Search results for " INFORMATION SYSTEMS"

showing 10 items of 940 documents

A Review on Applications of Big Data for Disaster Management

2017

International audience; The term " disaster management " comprises both natural and man-made disasters. Highly pervaded with various types of sensors, our environment generates large amounts of data. Thus, big data applications in the field of disaster management should adopt a modular view, going from a component to nation scale. Current research trends mainly aim at integrating component, building, neighborhood and city levels, neglecting the region level for managing disasters. Current research on big data mainly address smart buildings and smart grids, notably in the following areas: energy waste management, prediction and planning of power generation needs, improved comfort, usability …

[ INFO ] Computer Science [cs]Computer scienceBig data02 engineering and technology[INFO] Computer Science [cs]7. Clean energydisasters12. Responsible consumptionbig data020204 information systemsComponent (UML)11. Sustainability0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]Building automationEmergency managementbusiness.industry020207 software engineeringUsabilityEnergy consumptionDisaster managementsensor dataSystematic reviewSmart gridRisk analysis (engineering)13. Climate actionbusiness
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Adaptive Learning Process for the Evolution of Ontology-Described Classification Model in Big Data Context

2016

International audience; One of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic HMC was proposed. This process uses ontologies to describe the classification model. To prevent cold start and user overload, the classification process automatically learns the ontology-described classification model from a very large set of unstructured text documen…

[ INFO ] Computer Science [cs]Computer scienceMaintenanceBig dataAdaptive learningContext (language use)Multi-label classification02 engineering and technologyOntology (information science)[INFO] Computer Science [cs]Machine learningcomputer.software_genreAdaptive LearningData modeling[SPI.AUTO]Engineering Sciences [physics]/AutomaticMachine LearningCold start020204 information systems[ SPI.AUTO ] Engineering Sciences [physics]/AutomaticMachine learning0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]Multi-Label ClassificationMulti-label classificationbusiness.industryOntologyOntology-based data integration[SPI.AUTO] Engineering Sciences [physics]/Automatic020201 artificial intelligence & image processingAdaptive learningArtificial intelligencebusinesscomputer
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Comparative Study of the Mobile Learning Architectures

2016

International audience; With the emergence of mobile devices (Smart Phone, PDA, UMPC, game consoles, etc.), learning is changing from electronic learning (e-Learning) to mobile learning (m-learning). In fact, due to the mobility feature, it seems that the m-learning have to be adapted with the change within the context. Several researches addressed this issue and implemented a mobile learning environment to prove its usefulness and feasibility in various domains. In this article, we conduct a comparative study between a list of mobile learning architectures and methods that are presented in the literature. The performance of these architectures is evaluated based on several criteria, such a…

[ INFO ] Computer Science [cs]Computer science[ INFO.INFO-NI ] Computer Science [cs]/Networking and Internet Architecture [cs.NI]Mobile computingMobile TechnologyM-learning02 engineering and technology[INFO] Computer Science [cs]Context-change managementcomputer.software_genreRobot learning[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]020204 information systems0202 electrical engineering electronic engineering information engineeringMobile searchMobile technology[INFO]Computer Science [cs]AdaptationLearning methodMultimediaLearning environmentEducational technologyContextSynchronous learningE-LearningM-learning020201 artificial intelligence & image processingcomputer
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Privacy in Big Data

2016

International audience

[ INFO ] Computer Science [cs]Computer sciencebusiness.industryInternet privacyBig data02 engineering and technology[INFO] Computer Science [cs]World Wide Web020204 information systems0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]020201 artificial intelligence & image processingbusinessComputingMilieux_MISCELLANEOUS
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Interpreting Heterogeneous Geospatial Data Using Semantic Web Technologies

2016

International audience; The paper presents work on implementation of semantic technologies within a geospatial environment to provide a common base for further semantic interpretation. The work adds on the current works in similar areas where priorities are more on spatial data integration. We assert that having a common unified semantic view on heterogeneous datasets provides a dimension that allows us to extend beyond conventional concepts of searchability, reusability, composability and interoperability of digital geospatial data. It provides contextual understanding on geodata that will enhance effective interpretations through possible reasoning capabilities. We highlight this through …

[ INFO ] Computer Science [cs]Geospatial analysisComputer scienceInteroperabilitySemantification02 engineering and technologySDIcomputer.software_genreSocial Semantic Web020204 information systems0202 electrical engineering electronic engineering information engineeringSemantic analyticsGeospatial PDF[INFO]Computer Science [cs]Web Coverage ServiceSemantic Web StackSemantic WebData WebR2RMLInformation retrievalLand usebusiness.industryCIPcomputer.file_formatGeoSPARQLInteroperabilityGeoSPARQLSemantic technology020201 artificial intelligence & image processingHeterogeneitybusinesscomputer
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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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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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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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Towards A Twitter Observatory: A Multi-Paradigm Framework For Collecting, Storing And Analysing Tweets

2016

International audience; In this article we show how a multi-paradigm framework can fulfil the requirements of tweets analysis and reduce the waiting time for researchers that use computational resources and storage systems to support large-scale data analysis. The originality of our approach is to combine concerns about data harvesting, data storage, data analysis and data visualisation into a framework that supports inductive reasoning in multidisciplinary scientific research. Our main contribution is a polyglot storage system with a generic data model to support logical data independence and a set of tools that can provide a suitable solution for mixing different types of algorithms in or…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][ INFO ] Computer Science [cs]Computer scienceknowledge discovery02 engineering and technology[INFO] Computer Science [cs][INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]Data modelingmassive datasetsopen source softwareData visualization[ INFO.INFO-IT ] Computer Science [cs]/Information Theory [cs.IT]polyglot storage020204 information systems0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]Twitter analysis . SystemsComputingMilieux_MISCELLANEOUS[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]business.industryPolyglotInductive reasoningData science[SPI.TRON] Engineering Sciences [physics]/ElectronicsData independence[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsData model[INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT]Data architecturebusinessSoftware architecture
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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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