arXiv subjects

Christina Pavlopoulou

Publications and source records attributed to Christina Pavlopoulou.

6 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

Generating Query Context for Relational Databases

Relational databases are the systems of record for business applications, and there is growing demand to query them through natural language interfaces. A key challenge is that AI models need appropriate query context, i.e., sample questions paired with their corresponding data-model fragments, to generate accurate SQL. Today, creating this context is a manual, time-consuming process that requires expertise in both SQL and the database schema, leading to a cold start problem for new databases and an ongoing maintenance burden as schemas and query patterns evolve. We present an automated approach for generating query context for relational databases. Our method defines query flows that capture common patterns of data retrieval and analysis questions, systematically generates data models by traversing these flows, and instantiates them with specific values sampled from the database using weighted strategies that maximize diversity and coverage. The resulting question--data-model pairs can be used to guide natural language interfaces in accurately querying relational databases. We report on our experience deploying this approach across more than 50 real-world databases connected to Tursio.

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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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arXiv2022

Factor Windows: Cost-based Query Rewriting for Optimizing Correlated Window Aggregates

Window aggregates are ubiquitous in stream processing. In Azure Stream Analytics (ASA), a stream processing service hosted by Microsoft's Azure cloud, we see many customer queries that contain aggregate functions (such as MIN and MAX) over multiple correlated windows (e.g., tumbling windows of length five minutes and ten minutes) defined on the same event stream. In this paper, we present a cost-based optimization framework for optimizing such queries by sharing computation among multiple windows. In particular, we introduce the notion of factor windows, which are auxiliary windows that are not in the input query but may nevertheless help reduce the overall computation cost, and our cost-based optimizer can produce rewritten query plans that have lower costs than the original query plan by utilizing factor windows. Since our optimization techniques are at the level of query (plan) rewriting, they can be implemented on any stream processing system that supports a declarative, SQL-like query language without changing the underlying query execution engine. We formalize the shared computation problem, present the optimization techniques in detail, and report evaluation results over both synthetic and real datasets. Our results show that, compared to the original query plans, the rewritten plans output by our cost-based optimizer can yield significantly higher (up to 16.8x) throughput.

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arXiv2020

Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management Systems

Query Optimization remains an open problem for Big Data Management Systems. Traditional optimizers are cost-based and use statistical estimates of intermediate result cardinalities to assign costs and pick the best plan. However, such estimates tend to become less accurate because of filtering conditions caused either from undetected correlations between multiple predicates local to a single dataset, predicates with query parameters, or predicates involving user-defined functions (UDFs). Consequently, traditional query optimizers tend to ignore or miscalculate those settings, thus leading to suboptimal execution plans. Given the volume of today's data, a suboptimal plan can quickly become very inefficient. In this work, we revisit the old idea of runtime dynamic optimization and adapt it to a shared-nothing distributed database system, AsterixDB. The optimization runs in stages (re-optimization points), starting by first executing all predicates local to a single dataset. The intermediate result created from each stage is used to re-optimize the remaining query. This re-optimization approach avoids inaccurate intermediate result cardinality estimations, thus leading to much better execution plans. While it introduces the overhead for materializing these intermediate results, our experiments show that this overhead is relatively small and it is an acceptable price to pay given the optimization benefits. In fact, our experimental evaluation shows that runtime dynamic optimization leads to much better execution plans as compared to the current default AsterixDB plans as well as to plans produced by static cost-based optimization (i.e. based on the initial dataset statistics) and other state-of-the-art approaches.

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