Conduent Business Services, LLC

Conduent Busness System, LLC

Sharanya Eswaran

Koustuv Dasgupta

Tridib Mukherjee

Deepthi Chander

Shisagnee Banerjee

Todd Redmond

Meeralakshmi Radhakrishnan

Sakyajit Bhattacharya

Shruti Kunde

Arun Rajkumar

Gurulingesh Raravi

Archan Misra

Avantika Gupta

Sindhu Kiranmai Ernala

Sakshi Agarwal

Joydeep Banerjee

Nimmi Rangaswamy

Priyanka Sharma

Poorvi Agarwal

Mahima Suresh

Mridula Singh

Methods and systems for forecasting in sparse data streams. In an example embodiment, steps or operations can be implemented for mapping a time series data stream to generate forecast features using a neural network, transforming the forecast features into a space with transformed forecast features thereof using metric learning, clustering the transformed forecast features in a cluster, initializing a forecast learning algorithm with a combination of the transformed forecast features in the cluster corresponding to a sparse data stream, and displaying forecasts in a GUI dashboard with information indicative of how the forecasts were achieved, wherein the mapping, the transforming, the clustering, and the initializing together lead to increases in a speed of the forecasting and computer processing thereof.

Method and system for forecasting in sparse data streams via dense data streams

Datasets relating time information to crime occurrences in the geographical regions are received. Time based crime patterns are extracted. Based on similarities among the crime patterns, the geographical regions are clustered. A selected time series dataset is augmented with a second time series dataset from the same cluster. Based on the augmented time series dataset, a new crime pattern is extracted. Based on the new crime pattern, a crime forecast is made for the selected geographical region.

Crime analysis using domain level similarity

The disclosed embodiments illustrate a method and a system for managing crowd-sensed data, associated with events occurring in a geographical area. The method includes receiving crowd-sensed data from one or more data sources, wherein the crowd-sensed data comprises one or more event reports associated with at least a type of each of one or more events reported by the one or more data sources. The method further includes generating a data structure based on an aggregation of the received crowd-sensed data. Further, the method includes determining first information and second information based on at least the generated data structure, a reputation score of each of the one or more data sources and metadata associated with each of the one or more event reports. The method further includes displaying at least the determined first information and second information based on at least a prioritization of the one or more events.

Method and system for managing crowd-sensed data

A crime analysis system, method, and apparatus comprising at least one processor and a storage device communicatively coupled to the at least one processor, the storage device storing instructions which, when executed by the at least one processor, cause the processor to perform operations comprising receiving information provided by one or more data collection source, storing the information, wherein the stored information is formatted, processing the information to generate crime clustering data associated with at least one region and at least one crime, processing the crime clustering data associated with at least one region and at least one crime to generate benchmarking of the at least one region with at least one other region, and providing crime clustering data associated with at least one region and at least one crime, and benchmarking of the at least one region with at least one other region for presentation through a user interface.

Mixture model based time-series clustering of crime data across spatial entities

The present disclosure discloses methods and systems for enhancing shopping experience in physical stores. The method includes receiving at least one persona associated with a user based on one or more of: ethnographic data obtained from a user, demographic data associated with the user, buying behavioral data associated with the user, and social networking data associated with the user. After this, one or more historical activities of the user inside one or more physical stores are received. Also, one or more constraints associated with the user are received. Once received, the at least one persona, the one or more constraints, and the one or more historical activities are analyzed to generate a pre-defined number of personalized recommendations. Finally, the personalized recommendations are displayed to the user within a window of a user interface.

Systems and methods for enhancing shopping experience in physical stores

The disclosed embodiments illustrate methods and systems for processing customer data to predict behavioral characteristics of a customer in a physical store. The method includes receiving customer data, pertaining to a shopping episode, of the customer from one or more sensing devices. The method further includes segmenting the shopping episode into one or more segments of time durations. The method further includes categorizing each of the one or more segments of time durations into one of an in-aisle category and a non-aisle category. The method further includes identifying hand-related actions of the customer based on the received customer data. The method further includes determining a likelihood of occurrence of an action-related category based on at least the identified one or more hand-related actions. The method further includes predicting the behavioral characteristics of the customer based on at least the determined likelihood of occurrence of the action-related category.

Method and system for predicting behavioral characteristics of customers in physical stores

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Bengaluru, India

Sharanya Eswaran