It takes as input a Distributed Common Query Language (DCQL) query, breaks it into separate Common Query Language (CQL) queries, issues those queries to the appropriate underlying data services, receives the results, performs joins, aggregates data, and returns the results.
The distributed query engine takes as input a distributed common query language (DCQL) query, decomposes it into individual common query language (CQL) queries, issues those queries to the domain services, joins/aggregates the results, and returns the final results to the caller.
Fig. 1 Distribution of the number of queries issued per user for the different dataset.
We assume, for simplicity, here that each query issued is always answered, and answered accurately and truthfully.
Figure 1 depicts the Cumulative Distribution Function (CDF) of the number of queries issued by user in these 5 datasets.
Queries issued by different users will almost certainly be different, especially if they contain a floorplan graph.
Various sources of data are mined and modeled in order to detect these segments, such as the queries issued by the users.
While we do not prescribe the mechanism used by users to issue queries to other users, we assume that some cost is incurred per query issued (or per answer about a region obtained).
Interestingly, any query issued to the cloud server can be replied to with a valid proof based on the forward updated public key (PK^{prime }).
Much research on demographic prediction focuses on cyber behaviours investigate through Web pages browsed [2 5], queries issued [2, 6], mobile Apps installed, or Web contributed or commented upon (e.g., images, tweets, comments, likes) [7 9].
