{"id":4324,"date":"2022-06-18T13:00:51","date_gmt":"2022-06-18T16:00:51","guid":{"rendered":"https:\/\/sbbd.org.br\/2022\/chamada-para-db-ai-copy\/"},"modified":"2022-07-12T11:32:12","modified_gmt":"2022-07-12T14:32:12","slug":"call-for-db-ai","status":"publish","type":"page","link":"https:\/\/sbbd.org.br\/2022\/call-for-db-ai\/","title":{"rendered":"Call for DB+AI"},"content":{"rendered":"

IMPORTANT DATES<\/h3>\n

\u2022 10th July – Submission deadline
\u2022 29th July \u2013 Paper decision
\u2022 1st August \u2013 Camera Ready
\u2022 19th September- Workshop DB+AI<\/p>\n

DB+AI \u2013 1st Brazilian Workshop on the Integration of Databases and Artificial Intelligence (co-located with SBBD 2022, B\u00fazios, RJ)<\/h3>\n

The scientific community has witnessed a popularity explosion of Artificial Intelligence (AI), fostered by the development of novel techniques from several paradigms, including deep learning and ensembles. Therefore, several AI-based applications have emerged to support the regular tasks performed by different experts and domains, including Databases. In particular, the integration of AI and Database communities has already shown its potential, as seen in recent events organized by the field-leading SIGMOD and VLDB committees. AI applies to regular tasks manually performed by Database Management Systems (DBMSs) experts or its internal routines, such as query processing, database tuning, loading, balancing, and scaling. Although AI-based solutions do not always fully automate those tasks (as many require human-in-the-loop interactions), they are still valuable for warning experts about potentially unexpected behaviors that may require further actions to ensure the DBMS resiliency. While AI techniques can enhance DBMSs\u2019 maintenance and setups, Database techniques also apply to optimize the performance and life cycle of AI applications in a two-way integration. An example of DB design that supports AI applications is the coupling of similarity queries within DBMS search engines, which provide direct integration for case-based AI tasks such as distance-based classification, clustering, and content-based retrieval. The Database research community also has a solid history of efficient designs and solutions to (i) track data transformations and (ii) capture and manage applications\u2019 metadata, including those within trained models. Those solutions can provide new capabilities to AI specialists for solving large and complex data-driven problems. Accordingly, this workshop aims to offer Database and AI students, researchers, and professionals an opportunity to present their latest (and ongoing) results regarding this research topic.<\/p>\n

Submission Instructions<\/span><\/span><\/h3>\n

Papers must be written in Portuguese or English and not exceed 8 pages (including references), according to the SBC template. Papers submitted to DB+AI Workshop must not have been simultaneously submitted to any other forum (conference or journal), nor have already been published elsewhere. The acceptance of a paper implies that at least one of its authors will register for the SBBD to present it at the workshop. Papers must be submitted by the deadline through the JEMS website<\/a>. Submitted papers will undergo a single blind reviewing process, where the authors\u2019 identities are visible to the reviewers, and the reviewers\u2019 identities are hidden from the authors. The papers will be reviewed based on originality, relevance, technical soundness, and clarity of presentation.<\/p>\n

Format and duration<\/h3>\n

DB+AI will be an in-person workshop with live presentations. We expect to have one keynote speaker talks to be announced soon.<\/p>\n

Topics of Interest<\/h3>\n