# Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems
## 2 Related Work and Background
### Business Intelligence and Analytics  
Many of the applications discussed under the terms IIoT, smart factory, or smart manufacturing are tied to analytical solutions. Corresponding frameworks like the industrial Internet Reference Architecture by the IIC are designed as blueprints for building the data side of an IIoT solution [28]. This includes locating analytics functionality in overarching (cloud) platforms, cloud-like solutions that offload analytics to the application premises (fog computing) [29], or analytics conducted directly at the point of data origin (edge computing) [30]. Fog and edge computing have become particularly relevant in smart manufacturing environments owing to confidentiality and safety or for dealing with data that need to be processed with a high velocity (e.g., sensor data), or that is challenging to transmit because of its volume (e.g., video and image content) [31]. A schema for the specification of an analytics solution can be drawn from the literature on business intelligence. Since the beginning of the century, integrated approaches for decision support (that include the collection, transformation, and analysis of data) have been applied under the umbrella term business intelligence (BI) [32]. Recurring insight is the need to consider a logical architectural layering of analytical solutions with a data layer that handles the data ingestion, transformation, integration, and storage (including the storage of data histories), a _logic layer_ that includes the actual analytical processing, and an _access layer_ that handles the presentation and distribution of the analysis results [33]. The concrete specification of those layers can be performed with respect to different levels of abstraction from the hardware and the closeness to actual business content. Relevant levels of business specificity encompass:  
* The _infrastructure level_ (e.g., data storage based on a cloud-based object store, an independent edge device, or a federated relational database),
* The _software level_ (e.g., a dashboarding solution or an analytics-oriented programming language like Python or Julia), and
* The level of _actual business contents_ (e.g., data models, transformation logic, report design, and applied analytical models). Eventually, there is a distinction to be made between the provision or development phase (e.g., data modeling, report design, or machine learning (ML) training) and the operation of the service. An analytical solution can be defined by the interplay of services that span those architectural layers, levels of business specificity, and lifecycle phases [34]. Figure 1 illustrates this approach with examples in the domain of smart manufacturing. Figure 1: Schema for the specification of an analytics service, based on [34]. Notably, this logic can be applied to define purely local analytics solutions. However, as we strive to support IIoT business ecosystems, we deem it necessary to design all solutions in a way that they remain integrable into overarching cooperative solutions on a platform layer (the local solution acts as a sort of \"analytical atom\" in this regard [35]). While the overall structure of this framework has remained stable over the years, the internals of the individual architectural layers have undergone significant changes. Most notably, the data layer now regularly includes Big Data components that can handle high velocity, high volume, and high-variety data for which \"classical\" BI components like a relational data warehouse or data mart are not designed [36; 37]. The logical layer has seen a shift of attention toward more predictive analytical systems owing to the rise of ML [38]. In ML, models are not entered in detail by a modeler or programmer but rather are derived from data [39]. ML has made significant leaps, particularly within the field of artificial neural networks with a multitude of layers, that is, deep learning.