# Interoperable Data Analytics Reference Architectures Empowering Digital-Twin-Aided Manufacturing
## 2 Related Work
It can be observed that the architecture described in [17] has much in common with the streaming or IoT parts of the Azure-based architectures [14; 15]. This is not a coincidence, since most of the low-level data processing architectures are based on two high-level archetypes, which we are discussing next. High-level RAs represent a higher level of abstraction and may not be directly deployable, similar to low-level RAs. Such high-level RAs represent more generic architecture patterns that can be mapped to low-level RAs and specific technologies on demand. The first well-known example of a high-level Reference Architecture for data analytics is the Lambda Architecture [18]. It introduces two parallel pipelines for data processing. One pipeline is used for stream computation (\"speed layer\") and one for batch processing (\"batch layer\"). An extra layer (\"serving layer\") stores the output and responds to ad hoc queries. The batch layer utilizes a distributed processing system to perform processing on all available data. As processing takes time, the batch layer can push fewer updates to the serving layer. To fix computational or algorithmic errors, the system needs to recompute the complete dataset. The speed layer uses a time window and processes just recent data but provides a (near) real-time view. This layer was not intended to do a full computation on the whole dataset. The Kappa architecture [19] was introduced to overcome the shortcomings of the Lambda architecture, for example the need to duplicate data between the speed and batch layers and to reflect on the evolution of stream processing since the inception of the Lambda architecture. The main idea behind the Kappa architecture is to have a single streaming pipeline, which can process the entire dataset, e.g., perform a re-computation if an algorithm changes. Another well-known vendor-agnostic RA for data analytics is the FIWARE [20] middleware. It contains both high-level and low-level components as it provides higher-level functionality and multiple low-level alternatives. FIWARE was originally driven by the European Union to develop a platform for the Future Internet. FIWARE is a framework of open-source components for data management and for developing interoperable smart solutions. As part of the framework, FIWARE provides different components such as the \"Orion context broker\" for data management, IoT agents for connecting smart devices, an identity management component called \"Keyrock\", components for storing historical data such as \"Comet\" as well as components for data analytics among many others. FIWARE provides not just a standards-based platform, but an ecosystem as well. Edge devices can be considered as constrained environments in terms of data processing capabilities. [PERSON] et al. [21] introduce a high-level architecture that provides a declarative query model. This model allows accessing data independently of the capabilities of the available processing engines available on the edge devices. Finally, The Open Group Architecture Framework (TOGAF) [12] is a standardized framework for creating and managing software Reference Architectures. It is a widely used framework for enterprise architecture that includes an approach for designing, planning, delivering, and governing an enterprise information technology architecture. TOGAF incorporates both lower-level and higher-level components. It is not restricted to data analytics or any other domain. The TOGAF standard defines a building block as a potentially reusable component that encompasses an architectural or business functionality and can be used with other building blocks to provide architectures or complete solutions. It distinguishes between two building block types within architecture artifacts: Architecture Building Blocks (ABBs) and Solution Building Blocks (SBBs). An architectural artifact is a work product that describes a facet of the architecture.