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Related publications (4)

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

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

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

A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

Karl Aberer, Michael Ignaz Schumacher, Matteo Vasirani, Alevtina Dubovitskaya

In this paper, we address the problem of building an anonymized medical database from multiple sources. Our proposed solution defines how to achieve data integration in a heterogeneous network of many clinical institutions, while preserving data utility an ...

Springer International Publishing2015

Adaptive query execution for data management in the cloud

Anastasia Ailamaki, Debabrata Dash, Adrian Daniel Popescu

A major component of many cloud services is query processing on data stored in the underlying cloud cluster. The traditional techniques for query processing on a cluster are those offered by parallel DBMS. These techniques however, cannot guarantee high pe ...

ACM Press2010

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