Abstract: A data processing and analysis system that optimizes the resources to be used for data storage and refresh events. A partitioner module for a data analysis system can receive a first client criteria and a first client dataset that includes tabular data and calculate scores that are used to generate partitioning strategies. The selected partitioning strategy can be implemented to produce aggregated data that can be stored in an intelligent data mart. The partitions can then be accessed by a data visualization platform for intelligent, dynamic responses to user requests for data analyses and generation of visualizations. By providing synchronous partitioning of data (especially big data) and intelligent refresh, the data can move from the back-end to the front-end with minimal user clicks and minimal latency in performance.

Abstract: A data processing and analysis system that optimizes the resources to be used for data storage and refresh events. A partitioner module for a data analysis system can receive a first client criteria and a first client dataset that includes tabular data and calculate scores that are used to generate partitioning strategies. The selected partitioning strategy can be implemented to produce aggregated data that can be stored in an intelligent data mart. The partitions can then be accessed by a data visualization platform for intelligent, dynamic responses to user requests for data analyses and generation of visualizations. By providing synchronous partitioning of data (especially big data) and intelligent refresh, the data can move from the back-end to the front-end with minimal user clicks and minimal latency in performance.

Abstract: A system for auto classification of products includes an entity recognizer and a model selector. The entity recognizer receives training data including an attribute of a product. The model selector selects a feature from the training data using a first statistical model to provide a first feature and a second statistical model to provide a second feature, and trains a probabilistic classifier using the first and the second features for providing a first and a second classification models respectively. Further, the model selector calculates an accuracy score of the obtained classification models for each distinct category in a preset hierarchy of categories and selects a classification model from the obtained classification models based on the accuracy score. The selected classification model has a highest accuracy score for a corresponding category in the preset hierarchy.

Abstract: A system for auto classification of products includes an entity recognizer and a model selector. The entity recognizer receives training data including an attribute of a product. The model selector selects a feature from the training data using a first statistical model to provide a first feature and a second statistical model to provide a second feature, and trains a probabilistic classifier using the first and the second features for providing a first and a second classification models respectively. Further, the model selector calculates an accuracy score of the obtained classification models for each distinct category in a preset hierarchy of categories and selects a classification model from the obtained classification models based on the accuracy score. The selected classification model has a highest accuracy score for a corresponding category in the preset hierarchy.