Tata Consultancy Services Limited

Debnath Mukherjee

Snehasis Banerjee

Prateep Misra

Anupam Basu

Santa Maiti

Plaban Kumar Bhowmick

Suman Datta

Sourjya Sarkar

Siddharth Bhattacharya

This disclosure relates to systems and methods for analyzing sensor data using incremental autoregression techniques for generating a vector of autoregression coefficients is provided. The system processes a time series data to obtain blocks of observation values, reads the observation values, updates pre-stored convolution values with the observation values, updates a partial sum by adding each observation value to the partial sum, increments a count each time an observation value is read, repeats the steps of updates and increments until a last observation value from a last block is read to obtain an updated set of convolution values, partial sum, and count. The system further computes a first matrix and a second matrix using the updated set of convolutions values, or summation of observation values computed from the updated partial sum, or the updated count, and generates a vector of autoregression coefficients based on the first and the second matrix.

Systems and methods for analyzing sensor data using incremental autoregression techniques

Disclosed is a method and system for dynamically generating a customized, personalized and contextual alert for a user based upon personalized, contextual and background knowledge associated with the profile of the user. The system comprises a profile updater module configured to update the profile data of the user extracted either from the social web or from the user. Further, a reasoning module is configured to derive refined background knowledge in context with the updated profile data of the user. A monitoring module is configured to monitor the events sensed by a sensing module. A context extractor module is configured for extracting the context of the events and the context of the user. A knowledge converter module is configured to convert the extracted context into structured format. Finally, the refined background knowledge is steam reasoned to determine whether the events received are relevant to the user and accordingly transmitted.

System and method for smart public alerts and notifications

Disclosed is a method and a system for stream reasoning a plurality of data streams. The system comprises a processor and a memory coupled to the processor. The processor is capable of executing a plurality of modules embodied on the memory. The plurality of modules comprises an event module and a application managed window module. The event module is configured to receive a data stream associated with an event from a stream reasoner application. The data stream provides factual information about the event. Further, the data stream comprises a request. The request may be an add request or a delete request. The application managed window module is configured to insert the request associated with the event or delete a prior request associated with the event from the memory based upon a type of the request.

System and a method for reasoning and running continuous queries over data streams

The present disclosure provides system and method for executing SPARQL query on a SPARQL engine. For executing the SPARQL query, a function may be instantly integrated with the SPARQL query which leads to extension of the SPARQL query. The extension may be achieved through a user friendly interface which may allow transparent integration of code (i.e., the function) in any language such as JAVA, C, C++ and the like, supporting a particular functionality. The system may integrate the code by addition of newly added code to the SPARQL library after validation. Further, the system may analyze the functionality associated with the code to optimize decision making of a user. The system may further support auto compilation and rating of the functions based on the user feedback and re-usability of the code working in a collaborative environment. Further the system may enable also enable to integrate external tools and web services.

System and method for executing a SPARQL query

Disclosed is a method and Geographic Information System (GIS) for creating a user's proximity model in accordance with a user's feedback. The GIS creates the user's proximity model using a Dempster-Shafer technique. The GIS initializes the user's proximity model upon initializing a fuzzy set with a fuzzy membership function. The fuzzy set includes a plurality of points scattered around a reference point. The GIS creates an intermediate model using the user's proximity model by selecting a group of points from the plurality of points. The GIS receives a user feedback on the intermediate model. The GIS adapts the fuzzy membership function based on the user feedback. The GIS then updates the user's proximity model based on the fuzzy membership function which is adapted on basis of the user feedback.

Creating a user's proximity model in accordance with a user's feedback

A context aware recommendation system and method for recommending at least one program to the user responsive to dynamically varying user preferences, learned user behavior and contextual information is described. The recommended program is a television program, radio program or music file. The system and method further alert the user about change in user's preferences and guides the user to change stated preferences.

Method and system for context-aware recommendation

A system and method for generating a plan to complete a task by providing a framework facilitating use of heterogeneous data sources without altering a planning algorithm are disclosed. The method includes using a first dataset of logical atoms represented in predicate schema and second dataset of database atoms represented in non-predicate schema, and modifying a grammar rule, a domain definition, and a problem definition, and selecting and executing task methods and task operators to complete a task. Execution of task operator includes verifying a precondition, assigning variables with values when the precondition is valid, and modifying (delete and add) a plan state. Execution of the task method includes verifying a precondition of the task method, assigning variables with values when the precondition is valid, decomposing the task into sub-tasks, assigning arguments of task method to sub-tasks, and adding sub-tasks to a task list. Thereafter, a plan is generated.

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Kolkata, India

Debnath Mukherjee