Indexing and Querying XML Data for Regular Path Expressions

By Quanzhong Li, Bongki Moon. Very Large Data Bases Conference, 2001

888 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/03f2c2b0aab2af0f12c084c8087ed9b7a2b4b09b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Accelerating XPath location steps (2002, methodology): “As others have noted [7, 14, 18], one can use pre(v) and post(v) to efficiently characterize the descendants v′ of v.”
Covering indexes for branching path queries (2002, background): “There has been a considerable amount of work on indexing for semi-structured/XML data [4, 6, 7, 9, 11, 12, 17].”
Efficiently mining frequent trees in a forest: algorithms and applications (2005, methodology): “There has been active work in indexing and querying XMLdocuments [2], [14], [22], [42], which aremainly tree (or graph) structured.”
Efficiently mining frequent trees in a forest (2002, methodology): “To efficiently answer ancestordescendant queries various node numbering schemes similar to ours have been proposed [15, 23, 1].”
Holistic Twig Joins on Indexed XML Documents (2003, methodology): “…the pattern into a set of binary structural relationships (parent-child or ancestor-descendant) between pairs of nodes, then match each of the binary structural relationships against the XML database and (cid:12)nally stitch together the results from those basic matches [20, 9, 14, 4, 8, 19].”
APEX: an adaptive path index for XML data (2002, background): “Examples of such index structures include DataGuides [13], T-indexes [lS], the Index Fabric [S], and extensions of inverted indexes [16, 22].”
MonetDB/XQuery: a fast XQuery processor powered by a relational engine (2006, background): “The database research and industry communities have developed a variety of possible alternatives, among these [30, 25, 41, 38].”
Exploiting local similarity for indexing paths in graph-structured data (2002, background): “In [17], a numbering scheme for XML data trees is proposed that enables ancestor queries to be answered in constant time.”
Efficient keyword search for smallest LCAs in XML databases (2005, methodology): “A popular numbering scheme is to use a pair of numbers consist ing of preorder and postorder numbers [2, 17].”
Skew handling techniques in sort-merge join (2002, unknown): “Inequality predicates, such as found in band join(to be discussed in detail later), intemporal join[19] and temporal Cartesian product [22], and inmulti-predicate merge join[21] andEEJoin andEA-Join [14] proposed for queries on XML data, exacerbate the problem.”
Efficient Structural Joins on Indexed XML Documents (2002, methodology): “Path expression queries pose a much harder problem, requiring the computation of structural joins [1] [24].”
eXist: An Open Source Native XML Database (2002, methodology): “Backend implementation details are completely hidden from eXist’s core (see figure 1).”
From Region Encoding To Extended Dewey: On Efficient Processing of XML Twig Pattern Matching (2005, methodology): “…of designing a proper labeling scheme, various methods have been proposed that are based on tree - traversal order(e.g. extended preorder [12]), textual positions of the start and end tags (e.g. region encoding [3]), path expressions(e.g. Dewey ID [22], PID [2]) or prime numbers (e.g.…”