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Anastasia Ailamaki

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Publications associées (179)

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 ...

Springer2023

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

Anastasia Ailamaki, Viktor Sanca

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 ...

2023

Chaosity: Understanding Contemporary NUMA-architectures

Anastasia Ailamaki, Viktor Sanca, Syed Mohammad Aunn Raza, Hamish Mcniece Hill Nicholson, Andreea Nica

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 ...

2023

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

Pixels: An Efficient Column Store for Cloud Data Lakes

Anastasia Ailamaki, Haoqiong Bian

To benefit from the cloud’s higher elasticity and price-efficiency, most modern data-lake engines support S3-like cloud object storage (COS) services as their optional or preferred underlying storage. Meanwhile, the widespread column stores, such as Parque ...

IEEE2022

Dalton: Learned Partitioning for Distributed Data Streams

Anastasia Ailamaki, Ioannis Mytilinis, Eleni Zapridou

To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability. Deciding on a static partitioning for a ...

2022

Efficient GPU-accelerated Join Optimization for Complex Queries

Anastasia Ailamaki, Bikash Chandra, Srinivas Karthik Venkatesh, Riccardo Mancini, Vasileios Mageirakos

Analytics on modern data analytic and data warehouse systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processin ...

IEEE2022

The DB Community vis-a-vis Environmental, Health, and Societal Grand Challenges: Innovation Engine, Plumber, or Bystander?

Anastasia Ailamaki

This panel considers the role of the database research community in addressing humanity's greatest challenges. Are we an innovation engine, tool providers, or are we standing on the side while other research communities take the lead? ...

ASSOC COMPUTING MACHINERY2022

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

Efficient Massively Parallel Join Optimization for Large Queries

Anastasia Ailamaki, Bikash Chandra, Srinivas Karthik Venkatesh, Riccardo Mancini, Vasileios Mageirakos

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, find ...

ACM2022

Adaptive table placement in NUMA architectures

Anastasia Ailamaki, Iraklis Psaroudakis

Techniques and solutions are provided for performing adaptive database table placement in a non-uniform memory access (NUMA) architecture. The adaptive table placement can occur in response to changing workloads on the NUMA nodes. For example, if a particu ...

2022

Sampling-Based AQP in Modern Analytical Engines

Anastasia Ailamaki, Viktor Sanca

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 ...

ACM2022

The Formidable Mid-Career Crisis

Anastasia Ailamaki

ASSOC COMPUTING MACHINERY2022

Micro-architectural analysis of in-memory OLTP: Revisited

Anastasia Ailamaki, Danica Porobic, Utku Sirin

Micro-architectural behavior of traditional disk-based online transaction processing (OLTP) systems has been investigated extensively over the past couple of decades. Results show that traditional OLTP systems mostly under-utilize the available micro-archi ...

2021

Scalable Multi-Query Execution using Reinforcement Learning

Anastasia Ailamaki, Panagiotis Sioulas

The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time ...

Association for Computing Machinery2021

Storage Management in Smart Data Lake

Anastasia Ailamaki, Ioannis Mytilinis, Bikash Chandra, Haoqiong Bian

Data lakes are complex ecosystems where heterogeneity prevails. Raw data of diverse formats are stored and processed, while long and expensive ETL processes are avoided. Apart from data heterogeneity, data lakes also entail hardware heterogeneity. Typical ...

2021

Performance Characterization of HTAP Workloads

Anastasia Ailamaki, Utku Sirin, Sandhya Dwarkadas

Hybrid Transactional and Analytical Processing (HTAP) systems have become popular in the past decade. HTAP systems allow running transactional and analytical processing workloads on the same data and hardware. As a result, they suffer from workload interfe ...

IEEE COMPUTER SOC2021

A system design for elastically scaling transaction processing engines in virtualized servers

Anastasia Ailamaki, Hillel Avni, Raja Appuswamy, Angelos Christos Anadiotis

Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of re ...

ASSOC COMPUTING MACHINERY2020

Access pattern based optimization of memory access

Anastasia Ailamaki, Georgios Psaropoulos

A method for optimizing memory access for database operations is provided. The method may include identifying an access pattern associated with a database operation. The access pattern may include data required to perform the database operation. One or mor ...

2020

Cleaning Denial Constraint Violations through Relaxation

Anastasia Ailamaki, Manolis Karpathiotakis, Styliani Asimina Giannakopoulou

Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a p ...

ASSOC COMPUTING MACHINERY2020

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