The availability of data sources during the Big Data era provides the opportunity for new analytical applications in the networking domain, which are envisioned as one of the main enablers of the future autonomous networks. But the proliferation of heterogeneous data sources has resulted into a sea of data silos, in which finding data, understanding data, and dealing with the complexities of each data source becomes a challenge. Aiming to tackle the connection of data silos, the data fabric has appeared as a new paradigm that provides a uniform access to all the data, abstracting consumers from the underlying complexities of the data sources. In this regard, the knowledge graph has raised as a promising solution that can integrate data from heterogeneous silos based on common concepts captured in ontologies. Building upon knowledge graph standards, this paper introduces CANDIL, a federated data fabric to support the integration of data from distributed systems, mainly focused on networking domain aspects. CANDIL defines an ontology that captures network topology and interface concepts, along with a reference architecture to ingest and integrate data in a federated knowledge graph that spans across the Edge-Cloud continuum. The proposal is validated with a prototype implementation and two example use cases of network analytics.

中文翻译：

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CANDIL：用于网络分析的联合数据结构

大数据时代数据源的可用性为网络领域的新分析应用提供了机会，这些应用被视为未来自治网络的主要推动者之一。但异构数据源的激增导致了数据孤岛的海洋，其中查找数据、理解数据以及处理每个数据源的复杂性成为了挑战。为了解决数据孤岛的连接问题，数据结构作为一种新的范式出现，提供对所有数据的统一访问，将消费者从数据源的底层复杂性中抽象出来。在这方面，知识图谱被认为是一种有前景的解决方案，它可以根据本体中捕获的通用概念来集成来自异构孤岛的数据。本文基于知识图谱标准，介绍了 CANDIL，一种支持分布式系统数据集成的联邦数据结构，主要关注网络领域方面。 CANDIL 定义了一个捕获网络拓扑和接口概念的本体，以及一个参考架构，用于在跨越边缘-云连续体的联合知识图中摄取和集成数据。该提案通过原型实现和网络分析的两个示例用例进行了验证。