Recent challenges in information retrieval are related to cross media information in social networks including rich media and web based content. In those cases, the cross media content includes classical file and their metadata plus web pages, events, blog, discussion forums, comments in multilingual. This heterogeneity creates large complex problems in cross media indexing and retrieval for services that integrate qualified documents and user generated content together. Problems are also related to scalability, robustness and resilience to errors. Moreover, users expect to have fast and efficient indexing and searching services, from social media in best practice network services. This paper presents a model and an indexing and searching solution for cross media contents, addressing the above issues, developed for the ECLAP Social Network, in the domain of Performing Arts. Effectiveness and optimization analysis of the retrieval solution are presented with relevant metrics. The research aimed to cope with the complexity of a heterogeneous indexing semantic model, using stochastic optimization techniques, with tuning and discrimination of relevant metadata terms. The research was conducted in the context of the ECLAP European Commission project and services
a b s t r a c tGraph databases are taking place in many different applications: smart city, smart cloud, smart education, etc. In most cases, the applications imply the creation of ontologies and the integration of a large set of knowledge to build a knowledge base as an RDF KB store, with ontologies, static data, historical data and real time data. Most of the RDF stores are endowed with inferential engines that materialize some knowledge as triples during indexing or querying. In these cases, deleting concepts may imply the removal and change of many triples, especially if the triples are those modeling the ontological part of the knowledge base, or are referred by many other concepts. For these solutions, the graph database versioning feature is not provided at level of the RDF stores tool, and it is quite complex and time consuming to be addressed as black box approach. In most cases the indexing is a time consuming process, and the rebuilding of the KB may imply manually edited long scripts that are error prone. Therefore, in order to solve these kinds of problems, this paper proposes a lifecycle methodology and a tool supporting versioning of indexes for RDF KB store. The solution proposed has been developed on the basis of a number of knowledge oriented projects as Sii-Mobility (smart city), RESOLUTE (smart city risk assessment), ICARO (smart cloud). Results are reported in terms of time saving and reliability.
The detection and classification of defects is strongly useful for stopping in real time the cloth production when degenerative defects occur; for increasing the efficiency of production by limiting the decrement of price for cloth rolls. The paper describes the work performed for detecting defect of well-known manufacturers of cloths and machine builders for cloths (looms). The main goal has been to obtain a new and innovative production line endowed with a system for detecting defects in real-time. The system is based on image processing techniques with a special attention to the real-time constraints. An architecture separating an on-line defect detection and an off-line classification has been proposed. An intelligent optical head, assembled on the loom, has the duty to acquire images and to detect the defects in realtime. A server has the offline task to classify each defect detected by the head. In the paper, some new algorithms for defect detection have been proposed. These have been compared with a selection of the most interesting algorithms for the same purposed taken from the literature. The comparison has been conducted by on the basis of a large test set with several types of defects and by considering reliability, performance, and complexity.
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