## 1 Introduction
Additionally, another study [6] enhances iOS data extraction through timeline analysis and plugin development. A literature review [7] forms the basis for a unified digital business forensic investigation (DBFI) process, promoting systematic forensic practices. Furthermore, another research effort [8] explores encryption's impact on forensic data extraction, proposing a new acquisition model with a legal framework. Several studies [1; 9; 10; 11] investigate social networking and messaging-app forensics, contributing insights and methods for evidence retrieval. In sum, these studies collectively highlight the need for adaptive forensic techniques, standardized processes, and innovative approaches to address evolving technologies and data challenges in digital forensics. There are still some gaps and areas that require further investigation [12]:  
1. Standardization: To promote consistency and comparability in the extraction of SQLite evidence from mobile devices, there is a need for standardization. Current methods and tools used by researchers and practitioners can lead to inconsistencies and may compromise the reliability of findings. A unified framework is necessary to ensure an efficient and comprehensive approach to digital forensic investigations on various mobile phone platforms, accommodating different architectural models, storage paths, and device settings, as well as users' security preferences. This framework will provide investigators with a reliable and consistent method of acquiring and analyzing digital evidence from mobile devices, regardless of the specific characteristics of the devices. The adoption of a standardized approach will mitigate the risk of errors or omissions in the investigation process, ensuring that all relevant evidence is collected and analyzed thoroughly and reliably [13]. 2. Performance: The performance of SQLite evidence extraction techniques needs to be evaluated further. Specifically, there is a need to investigate the effectiveness of different methods in extracting data from SQLite databases of different sizes and complexities. 3. Privacy and Security: There is a need for research on the privacy and security implications of SQLite evidence extraction. Mobile devices may contain sensitive information that needs to be protected, and extracting data from SQLite databases may raise privacy concerns. 4. Automation: Automating the SQLite evidence extraction process can save time and reduce human errors. There is a need for research on the development of automated tools and techniques for SQLite evidence extraction in mobile devices. 5. Applicability: The applicability of SQLite evidence extraction in different types of mobile devices and operating systems needs to be investigated further. Some devices or operating systems may have different SQLite database structures or security measures that can affect evidence extraction. 6. Big Data: The growing volume of data and the emergence of big data pose significant challenges in digital forensics [12]. Investigating the generated data, data encoding process, and storage location of data to systematically follow the evidence and reconstruct user activities is complex and challenging. This process often requires controlled experiments, which can be challenging given the increasing number of mobile apps and their frequent updates [14; 15; 16; 17]. Therefore, this research proposes an automated tool, namely, Forensic Operations for Recognizing SQLite Content (FORC), for mobile forensic data acquisition, analysis, and reporting. With the increasing use of mobile devices and the volume of data that needs to be analyzed, automating certain aspects of the forensic investigation process can help improve efficiency and accuracy. Several mobile operating systems, including Android, iOS, KAIOS, Windows Phone, Blackberry, etc., exist. Android stands out as the dominant global mobile operating system, commanding a market share of 71.93% [18]. Consequently, this research conducted an experiment using the Chrome application on Android.