Publication
SmartNICs in the Cloud: The Why, What and How of In-network Processing for Data-Intensive Applications
Faeze Faghih; Tobias Ziegler; Zsolt István; Carsten Binnig
In: Pablo Barceló; Nayat Sánchez-Pi; Alexandra Meliou; S. Sudarshan (Hrsg.). Companion of the 2024 International Conference on Management of Data, SIGMOD/PODS 2024, Santiago, Chile, June 9-15, 2024. ACM SIGMOD International Conference on Management of Data (SIGMOD), Pages 556-560, ACM, 2024.
Abstract
Traditional query planners translate SQL queries into query plans
to be executed over relational data. However, it is impossible to
query other data modalities, such as images, text, or video stored in
modern data systems such as data lakes using these query planners.
In this paper, we propose Language-Model-Driven Query Planning,
a new paradigm of query planning that uses Language Models
to translate natural language queries into executable query plans.
Different from relational query planners, the resulting query plans
can contain complex operators that are able to process arbitrary
modalities. As part of this paper, we present a first GPT-4 based
prototype called CAESURA and show the general feasibility of this
idea on two datasets. Finally, we discuss several ideas to improve
the query planning capabilities of today’s Language Models.
