Publications by Viktor Sanca | EPFL Graph Search

Reproducibility Report for ACM SIGMOD 2024 Paper: 'A Unified Approach for Resilience and Causal Responsibility with Integer Linear Programming (ILP) and LP Relaxations'

We successfully reproduced the experimental results presented in the original paper. The authors provided clear documentation of the software and hardware requirements, along with well-structured scripts and datasets hosted in an organized GitHub repositor ...

ACM2025

Efficient Model-Relational Data Management: Challenges and Opportunities

Anastasia Ailamaki, Viktor Sanca

2024

Efficient Data Access Paths for Mixed Vector-Relational Search

Anastasia Ailamaki, Viktor Sanca

2024

Efficient Approximate Analytics via Adaptive Context-Conscious Query Processing

Viktor Sanca

Industry and academia rely on ad-hoc data analysis to extract new value and timely insights. At the same time, the growing data volume presents a challenge for interactive ad-hoc analytics for modern in-memory analytical execution engines. While sampling p ...

EPFL2024

Optimizing Context-Enhanced Relational Joins

Anastasia Ailamaki, Viktor Sanca

Collecting data, extracting value, and combining insights from relational and context-rich sources of many modalities in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators enable declarative and optim ...

IEEE2024

Efficient and Reusable Lazy Sampling

Anastasia Ailamaki, Viktor Sanca, Periklis Chrysogelos

2024

Analytical Engines With Context-Rich Processing: Towards Efficient Next-Generation Analytics

Anastasia Ailamaki, Viktor Sanca

As modern data pipelines continue to collect, produce, and store a variety of data formats, extracting and combining value from traditional and context-rich sources such as strings, text, video, audio, and logs becomes a manual process where such formats a ...

2023

LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

Anastasia Ailamaki, Viktor Sanca, Periklis Chrysogelos

Modern analytical engines rely on Approximate Query Processing (AQP) to provide faster response times than the hardware allows for exact query answering. However, existing AQP methods impose steep performance penalties as workload unpredictability increase ...

2023

Improving K-means Clustering Using Speculation

Anastasia Ailamaki, Viktor Sanca, Eleni Zapridou, Stefan Igescu

K-means is one of the fundamental unsupervised data clustering and machine learning methods. It has been well studied over the years: parallelized, approximated, and optimized for different cases and applications. With increasingly higher parallelism leadi ...

2023

E-Scan: Consuming Contextual Data with Model Plugins

Anastasia Ailamaki, Viktor Sanca

Extracting value and insights from increasingly heterogeneous data sources involves multiple systems combining and consuming the data. With multi-modal and context-rich data such as strings, text, videos, or images, the problem of standardizing the data mo ...

2023