APEX: an adaptive path index for XML data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Indexing and Querying XML Data for Regular Path Expressions
Proceedings of the 27th International Conference on Very Large Data Bases
ViST: a dynamic index method for querying XML data by tree structures
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Ctree: a compact tree for indexing XML data
Proceedings of the 6th annual ACM international workshop on Web information and data management
Index structures for matching XML twigs using relational query processors
Data & Knowledge Engineering
LCS-TRIM: dynamic programming meets XML indexing and querying
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Journal of Systems and Software
Querying Semistructured Data with Compression in Distributed Environments
ITNG '09 Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations
Object-Based Methodology for XML Data Partitioning (OXDP)
AINA '11 Proceedings of the 2011 IEEE International Conference on Advanced Information Networking and Applications
TwigTable: using semantics in XML twig pattern query processing
Journal on data semantics XV
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Content And Structure (CAS) index for XML data is an important index type that has not been widely researched, even though its role is important especially in multi domain applications. Most existing researches in XML Queries Optimization focus on structure index alone. Few have utilized the rich semantic of XML data to support CAS index and querying. In this paper, we propose two indexes namely Structural index and Content index, whose construction utilizes XML data semantics and schema. These indexes contribute to a better CAS queries performance. The experiments prove that our method improves the performance of CAS queries by reducing the cost of CPU time and the total number of scanned elements compared to a standard method.