Descendants, ancestors, children and parent: A set-based approach to efficiently address XPath primitives. Issue 3 (May 2016)

• Record Type: Journal Article
• Authors: Ferro, Nicola; Silvello, Gianmaria
• Is Part Of: Information processing & management. Volume 52:Issue 3(2016:May)
• Page Start: 399
• Page End: 429
• Publication Date: 2016-05
• Subjects: In-memory XPath processing -- NESTOR -- Set-based data models -- Data structures; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2015.11.001
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Set-based access to XML data. Improved XPath primitive operations. Up to eight orders of magnitude speed-up. Graphical abstract: Abstract: XML is a pervasive technology for representing and accessing semi-structured data. XPath is the standard language for navigational queries on XML documents and there is a growing demand for its efficient processing. In order to increase the efficiency in executing four navigational XML query primitives, namely descendants, ancestors, children and parent, we introduce a new paradigm where traditional approaches based on the efficient traversing of nodes and edges to reconstruct the requested subtrees are replaced by a brand new one based on basic set operations which allow us to directly return the desired subtree, avoiding to create it passing through nodes and edges. Our solution stems from the NEsted SeTs for Object hieRarchies ( NEASTOR ) formal model, which makes use of set-inclusion relations for representing and providing access to hierarchical data. We define in-memory efficient data structures to implement NESTOR, we develop algorithms to perform the descendants, ancestors, children and parent query primitives and we study their computational complexity. We conduct an extensive experimental evaluation by using several datasets: digital archives (EAD collections), INEX 2009 Wikipedia collection, and two widely-used synthetic datasets (XMark and XGen). We show that NESTOR-based data structures and query primitives consistently outperform state-of-the-art solutions for XPath processing at execution time and they are competitive in terms of both memory occupation and pre-processing time.