BIGDATA: F: DKM: Plato: A model-based database for compressed spatiotemporal sensor data

BIGDATA：F：DKM：Plato：基于模型的压缩时空传感器数据数据库

• 批准号: 1447943
• 负责人: Yannis Papakonstantinou
• 金额: $ 110万
• 依托单位: University of California-San Diego
• 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 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1447943&HistoricalAwards=false
• 关键词: BIGDATA; DKM; Plato; model; based

Sensor data of diverse types and large volumes need to be combined with the current standard SQL databases, which provide context and metadata for the sensor data. The combination will lead to a new generation of analytics in a number of areas, such as smart buildings that are based on building and environmental data collected by sensors. The project argues that this new generation of analytics must be based on the same healthy database technology cornerstones that the prior (non-sensor) business intelligence platforms were based on: Declarative queries, automatic optimization, efficient storage representations and multiple layers of abstraction lead to high productivity for the developer and the analyst. Such productivity is currently absent from sensor data analytics because database technology and sensor data processing currently do not mix well. Productivity is especially low in cases involving (a) many types of sensor data, (b) combinations of sensor data with conventional database data that provide context and (c) many types of analyses. Besides low productivity, the current (limited) state of the art poses very high expertise requirements on the analysts: They must be simultaneously experts in signal processing, statistics and big data management. The project will deliver a database system for sensor data, where the analyst can rapidly develop declarative queries that are automatically optimized. By doing so, the project will deliver the envisioned productivity gains and will lower the technical sophistication bar needed for acting in the space, therefore enabling many scientists and domain specialists to engage in analytics.This project argues that at the core of the failure of SQL databases in the management and analytics of sensor spatiotemporal data is the lack of a critical abstraction, which is the real world models, which capture the stochastic processes that generate the measurements. The proposed Plato database system will bring the real world model concept into SQL databases by using models (spatiotemporal continuous functions) as first class citizens. The delivery of Plato requires innovative solutions to multiple problems: The project will design and implement (a) a model-aware data model and respective query language features that allow seamless combination of conventional SQL querying with statistical signal processing, (b) learning algorithms that learn the model components of reduced-noise, additive model representations, which are naturally compressions of the original, (c) query processing algorithms that operate directly on the compressed representations and utilize the the relatively few bits necessary for the required confidence of the analytics, and (d) semiautomated algorithms that further compress the model representations by considering the dependencies (mutual entropy) between the models. Finally, the project will exercise the resulting system on large scale statistical sensor data processing cases, such as the ones presented by the UCSD Energy Dashboard. The exercise will measure the lines-of-code as well as the runtime efficiency of the analyses.For further information see the project web site at http://www.db.ucsd.edu/NSF14Plato

各种类型和大容量的传感器数据需要与当前标准的SQL数据库相结合，这些数据库为传感器数据提供上下文和元数据。这一结合将在许多领域带来新一代分析，例如基于传感器收集的建筑和环境数据的智能建筑。该项目认为，新一代分析必须基于以前(非传感器)商业智能平台所基于的相同的健康数据库技术基石：声明性查询、自动优化、高效的存储表示和多层抽象层，从而为开发人员和分析师带来高生产率。传感器数据分析目前缺乏这样的生产力，因为数据库技术和传感器数据处理目前不能很好地融合。在涉及(A)多种类型的传感器数据、(B)传感器数据与提供背景的常规数据库数据的组合以及(C)多种类型的分析的情况下，生产率尤其低。除了生产率低之外，目前(有限的)最先进水平对分析师提出了非常高的专业知识要求：他们必须同时是信号处理、统计和大数据管理方面的专家。该项目将为传感器数据提供一个数据库系统，分析师可以在其中快速开发自动优化的声明性查询。通过这样做，该项目将提供预期的生产率提高，并将降低在空间行动所需的技术复杂性门槛，从而使许多科学家和领域专家能够从事分析。该项目认为，SQL数据库在管理和分析传感器时空数据方面失败的核心是缺乏关键的抽象，即现实世界模型，它捕获了生成测量的随机过程。所提出的柏拉图数据库系统将把真实世界模型的概念引入到SQL数据库中，使用模型(时空连续函数)作为一等公民。柏拉图的交付需要针对多个问题的创新解决方案：该项目将设计和实现(A)可识别模型的数据模型和相应的查询语言功能，允许将传统的SQL查询与统计信号处理无缝结合；(B)学习算法，其学习降噪、加性模型表示的模型分量，其自然地是原始模型的压缩；(C)查询处理算法，其直接对压缩的表示进行操作，并利用分析所需的相对较少的位；以及(D)通过考虑模型之间的依赖关系(相互熵)来进一步压缩模型表示的半自动算法。最后，该项目将在大规模统计传感器数据处理案例上测试所产生的系统，例如由加州大学可持续发展分校能源仪表板提出的案例。本练习将测量代码行以及分析的运行时效率。有关更多信息，请参阅项目网站http://www.db.ucsd.edu/NSF14Plato