Publications associées à Optimisation de requête

Using Cloud Functions as Accelerator for Elastic Data Analytics

Anastasia Ailamaki, Haoqiong Bian, Tiannan Sha

Cloud function (CF) services, such as AWS Lambda, have been applied as the new computing infrastructure in implementing analytical query engines. For bursty and sparse workloads, CF-based query engine is more elastic than the traditional query engines runn ...

ACM2023

Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

Christoph Koch, Sachin Basil John, Zhekai Jiang, Peter Lindner

We present Sudokube, a novel system that supports interactive speed querying on high-dimensional data using partially materialized data cubes. Given a storage budget, it judiciously chooses what projections to precompute and materialize during cube constru ...

Association for Computing Machinery2023

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

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

Panagiotis Sioulas

Analytical workloads are evolving as the number of users surges and applications that submit queries in batches become popular. However, traditional analytical databases that optimize-then-execute each query individually struggle to provide timely response ...

EPFL2023

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

HPCache: memory-efficient OLAP through proportional caching revisited

Anastasia Ailamaki, Hamish Mcniece Hill Nicholson, Periklis Chrysogelos

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

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

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

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Karl Aberer, Quoc Viet Hung Nguyen, Thanh Trung Huynh, Thành Tâm Nguyên, Chi Thang Duong

The heterogeneity of today's Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user's information needs by different data modalities, such as keywords, hashtags, user profiles, and other media. Rec ...

IEEE COMPUTER SOC2022

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

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

HPCache: Memory-Efficient OLAP Through Proportional Caching

Anastasia Ailamaki, Hamish Mcniece Hill Nicholson, Periklis Chrysogelos

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

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

Edit Based Grading of SQL Queries

Bikash Chandra

Grading student SQL queries manually is a tedious and error-prone process. Earlier work on testing correctness of student SQL queries, such as the XData system, can be used to test the correctness of a student query. However, in case a student query is fou ...

ASSOC COMPUTING MACHINERY2021

Holistic, Efficient, and Real-time Cleaning of Heterogeneous Data

Styliani Asimina Giannakopoulou

Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. Existing solutions that attempt to automate t ...

EPFL2021

Query-driven Repair of Functional Dependency Violations

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 ...

IEEE COMPUTER SOC2020

Efficient Bundled Spatial Range Queries

Anastasia Ailamaki, Darius Sidlauskas, Thomas Heinis, Eleni Tzirita Zacharatou, Farhan Tauheed

Efficiently querying multiple spatial data sets is a growing challenge for scientists. Astronomers query data sets that contain different types of stars (e.g., dwarfs, giants, stragglers) while neuroscientists query different data sets that model different ...

ASSOC COMPUTING MACHINERY2019

Taster: Self-Tuning, Elastic and Online Approximate Query Processing

Anastasia Ailamaki, Raja Appuswamy, Matthaios Alexandros Olma, Odysseas Papapetrou

Current Approximate Query Processing (AQP) engines are far from silver-bullet solutions, as they adopt several static design decisions that target specific workloads and deployment scenarios. Offline AQP engines target deployments with large storage budget ...

IEEE2019

Timely and cost-efficient data exploration through adaptive tuning

Matthaios Alexandros Olma

Modern applications accumulate data at an exponentially increasing rate and traditional database systems struggle to keep up. Decision support systems used in industry, rely heavily on data analysis, and require real-time responses irrespective of data siz ...

EPFL2019

RAQ: Relationship-Aware Graph Querying in Large Networks

Akhil Arora, Arnab Bhattacharya

The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given query grap ...

ASSOC COMPUTING MACHINERY2019