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ED3: Enabling analytics over Diverse Distributed Datasources

ED3：支持对不同分布式数据源的分析

基本信息

批准号：
EP/N014359/1
负责人：
Ian Horrocks
金额：
$ 110.41万
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FN014359%2F1
关键词：
ED3 Enabling analytics over Diverse

项目摘要

Enterprises and government entities have a growing need for systems that provide decision support based on descriptive and predictive analytics over large volumes of data. Examples include supporting decisions on pricing and promotions based on analyses of revenue and demand data; supporting decisions on the operation of complex equipment based on analyses of sensor data; and supporting decisions on website content based on analyses of user behaviour. Such support may be critical for safety and regulatory compliance as well as for competitiveness.Current data analytics technology and workflows are well-suited to settings where the data has a uniform structure and is easy to access. Problems can arise, however, when performing data analytics in real-world settings, where as well as being large, datasources are often distributed, heterogeneous, and dynamic.Consider, for example, the case of Siemens Energy Services, which runs over 50 service centres, each of which provides remote monitoring and diagnostics for thousands of gas/steam turbines and ancillary equipment located in hundreds of power plants. Effective monitoring and diagnosis is essential for maintaining high availability of equipment and avoiding costly failures. A typical descriptive analytics procedure might be: "based on sensor data from an SGT-400 gas turbine, detect abnormal vibration patterns during the period prior to the shutdown and compare them with data on similar patterns in similar turbines over the last 5 years".Such diagnostic tasks employ sophisticated data analytics tools, and operate on many TBs of current and historical data. In order to perform the analysis it is first necessary to identify, acquire and transform the relevant data. This data may be stored on-site (at a power-plant), at the local service centre or at other service centres; it comes in a wide range of different formats, ranging from flat files to XML and relational stores; access may be via a range of different interfaces, and incur a range of different costs; and it is constantly being augmented, with new data arriving at a rate of more than 30 GB per centre per day.Acquiring the relevant data is thus very challenging, and is typically achieved via a combination of complex queries and bespoke data processing code, with numerous variants being required in order to deal with distribution and heterogeneity of the data. Given the large number of different analytics tasks that service centres need to perform, the development and maintenance of such procedures becomes a critical bottleneck.In ED3 we will address this problem by developing an abstraction layer that mediates between analytics tools and datasources. This abstraction layer will adapt Ontology Based Data Access (OBDA) techniques, using an ontology to provide a uniform conceptual schema, declarative mappings to establish connections between ontological terms and data sources, and logic-based rewriting techniques to transform ontological queries into queries over the data sources. For OBDA to be effective in this new setting, however, it will need to be extended in several different directions. Firstly, it needs to provide greatly extended support for basic arithmetic and aggregation operations. Secondly, it needs to deal more effectively with heterogeneous and distributed data sources. Thirdly, it will be necessary to support the development, maintenance and evolution of suitable ontologies and mappings.In ED3 we will address all of these issues, laying the foundations for a new generation of data access middleware with the conceptual modelling, query processing, and rapid-development infrastructure necessary to support analytic tasks. Moreover, we will develop a prototypical implementation of a suitable abstraction layer, and will evaluate our prototype in real-life deployments with our industrial partners.

企业和政府实体越来越需要基于对大量数据的描述性和预测性分析来提供决策支持的系统。例如，根据对收入和需求数据的分析，为定价和促销决策提供支持；根据传感器数据分析，为复杂设备的运行决策提供支持；根据用户行为分析，为网站内容决策提供支持。这种支持可能对安全和法规遵从性以及竞争力至关重要。当前的数据分析技术和工作流程非常适合数据具有统一结构和易于访问的环境。然而，在实际环境中执行数据分析时可能会出现问题，因为现实环境中的数据源不仅很大，而且通常是分布式、异类和动态的。例如，以西门子能源服务公司为例，该公司运营着50多个服务中心，每个服务中心都为数百家发电厂中的数千台燃气/蒸汽涡轮机和辅助设备提供远程监控和诊断。有效的监测和诊断对于保持设备的高可用性和避免代价高昂的故障至关重要。典型的描述性分析程序可能是：“基于SGT-400燃气轮机的传感器数据，检测停机前一段时间内的异常振动模式，并将它们与过去5年类似涡轮机中类似模式的数据进行比较”。此类诊断任务使用复杂的数据分析工具，并对大量当前和历史数据进行操作。为了进行分析，首先需要确定、获取和转换相关数据。这些数据可以存储在现场(发电厂)、本地服务中心或其他服务中心；它有各种不同的格式，从平面文件到XML和关系存储；访问可能通过一系列不同的接口，并产生一系列不同的成本；而且它正在不断扩大，每个中心每天以超过30 GB的速度到达新数据。因此，获取相关数据非常具有挑战性，通常通过复杂的查询和定制的数据处理代码的组合来实现，需要许多变体来处理数据的分布和异构性。鉴于服务中心需要执行大量不同的分析任务，此类程序的开发和维护成为一个关键瓶颈。在ED3中，我们将通过开发一个介于分析工具和数据源之间的抽象层来解决这个问题。该抽象层将采用基于本体的数据访问(OBDA)技术，使用本体来提供统一的概念模式，使用声明性映射来建立本体术语和数据源之间的连接，以及基于逻辑的重写技术来将本体查询转换为对数据源的查询。然而，为了让OBDA在这种新的环境中发挥作用，它需要在几个不同的方向上进行扩展。首先，它需要为基本的算术和聚合操作提供极大的扩展支持。其次，它需要更有效地处理异质和分布式数据源。在ED3中，我们将解决所有这些问题，为新一代数据访问中间件奠定基础，这些中间件具有支持分析任务所需的概念建模、查询处理和快速开发基础设施。此外，我们将开发合适抽象层的原型实现，并将与我们的行业合作伙伴一起在实际部署中评估我们的原型。