Abstract: The disclosure relates to system and method for continuously tracking humans in an area. The method includes receiving video data of the area from overhead cameras. Each of overhead cameras includes Field of View (FoV), FoV includes overlapping region and non-overlapping region, and overlapping region corresponds to region of intersection between at least two FoVs. The method further includes detecting presence humans in first FoV through object detection and classification models; for each human of humans, assigning unique global identity (ID) corresponding to human in first FoV, and reassigning unique global ID to human when human moves from first FoV to second FoV through overlapping region between first FoV and second FoV using weighted combination of resource assignment algorithm, intersection-over-union (IOU) based track detection, and velocity and direction estimation of subsequent frame of video data; and continuously tracking, in real-time, each of humans in the area through unique global ID.

Abstract: A method discloses obtaining a node template comprising a predetermined node structure and rules governing generation of node instances based on the node template. Data elements are received. A run-time node is generated in response to the reception of the data elements. The run-time node comprises the node template and a node instance. The node instance comprises the data elements mapped to the node template based on the rules of the node template. A run-time overlay comprising an overlay template and an overlay instance comprising processing logic implementing at least one generic rule of the overlay template are obtained. The executable run-time node is generated. It comprises a composition of the run-time node and the run-time overlay such that the processing logic of the run-time overlay is operable to interact with the run time node during execution of the executable run-time node.

Abstract: A method for transforming data organized in tabular structure is disclosed. In some embodiments, the method includes assigning a score to each of a plurality of cells within a table based on an associated set of orthogonal features characterizing a set of data. The set of orthogonal features comprises visual features, syntactic features, and language-based features. The method further includes identifying for each of the plurality of cells, a cell type based on the assigned score. The method further includes determining a table type based on the cell type and the set of orthogonal features determined for each of the plurality of cells. The table type comprises one of a row-oriented table, a column-oriented table, or a composite table.

Abstract: A method and/or system for Artificial Intelligence assisted service catalogue generation for network service provisioning is disclosed. The method comprising receiving input data which comprises either or combination of one or more specification documents or one or more configuration changes in network functions and/or network components. The entities and attributes of the entities are extracted from the input data which are then reconciled with graph database representing network function model to determine modifications in the input data. The graph database is updated based on the modifications identified in the input data, and recommendations comprising model elements are generated using AI engines which are displayed at the service modeler interface for generation of the service catalogue for network service provisioning.

Abstract: A method and/or system for automated estimation of 3D orientation of a physical asset using deep learning models and computer vision algorithms, according to one or more embodiments. The system may be configured to receive images of the physical asset and camera orientation data as input, use deep learning neural network models to isolate the physical assets across the images, track each physical asset instance throughout the images and derive a 3D point cloud model of each asset by projecting binary masks of the asset contours from different view-points. The 3D point cloud model is further processed and supplemented with camera orientation data to estimate the 3D orientation of one or more assets present in the images.

Abstract: This disclosure relates to a method and a system for managing anonymized data across computing devices in a blockchain network. The method includes generating the blockchain network including a blockchain ledger based on a dataset associated with each of a plurality of end users. The method of generating the blockchain network includes constructing the blockchain ledger corresponding to the dataset associated with each of the plurality of end users; generating a unique ledger key corresponding to the dataset associated with each of the plurality of end users; and storing the dataset associated with each of the plurality of end users in the blockchain ledger based on the unique ledger key. The method further includes communicating a joining request to a second computing device for joining the blockchain network. The method further includes granting access to the second computing device to the dataset associated with the plurality of end users.

Abstract: An overlay system including a storage element and processing circuitry is provided. The storage element stores an executable graph-based model. The processing circuitry receives a contextualized stimulus. Further, the processing circuitry identifies nodes associated with processing of the contextualized stimulus and determines an association between each identified node and a node layer present in the executable graph-based model. The processing circuitry loads the identified nodes, simultaneously, into the executable graph-based model based on the determined association such that one or more identified nodes are loaded into at least one node layer. The identified nodes loaded into the executable graph-based model form a projection. The processing circuitry further executes an operation associated with the contextualized stimulus based on the projection.

Abstract: A method for access management in graph-based model is provided. The graph-based model comprises a plurality of nodes and an overlay structure comprising processing logic. The overlay structure is associated with one or more nodes of the plurality of nodes. Processing circuitry determines a first node group of the graph-based model. The first node group comprises at least one node. The processing circuitry associates a first contract with the first node group such that the first contract is configured to act as a proxy for one or more nodes within the first node group in relation to requests from outside the first node group. The processing circuitry receives a stimulus and a context associated therewith. The stimulus is associated with the first contract. The processing circuitry maps the stimulus to the first contract to determine an access response. The processing circuitry processes the stimulus based on the access response.

Abstract: A method and system for cross-verification of alarms in real-time comprising identifying primary variables and secondary variables causing the event, labelling the primary variables and the secondary variables by primary engine and secondary engine based on Artificial Intelligence based predictive model building, predicting the labels by one or more inference engine based on previous history and data patterns, triggering secondary engine for cross-verification of alarms whenever there is a prediction from the primary engine, identifying the correlation between the labels from the primary engine and the secondary engine by validation engine, identifying alarm type based on correlation, recommending predictive maintenance and displaying on dashboard the cross-verification status of the alarms. The method reduces misclassification of alarm types based on predictive or preventive maintenance, reduces the maintenance costs of assets, and helps in prioritizing the critical alarms based on the alert type.

Abstract: A method and system can implement error and event log correlation in an apparatus and include extracting one or more log information associated with a storage location and creating a flexible structure of the one or more log information. The one or more log information is translated to a database store based on a user input. A match level is determined between an event and error data through the one or more log information extracted. When the match level exceeds a predetermined value, a relationship between the event and error data is created through an algorithm and a shareable entry is created for the relationship in a format usable by another apparatus.

Abstract: Onboarding a Virtual Network Function (VNF) package utilized by multiple network services is disclosed. The VNF package is received from a vendor for network services requested by a customer. The VNF package and its contents may be uploaded on an orchestrating manager, and a NFVI admin decides workflows for the VNF package, for validating and certifying the VNF package. The VNF package is validated using multiple checks and then parameters are identified for monitoring and certifying the VNF package. The VNF package is then ready for onboarding and instantiating the network services requested by the customer.

Abstract: A method and/or system for heterogeneous predictive models generation based on sampling of big data is disclosed. The method involves receiving a dataset and a target column associated with the dataset at a data processing engine from a distributed data warehouse. One or more columns associated with the dataset are classified at the data processing engine as a categorical column or a continuous column. One or more parameters in the dataset are identified to extract a sample data from the dataset. The sample data from the dataset is extracted based on the identified one or more parameters. One or more rank ordered machine learning algorithms are recommended to one or more users, to generate one or more predictive models from the sample data. One or more heterogeneous predictive models are generated based on the rank ordered algorithm through one or more iterations.

Abstract: A method generating a platform-agnostic abstract syntax tree (AST) comprises receiving data in a predefined format, through an input unit; subsequently parsing the data to extract model information corresponding to the predefined format of the data; and transforming, by a processing server, the model information to an abstract syntax tree (AST) structure. The above steps aid in generating, by the processing server, a platform-agnostic AST by combining predefined metadata and the abstract syntax tree (AST) structure.

Abstract: A method and system of a data join includes capture of metadata information associated with one of semi-structured data and unstructured data. A flattened structure for one of the semi-structured data and the unstructured data is defined, and an entity is extracted from the unstructured data. Further, one of the semi-structured data and an entity extracted unstructured data are flattened based on the flattened structure, and flattened semi-structured data and flattened entity extracted unstructured data with relational data are joined.

Abstract: Automated fault correction in a network environment comprises identifying a pattern in a set of network events and generating a set of substantiating data for the identified patterns. The method can also identify an occurrence probability value for each network event and generate root cause data based on a ranking for the network events using a set of parameters including the occurrence probability. The method can also be directed to performing a regression of the root cause data against a set of historic data and selecting the root cause with a predefined accuracy as an acceptable candidate. The acceptable candidate is then provided for assisted learning for automated fault correction.

Abstract: A system and method of recognizing data in a table area from unstructured data includes a computer network, one or more processors communicatively coupled with the computer network, a storage location, and a graph-theoretic engine that receives an input stream of unstructured data associated. A table area is recognized from unstructured data, through one or more computer processors, from an input stream of unstructured data received over a computer network. One or more table headers associated with the detected one or more table areas are recognized. Further, one or more column delimiters associated with each column of the detected one or more table areas are determined. One or more tabular data associated with the detected one or more table areas are extracted. The extracted tabular data is mapped to one or more target schema to store onto a relational database.

Abstract: This technique improves energy efficiency of MapReduce system by using system performance model without changing any component of the MapReduce system. This involves determining presence of any hardware bottleneck in any node of MapReduce system based on a system performance model and if any hardware bottleneck is present in any node, then the maximum bandwidth value of hardware associated with the bottleneck of each node is determined. Thereafter, an energy efficient value of Central Processing Unit (CPU) frequency of each node having the bottleneck is determined by using the system performance model and the maximum bandwidth value of hardware associated with the bottleneck. Further, the CPU frequency of each node having the bottleneck is set at the energy efficient value determined in the earlier step.

Abstract: This technology relates to a device, method, and non-transitory computer readable medium for allocating one or more resources optimally in a composite cloud environment. This technology involves configuring organization and service level quota values, describing service composition, service unit, service level agreement, defining allocation model and resource allocation optimization algorithm. Based on these predefined rules the infrastructure, software and manual resources are assigned, future allocation is forecasted and resources are allocated to complete the service requests received from the users.

Abstract: A method and system automates training named entity recognition in natural language processing to build configurable entity definitions includes receiving input documents or entities through an administration module and defining a domain for each entity. Further, one or more entities corresponding to the domain specific entity in the received documents are determined and a training file to one of pick a right parser, extract content and label the entity ambiguity is generated. One or more user actions are collected and maintained at a repository through a knowledge engine. Still further, one or more labelled ambiguous words are predicted and the knowledge engine is updated. Data may be fetched, through a training pipeline execution engine and each entity may be associated with one or more documents based on the fetched data from the document store to build configurable entity definitions.

Abstract: The technique relates to a system and method for data-driven anomaly detection. This technique involves identifying region of interest from the data based on dimensionality reduction technique and change point detection algorithm. A reference data can be obtained separately or can be obtained from the test data also, wherein the reference data represent the normal operating condition of a system. The reference data are classified into different groups representing different modes of operation of the system. A control limit is determined for the different groups. The data within the region of interest are mapped with the different groups of the reference data and it is determined if the mapped data fall outside of the control limit of the mapped group. Finally, at least one abnormal event is detected by applying a heuristic algorithm on the data within the region of interest which are outside the control limit.