Migration of Relational Database to Document-Oriented Database: Structure Denormalization and Data Transformation

Karnītis, Ģirts; Arnicans, Guntis

doi:10.1109/cicsyn.2015.30

Cited by 53 publications

(30 citation statements)

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“…The representative features of big data are 3Vs: volume, variety, velocity. International Data Corporation thinks that the 4th V of big data is value with sparse density but IBM regards as veracity is the 4th V [11].…”

Section: Outcome and Inferencementioning

confidence: 99%

Future Prospects and Challenges of Big Data with Big Data Problems

2019

IJITEE

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As we aware, every second, petabytes of data is being generated from various sources as mentioned in previous para at a rapid speed, other sources may be stock market transactions data, sales and marketing data, sensors data, web documents, internet images, movies, multimedia data and lot many. These Big Data are as volume, variety, velocity, veracity and value in 5 V’s, which explains the complexity of Big Data. Due to adoption of Big Data analytics, there is demand of efficient technologies to manage heterogeneous data]. 21st century has marked the advent of Rich-Data concept. Different societal application such as disaster management, urban planning & monitoring, health hazards etc. has reinvigorated the significance of Geographic Information Systems (GIS). Newer and advance technologies are generating means to produce location-based dataset called as geospatial datasets. Increasing influx of user-generated data has amplified geospatial data and has outdid conventional computation requirement limits. The similitude of Big data and data in consideration has evolved the concept and appellation as “Spatial Big Data” (SBD). This paper is an effort towards enlistment and analysis of the different SBD concepts and technologies in the contemporary time. The main endeavor of this paper is to critically analyze the different technologies in the present day and identify the existing technical inadequacies in the existing systems

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Section: Outcome and Inferencementioning

confidence: 99%

Future Prospects and Challenges of Big Data with Big Data Problems

2019

IJITEE

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“…The unity virtualization architecture [3] allows us to use both the relational databases and NoSQL databases. The migration from a Relational DB into a document-oriented DB and authors introduced two logical layers over physical data [4]. The graph based model helps in improving query performance by nesting related tables.…”

Section: Data Transformationmentioning

confidence: 99%

“…A virtualization system which allow users to retrieve data from both sql and NoSQL database by using source specific API [3] A paper on schema conversion model for transforming SQL database to NoSQL proposed a method of graph transforming algorithm which can provide high performance of join query with nesting relevant tables, and for containing all required content of join query in a table by offering correctly nested sequence [4].The research in which the Methodology presented for migration from RDBMS to NoSQL databases. The author used MySQL as RDBMS and MongoDB as NoSQL database.…”

Section: Comparison Between Nosql Databasesmentioning

confidence: 99%

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Approaches used in efficient migration from Relational Database to NoSQL Database

Koli¹,

Shinde²

2017

Annals of Computer Science and Information Systems

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Abstract-now p dpys, the emerging softwpre ppplicptions producing the lprge volume of dptp rppidly. Hence, sufficient mpnpgement of such p lprge dptp is mpjor chpllenge. The trpditionpl relptionpl dptpbpses pre inefficient to hpndle such p dynpmic dptp which would be in pny form (i.e. structurpl, semistructured or hybrid). The ever increpsing dptp hpve pushed the centrplized dptpbpses like orpcle, MySQL, SQL server to their limits. So, the NoSQL dptpbpses pre the new generption dptpbpses for tpckling such p problem with terpbyte to petpbyte of dptpsets. Hence, mpny lepding comppnies such ps fpcebook, Ampzon, eBpy, foursqupre pnd mpny more using NoSQL dptpbpses. Some comppnies' still using relptionpl dptpbpses pnd they wish to move towprds new technologies, this motivpted us to study the pppropches used for migrption of dptpbpses from relptionpl dptpbpse to NoSQL dptpbpse. There pre different techniques in the literpture introduced by people for such migrption. This ppper surveys different pppropches used for Migrption of structured dptpbpse to un-structured dptpbpse.

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“…Potey et al (2015) provide a tool to perform data conversion, in which the target database is an equivalent relational schema in a Document structure. Karnitis and Arnicans (2015) instead provide a semi-automatic approach, which allows a comprehension of the relationships that the tables carry one over the other by a classification strategy. Mpinda et al (2015) present a data conversion process that aggregates data tables, which are analyzed along with the established relationships.…”

Section: Related Workmentioning

confidence: 99%

An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

Dediź

Stanier

2016

Proceedings of the 18th International Conference on Enterprise Information Systems

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ICEIS 2016 received 257 paper submissions from 42 countries in all continents, which makes it one of the largest conferences in the World in the area of Information Systems, thus demonstrating the success and global dimension of this conference. From these, 42 papers were selected for publication and presentation at the Conference as full papers. These numbers, leading to a full-paper acceptance ratio of 16%, show the intention of preserving a high quality forum for this conference, a quality that we intend to maintain in the future, for the next editions of this conference.The high number and high quality of the received papers imposed difficult choices in the selection process. To evaluate each submission, a double blind paper review was performed by the Program Committee, whose members are highly qualified researchers in ICEIS topic areas.All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for indexation by Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP, EI (Elsevier Index) and Scopus.Additionally, a short list of presented papers will be selected to be expanded into a forthcoming book of ICEIS 2016 Selected Papers to be published by Springer in the LNBIP Series.The technical program of the conference included a panel and 4 invited talks delivered by internationally distinguished speakers, namely: Claudia Loebbecke (University of Cologne, Germany), Sergio Gusmeroli (TXT e-solutions SPA, Italy), Wil Van Der Aalst (Technische Universiteit Eindhoven, Netherlands) and Jan Vom Brocke (University of Liechtenstein, Liechtenstein). Their participation positively contributes to reinforce the overall quality of the Conference and to provide a deeper understanding of the fields addressed by the conference.Moreover, ICEIS 2016 had a Doctoral Consortium on Enterprise Information Systems and 1 tutorial. We are thankful to the Conference Co-chairs (Olivier Camp and José Cordeiro) and Program Co-chairs (Slimane Hammoudi, Leszek Maciaszek and Michele M. Missikoff) for their dedication and hard work in organizing these events.We sincerely thank all the authors for their submissions and participation in ICEIS 2016. Furthermore, we would like to thank all the members of the program committee and reviewers, who helped us with their expertise, dedication and time. We would also like to thank the invited speakers for their excellent contribution in sharing their knowledge and vision and the workshop/special session chairs whose collaboration with ICEIS 2015 was much appreciated. Finally, we gratefully acknowledge the professional support of the ICEIS 2016 team for all organizational processes. XIIIWe hope that all colleagues find this a fruitful and inspiring conference. We hope to contribute to the development of the Enterprise Information Systems community and look forward to having additional research results presented at the next edition of ICEIS, details of which are available at http://www.iceis.org. Slimane Hammoudi University of Cologne, GermanyAbstract:...

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Migration of Relational Database to Document-Oriented Database: Structure Denormalization and Data Transformation

Cited by 53 publications

References 1 publication

Future Prospects and Challenges of Big Data with Big Data Problems

Future Prospects and Challenges of Big Data with Big Data Problems

Approaches used in efficient migration from Relational Database to NoSQL Database

An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

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