A Query Processing Architecture for an XML Data Warehouse

In order to solve the problem of storage and query for massive XML data, a method of efficient storage and parallel query for a massive volume of XML data with Hadoop is proposed. This method can store massive XML data in Hadoop and the massive XML data is divided into many XML data blocks and loaded on HDFS. The parallel query method of massive XML data is proposed, which uses parallel XPath queries based on multiple predicate selection, and the results of parallel query can satisfy the requirement of query given by the user. In this chapter, the map logic algorithm and the reduce logic algorithm based on parallel XPath queries based using MapReduce programming model are proposed, and the parallel query processing of massive XML data is realized. In addition, the method of MapReduce query optimization based on multiple predicate selection is proposed to reduce the data transfer volume of the system and improve the performance of the system. Finally, the effectiveness of the proposed method is verified by experiment.

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XML Data Integration

Advanced Applications and Structures in XML Processing ◽

10.4018/978-1-61520-727-5.ch015 ◽

2010 ◽

pp. 333-360 ◽

Cited By ~ 1

Author(s):

Yan Qi ◽

Huiping Cao ◽

K. Selçuk Candan ◽

Maria Luisa Sapino

Keyword(s):

Data Integration ◽

Query Processing ◽

Multiple Sources ◽

Data Types ◽

Xml Data ◽

Data Schema ◽

Integration Data ◽

Feedback Techniques ◽

Different Sources ◽

Xml Data Integration

In XML Data Integration, data/metadata merging and query processing are indispensable. Specifically, merging integrates multiple disparate (heterogeneous and autonomous) input data sources together for further usage, while query processing is one main reason why the data need to be integrated in the first place. Besides, when supported with appropriate user feedback techniques, queries can also provide contexts in which conflicts among the input sources can be interpreted and resolved. The flexibility of XML structure provides opportunities for alleviating some of the difficulties that other less flexible data types face in the presence of uncertainty; yet, this flexibility also introduces new challenges in merging multiple sources and query processing over integrated data. In this chapter, the authors discuss two alternative ways XML data/schema can be integrated: conflict-eliminating (where the result is cleaned from any conflicts that the different sources might have with each other) and conflict-preserving (where the resulting XML data or XML schema captures the alternative interpretations of the data). They also present techniques for query processing over integrated, possibly imprecise, XML data, and cover strategies that can be used for resolving underlying conflicts.

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An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses

International Journal of Data Warehousing and Mining ◽

10.4018/jdwm.2013040105 ◽

2013 ◽

Vol 9 (2) ◽

pp. 89-109 ◽

Cited By ~ 1

Author(s):

Marie-Aude Aufaure ◽

Alfredo Cuzzocrea ◽

Cécile Favre ◽

Patrick Marcel ◽

Rokia Missaoui

Keyword(s):

Data Mining ◽

Knowledge Management ◽

Information Retrieval ◽

Query Processing ◽

Data Warehouse ◽

Multidimensional Data ◽

Data Warehouses ◽

Cooperative Query Answering ◽

User Centric ◽

Use Of Knowledge

In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).

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