1. Presented by: Dardan Xhymshiti Spring 2016:

2. Authors: Publication:  ICDM 2015 Type:  Research Paper 2 Sean Chester*Darius Sidlauskas`Ira Assent*Kenneth S. Bogh* *Data-Intensive Systems Group, Aarhus University, Denmakr `Data-Intensive Applications and Systems Laboratory, EPFL, Switzerland

3.  Skyline is expensive to compute especially in large datasets.  The recent multi-core skyline algorithms does not effectively reduce the dominance tests.  State-of-the art skyline algorithms outperform multi-core algorithms.  Most of the multi-core Skyline algorithms use the Divide-&-Conquer approach which has two drawbacks:  If the number of local skyline points is large, the merging step is expensive.  Increasing the cardinality of the dataset, the computation becomes expensive. 3

4.  To come up with a new multi-core algorithm, which eliminate as much as it can dominance tests. 4

5.  Provide an overview about skyline operator.  Introduces to the innovative skyline Hybrid algorithm.  Provide experiments which shows that this algorithm outperforms multi-core and sequential algorithms. 5

6.  How to increase the performance of skyline algorithms:  Implementation in GPUs  Implementation in Multi-Core CPUs.  Implementing in distributing environments like MapReduce.  The authors have developed an algorithm called: Hybrid  The authors have chosen the Multi-Core CPU to do the implementation of the algorithm because of:  Cheaper shared data structures and  Parallel work need not be isolated. 6

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8.  Sort-based algorithms (quickly detect dominance relationships)  SFS (Sort Filter Skyline)  LESS  SaLSa (Sort and Limit Skyline algorithm)  Object-based space partitioning (quickly detect incomparability)  Object-Space partitioning  BSky-Tree-P 8