{"id":89,"date":"2017-12-13T18:34:03","date_gmt":"2017-12-13T18:34:03","guid":{"rendered":"http:\/\/sbbd.org.br\/2018\/?page_id=89"},"modified":"2018-08-28T10:32:25","modified_gmt":"2018-08-28T10:32:25","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/sbbd.org.br\/2018\/tutorials\/","title":{"rendered":"Tutorials"},"content":{"rendered":"

In-Memory Analytic DBMSs: Design and Lessons Learned<\/b><\/span>
\nSpeaker: <\/b>Pedro Eugenio Pedreira (Facebook Inc.)
\nSat, 25 Aug [16:30] – Room: <\/b>Maracan\u00e3<\/span><\/p>\n

Abstract<\/b>
\nThis tutorial presents an overview and the motivation behind building in-memory analytic DBMS systems. As memory size grows and prices drop, in-memory analytic DBMSs have become a viable choice to provide interactive analytic capabilities to critical data marts which are part of much larger \u2013 and slower \u2013 data warehouses. In order to illustrate how this type of system is designed and built, some of the state-of-art in-memory database systems will be discussed, such as SAP HANA, Hyper, Hekaton, Oracle In-Memory and Cubrick, and the characteristics that differentiate them from the traditional DBMS design literature will be stressed. In addition, the author will discuss some of the lessons learned while building and providing Cubrick as a service at Facebook, and highlight some of the many research opportunity avenues.<\/p>\n


Coleta, Integra\u00e7\u00e3o e Pr\u00e9-processamento de Dados de M\u00faltiplas Fontes<\/b><\/span>
\nSpeakers: <\/b>Nat\u00e9rcia Batista (UFMG); Michele Brand\u00e3o (UFMG); Michele Brito (UFMG); Daniel Dalip (CEFET\/MG); Mirella Moro (UFMG)
\nSat, 25 Aug [16:30] – Room: <\/b>Catedral<\/span><\/p>\n

Tutorial slides<\/a><\/p>\n

Abstract<\/b>
\nDados extra\u00eddos da Web s\u00e3o cada vez mais heterog\u00eaneos e n\u00e3o estruturados, representando desafios para atividades de coleta, integra\u00e7\u00e3o e pr\u00e9-processamento de dados. Existem estudos que s\u00e3o “orientados a dados”, i.e., com base nos dados dispon\u00edveis, por\u00e9m seus resultados ficam restritos aos respectivos dados. Em contraponto, v\u00e1rios problemas existem antes de se identificar quais dados s\u00e3o necess\u00e1rios para solucion\u00e1-los, e muitas vezes, s\u00e3o necess\u00e1rios dados de m\u00faltiplas fontes. Nesse contexto, coletar, integrar e pr\u00e9-processar dados adequadamente possibilita criar conjuntos de dados para a solu\u00e7\u00e3o de problemas reais. Assim, este tutorial aborda essas atividades e apresenta seus principais desafios.<\/p>\n","protected":false},"excerpt":{"rendered":"

In-Memory Analytic DBMSs: Design and Lessons Learned Speaker: Pedro Eugenio Pedreira (Facebook Inc.) Sat, 25 Aug [16:30] – Room: Maracan\u00e3… <\/p>\n","protected":false},"author":10,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-89","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/sbbd.org.br\/2018\/wp-json\/wp\/v2\/pages\/89","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sbbd.org.br\/2018\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sbbd.org.br\/2018\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sbbd.org.br\/2018\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/sbbd.org.br\/2018\/wp-json\/wp\/v2\/comments?post=89"}],"version-history":[{"count":43,"href":"https:\/\/sbbd.org.br\/2018\/wp-json\/wp\/v2\/pages\/89\/revisions"}],"predecessor-version":[{"id":946,"href":"https:\/\/sbbd.org.br\/2018\/wp-json\/wp\/v2\/pages\/89\/revisions\/946"}],"wp:attachment":[{"href":"https:\/\/sbbd.org.br\/2018\/wp-json\/wp\/v2\/media?parent=89"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}