Semantic Based Visualization

Visualization is the process of mapping data into visual dimensions to create a visual representation to amplify cognition. Visual representations are essential aids to human cognitive tasks and are valued to the extent that they provide stable and external reference points upon which dynamic activities and thought processes may be calibrated and upon which models and theories can be tested and confirmed. The active use and manipulation of visual representations makes many complex and intensive cognitive tasks feasible. A visual representation is able to convey relationships among many elements in parallel and provides an individual with directly observable memory. A successful visualization allows the user to gain insight into the data, in other words to communicate different aspect of the data in an effective way. Even with today’s visualization systems that give the user a considerable control over the visualization process, it can be difficult to produce an effective visualization. To obtain useful results, a user had to know which questions to pose. Problems had to be framed in very precise terms. A strategy to improve this situation is to guide the user in the selection of the parameters involved in the visualization. Our research goal is the design of a visualization system that assist the user to do the work, by considering the semantic of the data together with the semantic of the stages through all the visualization process.Eje: Computación Gráfica, Visualización e ImágenesRed de Universidades con Carreras en Informática (RedUNCI

Semantic Self-Formation of Communities of Peers

The formation of semantic communities of peers plays a crucial role for realizing effective query propagation mechanisms on a semantic basis. In this paper, we propose a novel approach to the self-organization of autonomous communities of peers; we propose semantic handshake techniques based on semantic community aggregation and community-aware query propagation techniques exploiting dynamic ontology matching techniques for improving traditional P2P search and discovery capabilities

Semantic based visualization: a first approach

Visualization is the process of mapping data into visual dimensions to create a visual representation to amplify cognition. Visual representations are essential aids to human cognitive tasks and are valued to the extent that they provide stable and external reference points upon which dynamic activities and thought processes may be calibrated and upon which models and theories can be tested and confirmed. The active use and manipulation of visual representations makes many complex and intensive cognitive tasks feasible. A visual representation is able to convey relationships among many elements in parallel and provides an individual with directly observable memory. A successful visualization allows the user to gain insight into the data, in other words to communicate different aspect of the data in an effective way. Even with today’s visualization systems that give the user a considerable control over the visualization process, it can be difficult to produce an effective visualization. To obtain useful results, a user had to know which questions to pose. Problems had to be framed in very precise terms. A strategy to improve this situation is to guide the user in the selection of the parameters involved in the visualization. Our research goal is the design of a visualization system that assist the user to do the work, by considering the semantic of the data together with the semantic of the stages through all the visualization process.Eje: Computación Gráfica, Visualización e ImágenesRed de Universidades con Carreras en Informática (RedUNCI

Leveraging knowledge from different communities using ontologies

The purpose of this paper is to provide research based understanding of leveraging knowledge and managing knowledge within and across several communities using the poverty domain as a case study. We hypothesize that leveraging knowledge with a good taxonomy and a good integration process are good approaches to organize and share knowledge. Problems appear when a group of people in different communities share data and collaborate using different perceptions, different concepts, different terms (terminologies), and different semantics to represent the same reality. In this paper we present an approach to solve this problem. We will generate a common set of terms based on the terms of several different storage devices, used by different communities, in order to make data retrieval independent of the different perceptions and terminologies used by those communities. We use ontologies to represent the particular knowledge of each community and discuss the use of mapping and integration techniques to find correspondences between the concepts used in those ontologies

Solving problems of data heterogeneity, semantic heterogeneity and data inequality : an approach using ontologies

Knowledge is people’s personal map and people’s personal model of the world. Knowledge acquisition involves complex cognitive processes such as perception, communication, and reasoning. According to the knowledge differences, then it is possible for people have a different perception to attain awareness or understand the environment or reality. This paper provides a case study where there is a group of people in different communities managing data using different perceptions, different concepts, different terms (terminologies), and different semantics to represent the same reality. Perceptions are converted into data, and then saved into separate storage devices that are not connected to each other. Each user – belonging to different communities - use different terminologies in collecting data and as a consequence they also get different results of that exercise. It is not a problem if the different results are used for each community, the problem occur if people need to take data from another communities, sharing, collaborating and using it to get a bigger solution. In this paper we present an approach to generate a common set of terms based on the terms of several and different storage devices, used by different communities, in order to make data retrieval independent of the different perceptions and terminologies used by those communities. We use ontologies to represent the knowledge and discuss the use of mapping and integration techniques to find correspondences between the concepts used in those ontologies. We discuss too how to derive a common ontology to be used by all the communities. We can find in literature several documents about the theoretical concepts and techniques that can be used to solve the described problem. However, in this paper we are presenting a real implementation of a system using those concepts

Data Mining-based Fragmentation of XML Data Warehouses

With the multiplication of XML data sources, many XML data warehouse models have been proposed to handle data heterogeneity and complexity in a way relational data warehouses fail to achieve. However, XML-native database systems currently suffer from limited performances, both in terms of manageable data volume and response time. Fragmentation helps address both these issues. Derived horizontal fragmentation is typically used in relational data warehouses and can definitely be adapted to the XML context. However, the number of fragments produced by classical algorithms is difficult to control. In this paper, we propose the use of a k-means-based fragmentation approach that allows to master the number of fragments through its $k$ parameter. We experimentally compare its efficiency to classical derived horizontal fragmentation algorithms adapted to XML data warehouses and show its superiority

Semantic based visualization: a first approach

Visualization is the process of mapping data into visual dimensions to create a visual representation to amplify cognition. Visual representations are essential aids to human cognitive tasks and are valued to the extent that they provide stable and external reference points upon which dynamic activities and thought processes may be calibrated and upon which models and theories can be tested and confirmed. The active use and manipulation of visual representations makes many complex and intensive cognitive tasks feasible. A visual representation is able to convey relationships among many elements in parallel and provides an individual with directly observable memory. A successful visualization allows the user to gain insight into the data, in other words to communicate different aspect of the data in an effective way. Even with today’s visualization systems that give the user a considerable control over the visualization process, it can be difficult to produce an effective visualization. To obtain useful results, a user had to know which questions to pose. Problems had to be framed in very precise terms. A strategy to improve this situation is to guide the user in the selection of the parameters involved in the visualization. Our research goal is the design of a visualization system that assist the user to do the work, by considering the semantic of the data together with the semantic of the stages through all the visualization process.Eje: Computación Gráfica, Visualización e ImágenesRed de Universidades con Carreras en Informática (RedUNCI

Scientific Publication Packages: A Selective Approach to the Communication and Archival of Scientific Output

The use of digital technologies within research has led to a proliferation of data, many new forms of research output and new modes of presentation and analysis. Many scientific communities are struggling with the challenge of how to manage the terabytes of data and new forms of output, they are producing. They are also under increasing pressure from funding organizations to publish their raw data, in addition to their traditional publications, in open archives. In this paper I describe an approach that involves the selective encapsulation of raw data, derived products, algorithms, software and textual publications within "scientific publication packages". Such packages provide an ideal method for: encapsulating expert knowledge; for publishing and sharing scientific process and results; for teaching complex scientific concepts; and for the selective archival, curation and preservation of scientific data and output. They also provide a bridge between technological advances in the Digital Libraries and eScience domains. In particular, I describe the RDF-based architecture that we are adopting to enable scientists to construct, publish and manage "scientific publication packages" - compound digital objects that encapsulate and relate the raw data to its derived products, publications and the associated contextual, provenance and administrative metadata

Semantics-based color assignment in visualization

The active use and manipulation of visual representations makes many complex and intensive cognitive tasks feasible. A visual representation is able to convey relationships among many elements in parallel and it provides an individual with directly observable memory. A successful visualization allows the user to gain insight into the data, that is, to communicate different aspects of the data in an effective way. Even with today's visualization systems that give the user a considerable control over the visualization process, it can be difficult to produce an effective visualization. To obtain useful results, a user has to interrogate the visualization very precisely. A strategy to improve this situation is to guide the user with the selection of the parameters involved in the visualization. This paper presents the initial effort dedicated to achieve a visualization system that assists the user in the configuration and preparation of the visualization by considering both the semantic of the data and the semantic of the stages through all the visualization process. In this article we present a visualization system for file hierarchies where color assignment is made by a reasoning process through the use of an ontology. This work sets the way forward to integrate the visualization process with a reasoning process and configure a visualization based on the reasoner s results.Facultad de Informátic