III: Small: Collaborative Research: Stream-Based Active Mining at Scale: Non-Linear Non-Submodular Maximization

III：小型：协作研究：基于流的大规模主动挖掘：非线性非子模最大化

• 批准号: 1907472
• 负责人: Ding-Zhu Du
• 金额: $ 25万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1907472&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Stream

The past decades have witnessed enormous transformations of intelligent data analysis in the realm of datasets at an unprecedented scale. Analysis of big data is computationally demanding, resource hungry, and much more complex. With recent emerging applications, most of the studied objective functions have been shown to be non-submodular or non-linear. Additionally, with the presence of dynamics in billion-scale datasets, such as items are arriving in an online fashion, scalable and stream-based adaptive algorithms which can quickly update solutions instead of recalculating from scratch must be investigated. All of the aforementioned issues call for a scalable and stream-based active mining techniques to cope with enormous applications of non-submodular maximization in the era of big data. With the society's growing dependence on the cyberspace and computer technologies, the premium placed on the intelligent big data analysis for many emerging applications. Therefore, the success of this project has a high impact in almost any field that needs lightweight and near-optimal big data analysis. The findings of this project will also enrich the research on network science, graph theory, optimization, and big data analysis. In addition to creating new courses, undergrad and high school students will be involved in hands-on activities over the experimental platform. Outreach events targeted at under-represented groups and K-1This project develops a theoretical framework together with highly scalable approximation algorithms and tight theoretical performance bound guarantees for the class of non-submodular and non-linear optimization. In particular, the project lays the foundation for the novel data mining techniques, suitable to the new era of big data with emerging applications, as well as advance the research front of stochastic and stream-based algorithm designs, with several key innovations: 1) Rigorous mathematical techniques to analyze and design highly scalable approximation algorithms to the class of non-monotonic, non-submodular maximization, which underlies many emerging applications. 2) Attempt a new research direction by bridging the non-linear optimization and the combinatorial optimization, thereby bringing the new angles for the study of non-submodular optimization as well as getting deeper understanding of the problem structures. 3) Novel stream-based active mining at scale for multiple applications, focused on the two general models which unify many optimization problems in the domain of online social networks and privacy. It also provides a novel theoretical framework for adaptive non-submodular maximization, which has not been studied in the literature. 4) Extensive evaluation through a combination of various tools and methods, including the real-world datasets and applications that will bridge the gap between theory and practice.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的几十年里，智能数据分析在数据集领域发生了前所未有的巨大转变。大数据分析需要计算量大、资源匮乏且复杂得多。 随着最近出现的应用，大多数研究的目标函数已被证明是非子模或非线性的。此外，由于数十亿规模的数据集中存在动态，例如项目以在线方式到达，因此必须研究可快速更新解决方案而不是从头开始重新计算的可扩展和基于流的自适应算法。所有上述问题都需要可扩展的、基于流的主动挖掘技术来应对大数据时代非子模最大化的巨大应用。随着社会对网络空间和计算机技术的依赖日益加深，许多新兴应用的智能大数据分析受到重视。因此，这个项目的成功几乎对任何需要轻量级和近乎最优的大数据分析的领域都有很大的影响。该项目的研究成果也将丰富网络科学、图论、优化和大数据分析方面的研究。除了创建新课程外，本科生和高中生还将通过实验平台参与实践活动。针对代表性不足的群体和 K-1 的外展活动该项目开发了一个理论框架，以及高度可扩展的近似算法和针对非子模块和非线性优化类的严格理论性能界限保证。特别是，该项目为适合新兴应用的大数据新时代的新型数据挖掘技术奠定了基础，并推进了随机和基于流的算法设计的研究前沿，具有以下几项关键创新：1）严格的数学技术来分析和设计高度可扩展的近似算法，以实现非单调、非子模最大化类，这是许多新兴应用的基础。 2）尝试新的研究方向，将非线性优化与组合优化联系起来，从而为非子模优化的研究带来新的角度，加深对问题结构的理解。 3）针对多种应用的新型基于流的大规模主动挖掘，重点关注统一在线社交网络和隐私领域的许多优化问题的两个通用模型。它还为自适应非子模最大化提供了一种新颖的理论框架，该框架尚未在文献中进行过研究。 4) 通过结合各种工具和方法进行广泛的评估，包括现实世界的数据集和应用程序，这将弥合理论与实践之间的差距。该奖项反映了 NSF 的法定使命，并被认为值得通过使用基金会的智力评估进行支持优点和更广泛的影响审查标准。

项目成果

Union acceptable profit maximization in social networks

社交网络中联盟可接受的利润最大化

• DOI: 10.1016/j.tcs.2022.03.015
• 发表时间: 2022-03-01
• 期刊: Theor. Comput. Sci.
• 作者: Guoyao Rao;Yongcai Wang;Wenping Chen;Deying Li;Weili Wu
• 通讯作者: Weili Wu

Task-driven charger placement and power allocation for wireless sensor networks

无线传感器网络的任务驱动充电器放置和功率分配

• DOI: 10.1016/j.adhoc.2021.102556
• 发表时间: 2021-08-01
• 期刊: Ad Hoc Networks
• 影响因子: 4.8
• 作者: Xingjian Ding;Jianxiong Guo;Yongcai Wang;Deying Li;Weili Wu
• 通讯作者: Weili Wu

An Architecture for Distributed Energies Trading in Byzantine-Based Blockchains

基于拜占庭的区块链中的分布式能源交易架构

• DOI: 10.1109/tgcn.2022.3142438
• 发表时间: 2020-05-15
• 期刊: IEEE Transactions on Green Communications and Networking
• 影响因子: 4.8
• 作者: Jianxiong Guo;Xingjian Ding;Weili Wu
• 通讯作者: Weili Wu

Minimum Wireless Charger Placement with Individual Energy Requirement

满足个人能源需求的最小无线充电器放置位置

• DOI: 10.1007/978-3-030-64843-5_47
• 发表时间: 2024-09-13
• 作者: Xingjian Ding;Jianxiong Guo;Deying Li;D. Du
• 通讯作者: D. Du

Edge Server Deployment Scheme of Blockchain in IoVs

车联网区块链边缘服务器部署方案

• DOI: 10.1109/tr.2022.3142776
• 发表时间: 2020-06-16
• 期刊: IEEE Transactions on Reliability
• 影响因子: 5.9
• 作者: Liya Xu;Mingzhu Ge;Weili Wu
• 通讯作者: Weili Wu