Abstract: Different automatic tasks are facilitated via outlier detection in datasets using a Weighted Histogram-based Outlier Scoring (W-HBOS). An initial set of features is extracted from a processed dataset. The initial set of features is further filtered by applying robust statistics for size reduction. A second round of automatic feature selection is implemented based on maximum-entropy estimation so that a selected set of features that can give maximum possible information from different dimensions towards detecting anomalies are selected. The selected set of features are transformed to generate principal components that are provided to the W-HBOS-based model for outlier detection. A subset of outliers in one of the directions can be selected and reason codes are identified using back transformation for the execution of a desired automatic task.

Abstract: Different automatic tasks are facilitated via outlier detection in datasets using a Weighted Histogram-based Outlier Scoring (W-HBOS). An initial set of features is extracted from a processed dataset. The initial set of features is further filtered by applying robust statistics for size reduction. A second round of automatic feature selection is implemented based on maximum-entropy estimation so that a selected set of features that can give maximum possible information from different dimensions towards detecting anomalies are selected. The selected set of features are transformed to generate principal components that are provided to the W-HBOS-based model for outlier detection. A subset of outliers in one of the directions can be selected and reason codes are identified using back transformation for the execution of a desired automatic task.

Abstract: A system and method for improving and maintaining retail store customer loyalty through providing customers with quality services, timely assistances, and conveniences in all stages of product shopping. The system provides a web parts based collaborative online platform for retailers, product manufacturers, and service providers to share resources, distribute coupons, promote products and services, and communicate with customers. It further provides shopping assistances to help customers selectively receive coupons, redeem coupons online, build shopping lists online, retrieve shopping list and shopping reminders, find product shelf locations, place online orders, and manage budgets and nutrition. The system has the capability to collect customer shopping activity data, automatically analyze customer product purchase preferences, recommend products and services, and deliver relevant coupons and advertisements to customers.

Abstract: A system for retail store shelf stock status monitoring, predicting, and reporting that is capable of monitoring retail store current shelf stock capacity and shelf product freshness at product level, category level, department level, store level, and other product hierarchy levels. It is also capable of intelligently predicting future shelf stock status according to predicted future store sales activities. It uses shelf stock empty index, out-of-stock alert, shelf stock freshness, shelf stock expiration alert, and other shelf stock performance measures to present current shelf status, future shelf status, and past shelf stock performances through interactive store maps and other visual presentation means to optimally deliver retail store shelf stock performance information in real time. The system empowers store clerks, store managers, chain store management, and corporation analysts to monitor, review, and analyze store shelf stock status in real time from anywhere at any time.