A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction

This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization

Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources

Nowadays, the proliferation of heterogeneous data sources provided by different research and innovation projects and initiatives is proliferating more and more and presents huge opportunities. These developments create an increase in the number of different data sources, which could be involved in the process of decisionmaking for a specific purpose, but this huge heterogeneity makes this task difficult. Traditionally, the expert systems try to integrate all information into a main database, but, sometimes, this information is not easily available, or its integration with other databases is very problematic. In this case, it is essential to establish procedures that make a metadata distributed integration for them. This process provides a “mapping” of available information, but it is only at logic level. Thus, on a physical level, the data is still distributed into several resources. In this sense, this chapter proposes a distributed rule engine extension (DREE) based on edge computing that makes an integration of metadata provided by different heterogeneous data sources, applying then a mathematical decomposition over the antecedent of rules. The use of the proposed rule engine increases the efficiency and the capability of rule-based expert systems, providing the possibility of applying these rules over distributed and heterogeneous data sources, increasing the size of data sets that could be involved in the decision-making process

Messaging Rules as a Programming Model for Enterprise Application Integration

Rule-based systems and languages are successful in many application areas such as business rules or active database systems. The goal of the Demaq project is to investigate the feasibility and benefits of using a declarative, rule-based programming language to simplify the development of complex, distributed applications. For this purpose, we propose a novel programming paradigm based on messaging, queues and declarative rules. We focus on evaluating whether the proposed, rule-based approach can be used to implement complex application patterns. We use Enterprise Application Integration (EAI) as an example application domain, as EAI applications involve multiple, heterogeneous systems with complex interaction patterns. We discuss whether and how these application patterns can be implemented using our rule language

DATA CONSTRUCTORS: ON THE INTEGRATION OF RULES AND RELATIONS

Although the goals and means of rule-based and data-based systems are too different to be fully integrated at the present time, it seems appropriate to investigate a closer integration of language constructs and a better cooperation of execution models for both kinds of approaches. In this paper, we propose a new language construct called constructor that â when applied to a base relation â causes relation membership to become true for all tuples constructable through the predicates provided by the constructor definition. The approach is shown to provide expressive power at least equivalent to PROLOG's declarative semantics while blending well both with a strongly typed modular programming language and with a relational calculus query formalism. A three-step compilation, optimization, and evaluation methodology for expressions with constructed relations is described that integrates constructors with the surrounding database programming environment. In particular, many recursive queries can be evaluated more efficiently within the set-construction framework of database systems than with proof-oriented methods typical for a rule-based approach.Information Systems Working Papers Serie

Increasing the Efficiency of Rule-Based Expert Systems Applied on Heterogeneous Data Sources

Nowadays, the proliferation of heterogeneous data sources provided by different research and innovation projects and initiatives is proliferating more and more and presents huge opportunities. These developments create an increase in the number of different data sources, which could be involved in the process of decision-making for a specific purpose, but this huge heterogeneity makes this task difficult. Traditionally, the expert systems try to integrate all information into a main database, but, sometimes, this information is not easily available, or its integration with other databases is very problematic. In this case, it is essential to establish procedures that make a metadata distributed integration for them. This process provides a “mapping” of available information, but it is only at logic level. Thus, on a physical level, the data is still distributed into several resources. In this sense, this chapter proposes a distributed rule engine extension (DREE) based on edge computing that makes an integration of metadata provided by different heterogeneous data sources, applying then a mathematical decomposition over the antecedent of rules. The use of the proposed rule engine increases the efficiency and the capability of rule-based expert systems, providing the possibility of applying these rules over distributed and heterogeneous data sources, increasing the size of data sets that could be involved in the decision-making process

A New Methodology for Massive Alarm Management System in Electrical Power Administration

The paper presents a methodology that integrates several available techniques to manage the massive amount of alarm signals in electrical power dispatch control centers, as well as the contribution of each entity involved in the systems. Artificial intelligence techniques that can be used to solve this problem are reviewed here. The final objective is to find the root cause of avalanches of alarms (failure trees) and to reduce their number through grouping or clustering techniques, complying with the EEMUA 191 standards. Even though other contributions in this topic have been made before, the alarm management problem continues to be practically unsolved for many applications in industry. Here, the integration is developed using the ontology of the alarms. Additionally, in this methodology, a rule based expert systems is used to find the "Alarm Root Cause" and Clustering Technique (data segmentation) approach to treat the historical database of alarms.The paper presents a methodology that integrates several available techniques to manage the massive amount of alarm signals in electrical power dispatch control centers, as well as the contribution of each entity involved in the systems. Artificial intelligence techniques that can be used to solve this problem are reviewed here. The final objective is to find the root cause of avalanches of alarms (failure trees) and to reduce their number through grouping or clustering techniques, complying with the EEMUA 191 standards. Even though other contributions in this topic have been made before, the alarm management problem continues to be practically unsolved for many applications in industry. Here, the integration is developed using the ontology of the alarms. Additionally, in this methodology, a rule based expert systems is used to find the "Alarm Root Cause" and Clustering Technique (data segmentation) approach to treat the historical database of alarms

Embedding CLIPS in a database-oriented diagnostic system

This paper describes the integration of C Language Production Systems (CLIPS) into a powerful portable maintenance aid (PMA) system used for flightline diagnostics. The current diagnostic target of the system is the Garrett GTCP85-180L, a gas turbine engine used as an Auxiliary Power Unit (APU) on some C-130 military transport aircraft. This project is a database oriented approach to a generic diagnostic system. CLIPS is used for 'many-to-many' pattern matching within the diagnostics process. Patterns are stored in database format, and CLIPS code is generated by a 'compilation' process on the database. Multiple CLIPS rule sets and working memories (in sequence) are supported and communication between the rule sets is achieved via the export and import commands. Work is continuing on using CLIPS in other portions of the diagnostic system and in re-implementing the diagnostic system in the Ada language