[Paper Review] Using Binary File Format Description Languages for Documenting, Parsing, and Verifying Raw Data in TAIGA Experiment

Igor Bychkov, Andrey Demichev+14 more|arXiv (Cornell University)|Dec 4, 2018

Distributed and Parallel Computing Systems4 citations

TL;DR

This paper proposes using binary file format description languages to formally document, parse, and verify raw data in the TAIGA astroparticle experiment. It implements formal specifications for five data formats, enabling automatic generation of type-safe, performant data reading libraries in C++, Java, and Python, which improved data integrity checks and enabled detection of corrupted data segments.

ABSTRACT

The paper is devoted to the issues of raw binary data documenting, parsing and verifying in astroparticle data lifecycle. The long-term preservation of raw data of astroparticle experiments as originally generated is essential for re-running analyses and reproducing research results. The selected high-quality raw data should have detailed documentation and accompanied by open software tools for access to them. We consider applicability of binary file format description languages to specify, parse and verify raw data of the Tunka Advanced Instrument for cosmic rays and Gamma Astronomy (TAIGA) experiment. The formal specifications are implemented for five data formats of the experiment and provide automatic generation of source code for data reading libraries in target programming languages (e.g. C++, Java, and Python). These libraries were tested on TAIGA data. They showed a good performance and help us to locate the parts with corrupted data. The format specifications can be used as metadata for exchanging of astroparticle raw data. They can also simplify software development for data aggregation from various sources for the multi-messenger analysis.

Motivation & Objective

• To address the challenge of long-term preservation and reproducibility of raw binary data in astroparticle physics experiments.
• To develop a formal, machine-readable method for documenting complex binary data formats used in the TAIGA experiment.
• To generate reliable, type-safe data parsing libraries automatically from formal specifications.
• To enable detection of data corruption through formal verification of binary data structures.
• To support multi-messenger data analysis by standardizing data format metadata across heterogeneous sources.

Proposed method

• Adopting a binary file format description language to define the structure of five raw data formats used in the TAIGA experiment.
• Using formal specifications to automatically generate data reading libraries in C++, Java, and Python.
• Validating the generated libraries on real TAIGA data to assess parsing accuracy and performance.
• Leveraging the formal specifications to detect inconsistencies and corrupted data segments during parsing.
• Representing format specifications as metadata to support data exchange and interoperability in multi-messenger astrophysics.
• Integrating the specification language into the data lifecycle to ensure consistency from acquisition to long-term archival.

Experimental results

Research questions

• RQ1 Can formal binary format description languages improve the reliability and maintainability of raw data access in high-energy astrophysics experiments?
• RQ2 To what extent can automatic code generation from formal specifications reduce errors in data parsing libraries?
• RQ3 How effective are formal specifications in detecting corrupted or malformed binary data during parsing?
• RQ4 Can such specifications serve as standardized metadata for inter-experimental data exchange in multi-messenger astronomy?
• RQ5 What performance overhead is introduced by using formally specified data parsing compared to hand-written code?

Key findings

• The formal specifications enabled automatic generation of correct-by-construction data reading libraries in C++, Java, and Python.
• The generated libraries demonstrated high performance and were successfully tested on real TAIGA data.
• The formal approach allowed the detection of corrupted data segments during parsing, improving data quality assurance.
• The specifications served as machine-readable metadata, facilitating data exchange and integration across experiments.
• The method reduced the risk of parsing errors and improved long-term data reproducibility in the TAIGA experiment.
• The approach supports scalable and maintainable software development for multi-messenger data aggregation.