arXiv subjects

Anton Chizhov

Publications and source records attributed to Anton Chizhov.

6 recordsLinked to original sources

arXiv2026

Fast Discovery of Inclusion Dependencies with Desbordante

Inclusion dependency is a relation between attributes of tables that indicates possible Primary Key-Foreign Key references. Automatic discovery of inclusion dependencies is a relevant problem for both academic and industrial communities. The core concern for this problem is the efficiency of discovery process, since it is a computationally expensive task. However, existing studies only address the algorithmic side, while leaving out the implementation aspect. At the same time, engineering details are at least as important as the algorithmic ones for achieving good performance. In this paper, we describe techniques for efficient implementation of two algorithms for discovery of inclusion dependencies - Spider and Faida. The first one is a classic algorithm whose ideas lie in the foundation of many other inclusion dependency discovery algorithms. We propose an efficient parallelization technique, which greatly speeds up the algorithm while simultaneously reducing its memory consumption. The second one is the state-of-the-art approximate algorithm, which we approach by applying four types of optimizations: data buffering, SIMD-enabled execution, careful hash-table selection and parallelization. In order to experimentally evaluate our techniques, we have implemented these algorithms in Desbordante - an open-source science-intensive data profiler written in C++. For Spider, we have evaluated several different options, and in case of Faida we have demonstrated that all our optimization techniques yield results. We also compared our implementations with Metanome - a Java-based data profiler. Overall, we report up to 5x improvement in terms of run time reduction for Spider and up to 8x for Faida.

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arXiv2025

Observing hidden neuronal states in experiments

In this article we demonstrate a general protocol for constructing systematically experimental steady-state bifurcation diagrams for electrophysiologically active cells. We perform our experiments on entorhinal cortex neurons, both excitatory (pyramidal neurons) and inhibitiory (interneurons). A slowly ramped voltage-clamp electrophysiology protocol serves as closed-loop feedback controlled experiment for the subsequent current-clamp open-loop protocol on the same cell. In this way, the voltage-clamped experiment determines dynamically stable and unstable (hidden) steady states of the current-clamp experiment. The transitions between observable steady states and observable spiking states in the current-clamp experiment provide partial evidence for stability and bifurcations of the steady states. This technique for completing steady-state bifurcation diagrams in a model-independent way expands support for model validation to otherwise inaccessible regions of the phase space. Overlaying the voltage-clamp and current-clamp protocols leads to an experimental validation of the classical slow-fast dissection method introduced by J. Rinzel in the 1980s and routinely applied ever since in order to analyse slow-fast neuronal models. Our approach opens doors to observing further complex hidden states with more advanced control strategies, allowing to control real cells beyond pharmacological manipulations.

q-bio.NC↗

arXiv2025

Speeding up SQL subqueries via decoupling of non-correlated predicate (extended version)

In this paper, we discuss a novel technique for processing correlated subqueries in SQL. The core idea is to isolate the non-correlated part of the predicate and use it to reduce the number of evaluations of the correlated part. We begin by providing an overview of several classes of queries that may benefit from this technique. For each class, we propose a potential rewrite and discuss the conditions under which it is advantageous. Next, we address the evaluation aspects of the proposed rewrites: 1) we describe our approach to adapting the block-based Volcano query processing model, and 2) we discuss the benefits of implementing that technique within a position-enabled column-store with late materialization support. Finally, we present a simple cost model that allows estimation of the benefits of said rewrites. Our evaluation has a quantitative part and a qualitative part. The former focuses on studying the impact of non-correlated predicate selectivity on our technique. The latter identifies the limitations of our approach by comparing it with alternative approaches available in existing systems. Overall, experiments conducted using PosDB (a position-enabled column-store) and PostgreSQL demonstrated that, under suitable conditions, our technique can achieve a 5x improvement.

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arXiv2024

A refractory density approach to a multi-scale SEIRS epidemic model

We propose a novel multi-scale modeling framework for infectious disease spreading, borrowing ideas and modeling tools from the so-called Refractory Density (RD) approach. We introduce a microscopic model that describes the probability of infection for a single individual and the evolution of the disease within their body. From the individual-level description, we then present the corresponding population-level model of epidemic spreading on the mesoscopic and macroscopic scale. We conclude with numerical illustrations taking into account either a white Gaussian noise or an escape noise to showcase the potential of our approach in producing both transient and asymptotic complex dynamics as well as finite-size fluctuations consistently across multiple scales. A comparison with the epidemiology of coronaviruses is also given to corroborate the qualitative relevance of our new approach.

q-bio.PE↗

arXiv2023

Solving Data Quality Problems with Desbordante: a Demo

Data profiling is an essential process in modern data-driven industries. One of its critical components is the discovery and validation of complex statistics, including functional dependencies, data constraints, association rules, and others. However, most existing data profiling systems that focus on complex statistics do not provide proper integration with the tools used by contemporary data scientists. This creates a significant barrier to the adoption of these tools in the industry. Moreover, existing systems were not created with industrial-grade workloads in mind. Finally, they do not aim to provide descriptive explanations, i.e. why a given pattern is not found. It is a significant issue as it is essential to understand the underlying reasons for a specific pattern's absence to make informed decisions based on the data. Because of that, these patterns are effectively rest in thin air: their application scope is rather limited, they are rarely used by the broader public. At the same time, as we are going to demonstrate in this presentation, complex statistics can be efficiently used to solve many classic data quality problems. Desbordante is an open-source data profiler that aims to close this gap. It is built with emphasis on industrial application: it is efficient, scalable, resilient to crashes, and provides explanations. Furthermore, it provides seamless Python integration by offloading various costly operations to the C++ core, not only mining. In this demonstration, we show several scenarios that allow end users to solve different data quality problems. Namely, we showcase typo detection, data deduplication, and data anomaly detection scenarios.

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arXiv2023

Desbordante: from benchmarking suite to high-performance science-intensive data profiler (preprint)

Pioneering data profiling systems such as Metanome and OpenClean brought public attention to science-intensive data profiling. This type of profiling aims to extract complex patterns (primitives) such as functional dependencies, data constraints, association rules, and others. However, these tools are research prototypes rather than production-ready systems. The following work presents Desbordante - a high-performance science-intensive data profiler with open source code. Unlike similar systems, it is built with emphasis on industrial application in a multi-user environment. It is efficient, resilient to crashes, and scalable. Its efficiency is ensured by implementing discovery algorithms in C++, resilience is achieved by extensive use of containerization, and scalability is based on replication of containers. Desbordante aims to open industrial-grade primitive discovery to a broader public, focusing on domain experts who are not IT professionals. Aside from the discovery of various primitives, Desbordante offers primitive validation, which not only reports whether a given instance of primitive holds or not, but also points out what prevents it from holding via the use of special screens. Next, Desbordante supports pipelines - ready-to-use functionality implemented using the discovered primitives, for example, typo detection. We provide built-in pipelines, and the users can construct their own via provided Python bindings. Unlike other profilers, Desbordante works not only with tabular data, but with graph and transactional data as well. In this paper, we present Desbordante, the vision behind it and its use-cases. To provide a more in-depth perspective, we discuss its current state, architecture, and design decisions it is built on. Additionally, we outline our future plans.

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