MonetDB/XQuery: a fast XQuery processor powered by a relational engine

Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met

Relational Approach to Logical Query Optimization of XPath

To be able to handle the ever growing volumes of XML documents, effective and efficient data management solutions are needed. Managing XML data in a relational DBMS has great potential. Recently, effective relational storage schemes and index structures have been proposed as well as special-purpose join operators to speed up querying of XML data using XPath/XQuery. In this paper, we address the topic of query plan construction and logical query optimization. The claim of this paper is that standard relational algebra extended with special-purpose join operators suffices for logical query optimization. We focus on the XPath accelerator storage scheme and associated staircase join operators, but the approach can be generalized easily

Solving the intractable problem: optimal performance for worst case scenarios in XML twig pattern matching

In the history of databases, eXtensible Markup Language (XML) has been thought of as the standard format to store and exchange semi-structured data. With the advent of IoT, XML technologies can play an important role in addressing the issue of processing a massive amount of data generated from heterogeneous devices. As the number and complexity of such datasets increases there is a need for algorithms which are able to index and retrieve XML data efficiently even for complex queries. In this context twig pattern matching , finding all occurrences of a twig pattern query (TPQ), is a core operation in XML query processing. Until now holistic joins have been considered the state-of-the-art TPQ processing algorithms, but they fail to guarantee an optimal evaluation except at the expense of excessive storage costs which limit their scope in large datasets. In this article, we introduce a new approach which significantly outperforms earlier methods in terms of both the size of the intermediate storage and query running time. The approach presented here uses Child Prime Labels (Alsubai & North, 2018) to improve the filtering phase of bottom-up twig matching algorithms and a novel algorithm which avoids the use of stacks, thus improving TPQs processing efficiency. Several experiments were conducted on common benchmarks such as DBLP, XMark and TreeBank datasets to study the performance of the new approach. Multiple analyses on a range of twig pattern queries are presented to demonstrate the statistical significance of the improvements

Exploring run-time reduction in programming codes via query optimization and caching

Object oriented programming languages raised the level of abstraction by supporting the explicit first class query constructs in the programming codes. These query constructs allow programmers to express operations on collections more abstractly than relying on their realization in loops or through provided libraries. Join optimization techniques from the field of database technology support efficient realizations of such language constructs. However, the problem associated with the existing techniques such as query optimization in Java Query Language (JQL) incurs run time overhead. Besides the programming languages supporting first-class query constructs, the usage of annotations has also increased in the software engineering community recently. Annotations are a common means of providing metadata information to the source code. The object oriented programming languages such as C# provides attributes constraints and Java has its own annotation constructs that allow the developers to include the metadata information in the program codes. This work introduces a series of query optimization approaches to reduce the run time of the programs involving explicit queries over collections. The proposed approaches rely on histograms to estimate the selectivity of the predicates and the joins in order to construct the query plans. The annotations in the source code are also utilized to gather the metadata required for the selectivity estimation of the numerical as well as the string valued predicates and joins in the queries. Several cache heuristics are proposed that effectively cache the results of repeated queries in the program codes. The cached query results are incrementally maintained up-to-date after the update operations to the collections --Abstract, page iv

The relational XQuery puzzle: a look-back on the pieces found so far

Given the tremendous versatility of relational database implementations toward awide range of database problems, it seems only natural to consider them as back-ends for XML data processing. Yet, the assumptions behind the language XQuery are considerably different to those in traditional RDBMSs. The underlying data model is a tree, data and results carry an intrinsic order, queries are described using explicit iteration and, after all, problems are everything else but regular. Solving the relational XQuery puzzle, therefore, has challenged anumber of research groups over the past years. The purpose of this article is to summarize and assess some of the results that have been obtained during this period to solve the puzzle. Our main focus is on the Pathfinder XQuery compiler, afull reference implementation of apurely relational XQuery processor. As we dissect its components, we relate them to other work in the field and also point to open problems and limitations in the context of relational XQuery processin

Splicing Modulation as a Promising Therapeutic Strategy for Lysosomal Storage Disorders: The Mucopolysaccharidoses Example

This article belongs to the Special Issue New Insights into Lysosomal Storage Disorders and Other Rare Genetic Diseases.Over recent decades, the many functions of RNA have become more evident. This molecule has been recognized not only as a carrier of genetic information, but also as a specific and essential regulator of gene expression. Different RNA species have been identified and novel and exciting roles have been unveiled. Quite remarkably, this explosion of novel RNA classes has increased the possibility for new therapeutic strategies that tap into RNA biology. Most of these drugs use nucleic acid analogues and take advantage of complementary base pairing to either mimic or antagonize the function of RNAs. Among the most successful RNA-based drugs are those that act at the pre-mRNA level to modulate or correct aberrant splicing patterns, which are caused by specific pathogenic variants. This approach is particularly tempting for monogenic disorders with associated splicing defects, especially when they are highly frequent among affected patients worldwide or within a specific population. With more than 600 mutations that cause disease affecting the pre-mRNA splicing process, we consider lysosomal storage diseases (LSDs) to be perfect candidates for this type of approach. Here, we introduce the overall rationale and general mechanisms of splicing modulation approaches and highlight the currently marketed formulations, which have been developed for non-lysosomal genetic disorders. We also extensively reviewed the existing preclinical studies on the potential of this sort of therapeutic strategy to recover aberrant splicing and increase enzyme activity in our diseases of interest: the LSDs. Special attention was paid to a particular subgroup of LSDs: the mucopolysaccharidoses (MPSs). By doing this, we hoped to unveil the unique therapeutic potential of the use of this sort of approach for LSDs as a whole.This research was partially funded by the FCT (FCT/PTDC/BBB-BMD/6301/2014 and EXPL/BTM-SAL/0659/2021), the Portuguese Society for Metabolic Disorders (Sociedade Portuguesa de Doenças Metabólicas, SPDM—Bolsa SPDM de apoio à investigação Dr Aguinaldo Cabral 2018 (2019DGH1629/SPDM2018I&D) and 2019 (2020DGH1834)), the Sanfilippo Children’s Foundation (SCF Incubator Grant 2019: 2019DGH1656/SCF2019I&D) and the MPS Society (2019DGH1642).info:eu-repo/semantics/publishedVersio

WAYLA - Generating Images from Eye Movements

We present a method for reconstructing images viewed by observers based only on their eye movements. By exploring the relationships between gaze patterns and image stimuli, the "What Are You Looking At?" (WAYLA) system learns to synthesize photo-realistic images that are similar to the original pictures being viewed. The WAYLA approach is based on the Conditional Generative Adversarial Network (Conditional GAN) image-to-image translation technique of Isola et al. We consider two specific applications - the first, of reconstructing newspaper images from gaze heat maps, and the second, of detailed reconstruction of images containing only text. The newspaper image reconstruction process is divided into two image-to-image translation operations, the first mapping gaze heat maps into image segmentations, and the second mapping the generated segmentation into a newspaper image. We validate the performance of our approach using various evaluation metrics, along with human visual inspection. All results confirm the ability of our network to perform image generation tasks using eye tracking data

Splicing therapeutics in SMN2 and APOB

Splicing therapeutics are defined as the deliberate modification of RNA splicing to achieve therapeutic goals. Various techniques for splicing therapeutics have been described, and most of these involve the use of antisense oligonucleotide-based compounds that target key elements in the pre-mRNA to control splicing in the nucleus. In this review, recent developments in splicing therapeutics for the treatment of two specific diseases are described: correcting the alternative splicing of survival of motor neuron (SMN)2 pre-mRNA to compensate for the defective SMN1 gene in spinal muscular atrophy, and re-engineering the splicing of apolipoprotein B pre-mRNA to lower circulating cholesterol levels