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The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

机译：Massif平台：用于开发灵活的物联网服务的模块化和语义平台

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In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data processing platform for further analysis. Applications can have a notion of context awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet. In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services that can collaborate in a flexible manner to facilitate complex decision-making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated.
机译：在物联网（物联网）中，数据产生实体感知其环境，并将这些观察传输到数据处理平台以进行进一步分析。应用程序可以通过组合该感测数据或通过处理组合数据来具有语境意识的概念。组合数据的过程可以包括两个合并动态感测数据，以及融合所感测的数据与背景和历史数据。语义可以帮助这项任务，因为他们已经证明他们在数据集成，知识交流和推理中使用。在集成的感测数据上执行推理的语义服务，结合背景知识，例如简档数据，允许提取有用的信息并支持智能决策。但是，在这种感知数据和背景知识的结合上的先进推理仍然很难实现。此外，语义服务之间的协作允许达到复杂的决策。可以适应当前背景的这种协同工作流的动态组成尚未受到很多关注。在本文中，我们提出了Massif，一个数据驱动的平台，用于IOT数据的语义注释和推理。它允许集成多种模块化推理服务，这些服务可以以灵活的方式协作，以便于复杂的决策过程。通过让服务指定他们想要消耗的数据来启用数据驱动的工作流程。彻底处理后，这些服务可以决定与其他消费者分享他们的决定。通过定义数据，这些服务希望消耗，它们可以在数据子集上运行，提高推理效率。此外，这些服务中的每一个都可以在其自己的上下文模型中将消耗的数据与背景知识集成，以获得快速智能决策。为了展示平台的优势，将详细且彻底地评估两种用例。

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