An overview of XML warehouse design approaches and techniques

Ouaret, Zoubir; Chalal, Rachid; Boussaïd, Omar

doi:10.1504/ijicot.2013.059729

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…To the best of our knowledge, only [19] presents a benchmark for comparing NoSQL DW proposals; specifically, this benchmark is applied to MongoDB and Hbase. Some works also study the usage of XML DBMSs for warehousing XML data [20]. Although XML DWs represent a first effort towards native storage of semi-structured data, their querying performances do not scale well with size, and compression techniques must be adopted [21].…”

Section: Related Workmentioning

confidence: 99%

Data variety, come as you are in multi-model data warehouses

Bimonte

Gallinucci

Marcel

et al. 2022

Information Systems

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Section: Related Workmentioning

confidence: 99%

Data variety, come as you are in multi-model data warehouses

Bimonte

Gallinucci

Marcel

et al. 2022

Information Systems

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“…Sellami, Nabli and Gargouri [36] propose to use transformation rules for DW implementation in graph-based DBMSs for better handling social network data. Some works also use XML DBMSs for warehousing XML data Ouaret, Chalal and Boussaid, [32]. While this is a first effort towards native storage of semi-structured data, the querying performances do not scale well with size, and compression techniques must be adopted Boukraâ, Bouchoukh and Boussaïd, [5].…”

Section: Nosql Olapmentioning

confidence: 99%

Logical design of multi-model data warehouses

et al. 2022

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Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can store data according to the multidimensional model and, at the same time, let each of its elements be represented through the most appropriate model. An open challenge in this context is the lack of methods for logical design. Indeed, in a multi-model context, several alternatives emerge for the logical representation of dimensions and facts. The goal of this paper is to devise a set of guidelines for the logical design of multi-model data warehouses so that the designer can achieve the best trade-off between features such as querying, storage, and ETL. To this end, for each model considered (relational, document-based, and graph-based) and for each type of multidimensional element (e.g., non-strict hierarchy) we propose some solutions and carry out a set of intra-model and inter-model comparisons. The resulting guidelines are then tested on a case study that shows all types of multidimensional elements.

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“…The multidimensional schema is decided at design time and forced onto data at the time of writing them in the data warehouse, which entails better performances and simpler query formulation with no need for query rewriting. Schema-on-write approaches are based on the literature on (i) multidimensional design from NoSQL data [13,23] and (ii) NoSQL data warehouses, that aim at storing warehoused data in document/columnar/graph form by following design guidelines.…”

Section: Approximate Olapmentioning

confidence: 99%

“…Rather than being devised at design time, a multidimensional schema for accessing data is decided at querying time; while this requires OLAP queries to be rewritten over data sources on-the-fly and thus might give performance problems, it entails higher querying flexibility, simpler ETL, and lower effort for evolution. Schema-on-read approaches to enable OLAP on NoSQL data ground their roots into techniques for (i) schema discovery from XML/JSON documents, which deal with heterogeneity, quality, versioning, similarity, and comprehensiveness to produce unified schemas, schema matches, and skeleton schemas [3,22,24]; (ii) schema matching for XML/JSON documents using clustering or machine learning, in some cases considering a context [14,5]; (iii) multidimensional design from XML/JSON/columnar data, possibly by detecting and chasing functional dependencies [13,23].…”

Section: Introductionmentioning

confidence: 99%

OLAP and NoSQL: Happily Ever After

Rizzi

2022

Advances in Databases and Information Systems

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NoSQL databases are preferred to relational ones for storing heterogeneous data with variable schema and structure. However, their schemaless nature adds complexity to analytical applications, in which a single OLAP analysis often involves large sets of data with different schemas. In this tutorial we describe the main approaches to enable OLAP on NoSQL data. We start from schema-on-read approaches, where data are left unchanged in their structure until they are accessed by the user, so they are put into multidimensional form at query time. Specifically, we show how this enables a form of approximated OLAP that embraces the inherent variety of schemaless data. Then we move to schema-on-write approaches, where a fixed multidimensional structure is forced onto data, which are loaded into a data warehouse to be then queried. In particular, we introduce multi-model data warehouses as a way to store data in multidimensional form and, at the same time, let each piece of data be natively represented through the most appropriate NoSQL model.

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An overview of XML warehouse design approaches and techniques

Cited by 6 publications

References 55 publications

Data variety, come as you are in multi-model data warehouses

Data variety, come as you are in multi-model data warehouses

Logical design of multi-model data warehouses

OLAP and NoSQL: Happily Ever After

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