Grids and the Virtual Observatory

We consider several projects from astronomy that benefit from the Grid paradigm and associated technology, many of which involve either massive datasets or the federation of multiple datasets. We cover image computation (mosaicking, multi-wavelength images, and synoptic surveys); database computation (representation through XML, data mining, and visualization); and semantic interoperability (publishing, ontologies, directories, and service descriptions)

Computational Modelling for Bankruptcy Prediction: Semantic data Analysis Integrating Graph Database and Financial Ontology

In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company's financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database system. The Ontological layer defines the basic concepts of the financial risk management as well as the objects that serve as sources of knowledge for predicting a company's bankruptcy. The Graph Database layer utilises a powerful semantic data technology, which serves as a semantic data repository for our model. The article provides a detailed description of the construction of the Ontology and its informal conceptual representation. We also present a working prototype of the Graph Database system, constructed using the Neo4j application, and show the connection between well-known financial ratios. We argue that this methodology which utilises state of the art semantic data management mechanisms enables data processing and relevant computations in a more efficient way than approaches using the traditional relational database. These give us solid grounds to build a system that is capable of tackling the data of any complexity level

INVESTIGATION OF THE ROLE OF SERVICE LEVEL AGREEMENTS IN WEB SERVICE QUALITY

Context/Background: Use of Service Level Agreements (SLAs) is crucial to provide the value added services to consumers to achieve their requirements successfully. SLAs also ensure the expected Quality of Service to consumers. Aim: This study investigates how efficient structural representation and management of SLAs can help to ensure the Quality of Service (QoS) in Web services during Web service composition. Method: Existing specifications and structures for SLAs for Web services do not fully formalize and provide support for different automatic and dynamic behavioral aspects needed for QoS calculation. This study addresses the issues on how to formalize and document the structures of SLAs for better service utilization and improved QoS results. The Service Oriented Architecture (SOA) is extended in this study with addition of an SLAAgent, which helps to automate the QoS calculation using Fuzzy Inference Systems, service discovery, service selection, SLA monitoring and management during service composition with the help of structured SLA documents. Results: The proposed framework improves the ways of how to structure, manage and monitor SLAs during Web service composition to achieve the better Quality of Service effectively and efficiently. Conclusions: To deal with different types of computational requirements the automation of SLAs is a challenge during Web service composition. This study shows the significance of the SLAs for better QoS during composition of services in SOA

User Preference Web Search -- Experiments with a System Connecting Web and User

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware supporting top-k answers and a user interface for querying and evaluation of search results. We integrate several tools (implementing our models and methods) into one framework connecting user with the web. The model represents user preferences with fuzzy sets and fuzzy logic, here understood as a scoring describing user satisfaction. This model can be acquired with explicit or implicit methods. Model-theoretic semantics is based on fuzzy description logic f-EL. User preference learning is based on our model of fuzzy inductive logic programming. Our system works both for English and Slovak resources. The primary application domain are job offers and job search, however we show extension to mutual investment funds search and a possibility of extension into other application domains. Our top-k search is optimized with own heuristics and repository with special indexes. Our model was experimentally implemented, the integration was tested and is web accessible. We focus on experiments with several users and measure their satisfaction according to correlation coefficients