Holistic evaluation in multi-model databases benchmarking

Zhang, Chao; Lu, Jiaheng

doi:10.1007/s10619-019-07279-6

Cited by 21 publications

(11 citation statements)

References 25 publications

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“…However, since RDBMS stores many relations that can be related to graphs in the real application, combination of the two, could produce better results. AgensGraph [94] is a multi-model graph database based on PostgreSQL RDBMS. It enables developers to integrate the legacy relational data model and the flexible graph data model in one database.…”

Section: Big Data Models-interdependence and Complementary 511 Data Storage Modelmentioning

confidence: 99%

Growth of relational model: Interdependence and complementary to big data

Shetty

Rao

Prabhu

2021

IJECE

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A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system.

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Section: Big Data Models-interdependence and Complementary 511 Data Storage Modelmentioning

confidence: 99%

Growth of relational model: Interdependence and complementary to big data

Shetty

Rao

Prabhu

2021

IJECE

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“…• Generate an E-commence dataset with Unibench [15,16] • Hands-on experience for multi-model queries with ArangoDB.…”

Section: Tutorial Organizationmentioning

confidence: 99%

Multi-Model Data Query Languages and Processing Paradigms

Guo

Zhang

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Specifying users' interests with a formal query language is a typically challenging task, which becomes even harder in the context of multi-model data management because we have to deal with data variety. It usually lacks a unified schema to help the users issuing their queries, or has an incomplete schema as data come from disparate sources. Multi-Model DataBases (MMDBs) have emerged as a promising approach for dealing with this task as they are capable of accommodating and querying the multi-model data in a single system. This tutorial aims to offer a comprehensive presentation of a wide range of query languages for MMDBs and to make comparisons of their properties from multiple perspectives. We will discuss the essence of cross-model query processing and provide insights on the research challenges and directions for future work. The tutorial will also offer the participants hands-on experience in applying MMDBs to issue multi-model data queries.

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“…As data volume and variety increase at an ever-faster rate, the trend towards utilizing a unified platform to manage the relational and tree data together accelerates [19,38]. On one hand, all leading RDBMS platforms have supported tree data management using the SQL-extension query languages such as SQL/JSON [25] and SQL/XML [23].…”

Section: Introductionmentioning

confidence: 99%

“…On one hand, all leading RDBMS platforms have supported tree data management using the SQL-extension query languages such as SQL/JSON [25] and SQL/XML [23]. On the other hand, emerging multi-store systems [3] and multi-model databases [20,21,38,39] have developed new query languages to query the tabular and tree data simultaneously. Example 1.…”

Section: Introductionmentioning

confidence: 99%

“…There are also other cases that the estimates of query results are useful before evaluating the full query result. For instance, to design a benchmark query, a quick feedback about the join size of the query would help the user to decide whether to modify the query with another selection predicate or to configure other binding parameters for the same query [6,38]. Similarly, result size estimation can be valuable for the "next-step" decision in query refinement [24].…”

Section: Introductionmentioning

confidence: 99%

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Selectivity Estimation for Relation-Tree Joins

Zhang

2020

32nd International Conference on Scientific and Statistical Database Management

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Estimating the join selectivity is a crucial problem in many aspects of query processing, such as query optimization and query refinement. Selectivity estimation has been extensively studied for the relational joins in SQL queries and structural joins in pathoriented queries. However, as leading databases have supported the multi-model data management on relational and tree-structured data together, a new problem has arisen: the existing estimation techniques mainly work for a single model but not for the heterogeneous situation due to the cross-model joins. A straightforward combination of existing estimators cannot provide a satisfactory estimation quality.This paper studies the problem of selectivity estimation for crossmodel joins with relational and tree-structured data. Our estimator is based on the Kernel Density Estimation (KDE) model, which is a statistical approach using a data sample to approximate multivariate probability distribution. KDE has been successfully applied in relational databases to estimate the selectivity of range and join query. In this work, we propose an estimation method called location-value estimation (LVE) model based on KDE, which considers both value joins and structural joins in relational and tree-structured data. To boost the estimation efficiency in large data samples, we further propose the max-min approximation (MMA) and grid-based approximation (GBA) models to approximate the KDE contribution. Extensive experiments on four real and synthetic datasets demonstrate the effectiveness, efficiency, and scalability of our techniques.

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Holistic evaluation in multi-model databases benchmarking

Cited by 21 publications

References 25 publications

Growth of relational model: Interdependence and complementary to big data

Growth of relational model: Interdependence and complementary to big data

Multi-Model Data Query Languages and Processing Paradigms

Selectivity Estimation for Relation-Tree Joins

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