Abstract: Configurations for measuring sharing behavior and audience coverage for one or more users are provided. Some configurations can include the following components: an input processor (IP), a data processor (DP) and a visualization processor (VP). The IP extracts data from user logs or processes existing raw data, and prepares the data for the data processor. In one example, the IP transforms each user record into a structured file including time-ordered data corresponding to instances of information sharing activity, target users, and associated date-time(s) for the sharing activity. The DP takes the time-ordered data from the IP and performs statistical evaluations to classify and determine the audience coverage and sharing behavior. The VP receives processed data from the DP and generates different graphical visualizations (e.g., one or more graphs) based on the statistical analysis provided by the DP.

Abstract: Provided is a process and system for ascertaining the operating hours of a business. The process includes obtaining wireless-environment data indicative of the location of business and a timestamp; determining that a user device is located at the business responsive to both the wireless-environment data and wireless-environment data from checked-in user devices that have checked-in to the business via a social network; storing the timestamp in a timestamp data store; estimating operating hours of the business responsive to stored timestamps; and storing the estimated operating hours in a business-hours data store.

Abstract: Systems and methods for determining a group recommendation of an object, such as a restaurant, movie, or other object, from a plurality of candidate objects based on user comparisons of characteristic traits of the candidate objects are provided. In particular, keywords associated with characteristic traits are identified. The keywords are then presented to members of the group as a series of selection queries. The selection queries require a user to select or rank the keywords based on user preferences. The responses to the selection queries are used to generate a ranking score for each of the plurality of candidate objects and to select one or more of the candidate objects to recommend to the group.