Incremental Pattern Matching in Graph-Based State Space Exploration

Graph pattern matching is among the most costly operations in any graph transformation system. Incremental pattern matching aims at reducing this cost by incrementally updating, as opposed to totally recalculating, the possible matches of rules in the graph grammar at each step of the transformation. In this paper an implementation of one such algorithm is discussed with respect to the GROOVE toolset, with a special emphasis put on state space exploration. Specifically, we shall discuss exploration strategies that could better harness the positive aspects of incremental pattern matching in order to gain better performance

Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems

A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts) can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model) when it is compared to the second part (the fuzzy facts) for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree) and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs). The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler).Fuzzy Unification Tree, Dynamic Discrimination of Fuzzy Sets, DKMS, FRCOM

A New Strategy to Improve the Performance of PDP-Systems Simulators

One of the major challenges that current P systems simulators have to deal with is to be as efficient as possible. A P system is syntactically described as a membrane structure delimiting regions where multisets of objects evolve by means of evolution rules. According to that, on each computation step, the applicability of the rules for the current P system configuration must be calculated. In this paper we extend previous works that use Rete-based simulation algorithm in order to improve the time consumed during the checking phase in the selection of rules. A new approach is presented, oriented to the acceleration of Population Dynamics P Systems simulations.Ministerio de Economía y Competitividad TIN2012- 3743

Evaluation of Optimization Strategies for Incremental Graph Queries

The last decade brought considerable improvements in distributed storage and query technologies, known as NoSQL systems. These systems provide quick evaluation of simple retrieval operations and are able to answer certain complex queries in a scalable way, albeit not instantly. Providing scalability and quick response times at the same time for querying large data sets is still a challenging task. Evaluating complex graph queries is particularly difficult, as it requires lots of join, antijoin and filtering operations. This paper presents optimization techniques used in relational database systems and applies them on graph queries. We evaluate various query plans on multiple datasets and discuss the effect of different optimization techniques

CLIPS: A tool for the development and delivery of expert systems

The C Language Integrated Production System (CLIPS) is a forward chaining rule-based language developed by the Software Technology Branch at the Johnson Space Center. CLIPS provides a complete environment for the construction of rule-based expert systems. CLIPS was designed specifically to provide high probability, low cost, and easy integration with external systems. Other key features of CLIPS include a powerful rule syntax, an interactive development environment, high performance, extensibility, a verification/validation tool, extensive documentation, and source code availability. The current release of CLIPS, version 4.3, is being used by over 2,500 users throughout the public and private community including: all NASA sites and branches of the military, numerous Federal bureaus, government contractors, 140 universities, and many companies

Production system shell for mobile Devices

The CLIPS shell extension was proposed to enable the creation of applied production systems which can be used on mobile devices. The innovation was substantiated in terms of saving money resources to purchase specialized equipment for using for the resource-intensive production systems. The following studies were conducted: market research tablet devices, the most widely represented in Ukraine; production systems shells that can be used on mobile devices; freely distributable production systems shells were considered, important characteristics were identified and compared. The basic classes of match algorithms were overviewed. The results of studies about Rete and Treat match algorithms advisability were described for the general case of the applied problems. The proposed modeling environment for production systems for mobile devices will reduce development time and increase system efficiency by choosing the optimal match algorithm for minimal memory usag

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited