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: https://t.co/De3dQywCO3 (ICDE'23) and https://t.co/rqtcCcDN54 (TKDE invited issue)[5/5]
✈️ 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]
Paper will be available here: https://t.co/FINknSDQj8 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]
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.
💡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.
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): https://t.co/0lsOjSC6hy
#FNT#DataStructures#Textbook#Databases
Vector embeddings enable data analytics beyond similarity search.
Relational operators gain new semantics with embeddings (https://t.co/De3dQywCO3) - and many new challenges to solve.
In our preprint we dive deeper into embedding-based similarity joins: https://t.co/NkTwvcSMyt
#PhD admissions at #BU#CS are now open! More information for the CS PhD program can be found at: https://t.co/87ENRAEMQp
Application deadline: December 15, 2023
Apply at: https://t.co/fNDsbdkCyx
#FAQ: https://t.co/ClzZNinazr
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: https://t.co/rdEA2Gb5t4
The new book "Data Structures for Data-Intensive Applications" (by @manathan1984, Stratos, and Dennis) certainly made it to my reading list: https://t.co/aac6N4KCIl
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.
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.
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.