S
Sen Su
Researcher at Beijing University of Posts and Telecommunications
Publications - 206
Citations - 3803
Sen Su is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Web service. The author has an hindex of 27, co-authored 187 publications receiving 3144 citations. Previous affiliations of Sen Su include Peking University.
Papers
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Proceedings ArticleDOI
MASTER: across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation.
TL;DR: The MASTER framework, i.e., across Multiple social networks, integrate Attribute and STructure Embedding for Reconciliation to formulate the problem into a unified optimization, and demonstrates that MASTER outperforms the state-of-the-art approaches.
Proceedings ArticleDOI
Differentially private frequent itemset mining via transaction splitting
TL;DR: A differentially private FIM algorithm based on the FP-growth algorithm, which is referred to as PFP-growth, which substantially outperforms the state-of-the-art techniques and is shown to be ε-differentially private.
Proceedings ArticleDOI
Reducing Operational Costs through Consolidation with Resource Prediction in the Cloud
TL;DR: This work uses an Online Coloring Bin Packing problem to model the consolidation problem and devise an effective application-aware approximation algorithm to find a near-optimal solution, which shows a 1.7 asymptotic approximation ratio.
Journal ArticleDOI
Iterative selection algorithm for service composition in distributed environments
Sen Su,Fei Li,Fangchun Yang +2 more
TL;DR: An iterative service selection algorithm that can work on a centralized QoS registry as well as cross decentralized ones, and can be applied either before service execution or at service run-time without any modification is presented.
Journal ArticleDOI
Adaptive multi-objective artificial immune system based virtual network embedding
TL;DR: This paper first formulate the virtual network embedding problem into a multi-objective integer linear programming, then designs an artificial immune system based algorithm to solve this programming and shows that this algorithm outperforms the state-of-the-art algorithms in terms of the revenue and the energy consumption.