PRISM

Area of Application

PRISM is developed to represent the behaviors of system with a set of states of processes or actors with new concept, called Active Ontology. PRISM provides composition of two systems with handling the problem of state explosion and verifies the minimization of states between those system. Therefore, it can be used to solve the problem of size of complexities in domains or systems. It makes easier analyzing and testing of the models.

 

Abstract

PRISM is a final output of the project to develop a suite of tools to visualize an engineering process to model the behaviors of a domain. PRISM uses Behavior Ontology to model the behavior of a system that the ontology defines a behavior as a sequence of interactions among actors in a system, represented as a behavior lattice, called n:2-lattice. PRISM handles the problem of states explosion which causes by composition of systems. The composition of the systems can be performed by multiplication to the lattices for the systems, with respect to the common actors with the same cardinality. The composition can be interpreted as behavioral composition and reduce all the unnecessary composition not related to the behaviors in the lattices. Consequently, it can guarantee the minimal system states, compared with other approaches. PRISM models regular behaviors, abstract behaviors, behavior lattice, and merges behavior lattice for systems which are defined based on active ontology, and finally composes two systems. PRISM has been developed on the ADOxx Meta-Modeling Platform.

 

Approach

PRISM consists of the following components:

  1. Modeler
    1. Active Ontology (AO): The model component to describe classes, subclasses, and interactions.
    2. Behavior Specifier (BS): This is an engine to generate a set of regular, that is, basic behaviors from Active Ontology.
  1. Engine
    1. Multi-State Diagram (MSD): This is an engine to generate a unique graph of regular behaviors, that is, a state machine with one start node and one terminal node.
    2. Behavior Abstractor (BA): This is an engine to abstract a set of the regular behaviors from the A. i) with respect to the cardinality and the capacity, described in the previous chapters.
    3. Behavior Lattice Constructor (BLC): This is an engine to generate a behavior lattice of the abstract behaviors from the B. ii).
    4. Behavior Lattice Merger (BLM): This is an engine to merge two behavior lattices into an integrated lattice with respect to the same main actors with different cardinalities.

PRISM also includes of several models: Active Ontology, Regular Behavior, Multi-State Diagram, Abstract Behavior, Behavior Lattice, Composed Lattice of Behavior Lattices which are created by the modelers and engines.

 

Overview

Overview with Composition of EMS and HCS Example: (Video for Composition of EMS and HCS)

 

Manual

Manual: PRISM Manual.pdf

 

Examples

  1. Emergency Medical Services (EMS): EMS_PRISM.pdf
  2. Health Care Service (HCS): HCS_PRISM.pdf

 

Acknowledgements

This project was supported by Basic Science Research Programs through The National Research Foundation of Korea (NRF), funded by the Ministry of Education (2010-0023787), and the MISP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2016-H85011610120001002), supervised by the IITP (Institute for Information & communications Technology Promotion), and Space Core Technology Development Program through NRF, funded by the Ministry of Science, ICT and Future Planning (NRF-2014M1A3A3A02034792), and Basic Science Research Program through NRF, funded by the Ministry of Education (NRF-2015R1D1A3A01019282).

 

Publications

Here is the list of publications related to PRISM:

  • J. Song, M. Lee,  A Composition Method to Model Collective Behavior,  PoEM 2018: 121-137.
  • J. Song, M. Rahmani, M. Lee: Behavior Ontology to Model Collective Behavior of Emergency Medical Systems. ER Workshops 2017: 5-15.
  • M. Rahmani, J. Song, M. Lee: PRISM: A Knowledge Engineering Tool to Model Collective Behaviors of Real-time IoT Systems. PrOse@PoEM 2017.