Tata Consultancy Services Limited

Prateep Misra

Arpan Pal

Balamuralidhar Purushothaman

Aniruddha Sinha

Avik Ghose

Soma Bandyopadhyay

Puneet Kumar Agarwal

Chirabrata Bhaumik

Arijit Ukil

Tanushyam Chattopadhyay

Snehasis Banerjee

Debnath Mukherjee

Soumitra Naskar

Shubhrangshu Ghosh

Deepak N Swamy

Venkatramanan Siva Subrahmanian

Suvra Dutta

Abhisek Das

Satanik Panda

Sumanta Ghosh

Nilanjan Roy

Sunil Kumar Kopparapu

Ranjan Dasgupta

Sounak Dey

Amit Kumar Agrawal

Priyanka Sinha

Anupam Basu

Ankur Chakraborty

Suman Datta

Siddharth Bhattacharya

Arindam Halder

Sangram Dasharath Gaikwad

Tania Ghosh

Dibyendu Biswas

Satnik Panda

Hemanta Dutta

Abhijit Barman

Abhishek Roy Choudhury

Debnarayan Kar

Suman Adak

Deepak Swamy

Abhishek Choudhury

Indrajit Bhattachyarya

Amit Kumar Agarwal

State-of-the-art approaches have concentrated on building solution(s) to match the amplitude of a time series with a user given one. However, these have failed to implement solution(s) which enables searching for pattern(s) that can depict human vision psychology. Embodiments of the present disclosure determine occurrence of pattern of interest in time series data for anomaly detection, wherein time series data is obtained, and first order derivative is computed. Further an angle of change in direction is derived based on a gradient of change in value of the time series data. This angle is further converted to a measurement unit. The time series data is quantized into bins and a weighted finite state transducers diagram (WFSTD) is obtained based on domain knowledge which is then converted to specific pattern. The specific pattern is searched in the bins to determine occurrence/count of the specific pattern for anomaly detection.

Systems and methods for determining occurrence of pattern of interest in time series data

Data cataloging has become a necessity for empowering organizations with analytical ability. Conventional cataloging systems may fail to provide proper visualization of data to the different stakeholders of an organization. The present disclosure provides a hierarchical dynamic cataloging system so that visualization of data at different levels would be possible for different stake holders. In the present disclosure, a hierarchical structure of algorithms and multiple stake holders along with relevant metadata is generated. Further, a catalog is generated by performing a mapping across components comprised in the hierarchical structure and identifying relationship across the components based on mapping. The catalog gets dynamically updated and provides a dynamic view of the algorithms and associated metadata to the multiple stakeholders of an organization. Further, the disclosure supports reuse of already developed algorithms across multiple applications and domains resulting in optimization of resources and time.

Methods and systems for hierarchical dynamic cataloging

Sensor data (or IoT) analytics plays a critical role in taking business decisions for various entities (e.g., organizations, project owners, and the like). However, scaling of such analytical solutions beyond certain point requires adopting to various computing environments which seems to be challenging with the constrained resources available. Embodiments of the present disclosure provide system and method for analysing and executing sensor observational data in computing environments, wherein extract, transform, load (ETL) workflow pipeline created by users in the cloud, can be seamlessly deployed to job execution service available in cloud/edge without any changes in the code/config by end user. The configuration changes are internally handled by the system based on the selected computing environment and queries are executed either in distributed or non-distributed environments to output data frames. The data frames are further pre-processed in a desired computing environment and thereafter visualized accordingly.

Systems and methods for recommending execution environment for analysing sensor observational data

Machine Learning (ML) models are deployed in digital platforms for data analytics. However, it is realized that there is growing trends of recognition that machine learning models expose new vulnerabilities in software systems, for instance training data poisoning, adversarial responses, model extraction, and the like. Embodiments of the present disclosure provide systems and methods for safeguarding training dataset by exploiting immutability feature and generating immutable machine learning models for data analytics. More specifically, immutable records of events are governed by smart contracts within highly secure permissioned distributed ledger. This dataset is used for training multiple machine learning models which are immutable in nature and further utilized for triggering actions for incoming request(s) from IoT platforms.

Immutable records based generation of machine learning models for dynamically triggering actions

This disclosure relates to a distributed state exchanging model for assets of an organization or entity for an identified task in a domain of interest. The model provides a context aware autonomous Internet of things (IoT) based cognitive edge network, wherein each node is aware of the state of the other nodes based on a subscription. The state information is seamlessly updated and maintained by a certifying node of each group. The nodes are grouped based on associated capability definitions such that at least some of the groups form a hierarchy within the cognitive edge network for completing the identified task. Since the network is context aware, a new sub-task towards completing the identified task is autonomously selected by either groups or by the nodes based on the context thereby obviating a need for centralized scheduling.

Autonomous context aware state exchanging hierarchical cognitive edge network

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

This disclosure relates generally to surveillance, and more particularly to perform entry and exit monitoring of a user. In an embodiment, the system identifies an entry event (during which a user enters a room being surveilled) and/or an exit event (during which a user leaves a room being surveilled), based on a Bluetooth Low Energy (BLE) based system. This system uses two receivers one positioned outside and the other positioned inside the room being surveilled, to receive BLE beacon transmitted by a user who is in motion. Based on relative change in Received Signal Strength Indicator (RSSI) values of this beacon with respect to the first and second receivers, the system identifies direction of movement of the user, and in turn, determines one of the entry or exit events.

Method and system for entry and exit monitoring using bluetooth low energy (BLE) beacons

A method and system for real-time analytics of sensor-based data is disclosed. Also disclosed is a Cloud-based Paltform-as-a-Service (PaaS) offering for sensor driven applications with services and features for their complete life-cycle management including prompt development, testing, deployment and so forth. The method of the present disclosure enables real-time tracking of various physical parameters and attributes related to smart-spaces using sensor devices implemented in the premises of the smart-space environment and using crowd-sourced user input data. Further, the parameters obtained are sent to the cloud-computing server, wherein the analytics are performed in real-time based on the obtained parameters.

Computer platform for development and deployment of sensor data based applications and services

A system and method for consolidating a plurality of heterogeneous storage systems in a data center comprising collecting data from a plurality of heterogeneous storage devices using data collection tools, using Data Preparation Tool for extracting and translating the collected data, populating a Data Model stored in source storage configuration unit suitable for analysis, analyzing and classifying the collected data by an analysis unit based upon a plurality of attributes, comprising of a Consolidation Advisor that uses the analyzed data and candidate Target System Configurations, Preferences & Constraints for generating optimum number, specification & configuration of the Consolidate Target State infrastructure and mappings of logical units from as-is data center storage infrastructure to the target state, and iteratively validating the same in a Validation task till the final desired consolidation and objectives are met.

Method and system for consolidating a plurality of heterogeneous storage systems in a data center

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 system for identifying a sensor to be deployed in a physical environment. The method may comprise storing sensor data and metadata of the plurality of sensors in a data store. Further, the method may comprise deriving sensor information comprising at least one of thematic information, temporal information, and spatial information. The method may further comprise creating sensor ontology to define a relationship between the sensor data, the metadata, and the sensor information. The sensor ontology may be stored in a knowledge repository of the data store. The method may further comprise receiving and decomposing the search query into at least one of a basic query component and an inferred query component. Finally, the method may comprise executing the basic query component or the inferred query component on the data store and the knowledge repository respectively in order to identify the sensor.

Method and system for identifying a sensor to be deployed in a physical environment

A computing platform for intelligent development, deployment and management of vehicle telemetry applications is disclosed herein. Further, the present disclosure provides a method and system that enables provision of Intelligent Transportation Service on the Cloud-based Platform that facilitates creation and deployment of vehicle telemetry applications configured for enabling traffic measurements, traffic shaping, vehicle surveillance and other vehicle related services.

Computer platform for development and deployment of sensor-driven vehicle telemetry applications and services

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

A highly reliable data archival and retrieval method that enables fine grained control over data availability is implemented across a Quality of Service driven archival system, configured to fragment the data into data and parity chunks for storing onto the storage node. The technique employed by the archival system enables files to be read without having need to access any metadata, thereby tolerating complete loss of such metadata. Further, the Quality of Service driven system architecture improves upon the system performance and throughput by means of a storage node regeneration process which ensures balanced load on participating storage node during various storage, retrieval and regeneration operations.

Archival storage and retrieval system

A method and system for identifying personal context of a user having a portable mobile communication device at a particular location for deriving social interaction information of the user, wherein the user within a predefined range is identified using personal context of the user at the particular location and the identified personal context of the user is assigned with the confidence value. Further the current location information of the user within the particular location is obtained by fusing assigned confidence value. Further the proximity of the user in the current location is estimated by finding the accurate straight line distance between users. Further the two users having similar current location information at the particular location are grouped together with the predefined density criteria. Finally the social interaction information of the user is derived by multimodal sensor data fusion at the fusion engine and represented using a human network graph.

System and method for identifying and analyzing personal context of a user

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Kolkata, India

Prateep Misra