One approach for generating the estimate is to identify a quantity of stored objects (previously received as live objects) that correspond to the filtering parameter(s) and to project an estimate based on the count.
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Accordingly, it can be advantageous to generate a preliminary estimate of a size of the subset, which can be used to determine whether to proceed to identify the full subset or to modify the filtering parameter to produce a subset of the right size. Conversely, a large subset may prohibit distribution of the targeted content object as a result of a total distribution cost being too high. For example, a small subset may result in a determination that the cost of generating or distributing the targeted content object is too high on a per-recipient-device basis. The utility of receiving a response with a subset identification can depend on a size of the subset. Receipt of objects corresponding to the parameters can facilitate the requestor device to selectively transmit a targeted content object (e.g., advertisement for an electric car) to devices predicted (e.g., by a requestor) to be likely to interact with (e.g., click on) the content object. For example, a live object request may include filtering parameters indicative of a user demographic category (e.g., males, age 25-40) and a historical interaction with a webpage object (e.g., clicked on link involving electric cars). Each object in the subset can correspond to a specific user and/or specific device, such that a client system can coordinate selective distribution of content to be presented on a webpage to be rendered at corresponding specific user devices. More specifically, the one or more filtering parameters can be used to identify select live objects that identify particular characteristics of the represented interactions, where the particular characteristics corresponds to the filtering parameter(s). A request can be received for a subset of the generated live objects, and the subset can be defined by one or more filtering parameters identified within the request.
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Live objects corresponding to these interactions are generated as the interactions occur and are distributed based on active live object requests. objects, files, data records or log messages) can be generated that identify many details corresponding to the interactions (e.g., identifying the webpage, interaction type, involved content object, time, user device type, browser type, user login identifier, etc.). BRIEF SUMMARYĪs users interact with computers and/or webpages, data (e.g.
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Further, it can be difficult to estimate the quantity based on (for example) object structure variability, dynamic changes as to which objects are present in a data store, and limited access to object data.
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However, a response to such a request may be of variable utility depending on a quantity of parameter-matching objects.
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Object requests can identify particular parameters, so as to correspond to a request for information associated with each object in a data store that matches the parameter. 62/500,883 filed on May 3, 2017, entitled “CAMPAIGN SIMULATION,” which is hereby incorporated by reference in its entirety for all purposes. This application claims the benefit of and the priority to U.S.