Publications by Viktor Sanca | EPFL Graph Search

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Modern hardware is increasingly complex, requiring increasing effort to understand in order to carefully engineer systems for optimal performance and effective utilization. Moreover, established design principles and assumptions are not portable to modern ...

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 ...

As the data volume grows, reducing the query execution times remains an elusive goal. While approximate query processing (AQP) techniques present a principled method to trade off accuracy for faster queries in analytics, the sample creation is often consid ...

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 ...

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 ...

Analytical applications, such as exploratory data analysis and decision support, process complex workloads that include sequences of inter-dependent queries. While modern OLAP systems exploit data parallelism, dependencies force execution ordering constrai ...

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 ...

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 ...

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 ...

Modern data management systems aim to provide both cutting-edge functionality and hardware efficiency. With the advent of AI-driven data processing and the post-Moore Law era, traditional memory-bound scale-up data management operations face scalability ch ...

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 ...

Viktor Sanca

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Chaosity: Understanding Contemporary NUMA-architectures

Efficient Approximate Analytics via Adaptive Context-Conscious Query Processing

Sampling-Based AQP in Modern Analytical Engines

Optimizing Context-Enhanced Relational Joins

LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

Accelerating Complex Analytics using Speculation

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

E-Scan: Consuming Contextual Data with Model Plugins

Improving K-means Clustering Using Speculation

Post-Moore's Law Fusion: High-Bandwidth Memory, Accelerators, and Native Half-Precision Processing for CPU-Local Analytics

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

Efficient Model-Relational Data Management: Challenges and Opportunities

Efficient Data Access Paths for Mixed Vector-Relational Search

Efficient and Reusable Lazy Sampling