Pericles A. Mitkas

Protein classification with multiple algorithms

Nowadays, the number of protein sequences being stored in central protein databases from labs all over the world is constantly increasing. From these proteins only a fraction has been experimentally analyzed in order to detect their structure and hence their function in the corresponding organism. The reason is that experimental determination of structure is labor-intensive and quite time-consuming. Therefore there is the need for automated tools that can classify new proteins to structural families. This paper presents a comparative evaluation of several algorithms that learn such classification models from data concerning patterns of proteins with known structure. In addition, several approaches that combine multiple learning algorithms to increase the accuracy of predictions are evaluated. The results of the experiments provide insights that can help biologists and computer scientists design high-performance protein classification systems of high quality.

Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation

The fuzzy lattice reasoning (FLR) classifier is presented for inducing descriptive, decision-making knowledge (rules) in a mathematical lattice data domain including space R^N. Tunable generalization is possible based on non-linear (sigmoid) positive valuation functions; moreover, the FLR classifier can deal with missing data. Learning is carried out both incrementally and fast by computing disjunctions of join-lattice interval conjunctions, where a join-lattice interval conjunction corresponds to a hyperbox in R^N. Our testbed in this work concerns the problem of estimating ambient ozone concentration from both meteorological and air-pollutant measurements. The results compare favorably with results obtained by C4.5 decision trees, fuzzy-ART as well as back-propagation neural networks. Novelties and advantages of classifier FLR are detailed extensively and in comparison with related work from the literature.

A Hybrid Agent-Based Model for Estimating Residential Water Demand

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.

An agent‐based intelligent environmental monitoring system

Fairly rapid environmental changes call for continuous surveillance and on‐line decision making. There are two main areas where IT technologies can be valuable. In this paper we present a multi‐agent system for monitoring and assessing air‐quality attributes, which uses data coming from a meteorological station. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air‐quality, and, finally, to fire alarms to appropriate recipients, when needed. Data mining techniques have been used for adding data‐driven, customized intelligence into agents. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real‐world test case is demonstrated.

Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques

Abstract Enterprise Resource Planning systems tend to deploy Supply Chain Management and/or Customer Relationship Management techniques, in order to successfully fuse information to customers, suppliers, manufacturers and warehouses, and therefore minimize system-wide costs while satisfying service level requirements. Although efficient, these systems are neither versatile nor adaptive, since newly discovered customer trends cannot be easily integrated with existing knowledge. Advancing on the way the above mentioned techniques apply on ERP systems, we have developed a multi-agent system that introduces adaptive intelligence as a powerful add-on for ERP software customization. The system can be thought of as a recommendation engine, which takes advantage of knowledge gained through the use of data mining techniques, and incorporates it into the resulting company selling policy. The intelligent agents of the system can be periodically retrained as new information is added to the ERP. In this paper, we present the architecture and development details of the system, and demonstrate its application on a real test case.

Optics and supercomputing

Storage, interconnection, and processing are discussed. Various types of optical disks and page-oriented holographic memories are considered. It is shown that optical storage is advancing rapidly and holds the potential of hundreds of megabytes per second data rates from a single storage unit, which can provide many new opportunities for supercomputing. Module-to-module, board-to-board, and chip-to-chip interconnection and gate-to-gate communication are discussed. It is concluded that optical interconnection is, in many cases, superior to electronic interconnection and holds the key to the development of future electrooptic systems. Optical computing devices are discussed and various application areas where optical processing as well as storage and interconnection are expected to play a role in the future are considered. The authors believe that optical processing, while holding considerable promise, lags behind its electronic counterpart primarily due to the fact that digital optical device development is in its infancy. They predict near-term systems will be electrooptic, with each technology providing its strength to the problem at hand. >

Volume holographic storage for large relational databases

Computing applications continue to put increasing demands on secondary storage systems. These demands can be met by optical solutions such as volume holographic memories, which promise to provide the high storage capacities and data rates required by many input/ output intensive applications. Holographic memories appear especially suited for large database storage because of their page format and fast associative recall ability. A volume holographic database system that is being implemented in our laboratory is presented. The discussion includes a description of the optical system as well as the development and analysis of an error-correcting code suitable for the application. Also presented are experimental results in which good associative addressability is achieved for search arguments as small as 1/30 of a page.

Optoelectronic data filter for selection and projection

Database processing, like the majority of nonnumerical applications, exhibits a high degree of functional parallelism but does not require complex operations; therefore it is amenable to optical solutions. The architecture of an optoelectronic filter that is capable of performing selection and projection operations on a two-dimensional data array in a relational database environment is presented. The system receives input from a parallel optical memory, one page at a time, and performs logic operations by using optoelectronic smart pixels based on heterostructure phototransistors and vertical-cavity surfaceemitting lasers. Combinations of AND and XOR gates are used to realize row-lcolumn masking and comparisons of input data against user-supplied search arguments. The main goal of the filter is to reduce the effective data rate between the highly parallel optical storage and the low input data rate conventional electronic computer, thus efficiently interfacing currently available photonic and electronic technologies.

The impact of optics on data and knowledge base systems

The authors assess the possible impact of optics on database and knowledge base systems, focusing on storage, interconnection, and processing. Various types of optical disks and page-oriented holographic memories are discussed. In the interconnection section, data communication is discussed at a variety of levels. Under processing, applications involving optical content addressable memories, optical data/knowledge base machines, and optics applied to full text processing will be optoelectronic, with easy technology providing its strength to the problem under consideration. It is noted that optical interconnection is superior to electronic interconnection in many cases and that the rapid advance of optical storage holds the potential of hundreds of megabytes per second data rates from a single storage unit. It is noted that optical processing holds considerable promise but lags behind primarily because digital optical device development is in its infancy. >

A Framework for Constructing Multi-agent Applications and Training Intelligent Agents

As agent-oriented paradigm is reaching a significant level of acceptance by software developers, there is a lack of integrated high-level abstraction tools for the design and development of agent-based applications, In an effort to mitigate this deficiency, we introduce Agent Academy, an integrated development framework, implemented itself as a multi-agent system, that supports, in a single tool, the design of agent behaviours and reusable agent types, the definition of ontologies, and the instantiation of single agents or multi-agent communities. In addition to these characteristics, our framework goes deeper into agents, by implementing a mechanism for embedding rule-based reasoning into them. We call this procedure agent training and it is realized by the application of AI techniques for knowledge discovery on application-specific data, which may be available to the agent developer. In this respect, Agent Academy provides an easy-to-use facility that encourages the substitution of existing, traditionally developed applications by new ones, which follow the agent-orientation paradigm.