An effective algorithm for mining 3-clusters in vertically partitioned data
Proceedings of the 17th ACM conference on Information and knowledge management
Maintaining replicas in unstructured P2P systems
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Distribution fairness in Internet-scale networks
ACM Transactions on Internet Technology (TOIT)
Uniform Sampling for Directed P2P Networks
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Distributed online aggregations
Proceedings of the VLDB Endowment
Raptor packets: a packet-centric approach to distributed raptor code design
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 4
Time-based sampling of social network activity graphs
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Scalable Uniform Graph Sampling by Local Computation
SIAM Journal on Scientific Computing
Ubiquitous knowledge discovery
Ubiquitous knowledge discovery
Randomized load balancing by joining and splitting bins
Information Processing Letters
| Hi-index | 0.00 |
Uniform random sample is often useful in analyzing data. Usually taking a uniform sample is not a problem if the entire data resides in one location. However, if the data is distributed in a peer-to-peer (P2P) network with different amount of data in different peers, collecting a uniform sample of data becomes a challenging task. A random sampling can be performed using random-walk, but due to varying degrees of connectivity and different sizes of data owned by each peer, this random walk gives a biased sample. In this paper, we propose a random walk-based sampling algorithm that can be used to sample data tuples uniformly from a large, unstructured P2P network. We model the random walk as aMarkov chain and derive conditions to bound the length of the random walk necessary to achieve uniformity. A formal communication analysis shows logarithmic communication cost to discover a uniform data sample.