SLD Revolution: A Cheaper, Faster yet More Accurate Streaming Linked Data Framework

被引：2

作者：

Balduini, Marco ^{[1
]}

Della Valle, Emanuele ^{[1
]}

Tommasini, Riccardo ^{[1
]}

机构：

[1] Politecn Milan, DEIB, Milan, Italy

来源：

SEMANTIC WEB: ESWC 2017 SATELLITE EVENTS | 2017年 / 10577卷

关键词：

D O I：

10.1007/978-3-319-70407-4_37

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The RDF Stream Processing (RSP) is gaining momentum. The RDF stream data model is progressively adopted and many SPARQL extensions for continuous querying are converging to a unified RSP query language. However, the RSP community still has to investigate when transforming data streams in RDF streams pays off. In this paper, we report on several experiments on a revolutionized version of our Streaming Linked Data framework (namely, SLD Revolution). SLD Revolution (i) operates on time-stamped generic data items (events, tuples, trees and graphs), and (ii) it applies a lazy-transformation approach, i.e. it processes data according to their nature as long as possible. SLD Revolution results to be a cheaper (it uses less memory and has a smaller CPU load), faster (it reaches higher maximum input throughput), yet more accurate (it provides a smaller error rate in the results) solution than its ancestor SLD.

引用

页码：263 / 279

页数：17

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