Our experimental results demonstrate that our algorithm currently can only handle small problem sizes due to the limited number of qubits available on a gate-based quantum computer compared to a quantum computer based on quantum annealing.
no code implementations • 9 Apr 2021 • Sihem Amer-Yahia, Georgia Koutrika, Frederic Bastian, Theofilos Belmpas, Martin Braschler, Ursin Brunner, Diego Calvanese, Maximilian Fabricius, Orest Gkini, Catherine Kosten, Davide Lanti, Antonis Litke, Hendrik Lücke-Tieke, Francesco Alessandro Massucci, Tarcisio Mendes de Farias, Alessandro Mosca, Francesco Multari, Nikolaos Papadakis, Dimitris Papadopoulos, Yogendra Patil, Aurélien Personnaz, Guillem Rull, Ana Sima, Ellery Smith, Dimitrios Skoutas, Srividya Subramanian, Guohui Xiao, Kurt Stockinger
We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public.
In this paper we propose ValueNet light and ValueNet -- two end-to-end NL-to-SQL systems that incorporate values using the challenging Spider dataset.
In this paper, we provide an adapted benchmark data set that is based on a test collection originally used to evaluate information retrieval systems.
For this, we introduce an intermediate representation that is based on the logical query plan in a database called Operation Trees (OT).
The main idea of FOOP is to use a data-adaptive learning query optimizer that avoids exhaustive enumerations of join orders and is thus significantly faster than traditional approaches based on dynamic programming.
Semantic heterogeneity remains a problem when interoperating with data from sources of different scopes and knowledge domains.