Towards NoSQL-based Data Warehouse Solutions

Bičevska, Zane; Odītis, Ivo

doi:10.1016/j.procs.2017.01.080

Cited by 33 publications

(19 citation statements)

References 8 publications

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“…After that, the data will be stored in a back-end data repository that is managed by different servers for subsequent analysis (Gandomi and Haider, 2015;He et al, 2016;Bicevska and Oditis, 2017).…”

Section: Stage Two: Data Cleaning and Integrationmentioning

confidence: 99%

An analytic infrastructure for harvesting big data to enhance supply chain performance

Zhan

Tan

2020

European Journal of Operational Research

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Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this paper, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infrastructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating factbased operational decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm's operations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development.

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Section: Stage Two: Data Cleaning and Integrationmentioning

confidence: 99%

An analytic infrastructure for harvesting big data to enhance supply chain performance

Zhan

Tan

2020

European Journal of Operational Research

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“…Although there is a potential for the NoSQL integrative capability and opportunities offered by NoSQL databases and database management systems [31], few studies adopted such a technology in practice in the healthcare domain. Ercan and Lane [32] showed that the requirements of the application can determine the usability of NoSQL databases.…”

Section: Nosql Storage Solutionmentioning

confidence: 99%

GENE2D: A NoSQL Integrated Data Repository of Genetic Disorders Data

Samra

Soh

2020

Healthcare

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There are few sources from which to obtain clinical and genetic data for use in research in Saudi Arabia. Numerous obstacles led to the difficulty of integrating these data from silos and scattered sources to provide standardized access to large data sets for patients with common health conditions. To this end, we sought to contribute to this area and offer a practical and easy-to-implement solution. In this paper, we aim to design and implement a “not only SQL” (NoSQL) based integration framework to generate an Integrated Data Repository of Genetic Disorders Data (GENE2D) to integrate data from various genetic clinics and research centers in Saudi Arabia and provide an easy-to-use query interface for researchers to conduct their studies on large datasets. The major components involved in the GENE2D architecture consists of the data sources, the integrated data repository (IDR) as a central database, and the application interface. The IDR uses a NoSQL document store via MongoDB (an open source document-oriented database program) as a backend database. The application interface called Query Builder provides multiple services for data retrieval from the database using a custom query to answer simple or complex research questions. The GENE2D system demonstrates its potential to help grow and develop a national genetic disorders database in Saudi Arabia.

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“…Typical RDBMS are inefficient for handling un-structured big data generated by Web, in contrast NoSQL have the capabilities of handling such kind of data described by [6] and [1]. The aim of the research in [12] on NoSQL ETL is vital since currently DWs consist of structured data and this does not lead to storage of un-structured data in DW hence, strategic business decisions and data analysis can not be made for un-structured data.…”

Section: Related Workmentioning

confidence: 99%

“…The research by [6] is based on development and use of prototypes for NoSQL based DW. The authors concluded that creation of NoSQL based DW is possible only if a good balance is found between characteristics of SQL based DW and functionality offered by NoSQL DBMS.…”

Section: Related Workmentioning

confidence: 99%

“…The document-based databases allow the storage of documents made up of tagged elements and they seem to be the most popular NoSQL databases in practice (examples: Couchbase, MongoDB [4]; also in Latvia developed Clusterpoint [5]). While key-value stores use big hash tables of keys and values for fast accessing of data (examples: Riak, Amazon Dynamo) [6]. MongoDB is a NoSQL database which contains collections of documents which has no schema.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance Analysis of Not Only SQL Semi-Stream Join Using MongoDB for Real-Time Data Warehousing

Mehmood

Anees

2019

IEEE Access

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Data warehousing has been indispensable to enterprises for decades. However, infrequently updated data warehouse environment does not support quicker business decisions and faster data recovery in case of transformation or load issue. Implementation of real-time data warehouse provides solution to update problems of enterprises. Efficient stream processing for un-structured(NoSQL) and structured(SQL) data from various sources is required for the successful implementation of real-time data warehousing. We have done an analysis between un-structured and structured semi-stream join processing, using efficient database engine MongoDB at Extraction-Transformation-Loading phase. Semi-stream tuples coming from different sources are joined with disk-based master data, based on keys in memory, for both un-structured and structured documents(tuples) using MongoDB server, where the I/O rates are different for both inputs. Through experiments, in this paper we have analyzed the CPU and memory usage for real-time semi-stream join processing through two types of tests, un-structured and structured data streams using synthetic and real datasets. The results show that, memory usage and execution time remains consistent for a given specification irrespective of the nature of data streams (un-structured or structured), even when incoming semi-streams are growing.INDEX TERMS NoSQL/SQL, semi-stream join, real-time data warehousing, MongoDB.

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Towards NoSQL-based Data Warehouse Solutions

Cited by 33 publications

References 8 publications

An analytic infrastructure for harvesting big data to enhance supply chain performance

An analytic infrastructure for harvesting big data to enhance supply chain performance

GENE2D: A NoSQL Integrated Data Repository of Genetic Disorders Data

Performance Analysis of Not Only SQL Semi-Stream Join Using MongoDB for Real-Time Data Warehousing

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