Search results for "ACM : H.: Information Systems"

showing 3 items of 3 documents

Digitization and preservation of cultural heritage: The CEPROQHA approach

2017

The humanity has always learned from the previous experiences for many reasons. The national heritage proves to be a great way to discover a nation's history. As a result, these priceless cultural items have a special attention. However, Since the wide adoption of new digital technologies, documenting, storing, and exhibiting cultural heritage assets became more affordable and reliable. These digital records are then used in several applications. Researchers saw the opportunity to use digital heritage recordings for long-term preservation. In this paper, we present the research progress in cultural heritage digital processing and preservation, highlighting the most impactful advances. Addit…

History[ INFO ] Computer Science [cs]Content management system02 engineering and technologySemanticsDigital records01 natural sciences[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]World Wide WebACM: H.: Information SystemsCultural diversity3D Modeling0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs][ INFO.INFO-CL ] Computer Science [cs]/Computation and Language [cs.CL]Digital preservationCEPROQHA projectDigitizationComputingMilieux_MISCELLANEOUSDigital heritage010401 analytical chemistryACM : H.: Information Systems020207 software engineering0104 chemical sciencesCultural heritageSemantic enrichmentDigital preservationHumanityCultural heritageDigital heritage
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Extending SPARQL with Temporal Logic

2009

The data integration and sharing activities carried on in the framework of the Semantic Web lead to large knowledge bases that must be queried, analyzed, and exploited efficiently. Many of the knowledge representation languages of the Semantic Web, starting with RDF, are based on directed, labeled graphs, which can be also manipulated using graph algorithms and tools coming from other domains. In this paper, we propose an analysis approach of RDF graphs by reusing the verification technology developed for concurrent systems. To this purpose, we define a translation from the SPARQL query language into XTL, a general-purpose graph manipulation language implemented in the CADP verification too…

[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation[INFO.INFO-LO] Computer Science [cs]/Logic in Computer Science [cs.LO][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-WB ] Computer Science [cs]/WebInformationSystems_DATABASEMANAGEMENTlabeled transition system[INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]ACM : H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.3: Languages/H.2.3.3: Query languagesSPARQL[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulationmodel checkingRDFACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.4: Software/Program Verification/D.2.4.4: Model checking[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB]temporal logicACM : D.: Software/D.2: SOFTWARE ENGINEERING/D.2.4: Software/Program Verification/D.2.4.4: Model checking[INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB][ INFO.INFO-LO ] Computer Science [cs]/Logic in Computer Science [cs.LO]ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.3: Languages/H.2.3.3: Query languages[INFO.INFO-MO] Computer Science [cs]/Modeling and Simulationverification
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Semantic User Profiling for Digital Advertising

2015

International audience; With the emergence of real-time distribution of online advertising space (“real-time bidding”), user profiling from web navigation traces becomes crucial. Indeed, it allows online advertisers to target customers without interfering with their activities. Current techniques apply traditional methods as statistics and machine learning, but suffer from their limitations. As an answer, the proposed approach aims to develop and evaluate a semantic-based user profiling system for digital advertising.

[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]Data AnalysisBig DataACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.5: Online Information Services[ INFO ] Computer Science [cs]OntologyACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and IndexingACM : H.: Information SystemsUser ProfilingACM: H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONSReasoningACM : H.: Information Systems/H.4: INFORMATION SYSTEMS APPLICATIONS[INFO] Computer Science [cs][INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]ACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.5: User profiles and alert servicesACM: H.: Information SystemsInferenceACM : H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.5: Online Information Services[INFO]Computer Science [cs]Logical Rules[ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI]ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.1: Content Analysis and IndexingSWRLACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.5: User profiles and alert servicesSemantic Web
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