Publications de Manolis Karpathiotakis

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

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

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

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

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

No data left behind: real-time insights from a complex data ecosystem

Anastasia Ailamaki, Manolis Karpathiotakis

The typical enterprise data architecture consists of several actively updated data sources (e.g., NoSQL systems, data warehouses), and a central data lake such as HDFS, in which all the data is periodically loaded through ETL processes. To simplify query p ...

2017

Slalom: Coasting Through Raw Data via Adaptive Partitioning and Indexing

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. Hence, recent in-situ query proce ...

VLDB Endowment2017

The Case For Heterogeneous HTAP

Anastasia Ailamaki, Danica Porobic, Manolis Karpathiotakis, Raja Appuswamy

2017

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

Anastasia Ailamaki, Benjamin Cyrille Damien Gaidioz, 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

Just-in-time Analytics Over Heterogeneous Data and Hardware

Manolis Karpathiotakis

Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of datasets to gain insights. At the same time, data variety increases continuously across multiple axes. First, data comes in mu ...

EPFL2017

ReCache: Reactive Caching for Fast Analytics over Heterogeneous Data

Anastasia Ailamaki, Manolis Karpathiotakis, Tahir Azim

As data continues to be generated at exponentially growing rates in heterogeneous formats, fast analytics to extract meaningful information is becoming increasingly important. Systems widely use in-memory caching as one of their primary techniques to speed ...

2017

Fast Queries Over Heterogeneous Data Through Engine Customization

Anastasia Ailamaki, Ioannis Alagiannis, Manolis Karpathiotakis

Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of heterogeneous datasets to gain insights. The different data models and formats pose a significant challenge on performing anal ...

Assoc Computing Machinery2016

DBMS Data Loading: An Analysis on Modern Hardware

Anastasia Ailamaki, Ioannis Alagiannis, Manolis Karpathiotakis, Adam Dziedzic, Raja Appuswamy

2016

Towards the Identification of Disease Signatures

Anastasia Ailamaki, Thomas Heinis, Manolis Karpathiotakis

The identification of biological signatures of diseases will enable the development of new biologically grounded classifications of brain diseases, leading to a new systematic understanding of their causes, and new diagnostic tools. In this paper we presen ...

Springer International Publishing2015

Just-In-Time Data Virtualization: Lightweight Data Management with ViDa

Anastasia Ailamaki, Ioannis Alagiannis, Thomas Heinis, Miguel Sérgio De Oliveira Branco, Manolis Karpathiotakis

As the size of data and its heterogeneity increase, traditional database system architecture becomes an obstacle to data analysis. Integrating and ingesting (loading) data into databases is quickly becoming a bottleneck in face of massive data as well as i ...

2015

Adaptive Query Processing on RAW Data

Anastasia Ailamaki, Ioannis Alagiannis, Miguel Sérgio De Oliveira Branco, Manolis Karpathiotakis

Database systems deliver impressive performance for large classes of workloads as the result of decades of research into optimizing database engines. High performance, however, is achieved at the cost of versatility. In particular, database systems only op ...

2014