0000000000484363

AUTHOR

Richard Chbeir

showing 46 related works from this author

RSS Merger

2010

[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-WB] Computer Science [cs]/Web[SCCO.COMP] Cognitive science/Computer science[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
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Guest Editorial

2011

World Wide WebComputer Networks and CommunicationsComputer scienceJournal of Emerging Technologies in Web Intelligence
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Ninth International Conference on Signal-Image Technology & Internet-Based Systems, SITIS 2013, Kyoto, Japan, December 2-5, 2013

2013

International audience

[INFO]Computer Science [cs][INFO] Computer Science [cs]ComputingMilieux_MISCELLANEOUS
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Toward Approximate GML Retrieval Based on Structural and Semantic Characteristics

2010

International audience; GML is emerging as the new standard for representing geographic information in GISs on the Web, allowing the encoding of structurally and semantically rich geographic data in self describing XML-based geographic entities. In this study, we address the problem of approximate querying and ranked results for GML data and provide a method for GML query evaluation. Our method consists of two main contributions. First, we propose a tree model for representing GML queries and data collections. Then, we introduce a GML retrieval method based on the concept of tree edit distance as an efficient means for comparing semi-structured data. Our approach allows the evaluation of bo…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]Tree edit distanceSimilarity (geometry)[INFO.INFO-WB] Computer Science [cs]/WebComputer sciencecomputer.internet_protocol[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologycomputer.software_genre[SCCO.COMP] Cognitive science/Computer science020204 information systemsEncoding (memory)0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]GML SearchStructural & Semantic Similarity[INFO.INFO-WB]Computer Science [cs]/WebProcess (computing)[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]GISConstraint (information theory)[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Ranked retrieval020201 artificial intelligence & image processingData mining[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computerXMLDecision tree model
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Foto2Events: From Photos to Event Discovery and Linking in Online Social Networks

2014

International audience; — Online social networking has become the predominant activity in the digital world thanks to multimedia data (mainly photos) sharing (e.g., photos now represent 93% of the top posts on Facebook). Discovering events where users are involved using their own posts and those shared by their friends would be of great importance. In this paper, we address this issue by providing an original approach able to detect, enrich and also link user's events using photos shared within his online social networks. Using metadata, our approach provides a multi-dimensional gathering of similar photos using their temporal, geographical, and social facets. To validate our approach, we i…

World Wide WebSet (abstract data type)Metadata[ INFO ] Computer Science [cs]Event (computing)Computer sciencemetadata[INFO]Computer Science [cs][INFO] Computer Science [cs]Cluster analysis—image clusteringevent detection
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Towards semantic-based RSS merging

2009

Merging information can be of key importance in several XML-based applications. For instance, merging the RSS news from different sources and providers can be beneficial for end-users (journalists, economists, etc.) in various scenarios. In this work, we address this issue and mainly explore the relatedness relationships between RSS entities/ elements. To validate our approach, we also provide a set of experimental tests showing satisfactory results. © 2009 Springer-Verlag Berlin Heidelberg

Information retrievalComputer sciencecomputer.internet_protocolRSSINF/01 - INFORMATICAComputerApplications_COMPUTERSINOTHERSYSTEMScomputer.file_formatSet (abstract data type)Semantic similarityArtificial IntelligenceKey (cryptography)Document Object ModelcomputerXML
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Towards an XML Adaptation/Alteration Control Framework

2010

International audience

[INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL][ INFO.INFO-PL ] Computer Science [cs]/Programming Languages [cs.PL]ComputingMilieux_MISCELLANEOUS[INFO.INFO-PL] Computer Science [cs]/Programming Languages [cs.PL]
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Adding Knowledge Extracted by Association Rules into Similarity Queries

2010

International audience; In this paper, we propose new techniques to improve the quality of similarity queries over image databases performing association rule mining over textual descriptions and automatically extracted features of the image content. Based on the knowledge mined, each query posed is rewritten in order to better meet the user expectations. We propose an extension of SQL aimed at exploring mining processes over complex data, generating association rules that extract semantic information from the textual description superimposed to the extracted features, thereafter using them to rewrite the queries. As a result, the system obtains results closer to the user expectation than i…

[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-WB]Computer Science [cs]/Webuser expectation[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer scienceInformationSystems_DATABASEMANAGEMENTsimilarity queriescontent-based retrievalassociation rules[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][SCCO.COMP] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]SQL extensionquery rewriting[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]
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Signal Processing for Image Enhancement and Multimedia Processing

2008

Signal processingMultimediaComputer scienceSpeech recognitionImage enhancementcomputer.software_genrecomputer
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Detecting Inference Channels in Private Multimedia Data via Social Networks

2009

International audience; Indirect access to protected information has been one of the key challenges facing the international community for the last decade. Providing techniques to control direct access to sensitive information remain insufficient against inference channels established when legitimate data reveal classified facts hidden from unauthorized users. Several techniques have been proposed in the literature to meet indirect access prevention. However, those addressing the inference problem when involving multimedia objects (images, audio, video, etc.) remain few and hold several drawbacks. In essence, the complex structure of multimedia objects makes the fact of detecting indirect a…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer sciencemedia_common.quotation_subject[ INFO.INFO-WB ] Computer Science [cs]/WebInference[SCCO.COMP]Cognitive science/Computer scienceAccess control02 engineering and technologycomputer.software_genre01 natural sciences010104 statistics & probability[SCCO.COMP] Cognitive science/Computer science020204 information systems0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]0101 mathematicsSet (psychology)Function (engineering)media_common[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]Structure (mathematical logic)[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Social networkMultimediabusiness.industry[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]Information sensitivity[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer scienceKey (cryptography)[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]businesscomputer
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Towards a Novel Approach to Multimedia Data Mixed Fragmentation

2009

International audience; Distributed multimedia applications have emerged at an increasing rate during the last decade in several domains (video conferencing, e-health, virtual meeting rooms, etc). This has created several new challenging problems related to the data integration and fragmentation, user-oriented and adaptive interfaces, real time and network performances, etc. In this paper, we focus on the problem of data(base) fragmentation in a multimedia context. We recall in this respect that data fragmentation consists of reducing irrelevant data accesses by grouping data frequently accessed together in dedicated segments. We mainly address the issue of query and predicate implication r…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer science[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologycomputer.software_genreMultimedia DistanceVideoconferencing[SCCO.COMP] Cognitive science/Computer science020204 information systems0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Data partitioningData PartitionQuery Implication[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]MultimediaFunctional Dependency[INFO.INFO-WB]Computer Science [cs]/WebFragmentation (computing)[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]Multimedia FragmentationPartition (database)Predicate (grammar)[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computerData integration
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CSTST 2008: Proceedings of the 5th International Conference on Soft Computing as Transdisciplinary Science and Technology

2008

Edition des actes de la conférence, publiés par l'ACM

[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]ComputingMilieux_THECOMPUTINGPROFESSIONComputingMethodologies_DOCUMENTANDTEXTPROCESSING[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]GeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)GeneralLiterature_MISCELLANEOUS
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Semantic-based Merging of RSS Items

2009

Merging XML documents can be of key importance in several applications. For instance, merging the RSS news from same or different sources and providers can be beneficial for end-users in various scenarios. In this paper, we address this issue and explore the relatedness measure between RSS elements. We show here how to define and compute exclusive relations between any two elements and provide several predefined merging operators that can be extended and adapted to human needs. We also provide a set of experiments conducted to validate our approach. © Springer Science+Business Media, LLC 2009.

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer Networks and CommunicationsComputer sciencecomputer.internet_protocolRSS[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologycomputer.software_genreClusteringMergingSet (abstract data type)[SCCO.COMP] Cognitive science/Computer science020204 information systems0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Cluster analysisComputingMilieux_MISCELLANEOUS[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]Measure (data warehouse)[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Document relatedne[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]computer.file_formatRSSMerging operator[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]Hardware and Architecture[ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Key (cryptography)020201 artificial intelligence & image processingData mining[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computerSoftwareXML
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Novel Indexing Method of Relations Between Salient Objects

2011

Since the last decade, images have been integrated into several application domains such as GIS, medicine, etc. This integration necessitates new managing methods particularly in image retrieval. Queries should be formulated using different types of features such as low-level features of images (histograms, color distribution, etc.), spatial and temporal relations between salient objects, semantic features, etc. In this chapter, we propose a novel method for identifying and indexing several types of relations between salient objects. Spatial relations are used here to show how our method can provide high expressive power to relations in comparison to the traditional methods.

Information retrievalGeographic information systemRelational databasebusiness.industryComputer scienceSearch engine indexingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONExpressive powerSalient objectsSpatial relationHistogram[INFO]Computer Science [cs]businessImage retrieval
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XA2C Framework for XML Alteration/Adaptation

2010

XML has crossed the borders of software engineering and has spread to other areas such as e-commerce, identification, information storage, instant messaging and others. It is used to communicate crucial data over these domains. Thus, allowing non-expert programmers to manipulate and control their XML data is essential. In the literature, this issue has been dealt with from 3 perspectives: (i) XML alteration/adaptation techniques requiring a certain level of expertise to be implemented and are not unified yet, (ii) mashups, which are not formally defined yet and are not specific to XML data, and (iii) XML-oriented visual languages based on structural transformations and data extraction mainl…

[INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL]DatabaseProgramming languagecomputer.internet_protocolComputer science02 engineering and technologycomputer.software_genre[INFO.INFO-PL] Computer Science [cs]/Programming Languages [cs.PL]Set (abstract data type)Identification (information)Visual languageData extraction020204 information systems[ INFO.INFO-PL ] Computer Science [cs]/Programming Languages [cs.PL]0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingMashupControl (linguistics)Adaptation (computer science)computerXMLComputingMilieux_MISCELLANEOUS
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User profile matching in social networks

2010

International audience; Inter-social networks operations and functionalities are required in several scenarios (data integration, data enrichment, information retrieval, etc.). To achieve this, matching user profiles is required. Current methods are so restrictive and do not consider all the related problems. Particularly, they assume that two profiles describe the same physical person only if the values of their Inverse Functional Property or IFP (e.g. the email address, homepage, etc.) are the same. However, the observed trend in social networks is not fully compatible with this assumption since users tend to create more than one social network account (for personal use, for work, etc.) w…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]Matching (statistics)Computer science[SCCO.COMP]Cognitive science/Computer science02 engineering and technologySimilarity measurecomputer.software_genreElectronic mail[SCCO.COMP] Cognitive science/Computer science020204 information systemsFOAF0202 electrical engineering electronic engineering information engineeringPattern matchingUser profileSocial networkbusiness.industrycomputer.file_formatProfile MatchingSocial Networks[ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]020201 artificial intelligence & image processingData mining[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]businesscomputerData integration
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Integrating user preference to similarity queries over medical images datasets

2010

International audience; Large amounts of images from medical exams are being stored in databases, so developing retrieval techniques is an important research problem. Retrieval based on the image visual content is usually better than using textual descriptions, as they seldom gives every nuances that the user may be interested in. Content-based image retrieval employs the similarity among images for retrieval. However, similarity is evaluated using numeric methods, and they often orders the images by similarity in a way rather distinct from the user's intention. In this paper, we propose a technique to allow expressing the user's preference over attributes associated to the images, so simil…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer scienceComputed tomography02 engineering and technologyContent-based image retrievalSemanticsImage (mathematics)Similarity (network science)[SCCO.COMP] Cognitive science/Computer science020204 information systems0202 electrical engineering electronic engineering information engineeringmedicine[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Image retrieval[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]medicine.diagnostic_test[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]020207 software engineeringPreferenceImportant research[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
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Privacy preserving via tree augmented naïve Bayesian classifier in multimedia database

2011

International audience; In this paper, we propose a novel technique for privacy preserving in multimedia databases. Our technique is based on a multimedia co-occurrence matrix and a tree augmented naive Bayesian classifier (TAN) to detect possible data associations making confidential multimedia objects at risk.

Novel technique[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceMultimedia database[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologycomputer.software_genre[SCCO.COMP] Cognitive science/Computer science020204 information systems0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Naive bayesian classifier[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]Privacy preservingTree (data structure)[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]Data miningcomputerProceedings of the International Conference on Management of Emergent Digital EcoSystems
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Automatic Temporal Formatting of Multimedia Presentations Using Dynamic Petri Nets.

2009

An efficient authoring tool would provide support for automatic temporal formatting and modeling of multimedia presentations. Automatic temporal formatting is a process of converting the given presentation specifications into a required temporal format. This paper presents an algorithm that can convert a temporal layout into a dynamic petri net (DPN )w hich can represent iterative and interactive presentation components effectively. The prototype of the authoring tool extracts the temporal layout from any given SMIL file representation and uses the proposed algorithm to automatically convert it into a DPN. The DPN generated automatically at compile-time helps the run-time components in effe…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer sciencemedia_common.quotation_subject[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologycomputer.software_genreDisk formattingPresentation[SCCO.COMP] Cognitive science/Computer scienceFormal specificationSynchronization (computer science)0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Representation (mathematics)ComputingMilieux_MISCELLANEOUSmedia_common[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]MultimediaProgramming language[INFO.INFO-WB]Computer Science [cs]/WebProcess (computing)[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]020207 software engineeringPetri net[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computer
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CLEARMiner: a new algorithm for mining association patterns on heterogeneous time series from climate data

2010

International audience; Recently, improvements in sensor technology contributed to increasing in spatial data acquisition. The use of remote sensing in many countries and states, where agricultural business is a large part of their gross income, can provide a valuable source to improve their economy. The combination of climate and remote sensing data can reveal useful information, which can help researchers to monitor and estimate the production of agricultural crops. Data mining techniques are the main tools to analyze and extract relationships and patterns. In this context, this paper presents a new algorithm for mining association patterns in Geo-referenced databases of climate and satel…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]Association rule learning[INFO.INFO-WB] Computer Science [cs]/WebComputer scienceAssociation (object-oriented programming)[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer scienceContext (language use)computer.software_genreNOAA-AVHRR imagesImage-based Information Systemsassociation rules[SCCO.COMP] Cognitive science/Computer science[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Spatial analysisAgricultural crops[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Series (mathematics)[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]Remote sensing (archaeology)[ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Data mining[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]Vegetation IndexAlgorithmcomputer
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Building Semantic Trees from XML Documents

2016

International audience; The distributed nature of the Web, as a decentralized system exchanging information between heterogeneous sources, has underlined the need to manage interoperability, i.e., the ability to automatically interpret information in Web documents exchanged between different sources, necessary for efficient information management and search applications. In this context, XML was introduced as a data representation standard that simplifies the tasks of interoperation and integration among heterogeneous data sources, allowing to represent data in (semi-) structured documents consisting of hierarchically nested elements and atomic attributes. However, while XML was shown most …

Document Structure DescriptionComputer Networks and CommunicationsComputer sciencecomputer.internet_protocolSemantic analysis (machine learning)Efficient XML InterchangeInteroperabilityXML SignatureWord sense disambiguation02 engineering and technologycomputer.software_genreSemantic networkSemantic ambiguityXML Schema Editor020204 information systemsNode (computer science)0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]XML schemaContext representationcomputer.programming_languageXML treeInformation retrievalKnowledge basesSemi-structured dataXML validationcomputer.file_formatSemantic interoperabilityXMLHuman-Computer InteractionXML databaseSemantic similaritySemantic-aware processing020201 artificial intelligence & image processingWeb servicecomputerSoftwareXML
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Relating RSS News/Items

2009

Merging related RSS news (coming from one or different sources) is beneficial for end-users with different backgrounds (journalists, economists, etc.), particularly those accessing similar information. In this paper, we provide a practical approach to both: measure the relatedness, and identify relationships between RSS elements. Our approach is based on the concepts of semantic neighborhood and vector space model, and considers the content and structure of RSS news items. © 2009 Springer Berlin Heidelberg.

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceRSS[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologySimilarityTheoretical Computer ScienceWorld Wide Web[SCCO.COMP] Cognitive science/Computer science020204 information systemsSimilarity (psychology)0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Neighbourhood (mathematics)[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]Structure (mathematical logic)[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Measure (data warehouse)[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Information retrievalRelationship[INFO.INFO-WB]Computer Science [cs]/WebComputer Science (all)[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]computer.file_format[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]RSS Relatedne[ SCCO.COMP ] Cognitive science/Computer scienceVector space model020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]InformationSystems_MISCELLANEOUSNeighbourhoodcomputer
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RSS Merger

2010

[SCCO.COMP] Cognitive science/Computer science
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Extensible User-Based XML Grammar Matching

2009

International audience; XML grammar matching has found considerable interest recently due to the growing number of heterogeneous XML documents on the web and the increasing need to integrate, and consequently search and retrieve XML data originated from different data sources. In this paper, we provide an approach for automatic XML grammar matching and comparison aiming to minimize the amount of user effort required to perform the match task. We propose an open framework based on the concept of tree edit distance, integrating different matching criterions so as to capture XML grammar element semantic and syntactic similarities, cardinality and alternativeness constraints, as well as data-ty…

Document Structure Description[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]XML Encryption[INFO.INFO-WB] Computer Science [cs]/WebComputer sciencecomputer.internet_protocolEfficient XML Interchange[ INFO.INFO-WB ] Computer Science [cs]/WebXML Signature[SCCO.COMP]Cognitive science/Computer science02 engineering and technologycomputer.software_genreSchema matchingSimple API for XML[SCCO.COMP] Cognitive science/Computer scienceXML Schema Editor020204 information systemsStreaming XML0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]RELAX NGXML schemaBinary XMLSGML[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]computer.programming_language[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]XML validationcomputer.file_formatXML framework[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]XML databaseXML Schema (W3C)[ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Vector space model020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computerXMLXML Catalog
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XCDL: an XML-oriented visual composition definition language

2010

International audience; XML data flow has reached beyond the world of computer science and has spread to other areas such as data communication, e-commerce and instant messaging. Therefore, manipulating this data by non expert programmers is becoming imperative. On one hand, Mashups have emerged a few years ago, providing users with visual tools for web data manipulation but not necessarily XML specific. Mashups have been leaning towards functional composition but no formal languages have yet been defined. On the other hand, visual languages for XML have been emerging since the standardization of XML, and mostly relying on querying XML data for extraction or structure transformations. These…

Document Structure Description[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer scienceEfficient XML Interchange[ INFO.INFO-WB ] Computer Science [cs]/WebXML Signature[SCCO.COMP]Cognitive science/Computer sciencecomputer.software_genreWorld Wide WebXML Schema Editor[SCCO.COMP] Cognitive science/Computer science[ INFO.INFO-PL ] Computer Science [cs]/Programming Languages [cs.PL]Streaming XML[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]XML schemaComputingMilieux_MISCELLANEOUScomputer.programming_language[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Programming language[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]XML validationcomputer.file_formatXML database[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer scienceComputingMethodologies_DOCUMENTANDTEXTPROCESSING[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computer
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SITIS 2012 Foreword

2012

2012 Eighth International Conference on Signal Image Technology and Internet Based Systems
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Management and interaction with multimodal information content

2010

[Chbeir, Richard] Univ Bourgogne, CNRS, LE2I, Dept Comp Sci, F-21000 Dijon, France. [Coninx, Karin] Univ Hasselt, Expertise Ctr Digital Media EDM, BE-3590 Diepenbeek, Belgium. [Ferri, Fernando; Grifoni, Patrizia] CNR, Inst Res Populat & Social Policies, Rome, Italy.

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]Operations research[INFO.INFO-WB] Computer Science [cs]/WebComputer Networks and CommunicationsComputer science[ INFO.INFO-WB ] Computer Science [cs]/WebLibrary science[SCCO.COMP]Cognitive science/Computer science02 engineering and technologyDigital media[SCCO.COMP] Cognitive science/Computer science0202 electrical engineering electronic engineering information engineeringMedia Technology[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]ComputingMilieux_MISCELLANEOUS[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]business.industry05 social sciences[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]020207 software engineering[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]Hardware and Architecture[ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]0509 other social sciences050904 information & library sciencesbusinessSoftware
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THE IMAGE PROTECTOR A Flexible Security Rule Specification Toolkit

2011

International audience; The tremendous sharing of multimedia objects on the web shed the light on several privacy concerns related in essence to the safe publishing of end users' personal data. Providing techniques to protect multimedia objects faces several difficulties due to multimedia objects' heterogeneous and complex structure on one hand, and on the other hand, the wide range of information that could be used to describe their content. In this paper, we present a flexible security rule specification toolkit for multimedia objects. Our toolkit is based on a security model and a core ontology in which we populate the model's related information and multimedia objects data. To specify s…

[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]Multimedia Security[SCCO.COMP] Cognitive science/Computer science[ SCCO.COMP ] Cognitive science/Computer science[SCCO.COMP]Cognitive science/Computer scienceContent ProtectionPrivacy Preserving Security Rules[ INFO.INFO-CR ] Computer Science [cs]/Cryptography and Security [cs.CR]Security RulesPrivacy Preserving[INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]
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Eighth International Conference on Signal Image Technology and Internet Based Systems, SITIS 2012, Sorrento, Naples, Italy, November 25-29, 2012

2012

International audience

[INFO]Computer Science [cs][INFO] Computer Science [cs]ComputingMilieux_MISCELLANEOUS
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Emergent Web Intelligence: Advanced Semantic Technologies

2010

The future of the World Wide Web depended on its ability to understand and automatically process content to enable computers and people to work in cooperation. New advanced techniques and intelligent approaches are required more than ever to transform the Web into a universal reasoning and semantic-driven computing machine. The Web intelligence discipline attempts to deal with this challenge by exploits information technologies and artificial intelligence approaches to design next generation of web-empowered systems and services. The Emergent Web Intelligence: Advanced Semantic Technologies" book provides valuable references and cuttingedge technologies for: undergraduate and postgraduate s…

Web standardsmedicine.medical_specialtyWeb developmentbusiness.industryComputer scienceData scienceSocial Semantic WebWorld Wide WebWeb designmedicineSemantic Web StackWeb intelligencebusinessWeb modelingSemantic Web
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Enforcing role based access control model with multimedia signatures.

2009

International audience; Recently ubiquitous technology has invaded almost every aspect of the modern life. Several application domains, have integrated ubiquitous technology to make the management of resources a dynamic task. However, the need for adequate and enforced authentication and access control models to provide safe access to sensitive information remains a critical matter to address in such environments. Many security models were proposed in the literature thus few were able to provide adaptive access decisions based on the environmental changes. In this paper, we propose an approach based on our previous work [B.A. Bouna, R. Chbeir, S. Marrara, A multimedia access control languag…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer access controlComputer science[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer scienceXACMLAccess control02 engineering and technologycomputer.software_genreWorld Wide Web[SCCO.COMP] Cognitive science/Computer science020204 information systems0202 electrical engineering electronic engineering information engineeringRole-based access control[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Intelligent environmentcomputer.programming_language[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]Ambient intelligenceMultimediabusiness.industry[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]Computer security model[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]Hardware and Architecture[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]Web servicebusinesscomputerSoftware
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Algebraic Properties to Optimize kNN Queries

2011

International audience; New applications that are being required to employ Database Management Systems (DBMSs), such as storing and retrieving complex data (images, sound, temporal series, genetic data, etc.) and analytical data processing (data mining, social networks analysis, etc.), increasingly impose the need for new ways of expressing predicates. Among the new most studied predicates are the similarity-based ones, where the two commonest are the similarity range and the k-nearest neighbor predicates. The k-nearest neighbor predicate is surely the most interesting for several applications, including Content-Based Image Retrieval (CBIR) and Data Mining (DM) tasks, yet it is also the mos…

[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]similarity algebra[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]algebraic propertiesunary similarity queriesquery optimization
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Guest Editors' Introduction: Multimedia Metadata and Semantic Management

2009

This special issue assesses the current status and technologies and describes major challenges and proper solutions for effective multimedia production and management related to evolving Semantic Web strategies. The included articles, which cover different facets of the semantic management of multimedia and multimedia metadata from retrieval and processing to consumption and presentation, represent a step forward in research targeted at improving aspects of the semantic metadata life cycle.

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/WebComputer science[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologySemanticsWorld Wide Web[SCCO.COMP] Cognitive science/Computer scienceSemantic computing0202 electrical engineering electronic engineering information engineeringMedia Technology[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Semantic Web StackSemantic WebImage retrieval[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]business.industry[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]020207 software engineeringComputer Science ApplicationsMetadataSemantic grid[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]Hardware and Architecture[ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]Signal Processing020201 artificial intelligence & image processingThe Internet[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]businessSoftware
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MEDES '11: International ACM Conference on Management of Emergent Digital EcoSystems, San Francisco, CA, USA, November 21-24, 2011

2011

International audience

[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][SCCO.COMP] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]ComputingMilieux_MISCELLANEOUS[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]
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12th International Conference on Signal-Image Technology & Internet-Based Systems, SITIS 2016, Naples, Italy, November 28 - December 1, 2016

2016

International audience

[INFO]Computer Science [cs][INFO] Computer Science [cs]ComputingMilieux_MISCELLANEOUS
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Bridging Sensing and Decision Making in Ambient Intelligence Environments

2009

Context-aware and Ambient Intelligence environments represent one of the emerging issues in the last decade. In such intelligent environments, information is gathered to provide, on one hand, autonomic and easy to manage applications, and, on the other, secured access controlled environments. Several approaches have been defined in the literature to describe context-aware application with techniques to capture and represent information related to a specified domain. However and to the best of our knowledge, none has questioned the reliability of the techniques used to extract meaningful knowledge needed for decision making especially if the information captured is of multimedia types (image…

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Ambient intelligenceComputer science02 engineering and technologycomputer.software_genreBridging (programming)[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]uncertainty resolver modelHuman–computer interaction020204 information systemsResolver0202 electrical engineering electronic engineering information engineeringcontext-aware applicationsemantic-based020201 artificial intelligence & image processingData mining[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]computer
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An overview on XML similarity: Background, current trends and future directions

2009

In recent years, XML has been established as a major means for information management, and has been broadly utilized for complex data representation (e.g. multimedia objects). Owing to an unparalleled increasing use of the XML standard, developing efficient techniques for comparing XML-based documents becomes essential in the database and information retrieval communities. In this paper, we provide an overview of XML similarity/comparison by presenting existing research related to XML similarity. We also detail the possible applications of XML comparison processes in various fields, ranging over data warehousing, data integration, classification/clustering and XML querying, and discuss some…

Document Structure Description[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]General Computer Science[INFO.INFO-WB] Computer Science [cs]/WebComputer sciencecomputer.internet_protocolEfficient XML Interchange[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologycomputer.software_genreTheoretical Computer ScienceXML Schema Editor[SCCO.COMP] Cognitive science/Computer science020204 information systems0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]ComputingMilieux_MISCELLANEOUS[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Information retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]XML validationcomputer.file_formatXML frameworkXML database[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]ComputingMethodologies_DOCUMENTANDTEXTPROCESSING020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computerXMLXML Catalog
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Identifying Algebraic Properties to Support Optimization of Unary Similarity Queries

2009

International audience; Abstract. Conventional operators for data retrieval are either based on exact matching or on total order relationship among elements. Neither ofthem is appropriate to manage complex data, such as multimedia data, time series and genetic sequences. In fact, the most meaningful way tocompare complex data is by similarity. However, the Relational Algebra, employed in the Relational Database Management Systems (RDBMS),cannot express similarity criteria. In order to address this issue, we provide here an extension of the Relational Algebra, aimed at representingsimilarity queries in algebraic expressions. This paper identies fundamental properties to allow the integration…

[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/Websimilarity algebra[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer sciencealgebraic properties[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][SCCO.COMP] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]query optimiza-tion[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]unary similarity queries[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]
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Tenth International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014, Marrakech, Morocco, November 23-27, 2014

2014

International audience

[INFO]Computer Science [cs][INFO] Computer Science [cs]ComputingMilieux_MISCELLANEOUS
researchProduct

14th International Conference on Signal-Image Technology & Internet-Based Systems, SITIS 2018

2018

International audience; The SITIS conference is dedicated to research on the technologies used to represent, share and process information in various forms, ranging from signal, image, and multimedia data to traditional structured data and semi-structured data found in the web.SITIS spans two inter-related research domains that increasingly play a key role in connecting systems across network centric environments to allow distributed computing and information sharing. SITIS 2018 aims to provide a forum for high quality presentations on research activities centered on the following main tracks.The track titled "Signal Image & Vision Technologies" (SIVT) focuses on recent developments in digi…

[INFO]Computer Science [cs][INFO] Computer Science [cs]
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Editorial Preface

2009

[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-WB] Computer Science [cs]/Web[SCCO.COMP] Cognitive science/Computer science[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
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Special issue on context-aware and mobile multimedia databases and services

2010

International audience

[INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB] Computer Science [cs]/Web[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM][ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][SCCO.COMP] Cognitive science/Computer science[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]ComputingMilieux_MISCELLANEOUS[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM]
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A novel XML document structure comparison framework based-on sub-tree commonalities and label semantics

2012

International audience; XML similarity evaluation has become a central issue in the database and information communities, its applications ranging over document clustering, version control, data integration and ranked retrieval. Various algorithms for comparing hierarchically structured data, XML documents in particular, have been proposed in the literature. Most of them make use of techniques for finding the edit distance between tree structures, XML documents being commonly modeled as Ordered Labeled Trees. Yet, a thorough investigation of current approaches led us to identify several similarity aspects, i.e., sub-tree related structural and semantic similarities, which are not sufficient…

Document Structure DescriptionComputer Networks and Communicationscomputer.internet_protocolComputer scienceEfficient XML Interchange[SCCO.COMP]Cognitive science/Computer science0102 computer and information sciences02 engineering and technologycomputer.software_genre01 natural sciencesSemantic similarityXML Schema Editor020204 information systems0202 electrical engineering electronic engineering information engineeringXML schemacomputer.programming_languageInformation retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]XML validationcomputer.file_formatDocument clusteringHuman-Computer InteractionXML frameworkTree (data structure)XML databaseTree structure010201 computation theory & mathematics[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]020201 artificial intelligence & image processingSemi-structured dataEdit distancecomputerSoftwareXMLXML CatalogData integration
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XML document-grammar comparison: related problems and applications

2011

10.2478/s13537-011-0005-1; International audience; XML document comparison is becoming an ever more popular research issue due to the increasingly abundant use of XML. Likewise, a growing interest fosters the development of XML grammar matching and comparison, due to the proliferation of heterogeneous XML data sources, particularly on the Web. Nonetheless, the process of comparing XML documents with XML grammars, i.e., XML document and grammar similarity evaluation, has not yet received the attention it deserves. In this paper, we provide an overview on existing research related to XML document/grammar comparison, presenting the background and discussing the various techniques related to th…

Document Structure DescriptionXML grammarXML Encryptionselective disseminationGeneral Computer ScienceComputer scienceEfficient XML Interchange[SCCO.COMP]Cognitive science/Computer scienceWell-formed document02 engineering and technologyWorld Wide WebXML Schema Editor[SCCO.COMP] Cognitive science/Computer science020204 information systemsStreaming XML0202 electrical engineering electronic engineering information engineeringPROCESSAMENTO DE IMAGENSXML schemacomputer.programming_languageInformation retrievalXSDgrammar evolutionXML validationstructural similarityQA75.5-76.95computer.file_formatXMLDTDclassificationElectronic computers. Computer science[ SCCO.COMP ] Cognitive science/Computer scienceComputingMethodologies_DOCUMENTANDTEXTPROCESSING020201 artificial intelligence & image processingsemi-structured datacomputerclusteringstructure transformation
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Semantic to intelligent web era

2013

International audience; The Web has known a very fast evolution: going from the Web 1.0, known as Web of Documents where users are merely consumers of static information, to the more dynamic Web 2.0, known as social or collaborative Web where users produce and consume information simultaneously, and entering the more sophisticated Web 3.0, known as the Semantic Web by giving information a well-defined meaning so that it becomes more easily accessible by human users and automated processes. Fostering service intelligence and atomicity (the ability of autonomous services to interact automatically), remains one of the most upcoming challenges of the Semantic Web. This promotes the dawn of a ne…

Web standards[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR]medicine.medical_specialty[INFO.INFO-WB] Computer Science [cs]/WebComputer scienceInternet of Things[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer sciencecomputer.software_genreSPARQLData SemanticsSocial Semantic WebRDFKnowledge baseIntelligent ServicesWorld Wide Web[SCCO.COMP] Cognitive science/Computer sciencemedicine[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Semantic Web StackSemantic WebData WebSemantic WebOWL[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]business.industry[INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]XMLWeb[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]Web serviceWeb intelligencebusinesscomputerWeb modelingProceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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Semantic aware RSS query algebra

2010

International audience; Existing XML query algebras are not fully appropriate to retrieve RSS news items mainly due to three reasons: 1) RSS is text rich and its content is dependent on the wording and verbification of the author, thus semantic aware operators are needed; 2) news items are dynamic and consequently time oriented retrieval is needed; 3) a news item may evolve through time, or overlap with other news items and hence identifying relationships between items is also needed. In this paper, we aim to solve these issues by providing a dedicated RSS algebra based on semantic-aware operators that consider RSS characteristics. The provided operators are application domain specific and …

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-WB] Computer Science [cs]/Webcomputer.internet_protocolComputer scienceRSS[ INFO.INFO-WB ] Computer Science [cs]/Web[SCCO.COMP]Cognitive science/Computer science02 engineering and technologyQuery optimizationQuery algebraQuery expansion[SCCO.COMP] Cognitive science/Computer scienceApplication domain020204 information systems0202 electrical engineering electronic engineering information engineering[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB]Equivalence (formal languages)[ INFO.INFO-MM ] Computer Science [cs]/Multimedia [cs.MM][INFO.INFO-MM] Computer Science [cs]/Multimedia [cs.MM]Semantic queryInformation retrieval[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB][INFO.INFO-WB]Computer Science [cs]/Web[INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM]computer.file_format[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR][ SCCO.COMP ] Cognitive science/Computer science020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]computerXML
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