Session details: Data summarization

Author(s):  
Lei Chen
Keyword(s):  

Author(s):  
Kai Han ◽  
Shuang Cui ◽  
Tianshuai Zhu ◽  
Enpei Zhang ◽  
Benwei Wu ◽  
...  

Data summarization, i.e., selecting representative subsets of manageable size out of massive data, is often modeled as a submodular optimization problem. Although there exist extensive algorithms for submodular optimization, many of them incur large computational overheads and hence are not suitable for mining big data. In this work, we consider the fundamental problem of (non-monotone) submodular function maximization with a knapsack constraint, and propose simple yet effective and efficient algorithms for it. Specifically, we propose a deterministic algorithm with approximation ratio 6 and a randomized algorithm with approximation ratio 4, and show that both of them can be accelerated to achieve nearly linear running time at the cost of weakening the approximation ratio by an additive factor of ε. We then consider a more restrictive setting without full access to the whole dataset, and propose streaming algorithms with approximation ratios of 8+ε and 6+ε that make one pass and two passes over the data stream, respectively. As a by-product, we also propose a two-pass streaming algorithm with an approximation ratio of 2+ε when the considered submodular function is monotone. To the best of our knowledge, our algorithms achieve the best performance bounds compared to the state-of-the-art approximation algorithms with efficient implementation for the same problem. Finally, we evaluate our algorithms in two concrete submodular data summarization applications for revenue maximization in social networks and image summarization, and the empirical results show that our algorithms outperform the existing ones in terms of both effectiveness and efficiency.





2009 ◽  
pp. 1300-1304
Author(s):  
Egemen Tanin




Author(s):  
G Sundaresan ◽  
L Wu ◽  
H Yun ◽  
K Park ◽  
J Kim






Author(s):  
Ines Benali Sougui ◽  
Minyar Sassi Hidri ◽  
Amel Grissa Touzi

With the huge amount and the evolution of fuzzy data, the necessity to work with synthetic views became a challenge for many databases (DB) community researchers. Data summarization techniques are now considered as accurate tools to handle huge DB, in particular when precise data are not needed. Formal approaches have been proposed making possible the generation of an hierarchy of summaries from DB. The challenges arise on the question of how querying these fuzzy views according user requirements. In this work, we propose to handle with these challenges by query repairing and substitution. Two process were studied, the first process is used by modifying query while using the best fuzzy summaries which have the most near answers. The second one is applied to generate all substitution queries over the fuzzy summaries' hierarchy. This would be not only expensive but also unjustified for the part of the search hierarchy nodes.



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