Mining outliers in rule knowledge bases

The paper presents the results of the experiments in which an efficiency of decision support systems is analyzed. During the research it was noted that the most influenced on such efficiency is the method of representation of knowledge base. Following approaches are considered due to experiments: rules knowledge base with clusters of decision rules, rules knowledge base with partial decision rules and rules knowledge base with clusters of partial decision rules. The efficiency of choosing the best similarity measure is also analyzed.

In recent years, thanks to the ICT systems, there has been a continued growth and an increasing accumulation of the available information. Managing information has become one of the key activities in any sector. Indeed, information management emerged as the core and the basis of manufacturing enterprise. The research activity mainly focused on the development of a Knowledge Based Engineering (KBE) methodology for supporting a manufacturing company, in particular railway manufacturers, in reusing company knowledge. A Decision Support System (DSS) has been developed with the scope of identifying products already designed that fully or partly fit what required by new bids. The DSS has been built within a PLM software and part of the research has concentrated on comparing the PLM suites available in the market searching for the best tool able to act the role of a centralized management dashboard for knowledge reuse. The proposed methodology involves different business units and several expertise, emphasizing the importance of a collaborative environment and the need of a methodology for design review sessions. Contents of this thesis retrace these considerations: the second chapter reports a technology scouting on the available PLM tools; the third and the fourth chapters deal with a KBE methodology and the development of a DSS; the fifth one proposes the development of an immersive virtual reality environment aimed at deploying review sessions by design and manufacturing teams with interdisciplinary skills; at last, in the sixth chapter, it is reported a Discrete Event Simulation application as example of a procedural KBE application.

I: General Issues.- Modeling Knowledge: Model-based Decision Support and Soft Computations.- Benefits of Decision Support Using Soft Computing.- Evolving Connectionist-based Decision Support Systems.- An Agent-based Soft Computing Society with Application Applications in Financial Investment Planning.- A Rough Sets/Neoral Networks Approach to Knowledge Discovery for the Development of Decision Support Systems.- II: Applications and Implementations.- Decision Support Systems in Healthcare: Emerging Trends and Success Factors.- A View of Public and Private Sectors for Taiwan's BOT Project Financing Using Fuzzy Multi-Criteria Methods.- Relational Structures for the Analysis of Decision Information in an Electronic Market.- A Fuzzy Evaluation Model: A Case for Intermodal Terminals in Europe.- Application of Kemeny's Median for Group Decision Support.- An Internet-based Group Decision and Consensus Reaching Support System.- Limpio: A DSS for the Urban Waste Collection Problem.- A Decision Support System for Air Quality Control Based on Soft Computing Methods.- Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems.- Intelligent Information Systems for Crime Analysis.- Application of Fuzzy Decision Trees to Reservoir Recognition.- Introducing SACRA: A Decision Support System for the Construction of Cattle Diets.- Prediction of Parthenium Weed Dispersal Using Fuzzy Logic on GIS Spatial Image.

Fault tolerance of spacecraft remains one of the most complex problems in space missions. There are several ways to implement the “onboard intelligence allowing the recovery of a spacecraft in case of abnormal situations caused by hardware or software failures. The most common but inflexible way is “to disperse” the recovery logic in the source code of the flight control software. Our approach implies using onboard real-time knowledge base. The rules of the knowledge base could be added or refined from Earth over the radio channel on a timely basis. Currently, the rules of an onboard knowledge base should be specified in a table form, which entails some misunderstandings in the mission team and consequently leads to errors. The improved approach presented in the paper provides special tools–the visualizer and the visual builder of rules. The approach allows space mission operation engineers without special mathematical or programming background to define, visualize and refine knowledge base rules in a very easy manner. Tools prototypes are currently introduced at JSC Information Satellite Systems, Russia.

Knowledge is one of the most precious resources of an organization. Every organization wishes to preserve and fully utilize its knowledge. Within organization knowledge is present in various forms, may be in the minds of workers or in documented form. In documented form the knowledge has various representation schemes such as frames, scripts, lists, decision trees and decision tables etc. We have proposed a transformation method to optimally transform knowledge present in decision trees and decision tables into knowledge base. According to our proposal, the knowledge present in these two representation schemes should first be converted into corresponding set of human interpretable rules by using some existing transformation algorithms. The decision tree should be converted directly into set of human interpretable rules but for decision table transformation, two ways are adopted; either it should be converted first into decision tree and then to set of rules or, should be converted directly into set of rules. Once the set of rules is obtained then it will be optimized by using some existing optimization algorithms and unnecessary conditions will be omitted. After rules optimization process, comparison with the existing rules in the knowledge base is carried out. If these optimized rules are not found in the knowledge base, it should be added. If some rules need updation, then these should be added after updation. During comparison those rules which already exist in the knowledge base should be omitted.

The present Ph.D. thesis is devoted to the experimental and numerical analysis of the Pumps As Turbines (PATs) application in hydraulic systems and, specifically, in Water Distribution Networks (WDNs) to generate Energy Recovery (ER) in both urban and rural areas. The use of PATs in WDNs represents a relevant topic in the field of the innovative Best Management Practices (BMPs) of WDNs because addressed to both the pressure regulation and the hydropower generation. Thus, it could represent an alternative approach to the use of Pressure Reducing Valves (PRVs), benefitting of the exceed pressure to produce sustainable energy, otherwise dispersed. The PATs application consists in the use of pump models commercially available running in reverse mode, by inverting the flow direction and making use of the motor as an electrical generator. Their employment can be intended as an effective alternative to the traditional micro-turbines in WDNs, having lower investment and maintenance costs and allowing the model selection among a wide set of pump models available in the market. Conversely, PATs usually assure lower operative ranges, however expandable by arranging proper hydraulic and/or electrical regulations. In addition to the evaluation of the effective opportunities for pressure reduction in water systems, one of the main issue, concerning the PATs use for hydropower generation, regards the limited knowledge about their performance curves, rarely made available by manufactures. In the literature, several experimental, theoretical and numerical models have been proposed, aiming at both predicting the PAT performances and reproducing the internal fluid dynamics. In this regard, Computational Fluid Dynamics (CFD) models are widely applied, because able to both simulate the fluid dynamics of actual configurations and analyse the performance improvement obtainable by modifying the geometric properties of the considered turbo-machine. However, the current knowledge about PAT performances, available in the literature, does not allow to characterize the wide set of pump models, which are on the market, useful for running as turbines. With the aim of making this limitation smaller, in the present study an experimental and numerical investigation of centrifugal PAT models was performed, presenting an operative procedure for the optimal selection of centrifugal PATs in WDNs. A laboratory set-up was specfically installed at the Department of Civil, Architectural and Environmental Engineering (DICEA) of the University of Naples (IT) Federico II where several centrifugal PATs, having different geometric configurations and motor equipment, were tested. The experiments were performed in wide flow rate ranges, typical of WDNs supplying small-medium sized urban and rural centers, by varying the PAT rotational speed from 300 to 3000 rpm. A total of about 7400 operative configurations were investigated, in compliance with the laboratory set-up potentialities, achieving generated head drops up to about 70 m and produced electrical powers up to 16.3 kW. From the experimental analysis, with the aim of characterizing the investigated PATs, comparison with models from the literature was performed and, in order to provide effective tools for predicting the performances of centrifugal PATs, analytic relationships were derived, as a function of the geometric configuration, the number of stages and the motor efficiency class equipment. The attention was specifically drawn to the experimental characterization of: a) a classical Horizontal Axis Single-Stage PAT running at wider operative ranges than those discussed in the literature; b) PAT models, not adequately taken into account in the literature, such as centrifugal Single-Stage and Multi-Stage Vertical Axis PATs. Taking advantage of the experimental results, an operative procedure was developed for the optimal selection of PATs in WDNs, able to maximize the potential ER in WDNs. It could be intended as a basic tool for implementing a Decision Support System (DSS) for hydropower generation in WDNs. Finally, the development of a numerical CFD model, by applying the ANSYS® Fluent™ code, allowed to test the reliability of numerical simulations to both predict the PAT performances and investigate the fluid behaviour across centrifugal PATs.

Changing systems for supporting farmers' decisions: problems, paradigms, and prospects

Safety evaluation system for hydraulic metal structures based on knowledge engineering

Although recent studies have suggested the feasibility of integrating workflow and rule technology in a Clinical Decision Support System (CDSS), their implementation has not been verified yet. This paper proposes a knowledge engine which integrates workflow and rule engine as a tool for interpretation and execution of computer interpretable clinical guidelines. The objective of this paper is to validate its feasibility in two perspectives: clinical knowledge coverage and execution performance. The two open source engines which were selected and integrated were chosen due to their reliability and consistency. Implementation of workflow and rule engine integration has shown that the integrated knowledge engine (uBrain) is an effective CDSS for the execution of clinical guidelines.

The AAAI-99 Workshop Program (a part of the sixteenth national conference on artificial intelligence) was held in Orlando, Florida. The program included 16 workshops covering a wide range of topics in AI. Each workshop was limited to approximately 25 to 50 participants. Participation was by invitation from the workshop organizers. The workshops were Agent-Based Systems in the Business Context, Agents' Conflicts, Artificial Intelligence for Distributed Information Networking, Artificial Intelligence for Electronic Commerce, Computation with Neural Systems Workshop, Configuration, Data Mining with Evolutionary Algorithms: Research Directions (Jointly sponsored by GECCO-99), Environmental Decision Support Systems and Artificial Intelligence, Exploring Synergies of Knowledge Management and Case-Based Reasoning, Intelligent Information Systems, Intelligent Software Engineering, Machine Learning for Information Extraction, Mixed-Initiative Intelligence, Negotiation: Settling Conflicts and Identifying Opportunities, Ontology Management, and Reasoning in Context for AI Applications.

This paper introduces the Knowledge View concept and its application in software engineering. The work presented is aimed at bringing knowledge engineering and Semantic Web technologies closer to software engineers and programmers. It is done by disguising knowledge bases as relational or object databases and applying patterns known to software engineers.

Product development decisions represent challenging processes with various stakeholders from different disciplines and with different perspectives involved. Decisions can be highly complex due to the high amount of influencing factors that have to be considered simultaneously. Among other methods and tools, decision support systems (DSS) were developed to support decision-makers in handling complex decisions. One main disadvantage of DSS is the little adaptation to and integration of decision-maker needs and characteristics. Thus, current trends focus on the development of human- and decision-maker-centered DSS. This paper aims in providing a multilevel stepwise procedure for the modeling of decision-makers in order to enable a more effective decision support. The decision-maker modeling procedure is based on findings from literature research and results from research projects performed in cooperation with industry partners. The procedure serves as a guideline for generating diverse decision-maker models for different applications and for integrating human cognitive thinking procedures within the design and functionality of decision support methodologies and systems. The procedure represents an important design element of the human-centered decision support methodology currently being developed at the Institute of Product Development of the Technische Universität München (Germany).

Data warehouse technique is a new international pioneering research domain which is developed with the urgent demands of making full use of IT resources. Data Warehouse (DW), Data Mining (DM) and OLAP are introduced. The architecture and realization methods of an economic monitor and decision support system (DSS) based on DW are presented.

We propose a new statistical approach to the problem of inlier-based outlier detection, i.e.,finding outliers in the test set based on the training set consisting only of inliers. Our key idea is to use the ratio of training and test data densities as an outlier score; we estimate the ratio directly in a semi-parametric fashion without going through density estimation. Thus our approach is expected to have better performance in high-dimensional problems. Furthermore, the applied algorithm for density ratio estimation is equipped with a natural cross-validation procedure, allowing us to objectively optimize the value of tuning parameters such as the regularization parameter and the kernel width. The algorithm offers a closed-form solution as well as a closed-form formula for the leave-one-out error. Thanks to this, the proposed outlier detection method is computationally very efficient and is scalable to massive datasets. Simulations with benchmark and real-world datasets illustrate the usefulness of the proposed approach.

The tourist data is very interesting to be studied because the Indonesian tourism sector is an activator of the national economic which is potential to push higher economic growth in the future. Therefore, the forecast about tourist data is very needed for tourism business. The tourist data tend to fluctuate caused by many factors that affect the number of tourists extremely in an area, such as disasters, government regulation, social stability, violence and terrorism. That the extreme data can be assessed using intervention analysis and outlier detection. Intervention model is a time series model that can be used to forecast data consist of intervention of internal and external factors. In the intervention model, there are two kinds of intervention function, i.e., step and pulse functions. Step function is a form of intervention occurred in period of time while the pulse function is a form of intervention occurred only in a certain time. For the outlier detection, there are four types, such as additive outlier (AO), innovational outlier (IO), level shift (LS) and temporary change (TC). As an empirical studies was conducted by the domestic tourists data in Yogyakarta from January 2006 until December 2010 who staying on five-star hotels and motel throughout Yogyakarta. Based on the result of this research, known that the intervention occurred on January 2010 using the pulse function with MSE value 1172. Meanwhile based on the outliers detection, known any five outliers but only four outliers that significant included to the intervention model with MSE value 523,7167. So, the intervention model and outlier detection are chosen as a the best model based on the smallest MSE criterion. Keywords: Domestic tourists, intervention model, pulse function, outlier detection