%0 Conference Proceedings
%T Portable Intermediate Representation for Efficient Big Data Analytics
%+ Department of Informatics [Athens]
%A Tzouros, Giannis
%A Tsenos, Michail
%A Kalogeraki, Vana
%Z Part 2: Fault Tolerance and Big Data
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 21th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS)
%C Valletta, Malta
%Y Miguel Matos
%Y Fabíola Greve
%I Springer International Publishing
%3 Distributed Applications and Interoperable Systems
%V LNCS-12718
%P 74-80
%8 2021-06-14
%D 2021
%R 10.1007/978-3-030-78198-9_5
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X To process big data, applications have been utilizing data processing libraries over the last years, which are however not optimized to work together for efficient processing. Intermediate Representations (IR) have been introduced for unifying essential functions into an abstract interface that supports cross-optimization between applications. Still, the efficiency of an IR depends on the architecture and the tools required for compilation and execution. In this paper, we present a first glance at a framework that provides an IR by creating containers with executable code from structures of data analytics functions, described in an input grammar. These containers process data in query lists and they can be executed either standalone or integrated with other big data analytics applications without the need to compile the entire framework.
%G English
%Z TC 6
%Z WG 6.1
%2 https://inria.hal.science/hal-03384860/document
%2 https://inria.hal.science/hal-03384860/file/509420_1_En_5_Chapter.pdf
%L hal-03384860
%U https://inria.hal.science/hal-03384860
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-1
%~ IFIP-DAIS
%~ IFIP-LNCS-12718