BIGDATA: F: DKM: Collaborative Research: Making Big Data Active: From Petabytes to Megafolks in Milliseconds

在一个日益感知的世界里，每天都有大量的数字信息通过社交网络、博客、在线社区、新闻来源和移动应用程序产生。组织和研究人员认识到，通过捕获这些新兴数据并使其可用于查询和分析，可以获得巨大的价值和洞察力。第一代大数据管理工作本质上是被动的——查询、更新和/或分析任务主要是扩展到处理非常大量的数据。相比之下，该项目将开发新技术，以持续可靠地捕获大数据收集（来自社交、移动、Web和传感数据源），并将正确的信息及时传递给相关的最终用户。简而言之，该项目将为从大被动数据转向大主动数据提供可扩展的基础。应当开发技术，以便能够积累和监测数百万最终用户可能感兴趣的pb级数据；当“有趣的”新数据出现时，它应该在以（100毫秒）为单位的时间范围内传递给最终用户。本项目将构建这样一个Active大数据管理系统，并将其开源给社区使用。学生将接受与大主动数据管理和应用相关的技术培训；这样的培训对于解决当今社会媒体和移动网络推动的信息爆炸至关重要。大数据主动信息传播的通用基础将在公共安全和公共卫生等领域产生更广泛的影响。在为大活动数据建立基础的过程中存在许多挑战。在“数据导入”方面，包括在非常大规模的、基于lsm的存储系统中进行资源管理，以及为快速数据摄取提供高可用性、弹性的设施。在“数据处理”方面，挑战包括对多个高度分区数据集上的大量声明性数据订阅进行并行评估。需要在数据订阅中有效地支持空间、时间和相似性谓词，这加大了这一挑战。为了便于管理大型结果集，大数据还使得结果排序和多样化技术变得至关重要。在“数据输出”方面，挑战包括将感兴趣的数据可靠、及时地传播给规模空前的用户群。作为软件基础，该项目将通过AsterixDB（http://asterixdb.ics.uci.edu/）启动，这是一个开源的大数据管理系统，支持可扩展的存储、搜索和分析大量半结构化数据。欲了解更多信息，请访问项目网站https://www.ics.uci.edu/BigActiveData和http://www.cs.ucr.edu/~tsotras/BigActiveData

A wealth of digital information is being generated daily through social networks, blogs, online communities, news sources, and mobile applications in an increasingly sensed world. Organizations and researchers recognize that tremendous value and insight can be gained by capturing this emerging data and making it available for querying and analysis. First-generation Big Data management efforts have been passive in nature -- queries, updates, and/or analysis tasks were mainly scaled to handle very large volumes of data. In contrast, this project will develop new techniques for continuously and reliably capturing Big Data collections (arising from social, mobile, Web, and sensed data sources) and will enable timely delivery of the right information to the relevant end users. In short, this project will provide a scalable foundation for moving from Big Passive Data to Big Active Data. Techniques should be developed to enable the accumulation and monitoring of petabytes of data of potential interest to millions of end users; when "interesting" new data appears, it should be delivered to end users in a time frame measured in (100's of) milliseconds. This project will build such an Active Big Data Management system and make it available as open source to the community. Students will be trained in technologies related to Big Active Data management and applications; such training is critical to addressing the information explosion that social media and the mobile Web are driving today. The general-purpose foundation for active information dissemination from Big Data will have broader impacts in areas such as public safety and public health. There are many challenges involved in building a foundation for Big Active Data. On the "data in" side, these include resource management in very large scale, LSM-based storage systems and the provision of a highly available, elastic facility for fast data ingestion. On the "data processing" side, challenges include the parallel evaluation of a large number of declarative data subscriptions over multiple) highly partitioned data sets. Amplifying this challenge is a need to efficiently support spatial, temporal, and similarity predicates in data subscriptions. Big Data also makes result ranking and diversification techniques critical in order for large result sets to be manageable. On the "data out" side, challenges include the reliable and timely dissemination of data of interest to a sometimes-connected subscriber base of unprecedented scale. As a software base, this project will be jump-started by using AsterixDB(http://asterixdb.ics.uci.edu/), an open-source Big Data Management System that supports the scalable storage, searching, and analysis of mass quantities of semi-structured data. For further information see the project web sites at https://www.ics.uci.edu/BigActiveData and http://www.cs.ucr.edu/~tsotras/BigActiveData