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Anastasia Ailamaki

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Publications associées (177)

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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

NoDB: Efficient Query Execution on Raw Data Files

Anastasia Ailamaki, Ioannis Alagiannis, Renata Borovica-Gajic, Miguel Sérgio De Oliveira Branco

As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare and to load the data into the database before executing the desired queries. Many applications already av ...

Assoc Computing Machinery2015

Task Scheduling for Highly Concurrent Analytical and Transactional Main-Memory Workloads

Anastasia Ailamaki, Iraklis Psaroudakis

Task scheduling typically employs a worker thread per hardware context to process a dynamically changing set of tasks. It is an appealing solution to exploit modern multi-core processors, as it eases parallelization and avoids unnecessary context switches ...

2013

BLOCK: Efficient Execution of Spatial Range Queries in Main-Memory

Anastasia Ailamaki, Thomas Heinis, Farhan Tauheed, Matthaios Alexandros Olma

The execution of spatial range queries is at the core of many applications, particularly in the simulation sciences but also in many other domains. Although main memory in desktop and supercomputers alike has grown considerably in recent years, most spatia ...

ACM New York, NY, USA2017

How to Stop Under-Utilization and Love Multicores

Anastasia Ailamaki, Danica Porobic, Pinar Tözün, Iraklis Psaroudakis, Erietta Liarou

Designing scalable transaction processing systems on modern hardware has been a challenge for almost a decade. Hardware trends oblige software to overcome three major challenges against systems scalability: (1) Exploiting the abundant thread-level parallel ...

2014

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

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

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

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

E-Scan: Consuming Contextual Data with Model Plugins

Anastasia Ailamaki, Viktor Sanca

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 ...

2023

DiNoDB: Efficient Large-Scale Raw Data Analytics

Anastasia Ailamaki, Ioannis Alagiannis, Erietta Liarou

Modern big data workflows, found in e.g., machine learning use cases, often involve iterations of cycles of batch analytics and interactive analytics on temporary data. Whereas batch analytics solutions for large volumes of raw data are well established (e ...

2014

Improving K-means Clustering Using Speculation

Stefan Igescu, Anastasia Ailamaki, Viktor Sanca, Eleni Zapridou

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 ...

2023

CleanM: An Optimizable Query Language for Unified Scale-Out Data Cleaning

Benjamin Cyrille Damien Gaidioz, Anastasia Ailamaki, Manolis Karpathiotakis, 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. At the same time, the existing tools that att ...

Assoc Computing Machinery2017

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

Using Cloud Functions as Accelerator for Elastic Data Analytics

Anastasia Ailamaki, Haoqiong Bian

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

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

Spatio-Temporal Memory Streaming

Anastasia Ailamaki, Babak Falsafi

Recent research advocates memory streaming techniques to alleviate the performance bottleneck caused by the high latencies of off-chip memory accesses. Temporal memory streaming replays previously observed miss sequences to eliminate long chains of depende ...

2009

Practical Off-chip Meta-data for Temporal Memory Streaming

Anastasia Ailamaki, Babak Falsafi, Michael Ferdman

Prior research demonstrates that temporal memory streaming and related address-correlating prefetchers improve performance of commercial server workloads though increased memory level parallelism. Unfortunately, these prefetchers require large on-chip meta ...

2009

Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

Anastasia Ailamaki, Periklis Chrysogelos, Panagiotis Sioulas

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

Optimal service pricing for a cloud cache

Anastasia Ailamaki, Grégory François

Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management nece ...

Institute of Electrical and Electronics Engineers2011

Optimizing Goodput through Sharing for Batch Analytics with Deadlines

Anastasia Ailamaki, Panagiotis Sioulas, Ioannis Mytilinis, Srinivas Karthik Venkatesh

Modern big data systems process not only a huge volume of data, but also numerous concurrent queries. These queries usually span over distributed data and need to be processed within a strict deadline (e.g.: report generation, SLA, etc.). While users expec ...

2024

Enabling Scientific Discovery Via Innovative Spatial Data Management

Anastasia Ailamaki, Thomas Heinis, Farhan Tauheed, Mirjana Pavlovic

Researchers in several scientific disciplines are struggling to cope with the masses of data resulting from either increasingly precise instruments or from simulation runs on ever more powerful supercomputers. Efficiently managing this deluge of data has b ...

2013