BIGDATA: F: DKA: Collaborative Research: Dealing Efficiently with Big Social Network Data

BIGDATA：F：DKA：协作研究：有效处理社交网络大数据

• 批准号: 1447793
• 负责人: Shanmugavelayu Muthukrishnan
• 金额: $ 30万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1447793&HistoricalAwards=false
• 关键词: BIGDATA; DKA; Collaborative; Research; Dealing

The past decade has seen dramatic growth in systems that collect data from human activities. Online social networks record not just friendships, but interactions, messages, photos, and interests. Mobile devices track location via GPS information. Online stores monitor millions of customers as they explore and transact. Sensors, wearable and otherwise, produce detailed behavioral data. Collectively, this provides ever-larger collections of human social-activity information -- we refer to this as Big Social Data. While Big Social Data is growing rapidly, the available processing resources -- CPU, memory, communication -- are growing at a slower pace. To realize the promise of big social data, we need algorithms that use only sublinear resources, that is, resources growing much less than the growth of the data in suitable parameters. Designing these algorithms will be the core activity of this research project. This work will be in consultation with practitioners handling Big Social Data, leading to many opportunities for technology transfer. The research program both enables and benefits from an education and outreach program that will help develop the new breed of algorithmically-trained data scientists for Big Social Data.Emerging systems -- MapReduce, Hadoop, Spark, Storm, etc. -- use large scale distributed computation: clusters of machines not only gathering and storing data in parallel, but also working together to perform computations. Often, these systems and applications work via incremental processing, storing and returning only approximate solutions, trading off quality and certainty for efficiency. In addition, these systems take a data-centric view, wherein the data is stored as Key, Value pairs. This project will address fundamental problems with Big Social Data -- search, ranking, and optimization, etc. in these modern computing and data models. For these problems, this project will design algorithms that are sublinear in the relevant parameter -- number of keys, size of values, computing time per key or over all keys, and other variations that map to underlying storage, number of machines, bandwidth and other computational constraints.For further information, see the project web site at http://www.stanford.edu/~ashishg/socialdata.html .

过去十年，从人类活动中收集数据的系统取得了巨大的发展。在线社交网络不仅记录友谊，还记录互动、消息、照片和兴趣。移动设备通过 GPS 信息跟踪位置。在线商店监控数百万顾客的探索和交易。可穿戴式传感器和其他传感器可生成详细的行为数据。总的来说，这提供了越来越大的人类社会活动信息集合——我们将其称为大社会数据。虽然社交大数据正在快速增长，但可用的处理资源（CPU、内存、通信）增长速度却较慢。为了实现大社交数据的承诺，我们需要仅使用次线性资源的算法，即在合适的参数下资源增长远小于数据的增长。设计这些算法将是该研究项目的核心活动。这项工作将与处理大社交数据的从业者进行协商，从而带来许多技术转让的机会。该研究计划既支持教育和推广计划，也从教育和推广计划中受益，该计划将有助于为大社会数据培养新一代经过算法训练的数据科学家。新兴系统——MapReduce、Hadoop、Spark、Storm 等——大规模使用分布式计算：机器集群不仅并行收集和存储数据，而且还一起工作来执行计算。通常，这些系统和应用程序通过增量处理来工作，仅存储和返回近似解决方案，以质量和确定性换取效率。此外，这些系统采用以数据为中心的视图，其中数据存储为键、值对。该项目将解决大社交数据的基本问题——这些现代计算和数据模型中的搜索、排名和优化等。对于这些问题，该项目将设计相关参数次线性的算法——键的数量、值的大小、每个键或所有键的计算时间，以及映射到底层存储、机器数量、带宽和网络的其他变量。其他计算限制。有关更多信息，请参阅该项目网站：http://www.stanford.edu/~ashishg/socialdata.html。