24,324 research outputs found
Distributed top-k aggregation queries at large
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network
Finding groups in data: Cluster analysis with ants
Wepresent in this paper a modification of Lumer and Faietaās algorithm for data clustering. This approach
mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically
clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus
on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on
the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine,
and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more
conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant
clustering algorithms have received special attention, especially because they still require much
investigation to improve performance, stability and other key features that would make such algorithms
mature tools for data mining.
As a case study, this paper focus on the behavior of clustering procedures in those new approaches.
The proposed algorithm and its modifications are evaluated in a number of well-known benchmark
datasets. Empirical results clearly show that ant-based clustering algorithms performs well when
compared to another techniques
Variable Selection in General Multinomial Logit Models
The use of the multinomial logit model is typically restricted to applications with few predictors, because in
high-dimensional settings maximum likelihood estimates tend to deteriorate. In this paper we are proposing a sparsity-inducing penalty that accounts for the special structure of multinomial models. In contrast to existing methods, it penalizes the parameters that are linked to one variable
in a grouped way and thus yields variable selection instead of parameter selection. We develop a proximal gradient method that is able to efficiently compute stable estimates.
In addition, the penalization is extended to the important case of predictors that vary across response categories. We apply our estimator to the modeling of party choice of voters in Germany including voter-specific variables like age and gender but also party-specific features like stance on nuclear energy and immigration
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