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2. HInT: Hybrid and Incremental Type Discovery for Large RDF Data Sources

Author

Georgia Troullinou, Kenza Kellou-Menouer, Zoubida Kedad, Dimitris Plexousakis, Nikolaos Kardoulakis, Haridimos Kondylakis, Données et algorithmes pour une ville intelligente et durable - DAVID (DAVID), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Hellenic Foundation for Research and Innovation, ΕΛ.ΙΔ.Ε.Κ: 1147, and Work reported in this paper has been partially supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the '2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers' (iQARuS Project No 1147)

Subjects
Exploit, LSH, Process (engineering), Computer science, media_common.quotation_subject, 02 engineering and technology, Machine learning, computer.software_genre, RDF, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Quality (business), [INFO]Computer Science [cs], media_common, Complement (set theory), business.industry, Hybrid type discovery, Linked data, computer.file_format, Automatic summarization, Schema (genetic algorithms), 020201 artificial intelligence & image processing, Incrementality, Artificial intelligence, business, computer
Abstract

International audience; The rapid explosion of linked data has resulted into many weakly structured and incomplete data sources, where typing information might be missing. On the other hand, type information is essential for a number of tasks such as query answering, integration, summarization and partitioning. Existing approaches for type discovery, either completely ignore type declarations available in the dataset (implicit type discovery approaches), or rely only on existing types, in order to complement them (explicit type enrichment approaches). Implicit type discovery approaches are based on instance grouping, which requires an exhaustive comparison between the instances. This process is expensive and not incremental. Explicit type enrichment approaches on the other hand, are not able to identify new types and they can not process data sources that have little or no schema information. In this paper, we present HInT, the first incremental and hybrid type discovery system for RDF datasets, enabling type discovery in datasets where type declarations are missing. To achieve this goal, we incrementally identify the patterns of the various instances, we index and then group them to identify the types. During the processing of an instance, our approach exploits its type information, if available, to improve the quality of the discovered types by guiding the classification of the new instance in the correct group and by refining the groups already built. We analytically and experimentally show that our approach dominates in terms of efficiency, competitors from both worlds, implicit type discovery and explicit type enrichment while outperforming them in most of the cases in terms of quality.

Published
2021
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3. Checksum-Filtered List Decoding Applied to H.264 and H.265 Video Error Correction

Author

Patrick Corlay, Stephane Coulombe, Firouzeh Golaghazadeh, François-Xavier Coudoux, Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520 (IEMN-DOAE), Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), COMmunications NUMériques - IEMN (COMNUM - IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), This work was supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grant. This paper was recommended by Associate Editor Siwei Ma, Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)-Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), and Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)

Subjects
Computer science, Real-time computing, List decoding, 02 engineering and technology, Data_CODINGANDINFORMATIONTHEORY, Smacker video, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], "Video Transmission", [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [SPI]Engineering Sciences [physics], "H.265", 0202 electrical engineering, electronic engineering, information engineering, Media Technology, [INFO]Computer Science [cs], Electrical and Electronic Engineering, Bitstream, Motion compensation, "List Decoding", Network packet, "Video Error Correction", 020206 networking & telecommunications, computer.file_format, Coding tree unit, Scalable Video Coding, [SPI.TRON]Engineering Sciences [physics]/Electronics, "High Efficiency Video Coding (HEVC)", "H.264", Checksum, "Checksum", 020201 artificial intelligence & image processing, Multiview Video Coding, Error detection and correction, Algorithm, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

JIF=4.046; International audience; The latest video coding standards, H.264 and H.265, are highly vulnerable in error-prone networks. Reconstructed packets may exhibit significant degradation in terms of peak signal-to-noise ratio and visual quality. This paper presents a novel list-decoding approach exploiting the receiver side user datagram protocol (UDP) checksum. The proposed method identifies the possible locations of errors by analyzing the pattern of the calculated UDP checksum. This permits considerably reducing the number of candidate bitstreams in comparison to conventional list decoding approaches. When a packet composed of N bits contains a single-bit error, instead of considering N candidate bitstreams, as is the case in conventional list decoding approaches, the proposed approach considers N/32 candidate bitstreams, leading to a reduction of 97% of the number of candidates. For a two-bit error, the reduction increases to 99.6%. The method's performance is evaluated using H.264 and H.265 test model software. Our simulation results reveal that, on average, the error was corrected perfectly 80%-90% of the time (the original bitstream was recovered). In addition, the proposed approach provides, on average, a 2.79-dB gain over frame copy (FC) error concealment using the joint model and a 3.57-dB gain over our implementation of FC error concealment in the High Efficiency Video Coding test model.

Published
2018
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