A sample decreasing threshold greedy-based algorithm for big data summarisation

As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an efficient algorithm for maximising general non-nega...

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Vydané v:	Journal of big data Ročník 8; číslo 1; s. 1 - 21
Hlavní autori:	Li, Teng, Shin, Hyo-Sang, Tsourdos, Antonios
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 09.02.2021 Springer Nature B.V SpringerOpen
Predmet:	Algorithms Approximation Big Data Big data summarisation Communications Engineering Complexity Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Database Management Experiments Greedy algorithms Information Storage and Retrieval k-extendible system constraints Mathematical Applications in Computer Science Maximization Networks Optimization Personalised recommendation Random sampling Recommender systems Sampling Submodular maximisation Summarization Thresholds Personalised recommendation extendible system constraints Submodular maximisation Big data summarisation
ISSN:	2196-1115, 2196-1115
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Abstract	As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an efficient algorithm for maximising general non-negative submodular objective functions subject to k -extendible system constraints. Leveraging a random sampling process and a decreasing threshold strategy, this work proposes an algorithm, named Sample Decreasing Threshold Greedy (SDTG). The proposed algorithm obtains an expected approximation guarantee of 1 1 + k - ϵ for maximising monotone submodular functions and of k ( 1 + k ) 2 - ϵ in non-monotone cases with expected computational complexity of O n ( 1 + k ) ϵ ln r ϵ . Here, r is the largest size of feasible solutions, and ϵ ∈ 0 , 1 1 + k is an adjustable designing parameter for the trade-off between the approximation ratio and the computational complexity. The performance of the proposed algorithm is validated and compared with that of benchmark algorithms through experiments with a movie recommendation system based on a real database.
AbstractList	Abstract As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an efficient algorithm for maximising general non-negative submodular objective functions subject to k-extendible system constraints. Leveraging a random sampling process and a decreasing threshold strategy, this work proposes an algorithm, named Sample Decreasing Threshold Greedy (SDTG). The proposed algorithm obtains an expected approximation guarantee of $$\frac{1}{1+k}-\epsilon $$ 1 1 + k - ϵ for maximising monotone submodular functions and of $$\frac{k}{(1+k)^2}-\epsilon $$ k ( 1 + k ) 2 - ϵ in non-monotone cases with expected computational complexity of $$O\left(\frac{n}{(1+k)\epsilon }\ln \frac{r}{\epsilon }\right)$$ O n ( 1 + k ) ϵ ln r ϵ . Here, r is the largest size of feasible solutions, and $$\epsilon \in \left(0, \frac{1}{1+k}\right)$$ ϵ ∈ 0 , 1 1 + k is an adjustable designing parameter for the trade-off between the approximation ratio and the computational complexity. The performance of the proposed algorithm is validated and compared with that of benchmark algorithms through experiments with a movie recommendation system based on a real database. As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an efficient algorithm for maximising general non-negative submodular objective functions subject to k-extendible system constraints. Leveraging a random sampling process and a decreasing threshold strategy, this work proposes an algorithm, named Sample Decreasing Threshold Greedy (SDTG). The proposed algorithm obtains an expected approximation guarantee of 11+k-ϵ for maximising monotone submodular functions and of k(1+k)2-ϵ in non-monotone cases with expected computational complexity of On(1+k)ϵlnrϵ. Here, r is the largest size of feasible solutions, and ϵ∈0,11+k is an adjustable designing parameter for the trade-off between the approximation ratio and the computational complexity. The performance of the proposed algorithm is validated and compared with that of benchmark algorithms through experiments with a movie recommendation system based on a real database. As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an efficient algorithm for maximising general non-negative submodular objective functions subject to k -extendible system constraints. Leveraging a random sampling process and a decreasing threshold strategy, this work proposes an algorithm, named Sample Decreasing Threshold Greedy (SDTG). The proposed algorithm obtains an expected approximation guarantee of $$\frac{1}{1+k}-\epsilon $$ 1 1 + k - ϵ for maximising monotone submodular functions and of $$\frac{k}{(1+k)^2}-\epsilon $$ k ( 1 + k ) 2 - ϵ in non-monotone cases with expected computational complexity of $$O\left(\frac{n}{(1+k)\epsilon }\ln \frac{r}{\epsilon }\right)$$ O n ( 1 + k ) ϵ ln r ϵ . Here, r is the largest size of feasible solutions, and $$\epsilon \in \left(0, \frac{1}{1+k}\right)$$ ϵ ∈ 0 , 1 1 + k is an adjustable designing parameter for the trade-off between the approximation ratio and the computational complexity. The performance of the proposed algorithm is validated and compared with that of benchmark algorithms through experiments with a movie recommendation system based on a real database. As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses big data summarisation problems using the submodular maximisation approach and proposes an efficient algorithm for maximising general non-negative submodular objective functions subject to k -extendible system constraints. Leveraging a random sampling process and a decreasing threshold strategy, this work proposes an algorithm, named Sample Decreasing Threshold Greedy (SDTG). The proposed algorithm obtains an expected approximation guarantee of 1 1 + k - ϵ for maximising monotone submodular functions and of k ( 1 + k ) 2 - ϵ in non-monotone cases with expected computational complexity of O n ( 1 + k ) ϵ ln r ϵ . Here, r is the largest size of feasible solutions, and ϵ ∈ 0 , 1 1 + k is an adjustable designing parameter for the trade-off between the approximation ratio and the computational complexity. The performance of the proposed algorithm is validated and compared with that of benchmark algorithms through experiments with a movie recommendation system based on a real database.
ArticleNumber	30
Author	Tsourdos, Antonios Li, Teng Shin, Hyo-Sang
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References	Calinescu, Chekuri, Pál, Vondrák (CR28) 2011; 40 Nemhauser, Wolsey (CR27) 1978; 3 CR18 CR17 CR16 CR15 CR37 CR14 CR13 CR35 CR12 Buchbinder, Feldman, Schwartz (CR25) 2016; 42 CR34 CR11 CR33 CR10 CR32 CR31 CR30 Minoux (CR38) 1978 Nemhauser, Wolsey, Fisher (CR19) 1978; 14 Krause, Golovin (CR36) 2014; 3 Mirzasoleiman, Karbasi, Sarkar, Krause (CR8) 2016; 17 CR2 CR4 CR3 CR6 CR5 CR7 CR29 CR9 CR26 CR24 CR22 CR21 CR20 Harper, Konstan (CR23) 2016; 5 Jin, Wah, Cheng, Wang (CR1) 2015; 2 416_CR9 416_CR7 416_CR5 416_CR6 X Jin (416_CR1) 2015; 2 416_CR29 416_CR26 FM Harper (416_CR23) 2016; 5 416_CR24 416_CR21 416_CR22 N Buchbinder (416_CR25) 2016; 42 416_CR20 G Calinescu (416_CR28) 2011; 40 GL Nemhauser (416_CR27) 1978; 3 B Mirzasoleiman (416_CR8) 2016; 17 416_CR18 416_CR16 416_CR17 416_CR14 416_CR15 416_CR37 M Minoux (416_CR38) 1978 416_CR3 416_CR12 416_CR34 416_CR4 416_CR13 GL Nemhauser (416_CR19) 1978; 14 416_CR35 416_CR10 416_CR32 416_CR2 416_CR11 416_CR33 A Krause (416_CR36) 2014; 3 416_CR30 416_CR31
References_xml	– ident: CR22 – ident: CR18 – ident: CR4 – ident: CR14 – ident: CR2 – ident: CR16 – ident: CR37 – ident: CR12 – ident: CR30 – volume: 3 start-page: 71 year: 2014 end-page: 104 ident: CR36 article-title: Submodular function maximization publication-title: Tractability. – ident: CR10 – ident: CR33 – volume: 42 start-page: 308 issue: 2 year: 2016 end-page: 329 ident: CR25 article-title: Comparing apples and oranges: query trade-off in submodular maximization publication-title: Math Oper Res doi: 10.1287/moor.2016.0809 – ident: CR35 – ident: CR6 – ident: CR29 – volume: 14 start-page: 265 issue: 1 year: 1978 end-page: 294 ident: CR19 article-title: An analysis of approximations for maximizing submodular set functions—I publication-title: Math Program doi: 10.1007/BF01588971 – ident: CR21 – ident: CR3 – ident: CR15 – volume: 2 start-page: 59 issue: 2 year: 2015 end-page: 64 ident: CR1 article-title: Significance and challenges of big data research publication-title: Big Data Res doi: 10.1016/j.bdr.2015.01.006 – ident: CR17 – volume: 40 start-page: 1740 issue: 6 year: 2011 end-page: 1766 ident: CR28 article-title: Maximizing a monotone submodular function subject to a matroid constraint publication-title: SIAM J Comput doi: 10.1137/080733991 – ident: CR31 – ident: CR13 – ident: CR11 – ident: CR9 – ident: CR32 – volume: 3 start-page: 177 issue: 3 year: 1978 end-page: 188 ident: CR27 article-title: Best algorithms for approximating the maximum of a submodular set function publication-title: Math Oper Res doi: 10.1287/moor.3.3.177 – ident: CR34 – start-page: 234 year: 1978 end-page: 243 ident: CR38 article-title: Accelerated greedy algorithms for maximizing submodular set functions publication-title: Optimization techniques doi: 10.1007/BFb0006528 – ident: CR5 – ident: CR7 – volume: 5 start-page: 1 issue: 4 year: 2016 end-page: 19 ident: CR23 article-title: The movielens datasets: history and context publication-title: ACM Trans Interac Intell Syst (TIIS) doi: 10.1145/2827872 – ident: CR26 – ident: CR24 – volume: 17 start-page: 8330 issue: 1 year: 2016 end-page: 8373 ident: CR8 article-title: Distributed submodular maximization publication-title: J Mach Learn Res – ident: CR20 – ident: 416_CR11 doi: 10.1137/1.9781611975673.45 – start-page: 234 volume-title: Optimization techniques year: 1978 ident: 416_CR38 doi: 10.1007/BFb0006528 – volume: 17 start-page: 8330 issue: 1 year: 2016 ident: 416_CR8 publication-title: J Mach Learn Res – volume: 3 start-page: 177 issue: 3 year: 1978 ident: 416_CR27 publication-title: Math Oper Res doi: 10.1287/moor.3.3.177 – ident: 416_CR4 doi: 10.1109/GlobalSIP.2016.7906050 – ident: 416_CR21 – ident: 416_CR24 doi: 10.1609/aaai.v29i1.9486 – ident: 416_CR2 – ident: 416_CR15 – volume: 40 start-page: 1740 issue: 6 year: 2011 ident: 416_CR28 publication-title: SIAM J Comput doi: 10.1137/080733991 – ident: 416_CR12 doi: 10.1145/2623330.2623637 – ident: 416_CR13 – volume: 5 start-page: 1 issue: 4 year: 2016 ident: 416_CR23 publication-title: ACM Trans Interac Intell Syst (TIIS) doi: 10.1145/2827872 – volume: 2 start-page: 59 issue: 2 year: 2015 ident: 416_CR1 publication-title: Big Data Res doi: 10.1016/j.bdr.2015.01.006 – ident: 416_CR34 – volume: 42 start-page: 308 issue: 2 year: 2016 ident: 416_CR25 publication-title: Math Oper Res doi: 10.1287/moor.2016.0809 – ident: 416_CR30 – ident: 416_CR29 doi: 10.1109/FOCS.2011.46 – ident: 416_CR22 doi: 10.1137/1.9781611973402.110 – ident: 416_CR6 – ident: 416_CR16 doi: 10.1109/CVPR.2015.7298836 – ident: 416_CR37 doi: 10.1137/1.9781611973730.80 – ident: 416_CR26 – ident: 416_CR20 doi: 10.1007/11841036_48 – ident: 416_CR32 doi: 10.1007/978-3-642-17572-5_20 – volume: 3 start-page: 71 year: 2014 ident: 416_CR36 publication-title: Tractability. doi: 10.1017/CBO9781139177801.004 – ident: 416_CR14 – ident: 416_CR35 – ident: 416_CR18 – volume: 14 start-page: 265 issue: 1 year: 1978 ident: 416_CR19 publication-title: Math Program doi: 10.1007/BF01588971 – ident: 416_CR7 – ident: 416_CR17 doi: 10.1109/CVPR.2015.7298928 – ident: 416_CR33 – ident: 416_CR9 – ident: 416_CR31 – ident: 416_CR10 – ident: 416_CR3 – ident: 416_CR5
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Snippet	As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper addresses... Abstract As the scale of datasets used for big data applications expands rapidly, there have been increased efforts to develop faster algorithms. This paper...
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SubjectTerms	Algorithms Approximation Big Data Big data summarisation Communications Engineering Complexity Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Database Management Experiments Greedy algorithms Information Storage and Retrieval k-extendible system constraints Mathematical Applications in Computer Science Maximization Networks Optimization Personalised recommendation Random sampling Recommender systems Sampling Submodular maximisation Summarization Thresholds
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Title	A sample decreasing threshold greedy-based algorithm for big data summarisation
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