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PatiÑo Martinez, MartaAuthor

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June 9, 2019
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CloudMdsQL: querying heterogeneous cloud data stores with a common language

Publicated to: DISTRIBUTED AND PARALLEL DATABASES. 34 (4): 463-503 - 2016-12-01 34(4), DOI: 10.1007/s10619-015-7185-y

Authors:

Kolev, B; Valduriez, P; Bondiombouy, C; Jiménez-Peris, R; Pau, R; Pereira, J
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Affiliations

INESC, Braga, Portugal - Author
Inria, Zenith Team, Montpellier, France - Author
LeanXcale, Madrid, Spain - Author
Spars Technol, Barcelona, Spain - Author
Univ Politecn Madrid, Madrid, Spain - Author
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Abstract

The blooming of different cloud data management infrastructures, specialized for different kinds of data and tasks, has led to a wide diversification of DBMS interfaces and the loss of a common programming paradigm. In this paper, we present the design of a cloud multidatastore query language (CloudMdsQL), and its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores (relational and NoSQL) within a single query that may contain embedded invocations to each data store's native query interface. The query engine has a fully distributed architecture, which provides important opportunities for optimization. The major innovation is that a CloudMdsQL query can exploit the full power of local data stores, by simply allowing some local data store native queries (e.g. a breadth-first search query against a graph database) to be called as functions, and at the same time be optimized, e.g. by pushing down select predicates, using bind join, performing join ordering, or planning intermediate data shipping. Our experimental validation, with three data stores (graph, document and relational) and representative queries, shows that CloudMdsQL satisfies the five important requirements for a cloud multidatastore query language.
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Keywords

CloudHeterogeneous databasesMultistore query languageSql and nosql integrationXml

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal DISTRIBUTED AND PARALLEL DATABASES due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2016, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Hardware and Architecture. Notably, the journal is positioned en el Cuartil Q3 for the agency WoS (JCR) in the category Computer Science, Information Systems.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.12. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.95 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-24, the following number of citations:

  • WoS: 46
  • Scopus: 70
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 50.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 50 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 3.

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/40947/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 399
  • Downloads: 486
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: France; Portugal.

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Awards linked to the item

Work partially funded by the European Commission through the CoherentPaaS FP7 Project funded under contract FP7-611068 [5]. We want to thank Norbert Martinez-Bazan for his contributions on the first version of the CloudMdsQL query engine. We also thank the editor and reviewers for their careful readings and useful suggestions that helped improving our design and the paper. The work of Prof. Ricardo Jimenez was also partially funded by the Regional Government of Madrid (CAM) under Project Cloud4BigData (S2013/ICE-2894) cofunded by ESF & ERDF, and the Spanish Research Council (MICCIN) under Project BigDataPaaS (TIN2013-46883).
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