About
About the Unified Model Binary (UMB) Standard
The Unified Model Binary (UMB) standard represents a significant advancement in modeling techniques, establishing a common language for diverse models while excluding symbolic representations for clarity and focus. Designed to express the least common multiple of various models, UMB ensures comprehensive support for a wide range of scenarios, with a commitment to mathematical precision and usability. By utilizing standard primitive data structures in a JSON format, UMB achieves a unique balance of expressiveness, speed, and conciseness—positioning it as an efficient tool for model representation.
Ecosystem Surrounding UMB
UMB is supported by a robust ecosystem that includes UMBI, its reference implementation, which enhances user experience by providing tools for model conversion and input. The collaborative framework surrounding UMB features support from multiple observatories that facilitate integration with various modeling tools and environments. This infrastructure allows users to leverage UMB’s capabilities seamlessly, exploring previously unattainable modeling possibilities and enhancing overall usability.
Use Cases and Consequences
The UMB format and its ecosystem unlock numerous capabilities for both users and researchers in the field of Probabilistic Model Checking (PMC). Below are some notable scenarios where UMB provides significant advantages:
Benefits for Users of PMC
Access to the Entire Ecosystem: UMB support opens doors to tools like pySMT and PuLP, allowing users to chain various tools effortlessly. For instance, a complex model generated by Modest or STAMINA can be processed using Storm and then enhanced with player additions via UMBI, with final verification conducted in PRISM.
Visualization Capabilities: Users can visualize models easily. Many model-visualization tools such as StormVogel support UMB.
Portfolio Approaches: UMB’s design allows for the easy composition and modification of models, making it straightforward to implement various modeling approaches—like generating biology models from simple equations—where traditional formats like JANI or PRISM would struggle.
Ease of Modification: Users can dynamically change models—such as adjusting transition probabilities or representation styles—facilitating agile development and testing.
Parallelization Potential: Large models can be efficiently distributed across different servers.
Benefits for Researchers and Tool Developers
Independent Algorithm Testing: Researchers can conduct fair comparisons of algorithms without the distortions caused by state space exploration differences, achieving consistent benchmarks.
Prototyping Flexibility: UMB allows for easy prototyping of new model types, broadening the frontiers of expressiveness in PMC. If existing tools do not support a desired model, UMBI facilitates straightforward solutions for new implementations.
Random Model Generation: The ease of generating random models speeds up experimentation and testing, addressing inefficiencies in previous systems.