Efficient representative subset selection over sliding windows

• Main Authors: WANG, Yanhao, LI, Yuchen, TAN, Kian-Lee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Approximation algorithm; Approximation algorithms; Data mining; Data models; data stream; Data summarization; Heuristic algorithms; Indexes Kernel; Microsoft Windows; Sliding window; Submodular maximization; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/4092; https://ink.library.smu.edu.sg/context/sis_research/article/5095/viewcontent/08410031.pdf
• Institution: Singapore Management University

Representative subset selection (RSS) is an important tool for users to draw insights from massive datasets. Existing literature models RSS as submodular maximization to capture the "diminishing returns" property of representativeness, but often only has a single constraint, which limits its applications to many real-world problems. To capture the recency issue and support various constraints, we formulate dynamic RSS as maximizing submodular functions subject to general d -knapsack constraints (SMDK) over sliding windows. We propose a KnapWindow framework (KW) for SMDK. KW utilizes KnapStream (KS) for SMDK in append-only streams as a subroutine. It maintains a sequence of checkpoints and KS instances over the sliding window. Theoretically, KW is 1−ε1+d -approximate for SMDK. Furthermore, we propose a KnapWindowPlus framework ( KW+ ) to improve upon KW. KW+ builds an index SubKnapChk to manage the checkpoints. By keeping much fewer checkpoints, KW+ achieves higher efficiency than KW while guaranteeing a 1−ε′2+2d -approximate solution for SMDK. Finally, we evaluate KW and KW+ in real-world datasets. The experimental results demonstrate that KW achieves more than two orders of magnitude speedups over the batch baseline and preserves high-quality solutions for SMDK. KW+ further runs 5-10 times faster than KW while providing solutions with equivalent or better utilities.