Versatile XQuery Processing in MapReduce

Abstract

Abstract. The MapReduce (MR) framework has become a standard tool for performing large batch computations-usually of aggregative nature-in parallel over a cluster of commodity machines. A significant share of typical MR jobs involves standard database-style queries, where it becomes cumbersome to specify map and reduce functions from scratch. To overcome this burden, higher-level languages such as HiveQL, PigLatin, and JAQL have been proposed to allow the automatic generation of MR jobs from declarative queries. We identify two major problems of these existing solutions: (i) they introduce new query languages and implement systems from scratch for the sole purpose of expressing MR jobs; and (ii) despite solving some of the major limitations of SQL, they still lack the flexibility required by big data applications. We propose BrackitMR, an approach based on the XQuery language with extended JSON support. XQuery not only is an established query language, but also has a more expressive data model and more powerful language constructs, enabling a much greater degree of flexibility. From a system design perspective, we extend an existing single-node query processor, Brackit, adding MR as a distributed coordination layer. Such heavy reuse of the standard query processor not only provides performance, but also allows for a more elegant design which transparently integrates MR processing into a generic query engine.

Improving GANs for Long-Tailed Data Through Group Spectral Regularization

“…iNaturalist-2019 [49] is a real-world long-tailed dataset composed of 1010 different variants of species, some of which have fine-grained differences in their appearance. For such fine-grained datasets, ImageNet pre-trained discriminators [39,40] may not be useful, as augmentations used to train the model makes it invariant to fine-grained changes.…”

“…A large-scale conditional StyleGAN (i.e. StyleGAN-XL) on ImageNet was recently trained successfully by Sauer et al [40] using the ImageNet pre-trained model through the idea of a projection discriminator [39]. While the StyleGAN-XL uses additional pre-trained mod-* Equal Contribution.…”

Run-time performance optimization of a BigData query language

“…It was concluded that JAQL, with its expressive power and flexibility, is best suited for large-scale data processing in Big Data analytics. In [13] XQuery language is extended to support JSON data model and the XQuery processor is extended to support MapReduce execution.…”

Distributed Evaluation of XPath Axes Queries over Large XML Documents Stored in MapReduce Clusters

“…Unfortunately, all these approaches consider a very limited subset of XPath Axes including only root-to-leaf axis identifiers: child, and descendant or descendant-or-self. BrackitMR [12] is an XQuery engine translating queries into MR jobs. However, BrackitMR parallelizes only computation of FLOWR expression and does not solve parallel evaluation of XPath queries (single one, or as a part of XQuery expressions).…”