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Anastasia Ailamaki

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Publications associées (179)

Micro-architectural Analysis of OLAP: Limitations and Opportunities

Anastasia Ailamaki, Utku Sirin

Understanding micro-architectural behavior is important for efficiently using hardware resources. Recent work has shown that in-memory online transaction processing (OLTP) systems severely underutilize their core micro-architecture resources [29]. Whereas, ...

ASSOC COMPUTING MACHINERY2020

JIT works: decide when all data is known (VLDB Women in Database Research Award talk)

Anastasia Ailamaki

When the time comes to make a critical decision, it is of paramount importance to prepare enough so that all the information necessary is available at decision time. Under-preparation leads to uninformed decisions; over-preparation, however, may lead to co ...

ASSOC COMPUTING MACHINERY2020

Adaptive partitioning and indexing for in situ query processing

Anastasia Ailamaki, Manoussos Gavriil Athanassoulis, Ioannis Alagiannis, Matthaios Alexandros Olma, Manolis Karpathiotakis

The constant flux of data and queries alike has been pushing the boundaries of data analysis systems. The increasing size of raw data files has made data loading an expensive operation that delays the data-to-insight time. To alleviate the loading cost, in ...

SPRINGER2020

The Next 5 Years: What Opportunities Should the Database Community Seize to Maximize its Impact?

Anastasia Ailamaki

ASSOC COMPUTING MACHINERY2020

Adaptive HTAP through Elastic Resource Scheduling

Anastasia Ailamaki, Angelos Christos Anadiotis, Periklis Chrysogelos, Syed Mohammad Aunn Raza

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

Adaptive HTAP through Elastic Resource Scheduling

Anastasia Ailamaki, Angelos Christos Anadiotis, Periklis Chrysogelos, Syed Mohammad Aunn Raza

ASSOC COMPUTING MACHINERY2020

GPU-accelerated data management under the test of time

Anastasia Ailamaki, Panagiotis Sioulas, Angelos Christos Anadiotis, Periklis Chrysogelos, Syed Mohammad Aunn Raza

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

Task scheduling for highly concurrent analytical and transaction workloads

Anastasia Ailamaki, Iraklis Psaroudakis

Systems and method for a task scheduler with dynamic adjustment of concurrency levels and task granularity are disclosed for improved execution of highly concurrent analytical and transactional systems. The task scheduler can avoid both over commitment and ...

2020

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

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, Matthaios Alexandros Olma, Raja Appuswamy, 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

Dictionary Compression in Point Cloud Data Management

Anastasia Ailamaki, Mirjana Pavlovic

Nowadays, massive amounts of point cloud data can be collected thanks to advances in data acquisition and processing technologies such as dense image matching and airborne LiDAR scanning. With the increase in volume and precision, point cloud data offers a ...

ASSOC COMPUTING MACHINERY2019

Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

Anastasia Ailamaki, Panagiotis Sioulas, Periklis Chrysogelos

In order to improve their power efficiency and computational capacity, modern servers are adopting hardware accelerators, especially GPUs. Modern analytical DMBS engines have been highly optimized for multi-core multi-CPU query execution, but lack the nece ...

2019

Hardware-conscious Hash-Joins on GPUs

Anastasia Ailamaki, Manolis Karpathiotakis, Raja Appuswamy, Panagiotis Sioulas, Periklis Chrysogelos

Traditionally, analytical database engines have used task parallelism provided by modern multisocket multicore CPUs for scaling query execution. Over the past few years, GPUs have started gaining traction as accelerators for processing analytical queries d ...

IEEE2019

Bridging the Latency Gap between NVM and DRAM for Latency-bound Operations

Anastasia Ailamaki, Georgios Psaropoulos

Non-Volatile Memory (NVM) technologies exhibit 4× the read access latency of conventional DRAM. When the working set does not fit in the processor cache, this latency gap between DRAM and NVM leads to more than 2× runtime increase for queries dominated by ...

ACM2019

The Five-Minute Rule 30 Years Later and Its Impact on the Storage Hierarchy

Anastasia Ailamaki, Renata Borovica-Gajic, Raja Appuswamy

ASSOC COMPUTING MACHINERY2019

HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines

Anastasia Ailamaki, Manolis Karpathiotakis, Raja Appuswamy, Periklis Chrysogelos

Modern server hardware is increasingly heterogeneous as hardware accelerators, such as GPUs, are used together with multicore CPUs to meet the computational demands of modern data analytics workloads. Unfortunately, query parallelization techniques used by ...

2019

Interleaving with coroutines: a systematic and practical approach to hide memory latency in index joins

Anastasia Ailamaki, Georgios Psaropoulos

Index joins present a case of pointer-chasing code that causes data cache misses. In principle, we can hide these cache misses by overlapping them with computation: The lookups involved in an index join are parallel tasks whose execution can be interleaved ...

2019

Adaptive Cache Mode Selection for Queries over Raw Data

Anastasia Ailamaki, Azqa Nadeem, Tahir Azim

Caching the results of intermediate query results for future re-use is a common technique for improving the performance of analytics over raw data sources. An important design choice in this regard is whether to lazily cache only the offsets of satisfying ...

2018

QUASII: QUery-Aware Spatial Incremental Index

Anastasia Ailamaki, Thomas Heinis, Mirjana Pavlovic, Darius Sidlauskas

With large-scale simulations of increasingly detailed models and improvement of data acquisition technologies, massive amounts of data are easily and quickly created and collected. Traditional systems require indexes to be built before analytic queries can ...

2018

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