Abstract: Systems, methods, and software are described herein to support view-based storage. A service application that provides a user interface to a service receives data communicated by the service and identifies at least a service item to present in at least one of various views rendered in the user interface. The service application then identifies with which of the views to associate the service item and stores the service item in association with an identified view or views.

Abstract: Described technologies automatically evaluate contact information and other submitted textual content to identify suspect data. Different data validation technologies alone or in combination identify frivolous content such as profanity, gibberish, and mismatched meanings. These technologies may perform regular expression recognition, naïve Bayes or other probabilistic classifications, named entity recognition, and subsidiary functions such as data cleaning, gibberish generation, and model training. A predictor value indicates how likely it is that the submitted data is valid input, e.g., a valid person name or valid company name, as requested. A trained machine learning based content characterizer embodies rules for content characterization, including implicit rules produced by supervised machine learning. Some content characterizers identify suspect content other than range violations or data type violations, by identifying profanity or gibberish.

Abstract: Systems, methods, and software are described herein to support view-based storage. A service application that provides a user interface to a service receives data communicated by the service and identifies at least a service item to present in at least one of various views rendered in the user interface. The service application then identifies with which of the views to associate the service item and stores the service item in association with an identified view or views.