Viktor Sanca

@ashaikhha Congratulations! 🎉

This is part of my vision toward analytical engines that support both structured and unstructured data through extending the relational model and existing execution infrastructure and systems: ieeexplore.ieee.org/abstract/docum… (ICDE'23) and ieeexplore.ieee.org/abstract/docum… (TKDE invited issue)[5/5]

Paper will be available here: dl.acm.org/doi/10.1145/36… Looking forward to presenting on Monday! 📩If you're working in this domain, I'd be even happier to get in touch and exchange ideas and potentially collaborate - workloads and benchmarks should propel more research! [4/5]

✈️ I'm going to Santiago, Chile to attend @SIGMODConf and present our work at @DaMoN_workshop. ⚡Fast and efficient data access is the cornerstone of actionable analytical insights. But novel use-cases and vector embedding analytics challenge the current approaches. [1/5]

I look forward to presenting this work at @ComputerSociety @icdeconf in Utrecht, Netherlands! #vectorDB #relationalDB #analytics

💡Compared with a vector index, with relational filters and different access patterns, highly optimized exhaustive processing can be beneficial! We start from a column store, composable with traditional relational operators, towards our vision of model-relational analytics.

Optimizing Context-Enhanced Relational Joins has been accepted @icdeconf'24. Collaborating with Manos and @ailamaki, we explored using vector embeddings to enable data processing engines to reason about previously opaque context-rich data.

Our book on "Data Structures for Data-Intensive Applications" co-authored with Stratos Idreos (@HarvardDASlab) and @DennisShasha can be downloaded for free from the publisher for the next few days (until Feb 12): nowpublishers.com/article/Detail… #FNT #DataStructures #Textbook #Databases

Vector embeddings enable data analytics beyond similarity search. Relational operators gain new semantics with embeddings (ieeexplore.ieee.org/abstract/docum…) - and many new challenges to solve. In our preprint we dive deeper into embedding-based similarity joins: arxiv.org/abs/2312.01476

#PhD admissions at #BU #CS are now open! More information for the CS PhD program can be found at: bu.edu/cs/phd-program… Application deadline: December 15, 2023 Apply at: bu.edu/cas/admissions… #FAQ: bu.edu/cs/phd-program… Please RT!

It was a joy to give a talk to the graduate class and Prof. Angela Bonifati's DB group at Lyon 1 University. I presented my research under the umbrella of System, workload, and context-conscious analytics. Thank you @ang3ela for the photo and the fun discussion with your group!

I am looking for a #postdoc to work with me on #AI-driven #Databases as part of my #DECRA at #unimelb. If you have a PhD and do research in #databases, #AI (#ML, #RL, recommender systems, user intent prediction) OR #HCI, please apply! More details here: go.unimelb.edu.au/wv7s

The new book "Data Structures for Data-Intensive Applications" (by @manathan1984, Stratos, and Dennis) certainly made it to my reading list: nowpublishers.com/article/Detail…

6/ From modern hardware and its interactions - to ML and rethinking established algorithms, I look forward to several presentations at @VLDBconf #VLDB2023! If anyone is interested, I am happy to chat or catch up at the conference or in the #Vancouver area.

5/ Finally, we propose "Improving K-means Clustering Using Speculation" by sampling the data for fast cluster exploration, in parallel running over the full data for true clusters, merging the two phases for faster convergence at #AIDB with S. Igescu, @elenizapridou, @ailamaki.

1/ I'm very excited about having 4 workshop papers accepted at @VLDBconf #VLDB2023, outlining data management research directions and challenges I am passionate about. Research done at @dias_EPFL @ICepfl targets different parts of the data management pipeline.