## 2 Related Work
### Compliance Checks
#### 2.2.6 Key Issues with Compliance Checks  
_Expert knowledge_ is crucial in several key areas of traditional compliance procedures. First, understanding and interpreting complex regulations, standards, and guidelines is essential. Experts must identify critical compliance points and define specific entities (objects) and their relevant characteristics (attributes) to be checked. Second, compliance checking involves validating data against regulatory requirements, detecting anomalies, and interpreting data within the broader industry and regulatory context. Finally, handling arbitrary events requires expert decision making, clear explanations, and adaptability to changing circumstances. However, manual compliance checks that rely on experts in primary industries can be time-consuming, costly, and prone to human error. As regulatory landscapes become increasingly complex, the need for efficient and accurate compliance solutions becomes even more critical. Additionally, manual checks can be inconsistent, as different inspectors may interpret regulations differently. Scaling manual checks to accommodate growth and increased regulatory requirements can be difficult. Furthermore, periodic inspections may not be sufficient to identify and address real-time issues. To overcome these limitations, digital solutions offer a promising avenue for improving compliance efficiency and accuracy. For instance, IoT sensors and data analytics enable real-time monitoring of environmental conditions, livestock health, and crop growth [62; 127; 128; 129; 63]. Blockchain technology ensures product traceability and verifies ethical and sustainable practices [130; 131; 132]. AI and machine learning algorithms can identify patterns, predict risks, and automate routine tasks [133; 134; 135]. Remote sensing and satellite imagery facilitate land use monitoring and environmental compliance [136; 137; 138]. Drone technology offers efficient inspection, monitoring, and mapping capabilities [139; 140]. These digital tools can _automate_ routine tasks, analyze vast amounts of data, and identify potential compliance risks. Businesses and regulators can streamline compliance processes, reduce costs, and improve accuracy. For example, a cherry farm can use IoT sensors and blockchain technology to automate the tracking of pesticide use and environmental conditions, ensuring compliance with minimal manual intervention. The data collection at the producers' end about the products and their production process is streamlined and very well developed. This can be used for continuous monitoring by AI tools in real time. However, human users are unable to ask analytical or pre-emptive questions about these processes unless they are experts in interpreting and analyzing the collected data. Often, this is due to the complex nature of processes and a single human's inability to understand such complexities. The current research in automated legal text processing faces significant limitations. Firstly, it heavily relies on sentence-level analysis, which can be insufficient for capturing complex legal concepts [107]. Secondly, automation strategies often lack clear justifications for their decisions and either adopt a coarse-grained approach or require substantial manual effort to build [107]. These works also focus on a one-time compliance check. However, for continuous compliance checks, a lack of clear justification in compliance automation strategies within primary industries may lead to several significant problems. It can undermine trust in the automation system, especially when decisions have far-reaching consequences for producers and consumers. It can potentially hinder maintenance efforts. In contrast to one-time compliance checks [112], we consider a _continuous compliance check_ in this paper. It goes on for a time period during which the compliance rules may be altered due to external factors, and the events are expected in a specific order. In the single compliance check, all data products are gathered before being checked against a fixed set of rules.