Automatic schema suggestion model for NoSQL document-stores databases

Imam, Abdullahi Abubakar; Basri, Shuib; Ahmad, Rohiza; Watada, Junzu; González-Aparicio, María Teresa

doi:10.1186/s40537-018-0156-1

Cited by 27 publications

(11 citation statements)

References 17 publications

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“…A small scale of DSP was originally introduced in an earlier article [24,25] as SPM. In this extended presentation, we focus on one impotent parameter (i.e., performance) and also describe how DSP supports a broader class of query complexities, like single-selectivity, drill-down, and rollup.…”

mentioning

confidence: 99%

DSP: Schema Design for Non-Relational Applications

et al. 2020

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The way a database schema is designed has a high impact on its performance in relational databases, which are symmetric in nature. While the problem of schema optimization is even more significant for NoSQL (“Not only SQL”) databases, existing modeling tools for relational databases are inadequate for this asymmetric setting. As a result, NoSQL modelers rely on rules of thumb to model schemas that require a high level of competence. Several studies have been conducted to address this problem; however, they are either proprietary, symmetrical, relationally dependent or post-design assessment tools. In this study, a Dynamic Schema Proposition (DSP) model for NoSQL databases is proposed to handle the asymmetric nature of today’s data. This model aims to facilitate database design and improve its performance in relation to data availability. To achieve this, data modeling styles were aggregated and classified. Existing cardinality notations were empirically extended using synthetically generated queries. A binary integer formulation was used to guide the mapping of asymmetric entities from the application’s conceptual data model to a database schema. An experiment was conducted to evaluate the impact of the DSP model on NoSQL schema production and its performance. A profound improvement was observed in read/write query performance and schema production complexities. In this regard, DSP has significant potential to produce schemas that are capable of handling big data efficiently.

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confidence: 99%

DSP: Schema Design for Non-Relational Applications

et al. 2020

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“…In [55] A schema was suggested for the document-based database in three areas of NoSQL, namely architecture, algorithm, and association rules. The purpose of this model was to maintain a balance of compatibility, availability, and scalability features in the data model.…”

Section: Review Of the Selected Articlesmentioning

confidence: 99%

“…• Scalability: The data model efficient performance on multiplied size or scale • Performance: The data model efficiency in terms of data production volume and velocity and big data processing The determining factors in data model design are considered consistency, availability, and scalability [55]. Having reviewed various articles, we observed that scalability, performance, and schema-less with frequencies of 10% or more are important features in designing the data model (Figure 9).…”

Section: B Data Model Featuresmentioning

confidence: 99%

A Systematic Review of Data Models for the Big Data Problem

Mostajabi¹,

Safaei

Sahafi

2021

IEEE Access

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Nowadays, data are generated in a continuous streaming manner as the inputs of various applications. The sources of such generated data can be wired or wireless sensor networks commonly used in various fields of geographical, traffic, Internet of Things (IoT), financial tickers, Web2 and Web3, ecommerce, social networks, and online communities. The high volume, high variety, and high velocity of data have recently posed the challenge of 3Vs to this field, also known as the Big Data Problem. The 3Vs dimensions of complexities for the big data entails high-speed storage, scalability of database systems, suitable data models, real-time responsiveness and so on. Data model, as the representation schema of data is an essential issue since many others (e.g., DBMS systems' design, DB languages, etc.) rely on. So, the study of data models is a key and fundamental aspect in structuring, organizing, storing, and manipulating big data. It is also the essence in various areas of cloud migration, web-scale, and so forth. In this paper, we have systematically reviewed different types of data models, the rationale behind them, their applications and support capabilities, and the technologies to switch from one model to another. To address the user needs in various fields, a systematic review method is adopted to classify and present different types and characteristics of data models.

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“…In a different study, Imam et al 16 presented a schema suggestion model for NoSQL document‐model databases. The proposed model suggests a schema based on system requirements and parameters such as list of entities and CRUD (Create, Read, Update and Delete) statements which are used in the production of NoSQL database schemas.…”

Section: Related Workmentioning

confidence: 99%

“…There has been little research done on automatic database schema design for NoSQL non‐relational databases 12‐20 . All of the studies take an offline approach to the schema design problem and are not able to monitor the managed database system in order to detect changes and adapt the schema design accordingly.…”

Section: Introductionmentioning

confidence: 99%

CONST: Continuous online NoSQL schema tuning

2020

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Self‐tuning is a feature of autonomic databases that includes the problem of automatic schema design. Automating the schema design process is critical to provide an optimized schema that increases the performance of an application's workload in NoSQL databases. There has been important work on automated design tuning for NoSQL databases. Existing solutions take an offline approach to the schema design problem and leave several important decisions to database administrators. Although offline approaches recommend good and efficient schemas, their analysis is far too heavy‐weight to be run continuously. This paper develops a novel self‐tuning control loop, called CONST, for online monitoring, analysis, and schema tuning a managed NoSQL database system that automatically modify the current schema design reacting to changes in the application's workload. CONST provides components that fulfill IBM's MAPE‐K loop phases to support self‐tuning. We describe an implementation of the proposed CONST loop on top of the Cassandra wide column store and evaluate its performance experimentally. Our results show the low overhead of CONST and demonstrate its ability to tune a schema design in response to changes in the application's workload.

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Automatic schema suggestion model for NoSQL document-stores databases

Cited by 27 publications

References 17 publications

DSP: Schema Design for Non-Relational Applications

DSP: Schema Design for Non-Relational Applications

A Systematic Review of Data Models for the Big Data Problem

CONST: Continuous online NoSQL schema tuning

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