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Periklis Chrysogelos

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Related publications (13)

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LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

Anastasia Ailamaki, Periklis Chrysogelos, Viktor Sanca

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

HetCache: Synergising NVMe Storage and GPU acceleration for Memory-Efficient Analytics

Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson, Syed Mohammad Aunn Raza

Accessing input data is a critical operation in data analytics: i) slow data access significantly degrades performance, and ii) storing everything in the fastest medium, i.e., memory, incurs high operational and hardware costs. Further, while GPUs offer in ...

2023

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Most modern in-memory online transaction processing (OLTP) engines rely on multi-version concurrency control (MVCC) to provide data consistency guarantees in the presence of conflicting data accesses. MVCC improves concurrency by generating a new version o ...

ACM2023

HPCache: memory-efficient OLAP through proportional caching revisited

Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson

Analytical engines rely on in-memory data caching to avoid storage accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- and time-based caching decisions, however, are a proxy of the expected query ...

New York2023

Boosting Efficiency of External Pipelines by Blurring Application Boundaries

Anastasia Ailamaki, Periklis Chrysogelos, Anna Patricia Herlihy

Modern application development addresses increasingly specialized problems using domain-specific utilities, such as Optical Code Recognition and standalone statistical tools. The diversity of tooling, combined with the ever-growing volume of data, requires ...

2022

HPCache: Memory-Efficient OLAP Through Proportional Caching

Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson

Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution ...

ACM2022

Efficient Analytical Query Processing on CPU-GPU Hardware Platforms

Periklis Chrysogelos

Timely insights lead to business growth and scientific breakthroughs but require analytical engines that cope with the ever-increasing data processing needs. Analytical engines relied on rapid CPU improvements, yet the end of Dennard scaling stopped the fr ...

EPFL2022

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Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data processing engine that performs analytics on fresh data, which are ingested from a transactional engine. HTAP systems typically consider data freshness at design time, ...

2020

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ASSOC COMPUTING MACHINERY2020

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GPUs are becoming increasingly popular in large scale data center installations due to their strong, embarrassingly parallel, processing capabilities. Data management systems are riding the wave by using GPUs to accelerate query execution, mainly for analy ...

2020