Toward Scalable Demand-Driven JSON-to-Forensic Lucid Encoder in GIPSY

The objective of this paper is to describe the research performed on JSON-formatted files demand-driven formalization to Forensic Lucid, a forensic case specification and modeling language. JSON is a popular format for representing data transfers between many popular web services today on the Internet; as a result a lot of potential digital evidence may be available in this format. Our proposed solution relies on the General Intensional Programming System's (GIPSY) multi-tier architecture to implement problem-specific (PS) generator and worker tiers. The JSONCONVERTERDGT is the PS demand generator for parsing JSON files. It will listen for JSON parser demands from the user. It will then get the demand, which contains the JSON data structure, the type of parser required (generic vs. concrete, such as social media Twitter, Instagram, Facebook, etc.) and pass this information to the demand store. Our worker in the project is included in the class JSONCONVERTERDWT. It collects the JSON demands from the store, parses the arguments received from the generator, gets the JSON file that needs to be parsed, calls the appropriate concrete parser (if available), and creates a JAVA object that has the JSON structure with all its elements. It then sends this object as a result via the store to the generator, which will create the Forensic Lucid IPL file. We formally specify requirements and design specifications in part, and some implementation details in this work.