Manolis Karpathiotakis

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Manolis Karpathiotakis

Chat with Graph Search

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

Just-in-time Analytics Over Heterogeneous Data and Hardware

ReCache: Reactive Caching for Fast Analytics over Heterogeneous Data

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

Adaptive partitioning and indexing for in situ query processing

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

Adaptive Query Processing on RAW Data

Hardware-conscious Hash-Joins on GPUs

Towards the Identification of Disease Signatures

Cleaning Denial Constraint Violations through Relaxation

DBMS Data Loading: An Analysis on Modern Hardware

The Case For Heterogeneous HTAP