arXiv subjects

Jyoti Pandey

Publications and source records attributed to Jyoti Pandey.

5 recordsLinked to original sources

arXiv2026

Reverse Migration of Cloud Applications to On-premises

Cloud has become ubiquitous to modern applications due to its agility and scalability. However, regulated industries still prefer to deploy on-premises due to security and compliance reasons. This creates a paradox for vendors who need to develop in the cloud but deploy on-premises, leading to long release cycles and complex maintenance. In this paper, we present Diel, the Tursio On-premises Migrator, a tool that automates reverse migration of cloud applications to on-premises environments. Diel applies a combination of simulate, replicate, and delegate strategies to transform cloud services into on-premises counterparts. We describe the design and implementation of Diel, along with lessons learned from using it in practice. With Diel, we have been able to keep Tursio AI's cloud and on-premises versions in sync, releasing new stable versions every three weeks.

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arXiv2026

Guided Table Retrieval for Structured Data Search

Answering natural language questions over structured databases requires identifying the relevant tables and determining how to join them---a task that demands both schema knowledge and semantic understanding of the user's intent. We present guided table retrieval, a four-phase pipeline that combines deterministic grounding via hash-based predictors, structural exploration of join-graph reachability, LLM-powered disambiguation of sources and targets, and algorithmic merging into minimal, topologically ordered join trees. By decomposing the problem into phases with distinct responsibilities--- determinism, coverage, semantic reasoning, and coherence--- the pipeline avoids the brittleness of end-to-end LLM approaches while leveraging LLMs where their contextual judgment is most needed. We evaluate on BIRD-DEV and the enterprise-scale BEAVER benchmark, achieving 94% and 70% precision respectively, with 92% and 53% F1---substantially outperforming existing baselines on precision and F1 while producing exact join trees that can be directly consumed by downstream query compilers.

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arXiv2026

Making Databases Searchable with Deep Context

Databases are the most critical assets for enterprises, and yet they remain largely inaccessible to people who make the most important decisions. In this paper, we describe the Tursio search platform that builds an abstraction layer, aka semantic knowledge graph, over the underlying databases to make them searchable in natural language. Tursio infuses large language models (LLMs) into every part of the query processing stack, including data modeling, query compilation, query planning, and result reasoning. This allows Tursio to process natural language queries systematically using techniques from traditional query planning and rewriting, rather than black-box memorization. We describe the architecture of Tursio in detail and present a comprehensive evaluation on production workloads, and synthetic and realistic benchmarks. Our results show that Tursio achieves high accuracy while being efficient and scalable, making databases truly searchable for non-expert users.

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arXiv2023

On the Microscopic Level Density Models for Nuclei Near Z=28 Shell Closure

A comprehensive test of level density models for explaining the decay of excited compound nuclei, 54 Mn, 56 Fe, 58 Co, 60 Ni, 61 Ni and 63 Cu, in the energy range of 28 - 36 MeV has been performed. The compound nuclei of interest in the desired ranges are populated using 6 Li based transfer reactions. The proton decay spectrum for each excitation energy bins has been measured. The measured proton spectrum has been reproduced using statistical model calculations with different level density models. A variance minimised approach has been employed for analysing the prediction capability of different level density models. This approach has been converged to Gogny Hartree-Fock-Bogoliubov(HFB) microscopic level density model and which is attributed as the most accurate model for the desired nuclei.

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arXiv2022

Determination of photo-nuclear cross section of $^{61}$Ni($γ$,xp) reaction via surrogate ratio technique

The photo nuclear reaction cross section of $^{61}$Ni($γ$,xp) reaction have been measured by employing surrogate reaction technique. This indirect method is used for the first time to obtain the cross section of photo nuclear reaction. The compound nucleus $^{61}$Ni$^{*}$ was populated using the transfer reaction $^{59}$Co($^{6}$Li,$α$) at E$_{lab}=$ 40.5 MeV. To calculate the surrogate ratio, $^{60}$Ni($γ$,xp) was selected as reference reaction and the corresponding compound nucleus $^{60}$Ni$^{*}$ was populated using the transfer reaction $^{56}$Fe($^{6}$Li,d) at E$_{lab}=$ 35.9 MeV. The experimental cross section data of the reference reaction has been taken from EXFOR data libraries. Compound nuclear cross section calculations have been done using EMPIRE 3.2.3 code.

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