OASIS: Ontology Reasoning over Frequently-changing and Streaming Data

依赖于松散结构和大规模数据集的智能管理的高级应用程序在医疗保健、商业和政府等领域发挥着关键作用。基于本体的技术是许多此类应用程序的核心。简而言之，基于本体论的数据管理系统（ODMS）通过提供在本体论中表示应用程序的背景知识的方法，并利用自动推理技术来推断数据和本体论中隐含的信息，从而实现智能信息处理。然而，最先进的odms并不适合需要实时分析快速变化的数据的应用程序。例如，石油和天然气公司持续监测传感器读数，以检测设备故障并预测维护需求；网络提供商分析流量数据，以识别流量异常和拒绝服务攻击；知识图谱不断更新；以及智能城市等物联网（IoT）应用需要对来自多种类型设备的数据进行实时分析。odms经常借用数据库文献中的实现技术，其中使用两种主要方法处理快速变化数据的实时分析。(1)在流处理系统中，输入数据在概念上被视为流经系统的时间戳元组的无界序列；数据只能一次性处理，系统存储的信息本质上是不完整的。流作业是长时间运行的：查询被部署一次，并继续产生结果，直到删除。最先进的系统，如Apache Storm、Apache Spark Streaming、b谷歌的Millwheel、Linked In的Samza和Apache Flink，通过在集群中分配流工作负载来实现亚秒级延迟，这需要复杂的调度和容错技术。(2)在实时数据库中，数据被看作是不断发展的有限记录集合。这种有限和持久集合的传统概念在数据库世界中普遍存在，非常适合需要一致和完整数据视图的应用程序。将实时数据库与传统数据库区分开来的关键特性是，与流系统类似，它们允许客户订阅长时间运行的连续查询，从而即时推送增量更新。然而，在将这些方法应用于ODMSs时，出现了许多理论和实践上的困难。在OASIS项目中，我们将解决这些困难，并为能够实时摄取和处理快速变化的数据的新一代odms奠定基础。这样的系统将通过支持智能决策的复杂分析管道的快速执行来支持上述应用程序。此外，我们将利用由此产生的见解来实现原型并在实际部署中对其进行测试。

Advanced applications relying on intelligent management of loosely structured and large-scale datasets play a key role in domains such as healthcare, business and government. Ontology-based technologies lie at the core of many such applications. In a nutshell, an ontology-based data management system (ODMS) enables intelligent information processing by providing means for representing background knowledge about the application in an ontology, and exploiting automated reasoning techniques to infer information that is implicit in the data and the ontology.State-of-the art ODMSs are, however, not well-suited for applications which require real-time analysis of rapidly changing data. For instance, oil and gas companies continuously monitor sensor readings to detect equipment malfunction and predict maintenance needs; network providers analyse flow data to identify traffic anomalies and Denial of Service attacks; knowledge graphs are continuously updated; and Internet of Things (IoT) applications such as Smart Cities require real-time analysis of data stemming from multiple types of device.ODMSs often borrow implementation techniques from the database literature, where real-time analysis of rapidly changing data has been tackled using two main approaches.(1) In a stream processing system, the input data is conceptually seen as an unbounded sequence of time-stamped tuples that flow through the system; data is only available for processing in a single pass and information stored by the system is inherently incomplete. Streaming jobs are long-running: queries are deployed once and continue to produce results until removed.State-of-the art systems, such as Apache Storm, Apache Spark Streaming, Google's Millwheel, Linked In's Samza, and Apache Flink, achieve sub-second latencies by distributing the streaming workload in a cluster, which requires sophisticated scheduling and fault-tolerance techniques. (2) In a real-time database, the data is seen as a finite collection of records that is continuously evolving. This traditional concept of a finite and persistent collection is ubiquitous in the database world is well-suited for applications requiring a consistent and complete view of the data.The key feature that distinguishes real-time from traditional databases is that, similarly to streaming systems, they allow clients to subscribe to long-running continuous queries that instantaneously push incremental updates. Many theoretical and practical difficulties arise, however, when adapting these approaches to ODMSs. In the OASIS project, we will address these difficulties and lay the foundations for a new generation of ODMSs capable of ingesting and processing rapidly changing data in real time. Such systems will support the aforementioned applications by enabling fast execution of complex analytics pipelines supporting intelligent decisions. Moreover, we will exploit the resulting insights to implement a prototype and test it in real-life deployments.