Optimizing Update Frequencies for Decaying Information
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Many kinds of information, e.g., addresses, crawls of webpages, or academic affiliations, are prone to becoming outdated over time. Therefore, if data quality shall be maintained over time, often periodical refreshing is done. As refreshing data usually has a cost, for instance computation time, network bandwidth or human work time, a problem is to find the right update frequency depending on the benefit gained from the information and on the speed with which the information is expected to get outdated. This is especially important since often entities exhibit a different speed of getting outdated, e.g., addresses of students change more frequently than addresses of retirees, or news portals change more frequently than homepages. Consequently, there is no uniform best update frequency for all entities. Previous work on data freshness has investigated how to best distribute a fixed number of updates among entities, in order to maximize average freshness. For businesses that are able to adapt their resources, another question is to determine the number of updates that optimizes the income derived from the data. In this paper we present a model for describing the relationship between update frequency and income derived from data, present solutions for calculating the optimal update frequency for two common classes of functions for describing decay behaviour, and validate the benefits of our framework.