EAGER: N-Body Algorithms for Mobile and Social Data

EAGER：适用于移动和社交数据的 N-Body 算法

• 批准号: 1939237
• 负责人: Athina Markopoulou
• 金额: $ 30万
• 依托单位: University of California-Irvine
• 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: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939237&HistoricalAwards=false
• 关键词: EAGER; Body; Algorithms; Mobile; Social

Today's fast-growing mobile device and online social media infrastructure is producing an unprecedented volume of data on communication, system and device-level interaction, and user behavior. Examples include cellphone data (including voice calls, SMS and Internet data, cellular signal strength) and activity on online social media (itself often sent from mobile devices). This presents both opportunities and challenges. On one hand, the increasing availability, high quality, and relatively easy collection of such data provides a way to better understand online human activity, infer activity patterns, and design new services. On the other hand, the combination of big data and powerful inferential techniques pose both computational challenges and privacy concerns. In both cases, there is a need for efficient algorithmic techniques that can support learning from mobile and social data sets, and that enable principled creation of synthetic data at similar scale for modeling, testing and anonymization purposes. This project aims to address problems in the above space by framing them in terms that are traditionally the domain of the physical sciences.This project is based on the core insight that many problems in the above space can be framed in terms of pairwise interactions among spatially embedded entities, traditionally the domain of N-body problems in the physical sciences. As a consequence, revisiting and adapting N-body algorithms specifically to mobile and social data analysis and learning can increase our capacity to (i) work with such data at scale and (ii) do so in a privacy-preserving way. The methodological contributions will include novel N-body and parallel algorithms, specifically designed for processing hierarchical, and geospatially embedded, mobile and social data, where the size or access to datasets is prohibitive. In addition to the algorithmic design, this project will develop software modules (e.g., special purpose compilers, crowdsourcing tools and generators of synthetic datasets) that implement these methods. The design willbe primarily applied to improve cellular network monitoring and control, mobile data privacy, and smart city applications.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.

当今快速增长的移动终端和在线社交媒体基础设施正在产生关于通信、系统和设备级交互以及用户行为的前所未有的数据量。例子包括手机数据（包括语音通话、短信和互联网数据、手机信号强度）和在线社交媒体上的活动（本身通常从移动的设备发送）。这既是机遇，也是挑战。一方面，这些数据的可用性、高质量和相对容易的收集提供了一种更好地理解在线人类活动、推断活动模式和设计新服务的方法。另一方面，大数据和强大的推理技术的结合带来了计算挑战和隐私问题。在这两种情况下，都需要有效的算法技术，这些技术可以支持从移动的和社交数据集学习，并且能够以类似的规模原则性地创建合成数据，以用于建模、测试和匿名化目的。 本项目的目的是解决上述空间中的问题，即用传统上属于物理学领域的术语来构建这些问题。本项目的核心观点是，上述空间中的许多问题可以用空间嵌入实体之间的成对相互作用来构建，传统上属于物理学中的N体问题。因此，重新审视和调整专门针对移动的和社交数据分析和学习的N体算法，可以提高我们（i）大规模处理此类数据的能力，以及（ii）以保护隐私的方式这样做。 方法学方面的贡献将包括新的N体和并行算法，这些算法专门用于处理层次结构、地理空间嵌入式、移动的和社会数据，这些数据的规模或访问权限都是禁止的。除了算法设计，该项目还将开发软件模块（例如， 专用编译器、众包工具和合成数据集生成器）来实现这些方法。该设计将主要应用于改善蜂窝网络监测和控制、移动的数据隐私和智慧城市应用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

adPerf: Characterizing the Performance of Third-party Ads

adPerf：描述第三方广告的性能

• DOI: 10.1145/3447381
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Measurement and Analysis of Computing Systems
• 作者: Pourghassemi, Behnam;Bonecutter, Jordan;Li, Zhou;Chandramowlishwaran, Aparna
• 通讯作者: Chandramowlishwaran, Aparna

Only Relative Speed Matters: Virtual Causal Profiling

只有相对速度才重要：虚拟因果分析

• DOI: 10.1145/3453953.3453979
• 发表时间: 2021
• 期刊: ACM SIGMETRICS Performance Evaluation Review
• 作者: Pourghassemi, Behnam;Amiri Sani, Ardalan;Chandramowlishwaran, Aparna
• 通讯作者: Chandramowlishwaran, Aparna

A dynamic process reference model for sparse networks with reciprocity

具有互易性的稀疏网络动态过程参考模型

• DOI: 10.1080/0022250x.2020.1795652
• 发表时间: 2020
• 期刊: The Journal of Mathematical Sociology
• 作者: Butts, Carter T.

A Unified Prediction Framework for Signal Maps: Not All Measurements are Created Equal

信号图的统一预测框架：并非所有测量结果都是平等的

• DOI: 10.1109/tmc.2022.3221773
• 发表时间: 2022
• 期刊: IEEE Transactions on Mobile Computing
• 影响因子: 7.9
• 作者: Alimpertis, Emmanouil;Markopoulou, Athina;Butts, Carter T.;Bakopoulou, Evita;Psounis, Konstantinos
• 通讯作者: Psounis, Konstantinos