The skyline operator and its variants such as dynamic skyline and reverse skyline operators have attracted considerable attention recently due to their broad applications. However, computations of such operators are challenging today since there is an increasing trend of applications to deal with big data. For such data-intensive applications, the MapReduce framework has been widely used recently.In this paper, we propose efficient parallel algorithms for processing the skyline and its variants using MapReduce. We first build histograms to effectively prune out non-skyline (non-reverse skyline) points in advance. We next partition data based on the regions divided by the histograms and compute candidate (reverse) skyline points for each region independently using MapReduce. Finally, we check whether each candidate point is actually a (reverse) skyline point in every region independently. Our performance study confirms the effectiveness and scalability of the proposed algorithms.

No abstract

No abstract

People engage with many overlapping social networks and enact diverse social roles across different facets of their lives. Unfortunately, many online social networking services reduce most people's contacts to "friend." A richer computational model of relationships would be useful for a number of applications such as managing privacy settings and organizing communications. In this paper, we take a step towards a richer computational model by using call and text message logs from mobile phones to classifying contacts according to life facet (family, work, and social). We extract various features such as communication intensity, regularity, medium, and temporal tendency, and classify the relationships using machine-learning techniques. Our experimental results on 40 users showed that we could classify life facets with up to 90.5% accuracy. The most relevant features include call duration, channel selection, and time of day for the communication.

No abstract