Periklis Mitkas

Bio: Periklis Mitkas is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Data quality & Decision support system. The author has an hindex of 3, co-authored 12 publications receiving 26 citations.

Quality control of national genetic evaluation results using data-mining techniques; a progress report

Abstract: Data quality constitutes one of the most critical issues in genetic evaluations both at national and international level. International genetic evaluations computed by Interbull are based on the analysis of national genetic evaluation results. Therefore, the validity of international comparisons depends on the quality of the output of the various national genetic evaluation systems. The current method for data quality assurance is mainly determined by the consistency of consecutive evaluation results and is based on thorough statistical examination (Klei et al., 2002). In a separate project, national genetic evaluation programs are being tested on simulated datasets with known properties (Taubert et al., 2002).

DAWN: A platform for evaluating water-pricing policies using a software agent society

Abstract: Lately there is a transition in water management: policy makers leave aside traditional methods focused on additional-supply policies and focus on water conservation using demand control methods. Water Agencies use water-pricing policies as an instrument for controlling residential water demand. However, design and evaluation of a water-pricing policy is a complex task, as economic, social and political constraints have to be incorporated. In order to support policy makers in their tasks, we developed DAWN, a software tool for evaluating water-pricing policies, implemented as a multi-agent system. DAWN simulates the residential water demand-supply chain and enables the design, creation, modification and execution of different scenarios. Software agents behave as water consumers, while econometric and social models are incorporated into them for estimating future consumptions. Scenarios and models can be parameterized through a friendly graphical user interface and software agents are instantiated at runtime. DAWN’s main advantage is that it supports social interaction between consumers, which is activated using agent communication. Thus, variables affecting water consumption and associated with consumer’s social behavior can be included into DAWN scenarios. In this paper, DAWN’s agent architecture is detailed and agent communication using ontologies is discussed. Focus is given on the econometric and social simulation models used for agent reasoning. Finally, the platform developed is presented along with real-world results of its application at the region of Thessaloniki, Greece.

G-Class: A Divide and Conquer Application for Grid Protein Classification

Abstract: Protein classification has always been one of the major challenges in modern functional proteomics. The presence of motifs in protein chains can make the prediction of the functional behavior of proteins possible. The correlation between protein properties and their motifs is not always obvious, since more than one motif may exist within a protein chain. Due to the complexity of this correlation most data mining algorithms are either non efficient or time consuming. In this paper a data mining methodology that utilizes grid technologies is presented. First, data are split into multiple sets while preserving the original data distribution in each set. Then, multiple models are created by using the data sets as independent training sets. Finally, the models are combined to produce the final classification rules, containing all the previously extracted information. The methodology is tested using various protein and protein class subsets. Results indicate the improved time efficiency of our technique compared to other known data mining algorithms.

AGELI: An integrated platform for the Assessment of national Genetic Evaluation results by Learning and Informing

Abstract: We present an integrated platform for preprocessing, analysis, alarm issuing and presentation of national genetic evaluation data based on data-mining. Our goal is the integrated qualitative description of national genetic evaluation results, concerning three milk yield traits that constitute a critical issue in the range of services provided by Interbull. Although the standard method for quality assurance appears sufficiently functional (Klei et al., 2002), during the last years there has been a progress concerning an alternative validation method of genetic evaluation results using data-mining (Banos et al., 2003; Diplaris et al., 2004; Han and Kamber, 2000), potentially leading to inference on data quality. A new alarming technique based on multiple criteria was recently established in order to assess and assure data quality (Diplaris et al., 2004). The whole idea was to exploit datamining techniques, i.e. decision trees, and then apply a goodness of fit test to individual tree nodes and an F-test in corresponding nodes from consecutive evaluation runs, aiming at discovering possible abnormalities in bull proof distributions at various regions. In a previous report (Banos et al., 2003) predictions led to by associations discovered had been qualitatively compared to actual proofs and discrepancies had been confirmed using a data set with known errors.

A Hybrid Agent-Based Model for Estimating Residential Water Demand

Abstract: The global effort toward sustainable development has initiated a transition in water management. Water utility companies use water-pricing policies as an instrument for controlling residential water demand. To support policy makers in their decisions, the authors have developed DAWN, a hybrid model for evaluating water-pricing policies. DAWN integrates an agent-based social model for the consumer with conventional econometric models and simulates the residential water demand-supply chain, enabling the evaluation of different scenarios for policy making. An agent community is assigned to behave as water consumers, while econometric and social models are incorporated into them for estimating water consumption. DAWN's main advantage is that it supports social interaction between consumers, through an influence diffusion mechanism, implemented via inter-agent communication. Parameters affecting water consumption and associated with consumers' social behavior can be simulated with DAWN. Real-world results of DAWN's application for the evaluation of five water-pricing policies in Thessaloniki, Greece, are presented.

A multi-agent system for meteorological radar data management and decision support

Abstract: The continuous processing and evaluation of meteorological radar data require significant efforts by scientists, both for data processing, storage, and maintenance, and for data interpretation and visualization. To assist meteorologists and to automate a large part of these tasks, we have designed and developed Abacus, a multi-agent system for managing radar data and providing decision support. Abacus' agents undertake data management and visualization tasks, while they are also responsible for extracting statistical indicators and assessing current weather conditions. Abacus agent system identifies potentially hazardous incidents, disseminates preprocessed information over the web, and enables warning services provided via email notifications. In this paper, Abacus' agent architecture is detailed and agent communication for information diffusion is presented. Focus is also given on the customizable logical rule-bases for agent reasoning required in decision support. The platform has been tested with real-world data from the Meteorological Service of Cyprus.

Social influence and water conservation: an agent-based approach

Abstract: Every day, consumers are exposed to advertising campaigns that attempt to influence their decisions and affect their behavior. Word-of-mouth communication - the informal channels of daily interactions among friends, relatives, coworkers, neighbors, and acquaintances - plays a much more significant role in how consumer behavior is shaped, fashion is introduced, and product reputation is built. Macrolevel simulations that include this kind of social parameter are usually limited to generalized, often simplistic assumptions. In an effort to represent the phenomenon in a semantically coherent way and model it more realistically, we developed an influence-diffusion mechanism that follows agent-based social simulation primitives. The model is realized as a multiagent software platform, which we call Dawn (for distributed agents for water simulation).