TL;DR: In this paper, a server arrangement for managing observation data of wireless devices is presented, including data input logic for obtaining observation data from wireless devices, the obtained data including behavioral and contextual raw data relative to the devices, data mining logic for establishing a number of derived data elements, on the basis of processing and analyzing the obtained observation and optional supplementary data.
Abstract: A server arrangement for managing observation data of wireless devices, including data input logic for obtaining observation data from wireless devices, the obtained data including behavioral and contextual raw data relative to the wireless devices, data mining logic for establishing a number of derived data elements, on the basis of processing and analyzing the obtained observation and optional supplementary data, the processing and analyzing incorporating aggregation procedures. At least one derived data element includes usage metrics with contextual dimension relative to applications or other features of wireless devices and users, data storage for storing the obtained data and the number of derived information elements, and a data distribution logic providing derived data. The distribution logic may serve a data query constructed by an external entity through provision of derived information from derived data elements according to the query parameters. A corresponding method for execution by the server arrangement is presented.
TL;DR: The experimental results show that a predictor for the optimal solution cannot be obtained in any intuitive or analytic way, due to the complexity of the involved considerations; thus, there is no obvious way to achieve these results without using the optimization model.
Abstract: The research in materialization of derived data elements has dealt so far with the if issue of whether to physically store derived data elements. In the active database area, there has been some research on the how issue. We deal with the when issue, devising an optimization model to determine the optimal materialization strategy. The decision problem confronted by the optimization model deals with devising the materialization strategy that consists of a set of interdependent decisions about each derived data element. Each decision relates to two issues: Should the value of a derived data element be persistent? What is the required level of consistency of a derived value with respect to its derivers? For each derived data element, the decision is based on both its local properties and its interdependencies with other derived values. The optimization model is based on a heuristic algorithm that finds a local optimum in O(N/sup 2/) and a monitor that obtains feedback about the actual database performance. This optimization model is general and is not specific to any data model. Our experimental results show that a predictor for the optimal solution cannot be obtained in any intuitive or analytic way, due to the complexity of the involved considerations; thus, there is no obvious way to achieve these results without using the optimization model. This fact is a strong motivation for applying such an optimization model.
TL;DR: In this paper, a query is received, and one or more computers determine that the query involves an operation that satisfies a set of criteria, and the data is saved that indicates a derived data element corresponding to the operation.
Abstract: Methods, systems, apparatus, and computer-readable media for deriving data elements from queries. In some implementations, a query is received, and one or more computers determine that the query involves an operation that satisfies one or more criteria. In response data is saved that indicates a derived data element corresponding to the operation. The one or more computers provide data causing a representation of the derived data element to be presented, such as data causing an interactive control representing the derived data element to be presented on a user interface.