{"id":86,"date":"2017-03-22T13:58:36","date_gmt":"2017-03-22T12:58:36","guid":{"rendered":"https:\/\/project.inria.fr\/bda2017\/?page_id=86"},"modified":"2017-11-21T13:50:14","modified_gmt":"2017-11-21T12:50:14","slug":"programme","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/bda2017\/programme\/","title":{"rendered":"Programme"},"content":{"rendered":"<h2>Emploi du temps<\/h2>\n<p><iframe loading=\"lazy\" src=\"https:\/\/calendar.google.com\/calendar\/embed?title=BDA%202017&amp;showNav=0&amp;showDate=0&amp;mode=WEEK&amp;height=1000&amp;wkst=2&amp;hl=fr&amp;bgcolor=%23ffffff&amp;src=gm95us06359qrprjthqj5fm6gs%40group.calendar.google.com&amp;color=%236B3304&amp;src=kvr0gjcv0qdpdraqbo4uorkusk%40group.calendar.google.com&amp;color=%23125A12&amp;src=geufddmh74no0cahqil3nqa7mk%40group.calendar.google.com&amp;color=%232952A3&amp;ctz=Europe%2FParis&amp;dates=20171113%2F20171119\" style=\"border-width:0\" width=\"1200\" height=\"1000\" frameborder=\"0\" scrolling=\"no\"><\/iframe><\/p>\n<h2>Tutoriels<\/h2>\n<h4>Mardi, 11:00-13:00 : IoT Big Data Stream Mining (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/tutoriel1.pdf\">slides<\/a>)<\/h4>\n<h5>Albert Bifet, T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris-Saclay<\/h5>\n<h5>Pr\u00e9sident de session : Joachim Niehren (Inria Lille)<\/h5>\n<p>The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in IoT stream mining. This tutorial is a gentle introduction to mining IoT big data streams. The first part introduces data stream learners for classification, regression, clustering, and frequent pattern mining. The second part deals with scalability issues inherent in IoT applications, and discusses how to mine data streams on distributed engines such as Spark, Flink, Storm, and Samza.<\/p>\n<p><img decoding=\"async\" style=\"float: left; margin-top: .5em; width: 150px;\" src=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/03\/bifet-150x150.jpg\" alt=\"\" \/><\/p>\n<div style=\"margin-left: 170px;\">Albert Bifet is Associate Professor at Telecom ParisTech. Previously he worked at Huawei Noah&rsquo;s Ark Lab in Hong Kong, Yahoo Labs in Barcelona, University of Waikato and UPC BarcelonaTech. He is the author of a book on Adaptive Stream Mining and Pattern Learning and Mining from Evolving Data Streams. He is one of the leaders of MOA and Apache SAMOA software environments for implementing algorithms and running experiments for online learning from evolving data streams. He was serving as Co-Chair of the Industrial track of IEEE MDM 2016, ECML PKDD 2015, and as Co-Chair of BigMine (2017-2012), and ACM SAC Data Streams Track (2018-2012).<\/div>\n<h4>Mardi, 14:00-16:00 : Knowledge Graph Expansion and Enrichment (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/tutorial2.pdf\">slides<\/a>)<\/h4>\n<h5>Fatiha Sa\u00efs, Universit\u00e9 Paris-Sud, Universit\u00e9 Paris-Saclay<\/h5>\n<h5>Pr\u00e9sident de session : Pierre Senellart (\u00c9cole normale sup\u00e9rieure &amp; Inria Paris)<\/h5>\n<p>Today, we are experiencing an unprecedented production of resources, published as Linked Open Data (LOD, for short). This is leading to the creation of knowledge graphs (KGs) containing billions of RDF (Resource Description Framework) triples, such as DBpedia, YAGO and Wikidata on the academic side, and the Google Knowledge Graph or Microsoft\u2019s Satori graph on the commercial side. These KGs contain millions of entities (such as people, proteins, or books), and millions of facts about them. This knowledge is typically expressed in RDF (Resource Description Framework), i.e., as triples of the form \u27e8Macron, presidentOf, France\u27e9. Some KGs provide an ontology expressed in OWL2 (Web Ontology Language), which describes the vocabulary (the classes and properties) for the RDF facts. However, to exploit and take benefits from the richness of this available data and knowledge, several problems have to be faced, namely, data linking, data fusion and knowledge discovery, when data is of big volume, heterogeneous and evolving. In this tutorial we will first give an overview of exiting data linking and key discovery approaches. Then, we will discuss the problem of identity crisis caused by the misuse of owl:sameAs predicate and give some possible solutions. We will finish by highlighting some current challenges in this research area.<\/p>\n<p><img decoding=\"async\" style=\"float: left; margin-top: .5em; width: 150px;\" src=\"https:\/\/www.lri.fr\/~sais\/images\/me-FS-2012.jpg\" alt=\"\" \/><\/p>\n<div style=\"margin-left: 170px;\">Fatiha Sa\u00efs is an Associate Professor at Paris Sud University in France. She obtained her Ph.D. in Computer Science at the University of Paris Sud. Her research interest are ontology-based data linking and fusion, RDF data evolution and knowledge discovery from RDF graphs. Her work has been included in several national, industrial and European projects. She has published more than 50 research papers in national and international conferences (AAAI, ISWC, K-Cap) and journals (JWS, KBS and JoDS).<\/div>\n<h4 style=\"clear:both\">Jeudi, 9:30-11:30 : Preference-based Pattern Mining (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/tutoriel3.pdf\">slides<\/a>)<\/h4>\n<h5>Bruno Cr\u00e9milleux, Universit\u00e9 de Caen Normandie<br \/>\nMarc Plantevit, Universit\u00e9 de Lyon<br \/>\nArnaud Soulet, Universit\u00e9 Fran\u00e7ois Rabelais de Tours<\/h5>\n<h5>Pr\u00e9sident de session : Amedeo Napoli (CNRS, LORIA &amp; Inria Nancy)<\/h5>\n<p>This tutorial focuses on the recent shift from constraint-based pattern mining to preference-based pattern mining and interactive pattern mining. Constraint-based pattern mining, which shares common notions with FCA, is now a mature domain of data mining that makes it possible to handle various different pattern domains (e.g., itemsets, sequences, graphs) with a large variety of constraints thanks to solid theoretical foundations and an efficient algorithmic machinery. Even though, it has been realized for a long time that it is difficult for the end-user to model her interest in term of constraints and above to overcome the well-known thresholding issue, researchers have only recently intensified their study of methods for finding high-quality patterns according to the user\u2019s preferences.<\/p>\n<p>In this tutorial, we discuss the need of preferences in pattern mining, the principles and methods of the use of preferences in pattern mining. Many methods are derived from constraint-based pattern mining by integrating utility functions or interestingness measures as quantitative preference model. This approach transforms pattern mining in an optimization problem guided by user specified preferences. However, in practice, the user has only a vague idea of what useful patterns could be. The recent research field of interactive pattern mining relies on the automatic acquisition of these preferences and the development of the instant data mining field.<\/p>\n<p>Bruno Cr\u00e9milleux is professor in computer science at the University of Caen-Normandie. He received his PhD in computer science at the University of Grenoble. His main research interests are pattern (set) discovery, Constraint Satisfaction Problems and data mining, preference queries and exploratory data mining.<\/p>\n<p>Marc Plantevit is associate professor in computer sciences at the University of Lyon. He received his PhD in computer science from the University of Montpellier. His research interest include constraint-based pattern mining in general. Currently, he is very interested with sophisticate pattern domains (dynamic\/ attributed graphs) and in incorporating background knowledge into pattern mining.<\/p>\n<p>Arnaud Soulet is associate professor in computer science at the University Fran\u00e7ois Rabelais of Tours. He received his PhD at the University of Caen. He has an expertise in constraint-based pattern mining and involvement in the mining process like pattern mining techniques for preference elicitation.<\/p>\n<h2 style=\"clear: both;\">Sessions techniques<\/h2>\n<h4>Session 1 : Incertitude et s\u00e9curit\u00e9<\/h4>\n<h5>Mardi, 16:30-17:30. Pr\u00e9sid\u00e9e par David Gross Amblard (Universit\u00e9 Rennes).<\/h5>\n<ul>\n<li>Sebastian Link et Henri Prade. Conception de Sch\u00e9mas de Bases de Donn\u00e9es Relationnelles en pr\u00e9sence de Donn\u00e9es Incertaines.<\/li>\n<li>Paul Tran-Van, Nicolas Anciaux et Philippe Pucheral. SWYSWYK: a Privacy-by-Design Paradigm for Personal Information Management Systems. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk1-2.pdf\">slides<\/a>)<\/li>\n<\/ul>\n<h4>Session 2 : donn\u00e9es graphes<\/h4>\n<h5>Mardi, 17:30-19:00. Pr\u00e9sid\u00e9e par Micha\u00ebl Thomazo (Inria Saclay).<\/h5>\n<ul>\n<li>Sara El Hassad, Francois Goasdoue et Helene Jaudoin. Learning Commonalities in SPARQL. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk2-1.pdf\">slides<\/a>)<\/li>\n<li>Abdullah Abbas, Pierre Genev\u00e8s, C\u00e9cile Roisin et Nabil Laya\u00efda. Optimisation de l&rsquo;\u00e9valuation de requ\u00eates SPARQL en pr\u00e9sence de contraintes ShEx.<\/li>\n<li>Damien Graux, Louis Jachiet, Pierre Genev\u00e8s et Nabil Layaida. Une classification exp\u00e9rimentale multi-crit\u00e8res des \u00e9valuateurs SPARQL r\u00e9partis. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk2-3.pdf\">slides<\/a>)<\/li>\n<\/ul>\n<h4>Session 3 : Fouille de donn\u00e9es<\/h4>\n<h5>Mercredi, 10:30-12:00. Pr\u00e9sid\u00e9e par Amedeo Napoli (CNRS, LORIA &#038; Inria Nancy).<\/h5>\n<ul>\n<li>Pierre Gan\u00e7arski, Antoine Cornu\u00e9jols, C\u00e9dric Wemmert et Youn\u00e8s Bennani. Clustering collaboratif : Principes et mise en \u0153uvre. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk3-1.pdf\">slides<\/a>)<\/li>\n<li>Mehdi Zitouni, Reza Akbarinia, Sadok Ben Yahia et Florent Masseglia. Massively Distributed Environments and Closed Itemset Mining the DCIM Approach. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk3-2.pdf\">slides<\/a>)<\/li>\n<li>Raef Mousheimish, Yehia Taher et Karine Zeitouni. Apprentissage automatique de r\u00e8gles CEP pr\u00e9dictives: combler le gap entre fouille de donn\u00e9es et traitement des \u00e9v\u00e9nements complexes. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk3-3.pdf\">slides<\/a>)<\/li>\n<\/ul>\n<h4>Session 4 : Donn\u00e9es semi-structur\u00e9es<\/h4>\n<h5>Mercredi, 12:00-13:00. Pr\u00e9sid\u00e9e par Bernd Amann (Universit\u00e9 Pierre-et-Marie-Curie)<\/h5>\n<ul>\n<li>Pierre Bourhis, Juan Reuters, Fernando Su\u00e1rez et Domagoj Vrgo\u010d. JSON: Mod\u00e8le de donn\u00e9es, langage de requ\u00eate et de sch\u00e9ma JSON: data model, query languages and schema specification. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk4-1.pdf\">slides<\/a>)<\/li>\n<li>Mohamed-Amine Baazizi, Dario Colazzo, Giorgio Ghelli et Carlo Sartiani. Counting Types for Massive JSON Datasets. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk4-2.pdf\">slides<\/a>)<\/li>\n<\/ul>\n<h4>Session 5 : Syst\u00e8mes et applications<\/h4>\n<h5>Mercredi, 16:00-18:10. Pr\u00e9sid\u00e9e par Soror Sahri (Universit\u00e9 Paris-Descartes).<\/h5>\n<ul>\n<li>Abdeslem Belghoul, Mourad Baiou, Radu Ciucanu et Farouk Toumani. Optimizing Communication Time via Middleware Tuning.<\/li>\n<li>Ji Liu, Luis Pineda, Esther Pacitti, Alexandru Costan, Patrick Valduriez, Gabriel Antoniu et Marta Mattoso. Efficient Scheduling of Scientific Workflows using Hot Metadata in a Multisite Cloud. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk5-2.pdf\">slides<\/a>)<\/li>\n<li>Jocelyn De Go\u00ebr, Myoung-Ah Kang, Xavier Bailly et Engelbert Mephu-Nguifo. PSH-DB, un syst\u00e8me cl\u00e9-valeur permettant l\u2019indexation et la recherche de s\u00e9quences ADN. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk5-3.pdf\">slides<\/a>)<\/li>\n<li>Benjamin Billet, Micka\u00ebl Jurret, Didier Parigot et Patrick Valduriez. End-to-end Graph Mapper. <em>(article court)<\/em> (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk5-4.pdf\">slides<\/a>)<\/li>\n<li>Marie Le Guilly, Jean-Marc Petit et Marian Scuturici. Retour d&rsquo;exp\u00e9rience sur l&rsquo;analyse des donn\u00e9es d&rsquo;un tunnelier.  <em>(article court)<\/em><\/li>\n<\/ul>\n<h4>Session 6 : Th\u00e9orie des BD<\/h4>\n<h5>Jeudi, 14:00-16:00. Pr\u00e9sid\u00e9e par Victor Vianu (U. C. San Diego, ENS &#038; Inria Paris).<\/h5>\n<ul>\n<li>Momar Sakho, Iovka Boneva et Joachim Niehren. Complexity of Earliest Query Answering for Hyperstreams. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk6-1.pdf\">slides<\/a>)<\/li>\n<li>Nadime Francis et Leonid Libkin. Schema Mappings for Data Graphs. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk6-2.pdf\">slides<\/a>)<\/li>\n<li>Luc Segoufin et Alexandre Vigny. \u00c9num\u00e9ration des requ\u00eates du premier ordre sur classes de bases de donn\u00e9es avec local bounded expansion. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk6-3.pdf\">slides<\/a>)<\/li>\n<li>Antoine Amarilli, Pierre Bourhis, Louis Jachiet et Stefan Mengel. Une approche par circuit pour une \u00e9num\u00e9ration efficace. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk6-4.pdf\">slides<\/a>)<\/li>\n<\/ul>\n<h4>Session 7 : R\u00e9seaux sociaux<\/h4>\n<h5>Vendredi, 10:30-13:00. Pr\u00e9sid\u00e9e par Pierre Senellart (\u00c9cole normale sup\u00e9rieure &#038; Inria Paris).<\/h5>\n<ul>\n<li>Jean-Benoit Griesner, Talel Abdessalem, Hubert Naacke et Pierre Dosne. ALGeoSPF: A Hierarchical Geographical Factorization Model for POI Recommendation.<\/li>\n<li>Quentin Grossetti, Camelia Constantin, C\u00e9dric Du Mouza et Nicolas Travers. Enhance micro-blogging recommendations of posts with an homophily-based graph. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk7-2.pdf\">slides<\/a>)<\/li>\n<li>Abdulhafiz Alkhouli et Dan Vodislav. Continuous processing of diversity-aware top-k queries in social networks.<\/li>\n<li>Paul Lagr\u00e9e, Olivier Cappe, Bogdan Cautis et Silviu Maniu. Maximisation en ligne et \u00e0 grande \u00e9chelle de l&rsquo;influence sur les r\u00e9seaux sociaux. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk7-4.pdf\">slides<\/a>\u00a0|\u00a0<a href=\"https:\/\/github.com\/smaniu\/oim\n\">code<\/a>)<\/li>\n<li>Maximilien Danisch, Hubert Chan et Mauro Sozio. Large Scale Density-friendly Graph Decomposition via Convex Programming. (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/talk7-5.pdf\">slides<\/a> | <a href=\"https:\/\/github.com\/maxdan94\/Density-Friendly\">code<\/a>)<\/li>\n<\/ul>\n<h4>D\u00e9monstrations<\/h4>\n<h5>Jeudi, 16:00-17:30<\/h5>\n<ul>\n<li>Angela Bonifati, Ioana Ileana et Michele Linardi.<br \/>\nChaseFUN: Un moteur d&rsquo;\u00c9change de Donn\u00e9es efficace avec (et malgr\u00e9) les d\u00e9pendances fonctionnelles (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/demo1.pdf\">poster<\/a>).\n<\/li>\n<li>Cyrille Ponchateau, Ladjel Bellatreche, Carlos Ordonez et Mickael Baron.<br \/>\nMathMOuse: A Mathematical MOdels WarehoUSE to handle both Theoretical and Numerical Data (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/demo2.pdf\">poster<\/a>\u00a0|\u00a0<a href=\"https:\/\/forge.lias-lab.fr\/projects\/mathmouse\/\">code<\/a>)\n<\/li>\n<li>\nKarima Rafes, Sarah Cohen-Boulakia et Serge Abiteboul.<br \/>\nUne infrastructure d&rsquo;autocompl\u00e9tion pour SPARQL g\u00e9n\u00e9rique et multi-services (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/demo3.pdf\">poster<\/a> | <a href=\"https:\/\/www.slideshare.net\/BorderCloud\/initiation-sparql-avec-wikidata\">tutoriel<\/a> | <a href=\"http:\/\/linkedwiki.com\/exampleInsertUpdate.php\">d\u00e9monstrateur<\/a> | <a href=\"http:\/\/www.bordercloud.com\/LinkedWikiPlatform.php?lang=fr_FR\">logiciel<\/a>)\n<\/li>\n<li>\nXiangnan Ren, Olivier Cur\u00e9, Ke Li, Jeremy Lhez, Badre Belabbess, Tendry Randriamalala, Yufan Zheng et Gabriel Kepeklia.<br \/>\nStrider: An Adaptive, Inference-enabled Distributed RDF Stream Processing Engine (<a href=\"https:\/\/github.com\/renxiangnan\/strider\">code<\/a>)\n<\/li>\n<li>\nOlivier Rodriguez, Corentin Colomier, Cecilie Rivi\u00e8re, Reza Akbarinia et Federico Ulliana.<br \/>\nParallelizing Query Rewriting for Key-Value Stores Under Simple Semantic Constraints (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/demo5.pdf\">poster<\/a> | <a href=\"https:\/\/github.com\/zuri66\/keyval-qrewriting\">code<\/a>)\n<\/li>\n<\/ul>\n<h4>Session doctorant(e)s<\/h4>\n<h5>Mercredi, 14:00-15:30<\/h5>\n<ul>\n<li>\nJoris Dugu\u00e9p\u00e9roux. Garanties de confidentialit\u00e9 et d&rsquo;efficacit\u00e9 sur les plate-formes de crowdsourcing (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/phd1.pdf\">poster<\/a>)\n<\/li>\n<li>\nLouis Jachiet, Pierre Geneves, Nabil Layaida et Nils Gesbert. Une nouvelle alg\u00e8bre pour SPARQL permettant l&rsquo;optimisation des requ\u00eates contenant des Property Paths (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/phd2.pdf\">poster<\/a>)\n<\/li>\n<li>\nNyoman Juniarta, Chedy Ra\u00efssi et Amedeo Napoli.\tEchantillonnage et fouille de motifs s\u00e9quentiels &#8211; Application \u00e0 l&rsquo;analyse de trajectoires de visiteurs (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/phd3.pdf\">poster<\/a>)\n<\/li>\n<li>\nMarie Le Guilly. Langages de requ\u00eates interactifs pour l&rsquo;exploration de donn\u00e9es\n<\/li>\n<li>\nRutian Liu. Computing Schema Complements over Analytical Datasets (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/phd5.pdf\">poster<\/a>)\n<\/li>\n<li>\nPierre Monnin, Amedeo Napoli et Adrien Coulet. Confirming and Suggesting Subsumption Relations in an Ontology using Formal Concept Analysis (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/phd6.pdf\">poster<\/a>)\n<\/li>\n<li>\nChao Zhang, Farouk Toumani et Emmanuel Gangler. Symmetric and Asymmetric Aggregate Function in Massively Parallel Computing (<a href=\"https:\/\/project.inria.fr\/bda2017\/files\/2017\/11\/phd7.pdf\">poster<\/a>)\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Emploi du temps Tutoriels Mardi, 11:00-13:00 : IoT Big Data Stream Mining (slides) Albert Bifet, T\u00e9l\u00e9com ParisTech, Universit\u00e9 Paris-Saclay Pr\u00e9sident de session : Joachim Niehren (Inria Lille) The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities\u2026<\/p>\n<p> <a class=\"continue-reading-link\" href=\"https:\/\/project.inria.fr\/bda2017\/programme\/\"><span>Continue reading<\/span><i class=\"crycon-right-dir\"><\/i><\/a> <\/p>\n","protected":false},"author":1191,"featured_media":0,"parent":0,"menu_order":25,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-86","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/bda2017\/wp-json\/wp\/v2\/pages\/86","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/bda2017\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/bda2017\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/bda2017\/wp-json\/wp\/v2\/users\/1191"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/bda2017\/wp-json\/wp\/v2\/comments?post=86"}],"version-history":[{"count":84,"href":"https:\/\/project.inria.fr\/bda2017\/wp-json\/wp\/v2\/pages\/86\/revisions"}],"predecessor-version":[{"id":395,"href":"https:\/\/project.inria.fr\/bda2017\/wp-json\/wp\/v2\/pages\/86\/revisions\/395"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/bda2017\/wp-json\/wp\/v2\/media?parent=86"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}