2,557 research outputs found
Significance of Semantic Reconciliation in Digital Forensics
Digital forensics (DF) is a growing field that is gaining popularity among many computer professionals, law enforcement agencies and other stakeholders who must always cooperate in this profession. Unfortunately, this has created an environment replete with semantic disparities within the domain that needs to be resolved and/or eliminated. For the purpose of this study, semantic disparity refers to disagreements about the meaning, interpretation, descriptions and the intended use of the same or related data and terminologies. If semantic disparity is not detected and resolved, it may lead to misunderstandings. Even worse, since the people involved may not be from the same neighbourhood, they may not be aware of the existence of the semantic disparities, and probably might not easily realize it. The aim of this paper, therefore, is to discuss semantic disparity in DF and further elaborates on how to manage it. In addition, this paper also presents the significance of semantic reconciliation in DF. Semantic reconciliation refers to reconciling the meaning (including the interpretations and descriptions) of terminologies and data used in digital forensics. Managing semantic disparities and the significance of semantic reconciliation in digital forensics constitutes the main contributions of this paper.
Keywords: Digital forensics, semantic disparity, managing semantic disparity, semantic reconciliation, significance of semantic reconciliatio
An Ontology Based Method to Solve Query Identifier Heterogeneity in Post-Genomic Clinical Trials
The increasing amount of information available for biomedical research has led to issues related to knowledge discovery in large collections of data. Moreover, Information Retrieval techniques must consider heterogeneities present in databases, initially belonging to different domainsâe.g. clinical and genetic data. One of the goals, among others, of the ACGT European is to provide seamless and homogeneous access to integrated databases. In this work, we describe an approach to overcome heterogeneities in identifiers inside queries. We present an ontology classifying the most common identifier semantic heterogeneities, and a service that makes use of it to cope with the problem using the described approach. Finally, we illustrate the solution by analysing a set of real queries
A conceptual framework and a risk management approach for interoperability between geospatial datacubes
De nos jours, nous observons un intĂ©rĂȘt grandissant pour les bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Ces bases de donnĂ©es sont dĂ©veloppĂ©es pour faciliter la prise de dĂ©cisions stratĂ©giques des organisations, et plus spĂ©cifiquement lorsquâil sâagit de donnĂ©es de diffĂ©rentes Ă©poques et de diffĂ©rents niveaux de granularitĂ©. Cependant, les utilisateurs peuvent avoir besoin dâutiliser plusieurs bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Ces bases de donnĂ©es peuvent ĂȘtre sĂ©mantiquement hĂ©tĂ©rogĂšnes et caractĂ©risĂ©es par diffĂ©rent degrĂ©s de pertinence par rapport au contexte dâutilisation. RĂ©soudre les problĂšmes sĂ©mantiques liĂ©s Ă lâhĂ©tĂ©rogĂ©nĂ©itĂ© et Ă la diffĂ©rence de pertinence dâune maniĂšre transparente aux utilisateurs a Ă©tĂ© lâobjectif principal de lâinteropĂ©rabilitĂ© au cours des quinze derniĂšres annĂ©es. Dans ce contexte, diffĂ©rentes solutions ont Ă©tĂ© proposĂ©es pour traiter lâinteropĂ©rabilitĂ©. Cependant, ces solutions ont adoptĂ© une approche non systĂ©matique. De plus, aucune solution pour rĂ©soudre des problĂšmes sĂ©mantiques spĂ©cifiques liĂ©s Ă lâinteropĂ©rabilitĂ© entre les bases de donnĂ©es gĂ©ospatiales multidimensionnelles nâa Ă©tĂ© trouvĂ©e. Dans cette thĂšse, nous supposons quâil est possible de dĂ©finir une approche qui traite ces problĂšmes sĂ©mantiques pour assurer lâinteropĂ©rabilitĂ© entre les bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Ainsi, nous dĂ©finissons tout dâabord lâinteropĂ©rabilitĂ© entre ces bases de donnĂ©es. Ensuite, nous dĂ©finissons et classifions les problĂšmes dâhĂ©tĂ©rogĂ©nĂ©itĂ© sĂ©mantique qui peuvent se produire au cours dâune telle interopĂ©rabilitĂ© de diffĂ©rentes bases de donnĂ©es gĂ©ospatiales multidimensionnelles. Afin de rĂ©soudre ces problĂšmes dâhĂ©tĂ©rogĂ©nĂ©itĂ© sĂ©mantique, nous proposons un cadre conceptuel qui se base sur la communication humaine. Dans ce cadre, une communication sâĂ©tablit entre deux agents systĂšme reprĂ©sentant les bases de donnĂ©es gĂ©ospatiales multidimensionnelles impliquĂ©es dans un processus dâinteropĂ©rabilitĂ©. Cette communication vise Ă Ă©changer de lâinformation sur le contenu de ces bases. Ensuite, dans lâintention dâaider les agents Ă prendre des dĂ©cisions appropriĂ©es au cours du processus dâinteropĂ©rabilitĂ©, nous Ă©valuons un ensemble dâindicateurs de la qualitĂ© externe (fitness-for-use) des schĂ©mas et du contexte de production (ex., les mĂ©tadonnĂ©es). Finalement, nous mettons en Ćuvre lâapproche afin de montrer sa faisabilitĂ©.Today, we observe wide use of geospatial databases that are implemented in many forms (e.g., transactional centralized systems, distributed databases, multidimensional datacubes). Among those possibilities, the multidimensional datacube is more appropriate to support interactive analysis and to guide the organizationâs strategic decisions, especially when different epochs and levels of information granularity are involved. However, one may need to use several geospatial multidimensional datacubes which may be semantically heterogeneous and having different degrees of appropriateness to the context of use. Overcoming the semantic problems related to the semantic heterogeneity and to the difference in the appropriateness to the context of use in a manner that is transparent to users has been the principal aim of interoperability for the last fifteen years. However, in spite of successful initiatives, today's solutions have evolved in a non systematic way. Moreover, no solution has been found to address specific semantic problems related to interoperability between geospatial datacubes. In this thesis, we suppose that it is possible to define an approach that addresses these semantic problems to support interoperability between geospatial datacubes. For that, we first describe interoperability between geospatial datacubes. Then, we define and categorize the semantic heterogeneity problems that may occur during the interoperability process of different geospatial datacubes. In order to resolve semantic heterogeneity between geospatial datacubes, we propose a conceptual framework that is essentially based on human communication. In this framework, software agents representing geospatial datacubes involved in the interoperability process communicate together. Such communication aims at exchanging information about the content of geospatial datacubes. Then, in order to help agents to make appropriate decisions during the interoperability process, we evaluate a set of indicators of the external quality (fitness-for-use) of geospatial datacube schemas and of production context (e.g., metadata). Finally, we implement the proposed approach to show its feasibility
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