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

不同类型和大量的传感器数据需要与当前的标准SQL数据库相结合，后者为传感器数据提供上下文和元数据。这一组合将在许多领域带来新一代的分析，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与先前（非传感器）商业智能平台相同的健康数据库技术基石：声明式查询，自动优化，高效的存储表示和多层抽象，为开发人员和分析师带来高生产力。目前传感器数据分析缺乏这种生产力，因为数据库技术和传感器数据处理目前没有很好地融合。在涉及（a）许多类型的传感器数据、（B）传感器数据与提供上下文的常规数据库数据的组合以及（c）许多类型的分析的情况下，生产率特别低。除了生产率低之外，目前（有限的）技术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，分析人员可以快速开发自动优化的声明式查询。通过这样做，该项目将实现预期的生产力提高，并将降低在空间中行动所需的技术复杂性，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数据库在传感器时空数据的管理和分析中失败的核心是缺乏关键的抽象，这是真实的世界模型，其捕获生成测量的随机过程。柏拉图数据库系统将把真实的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。柏拉图的交付需要创新的解决方案，以解决多个问题：该项目将设计和实现（a）模型感知数据模型和相应的查询语言功能，允许传统SQL查询与统计信号处理的无缝结合，（B）学习算法，学习降噪的模型组件，加法模型表示，这是原始模型的自然压缩，（c）查询处理算法，其直接对压缩的表示进行操作，并利用分析的所需置信度所需的相对较少的比特，以及（d）半自动化算法，其通过考虑模型之间的依赖性（互熵）来进一步压缩模型表示。最后，该项目将在大规模统计传感器数据处理案例中使用由此产生的系统，例如UCSD能源仪表板所提供的案例。该练习将测量代码行以及分析的运行时效率。有关更多信息，请访问项目网站http://www.db.ucsd.edu/NSF14Plato

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