Tata Consultancy Services Limited

Shubhrangshu Ghosh

Prateep Misra

Tanushyam Chattopadhyay

Suvra Dutta

Abhisek Das

Chirabrata Bhaumik

Amit Kumar Agrawal

Sangram Dasharath Gaikwad

Tania Ghosh

Abhijit Barman

Arindam Halder

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

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

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

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Kolkata, India

Shubhrangshu Ghosh