9741529
Mining frequent itemsets in distributed and dynamic databases
Traditional methods for frequent itemset mining typically assume that data is centralized and static. Such methods impose excessive communication overhead when data is distributed, and they waste computational resources when data is dynamic. We present what we believe to be the first unified approach that overcomes these assumptions. Our approach makes use of parallel and incremental techniques to generate frequent itemsets in the presence of data updates without examining the entire database, and imposes minimal communication overhead when mining distributed databases. Further, our approach is able to generate both local and global frequent itemsets. This ability permits our approach to identify high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.
46393116
Learning Approximate MRFs from Large Transaction Data
In this paper we consider the problem of learning approximate Markov Random Fields (MRFs) from large transaction data. We rely on frequent itemsets to learn MRFs on the data. Since learning exact large MRFs is generally intractable, we resort to learning approximate MRFs. Our proposed modeling approach first employs graph partitioning to cluster variables into balanced disjoint partitions, and then augments important interactions across partitions to capture interdependencies across them. A novel treewidth based augmentation scheme is proposed to boost performance. We learn an exact local MRF for each partition and then combine all the local MRFs together to derive a global model of the data. A greedy approximate inference scheme is developed on this global model. We demonstrate the use of the learned MRFs on the selectivity estimation problem. Empirical evaluation on real datasets demonstrates the advantage of our approach over extant solutions.
1928330
A near-optimal broadcasting algorithm in all-port wormhole-routed hypercubes
For massively parallel computing mechanism, broadcasting is widely used in a variety of applications. When the computation is distributed among the powerful processors, communication overhead always limits the speedup. To reduce the influence of communication latency, a variety of routing methods have been discussed. In this paper, we propose an efficient routing method of broadcasting on all-port worrnhole-routed hybercubes. By exploiting the distance-insensitivity of wormhole switching, this efficient broadcasting algorithm reduce communication latency to l_rzl_logz(n+l )11steps in an n-dimensional hypercube, which is much better than previous results and very close to the theoretical optimum, [rz/log2(n+l )1.Further, by adopting the Hamming code, a well-known algebraic approach in coding theory, this proposed algorithm is contention-free in each broadcasting step.
46728752
Some more remarks on domination in cubic graphs
Abstract We give sharp upper bounds on the upper signed domination number of nearly regular graphs and on the minus domination number of connected cubic graphs.
53757025
The Compositional Far Side of Image Computation
5531222
Divide and Compose: SCC Refinement for Language Emptiness
We propose a refinement approach to symbolic SCC analysis, which performs large parts of the computation on abstracted systems, and on small subsets of the state space. For language-emptiness checking, it quickly discards uninteresting parts of the state space; for the remaining states, it adapts the model checking computation to the strength of the SCCs at hand. 
 
We present a general framework for SCC refinement, which uses a compositional approach to generate and refine overapproximations. We show that our algorithm significantly outperforms the one of Emerson and Lei.
28624321
Improving Event-to-Sink Throughput in Wireless Sensor Networks
Disaster relief is an important application of sensor networks, in which bursting data needs to be collected in a short period to the sink through a multi-hop wireless network. In general, packets containing the reported data have few correlations among each other, such that meaningful information can be inferred from partially received packets. For better understanding of monitored events, it is more important to capture the total number of unique reports rather than to reliably deliver each individual packet. Therefore, in the case of monitoring disaster filed with bursting data, communication channel throughput has higher priority than the channel reliability. Under this circumstance, we revisit the sensor network transport protocols, which use hop-by-hop recovery to provide reliable data transmission over unreliable links. We found that the complex recovery mechanism, while assuring high reliable individual packet delivery, reduces channel throughput when measured data is reported at high rate. To provide optimal data transport in a sensor network with bursting data generation, we propose a light weight sink centric transport protocol, which maximizes the channel throughput by minimizing the interference of packet recovery process. We implement the sink centric transport protocol in TinyOS and evaluate its performance in the TOSSIM simulator. The comparison shows that our proposed approach outperforms the hop-by-hop recovery approach in terms of event reporting throughput and transmission costs.