a user, wherein the query is one of a registered query and an ad-hoc query;

determining the data based upon the query, wherein the data comprises the dataset and a part of the pre-stored dataset or the pre-stored dataset;

resizing the window to merge the part of the pre-stored dataset or the pre-stored dataset with the dataset, thereby generating a resized window, wherein the window is resized when the query is the registered query;

selecting a maximum size window having a maximum size amongst the plurality of windows, wherein the maximum size window selected when the query is the ad-hoc query; and

providing the data in one of the resized window and the maximum size window in order to resolve the query, wherein the receiving the dataset, the creating, the receiving the query, the determining, the resizing, the selecting and the providing are performed by a processor using programmed instructions stored in a memory.

2. The method of claim 1, wherein the plurality of sensors comprises at least one of a soft sensor and a hard sensor, and wherein the soft sensor comprises RSS feed, twitter feeds, web updates, and wherein the hard sensor comprises a temperature sensor, a humidity sensor, an accelerometer sensor, a global positioning (GPS) sensor and combinations thereof.

3. The method of claim 1, wherein the receiving the dataset further comprises:

extracting a metadata from an external repository, wherein the metadata is associated to the dataset, and

unifying the dataset and the metadata in a unified format by using a data unification algorithm.

4. The method of claim 3, wherein the metadata comprises geo-spatial data, profile data of a person.

5. The method of claim 3, wherein the dataset and the metadata is unified by using at least one of an information extraction technique, a machine learning technique, a natural language processing technique and a combination thereof.

6. The method of claim 1, wherein the pre-stored data is stored in a repository based on priority associated the pre-stored data, wherein the priority is associated using plurality of priority parameters, and wherein the plurality of priority parameters comprises data source, data pattern, logic of the query, and volume of the data.

7. The method of claim 1, wherein the maximum size window is selected by using a context aware windowing technique, and wherein the context aware windowing technique facilitates to learn the maximum size window based on context of the dataset.

8. A system for providing data required for resolving a query, the system comprising:

a processor; and

a memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, and wherein the plurality of module comprising;

a data receiving module configured to receive a dataset captured by a plurality of sensors, wherein the dataset is received based on a pre-defined set of rules;

a logic manager module configured to

create a space for storing the dataset in a window, of a plurality of windows, of the database by removing pre-stored data from the window, wherein the pre-stored data is removed on arrival of the dataset, and wherein the pre-stored data is stored in a repository post removal from the window;

receive the query from a user, wherein the query is one of a registered query and an ad-hoc query;

a window manager module configured to

determine the data based upon the query, wherein the data comprises the dataset and a part of the pre-stored dataset or the pre-stored dataset;

resize the window to merge the part of the pre-stored dataset or the pre-stored dataset with the dataset, thereby generating a resized window, wherein the window is resized when the query is the registered query;

a learning module configured to select a maximum size window having a maximum size amongst the plurality of windows, wherein the maximum size window selected when the query is the ad-hoc query; and

a data facilitating module configured to provide the data in one of the resized window and the maximum size window in order to resolve the query.

9. The system of claim 8, wherein the learning module is further configured to select the maximum size window using a context aware windowing technique, and wherein the context aware windowing technique facilitates to add context to the dataset stored in the plurality of windows.

10. A non-transitory computer readable medium embodying a program executable in a computing device for providing data required for resolving a query, the program comprising:

a program code for receiving a dataset captured by a plurality of sensors, wherein the dataset is received based on a pre-defined set of rules;

a program code for creating a space for storing the dataset in a window, of a plurality of windows, of the database by removing pre-stored data from the window, wherein the pre-stored data is removed on arrival of the dataset, and wherein the pre-stored data is stored in a repository post removal from the window;

a program code for receiving the query from a user, wherein the query is one of a registered query and an ad-hoc query;

a program code for determining the data based upon the query, wherein the data comprises the dataset and a part of the pre-stored dataset or the pre-stored dataset;

a program code for resizing the window to merge the part of the pre-stored dataset or the pre-stored dataset with the dataset, thereby generating a resized window, wherein the window is resized when the query is the registered query;

a program code for selecting a maximum size window having a maximum size amongst the plurality of windows, wherein the maximum size window selected when the query is the ad-hoc query; and

a program code for providing the data in one of the resized window and the maximum size window in order to resolve the query.