"This book presents quality articles focused on key issues concerning the planning, design, maintenance, and management of telecommunications and networking technologies"--Provided by publisher.

This book contains the collection of full papers accepted at the 11th International Conference on Enterprise Information Systems (ICEIS 2009), organized by the Ins- tute for Systems and Technologies of Information Control and Communication (INSTICC) in cooperation with the Association for Advancement of Artificial Intel- gence (AAAI) and ACM SIGMIS (SIG on Management Information Systems), and technically co-sponsored by the Japanese IEICE SWIM (SIG on Software Interprise Modeling) and the Workflow Management Coalition (WfMC). ICEIS 2009 was held in Milan, Italy. This conference has grown to become a - jor point of contact between research scientists, engineers and practitioners in the area of business applications of information systems. This year, five simultaneous tracks were held, covering different aspects related to enterprise computing, including: “- tabases and Information Systems Integration,” “Artificial Intelligence and Decision Support Systems,” “Information Systems Analysis and Specification,” “Software Agents and Internet Computing” and “Human–Computer Interaction”. All tracks describe research work that is often oriented toward real-world applications and hi- light the benefits of information systems and technology for industry and services, thus making a bridge between academia and enterprise. ICEIS 2009 received 644 paper submissions from 70 countries in all continents; 81 papers were published and presented as full papers, i.e., completed research work (8 pages/30-minute oral presentation). Additional papers accepted at ICEIS, including short papers and posters, were published in the regular conference proceedings.

Provides a collection of authoritative articles from distinguished international researchers in information technology and Web engineering.

Food process modelling provides an authoritative review of one of the most exciting and influential developments in the food industry. The modelling of food processes allows analysts not only to understand such processes more clearly but also to control them more closely and make predictions about them. Modelling thus aids the search for greater and more consistent food quality. Written by a distinguished international team of experts, Food process modelling covers both the range of modelling techniques and their practical applications across the food chain.

Ontology Learning for the Semantic Web explores techniques for applying knowledge discovery techniques to different web data sources (such as HTML documents, dictionaries, etc.), in order to support the task of engineering and maintaining ontologies. The approach of ontology learning proposed in Ontology Learning for the Semantic Web includes a number of complementary disciplines that feed in different types of unstructured and semi-structured data. This data is necessary in order to support a semi-automatic ontology engineering process. Ontology Learning for the Semantic Web is designed for researchers and developers of semantic web applications. It also serves as an excellent supplemental reference to advanced level courses in ontologies and the semantic web.

Shared-workspace systems with structured graphical representations allow for the free user interaction and the joint construction of problem solutions for potentially open-ended tasks. However, group modelling in shared workspaces has to take on a process-orientated perspective due to the reduced system control in shared workspaces. This text is defined as the monitoring of user actions and the abstraction and interpretation of the raw data in the context of the group interaction and the problem representation. Formally based on plan recognition and the situation calculus, an approach has been developed that incorporates an operational hierarchy for generally modelling activities. The system performs an automatic inline analysis of group interactions and the results are visualized in different forms to give feedback and stimulating self-reflection.

This thesis presents an approach to learning good search guiding heuristics for the supposition-based theorom prover E in equational deductions. Search decisions from successful proof searches are represented as sets annotated clause patterns. Term Space Mapping, an alternative learning method for recursive structures is used to learn heuristic evaluation functions for the evaluation of potential new consequences. Experimental results with extended system E/TSM show the success of the approach. Additional contributions of the thesis are an extended superposition calculus and a description of both the proof procedure and the implementation of a state-of-the-art equational theorem prover.

Automated deduction is one of the fundamental disciplines in the field of artificial intelligence. The purpose of systems for automated deduction is to find formal proofs for given conjectures by drawing conclusions from formally specified knowledge. Their main strength is that they allow a purely declarative description of knowledge, i.e., procedural information on the drawing of conclusions need not be provided. In combination with the indeterminism in the drawing of possible conclusions, however, the ability to handle declarative specifications introduces the aspect of search into the deduction process. Usually, tremendous search spaces have to be explored in order to find a proof. Successful systems for automated deduction can be built, for example, on the basis of connection tableau calculi. In this thesis, an approach to a more intelligent search in connection tableau calculi is made. The approach is based on the compression of structurally similar formulas given to and derived by connection tableau calculi. Disjunctive constraints over first order terms are used to express the results of the compression. There are two main theoretical results of the thesis. Firstly, it introduces a new class of sound and complete connection tableau calculi, the so-called constrained-connection-tableau calculi, which are compatible with the most important search pruning techniques of conventional connection tableau calculi. Secondly, intelligent algorithms for solving disjunctive constraints over first order terms are developed. As a practical result, the implementation of the approach leads to a powerful system for automated deduction which demonstrates the high potential of the new developments.