Partitioning methods for multi-version XML data warehouses

Rusu, Laura-Cristina; Rahayu, Wenny; Taniar, David

doi:10.1007/s10619-009-7034-y

Cited by 13 publications

(2 citation statements)

References 18 publications

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“…This definition closely resembles the way Rusu et al [119] extend structure-based fragmentation approaches for XML data by adding explicit support for storing multiple versions of the same document. As the authors point out, this is particularly useful in a data warehouse, where historic versions of data need to be preserved.…”

Section: Structure-based Fragmentationmentioning

confidence: 93%

Distributed XML Query Processing

Özsu

Kling

2010

Database and XML Technologies

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While centralized query processing over collections of XML data stored at a single site is a well understood problem, centralized query evaluation techniques are inherently limited in their scalability when presented with large collections (or a single, large document) and heavy query workloads. In the context of relational query processing, similar scalability challenges have been overcome by partitioning data collections, distributing them across the sites of a distributed system, and then evaluating queries in a distributed fashion, usually in a way that ensures locality between (sub-)queries and their relevant data. This thesis presents a suite of query evaluation techniques for XML data that follow a similar approach to address the scalability problems encountered by XML query evaluation.Due to the significant differences in data and query models between relational and XML query processing, it is not possible to directly apply distributed query evaluation techniques designed for relational data to the XML scenario. Instead, new distributed query evaluation techniques need to be developed. Thus, in this thesis, an end-to-end solution to the scalability problems encountered by XML query processing is proposed.Based on a data partitioning model that supports both horizontal and vertical fragmentation steps (or any combination of the two), XML collections are fragmented and distributed across the sites of a distributed system. Then, a suite of distributed query evaluation strategies is proposed. These query evaluation techniques ensure locality between each fragment of the collection and the parts of the query corresponding to the data in this fragment. Special attention is paid to scalability and query performance, which is achieved by ensuring a high degree of parallelism during distributed query evaluation and by avoiding access to irrelevant portions of the data.For maximum flexibility, the suite of distributed query evaluation techniques proposed in this thesis provides several alternative approaches for evaluating a given query over a given distributed collection. Thus, to achieve the best performance, it is necessary to predict and compare the expected performance of each of these alternatives. In this work, this is accomplished through a query optimization technique based on a distribution-aware cost model. The same cost model is also used to fine-tune the way a collection is fragmented to the demands of the query workload evaluated over this collection. v To evaluate the performance impact of the distributed query evaluation techniques proposed in this thesis, the techniques were implemented within a production-quality XML database system. Based on this implementation, a thorough experimental evaluation was performed. The results of this evaluation confirm that the distributed query evaluation techniques introduced here lead to significant improvements in query performance and scalability both when compared to centralized techniques and when compared to existing distributed query evaluation tec...

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Section: Structure-based Fragmentationmentioning

confidence: 93%

Distributed XML Query Processing

Özsu

Kling

2010

Database and XML Technologies

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“…The underlying differences between the relational and XML data models present many challenges. It is difficult to provide a logical and direct mapping from one data model to the other due to the impedance mismatch between them (Rusu et al, 2009). In our previous works (Gui and Roantree, 2012) (Gui and Roantree, 2013), we have proposed a pipeline design based on an OLAP data cube construction framework designed for real time web generated sensor data.…”

Section: Introductionmentioning

confidence: 99%

Data Cube Computational Model with Hadoop MapReduce

Wang

Gui

Roantree

et al. 2014

Proceedings of the 10th International Conference on Web Information Systems and Technologies

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Abstract:XML has become a widely used and well structured data format for digital document handling and message transmission. To find useful knowledge in XML data, data warehouse and OLAP applications aimed at providing supports for decision making should be developed. Apache Hadoop is an open source cloud computing framework that provides a distributed file system for large scale data processing. In this paper, we discuss an XML data cube model which offers us the complete views to observe XML data, and present a basic algorithm to implement its building process on Hadoop. To improve the efficiency, an optimized algorithm more suitable for this kind of XML data is also proposed. The experimental results given in the paper prove the effectiveness of our optimization strategies.

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Hybrid Fragmentation of XML Data Warehouse Using K-Means Algorithm

Kechar

Bahloul

2014

Advances in Databases and Information Systems

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Partitioning methods for multi-version XML data warehouses

Cited by 13 publications

References 18 publications

Distributed XML Query Processing

Distributed XML Query Processing

Data Cube Computational Model with Hadoop MapReduce

Hybrid Fragmentation of XML Data Warehouse Using K-Means Algorithm

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