Skyline Recommendation in Distributed Networks

Skyline recommendation technology has recently received a lot of attention in the database community. However, the existing works mostly focus on how to obtain skyline objects from fine-grained data in centralized environments. And the time cost of skyline recommendation will increase exponentially as the number of data and skyline recommendation instructions increases, which will seriously influence the recommendation efficiency. Motivated by the above fact, this paper proposes an efficient algorithm Skyline Recommendation Algorithm in Distributed Networks (SRADN) in Super-Peer Architecture (SPA) distributed networks to handle multiple subspace skyline recommendations by prestoring the set of skyline snapshots under the cost constraint of maintenance and communication. The proposed SRADN algorithm fully considers the characteristic of storage and communication of SPA networks, and uses the map/reduce distributed computation model. The SRADN algorithm can quickly produce the optimal set of skyline snapshots through the following two phases: Heuristically constructing the initial set of snapshots, and adjusting the set of snapshots based on the genetic algorithm. The detailed theoretical analyses and extensive experiments demonstrate that the proposed SRADN algorithm is both efficient and effective