Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Summarizability in OLAP and Statistical Data Bases
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
WWW '03 Proceedings of the 12th international conference on World Wide Web
Structural Joins: A Primitive for Efficient XML Query Pattern Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Specifying OLAP Cubes on XML Data
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
The XML Stream Query Processor SPEX
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Building the Data Warehouse
Analytical processing of XML documents: opportunities and challenges
ACM SIGMOD Record
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Integrating Data Warehouses with Web Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Warehousing complex data from the web
International Journal of Web Engineering and Technology
A relational–XML data warehouse for data aggregation with SQL and XQuery
Software—Practice & Experience
Dwarfs in the rearview mirror: how big are they really?
Proceedings of the VLDB Endowment
A schemaguide for accelerating the view adaptation process
ER'10 Proceedings of the 29th international conference on Conceptual modeling
Classification of index partitions to boost XML query performance
ER'10 Proceedings of the 29th international conference on Conceptual modeling
Efficient topological OLAP on information networks
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Optimizing queries for web generated sensor data
ADC '11 Proceedings of the Twenty-Second Australasian Database Conference - Volume 115
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The usage of online analytical processing is now widespread, having emerged primarily in areas of business information systems using relational databases. However, the XML tree-based model is quite different from the relational model in mainstream OLAP. In terms of conceptual modelling, this provides new challenges, for example with operations such as cube and roll-up. However, our view is that it should be possible to exploit the more expressive XML model to deliver efficiencies not possible in more traditional OLAP systems. To be precise, XML data contains inherent structure and semantics not available in relational data. In this paper, we analyse the distinct characteristics and requirements of a more structured OLAP to make comprehensive comparisons between structural and flat dimensions. In order to build our conceptual model, we examined different XML cube construction models for commonly used XML recursive structures. This construction process requires only a single scan of input data and captures structural information on the fly, delivering both standard and structural OLAP support simultaneously.