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Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Relational databases.'
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Maatuk, Abdelsalam, M. Akhtar Ali, and Nick Rossiter. "Converting Relational Databases into Object-relational Databases." Journal of Object Technology 9, no. 2 (2010): 145. http://dx.doi.org/10.5381/jot.2010.9.2.a3.
Garvey, M. "Relational databases." Information and Software Technology 34, no. 12 (December 1992): 825. http://dx.doi.org/10.1016/0950-5849(92)90125-9.
Finkelstein, S., M. Schkolnick, and P. Tiberio. "Physical database design for relational databases." ACM Transactions on Database Systems 13, no. 1 (March 1988): 91–128. http://dx.doi.org/10.1145/42201.42205.
Sharma, Yashraj, and Yashasvi Sharma. "CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM." International Journal of Research -GRANTHAALAYAH 7, no. 7 (July 31, 2019): 351–59. http://dx.doi.org/10.29121/granthaalayah.v7.i7.2019.777.
On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed.
In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced.
There are a lot of advantages of NoSQL which not only involve its own features but also some features of relational database management system. The severe benefit of NoSQL database is that it is an open source system which helps to adapt many numbers of features for newly generated applications.
This paper is focused on understanding the concepts of non-relational database system architecture with relational database system architecture and figure out the advantages and disadvantages of both simultaneously.
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Sliusarenko, Tetiana, and Valentin Filatov. "RELATIONAL VS NON-RELATIONAL DATABASES." Grail of Science, no. 23 (January 4, 2023): 269–71. http://dx.doi.org/10.36074/grail-of-science.23.12.2022.41.
In this paper we’re going to talk about the difference between relational and non-relational databases. These are two different ways in which clients store the data that they have and operationalize it. And we know there is so much data that is coming into every single company today that it’s important that customers have options for how they want to store that data.
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NAVNEET KUMAR, KASHYAP, PANDEY B.K, MANDORIA H.L, and KUMAR ASHOK. "A REVIEW OF LEADING DATABASES: RELATIONAL and NON-RELATIONAL DATABASE." i-manager's Journal on Information Technology 5, no. 2 (2016): 34. http://dx.doi.org/10.26634/jit.5.2.6002.
Thakur, Nimesh, and Nishi Gupta. "Relational and Non Relational Databases: A Review." Journal of University of Shanghai for Science and Technology 23, no. 08 (August 4, 2021): 117–21. http://dx.doi.org/10.51201/jusst/21/08341.
Relational and non-relational databases are the two types of databases that are used to store data and perform dierent operations on it. For data storage, they use a variety of formats. In this paper, we’ll try to gure out what they’re all about and what the main dierences are. Databases serve as a data centre from which information is collected and processed. Data science is a multidisciplinary eld that combines mathematics, statistics, and programming to research data. For a data scientist, a basic understanding of databases is a must-have ability. We’ll look at how a data scientist can make the most of dierent database types.
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Nisa, Behjat U. "A Comparison between Relational Databases and NoSQL Databases." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 845–48. http://dx.doi.org/10.31142/ijtsrd11214.
., Vinay Goyal. "REENGINEERING OF RELATIONAL DATABASES TO OBJECTORIENTED DATABASE." International Journal of Research in Engineering and Technology 03, no. 01 (January 25, 2014): 112–15. http://dx.doi.org/10.15623/ijret.2014.0301018.
Dissertations / Theses on the topic "Relational databases":
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Bielecki, Pavel. "Distributed relational database system of occasionally connected databases." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2000. http://handle.dtic.mil/100.2/ADA378092.
This thesis is concerned with the design and im plementation of a Relational Multimedia Database System, in short RMDBS. RMDBS is designed to efficiently use storage space and manipulate various kinds of data; attribute data, bit-m apped pictures, and program s in binary code. RMDBS is an integrated system which enables the user to manage and control operations on the different forms of data in a user friendly manner. This means that even nonexperienced users can work with the system. The work described in this thesis is novel in that a true multimedia database has been implemented within the framework of a traditional relational DBMS. Previous work in this area has concentrated either in building data base management systems for storing picture-based data or multimedia databases which are not true data base management systems. RMDBS is implemented using the Revelation data base management system.
Almost all of the database products developed over the past few years are based on what is called the relational approach.The purpose of this thesis is to characterize a relational data base management system, we do this by studying the relational model in some depth.The relational model is not static, rather it has been evolving over time. We trace the evolution of the relational model. We will also consider the ramifications of the relational model for modern database systems. Department of Computer Science
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Karlapalem, Kamalakar. "Redesign of distributed relational databases." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/9173.
Zavodny, Jakub. "Factorisation in relational databases." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:54c9a3a7-caac-40d9-90fb-83797ced9c5a.
We study representation systems for relational data based on relational algebra expressions with unions, products, and singleton relations. Algebraic factorisation using the distributivity of product over union allows succinct representation of many-to-many relationships; further succinctness is brought by sharing repeated subexpressions. We show that these techniques are especially applicable to results of conjunctive queries. In the first part of the dissertation we derive tight asymptotic size bounds for two flavours of factorised representations of results of conjunctive queries. Any conjunctive query is characterised by rational parameters that govern the factorisability of its results independently of the database instance. We relate these parameters to fractional edge covers and fractional hypertree decompositions. Factorisation naturally extends from relational data to its provenance. We characterise conjunctive queries by tight bounds on their readability, which captures how many times each input tuple is used to contribute to an output tuple, and we define syntactically the class of queries with bounded readability. In the second part of the dissertation we describe FDB, a relational database engine that uses factorised representations at the physical layer to reduce data redundancy and boost query performance. We develop algorithms for optimisation and evaluation of queries with selection, projection, join, aggregation and order-by clauses on factorised representations. By introducing novel operators for factorisation restructuring and a new optimisation objective to maintain intermediate and final results succinctly factorised, we allow query evaluation with lower time complexity than on flat relations. Experiments show that for data sets with many-to-many relationships, FDB can outperform relational engines by orders of magnitude.
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Sullivan, Larry. "Performance issues in mid-sized relational database machines /." Online version of thesis, 1989. http://hdl.handle.net/1850/10445.
Jermaine, Christopher. "Approximate answering of aggregate queries in relational databases." Diss., Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/9221.
Relational databases are a popular method for organizing and storing data. Unfortunately, many machine-learning techniques are unable to handle complex relational models. The Probabilistic Relational Model (PRM) is an extension of the Bayesian Network framework that can express relational structure as well as probabilistic dependencies. In this thesis, we significantly expand and improve an implementation of PRMs that allows defining conditional probability distributions over discrete and continuous variables. The thesis uses as starting point an implementation that has various problems, and runs very slowly when using a database management system (DBMS) as storage. This thesis discusses alternative algorithms that improve the accuracy of the learned models, the computing performance, and correct the inference problems of the existing implementation. The focus is on techniques used to reduce the running time of the algorithms when the implementation is used to learn from data stored on a DBMS. The thesis provides experimental results using this package on both synthetic and real data sets.
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Henderson, Rebecca Sue. "Technology in action : the design and use of relational databases /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/8880.
Monat, Andre Soares. "Exceptional values in relational databases." Thesis, University of East Anglia, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359326.
E, Robinson A., Wolverhampton Polytechnic. School of Computing and Information Technology., and British Computer Society, eds. Beyond relational databases. Wolverhampton: School of Computing and Information Technology, The Polytechnic Wolverhampton, 1989.
Book chapters on the topic "Relational databases":
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Lake, Peter, and Paul Crowther. "Relational Databases." In Undergraduate Topics in Computer Science, 69–96. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5601-7_4.
Greco, Sergio, Cristian Molinaro, and Francesca Spezzano. "Relational Databases." In Incomplete Data and Data Dependencies in Relational Databases, 3–13. Cham: Springer International Publishing, 2012. http://dx.doi.org/10.1007/978-3-031-01893-0_2.
Domdouzis, Konstantinos, Peter Lake, and Paul Crowther. "Relational Databases." In Undergraduate Topics in Computer Science, 71–100. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-42224-0_4.
Wagner, Gerd. "Object-Relational Databases." In Foundations of Knowledge Systems: with Applications to Databases and Agents, 71–83. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5723-4_3.
Moal, Frédéric, Teddy Turmeaux, and Christel Vrain. "Mining Relational Databases." In Principles of Data Mining and Knowledge Discovery, 536–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45372-5_63.
Marcou, Gilles, and Alexandre Varnek. "Relational Chemical Databases." In Tutorials in Chemoinformatics, 37–66. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119161110.ch2.
Conference papers on the topic "Relational databases":
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Guo, Fei, Jianmin Wang, and Deyi Li. "Fingerprinting relational databases." In the 2006 ACM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1141277.1141391.
Beaubouef, Theresa, and Frederick E. Petry. "Database security issues in rough relational databases." In 2010 42nd Southeastern Symposium on System Theory (SSST 2010). IEEE, 2010. http://dx.doi.org/10.1109/ssst.2010.5442811.
"Database Schema Elicitation to Modernize Relational Databases." In 14th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003980801260132.
Chung, Jen-Yao, Yi-Jing Lin, and Daniel T. Chang. "Object and relational databases." In Addendum to the proceedings of the 10th annual conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/260094.260273.
Maatuk, Abdelsalam, M. Akhtar Ali, and Nick Rossiter. "Re-engineering relational databases." In the 2011 International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1980822.1980839.
"From Relational Databases to Ontology-Based Databases." In 15th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004454802890297.
Hassan, Mohammad A. "Relational and NoSQL Databases: The Appropriate Database Model Choice." In 2021 22nd International Arab Conference on Information Technology (ACIT). IEEE, 2021. http://dx.doi.org/10.1109/acit53391.2021.9677042.
Lei, Yuxia, Yuefei Sui, and Cungen Cao. "Complementary Relations and Their Concept Lattices in Relational Databases." In 2009 Fifth International Conference on Semantics, Knowledge and Grid. IEEE, 2009. http://dx.doi.org/10.1109/skg.2009.36.
Ruiz, Guillermo, Edgar Chavez, and Eric S. Tellez. "Towards Self-Indexing Relational Databases." In 2013 Mexican International Conference on Computer Science (ENC). IEEE, 2013. http://dx.doi.org/10.1109/enc.2013.9.
Amin, Alerk, Ingo Barko, Stefan Kramer, David Schiller, and Jeremy Williams. Representing and Utilizing DDI in Relational Databases. Inter-university Consortium for Political and Social Research (ICPSR), 2011. http://dx.doi.org/10.3886/ddiotherpapers02.
Amin, Alerk, Ingo Barko, Stefan Kramer, David Schiller, and Jeremy Williams. Representing and Utilizing DDI in Relational Databases. Inter-university Consortium for Political and Social Research (ICPSR), 2011. http://dx.doi.org/10.3886/ddiothertopics02.
Rintoul, Mark Daniel, and Andrew T. Wilson. Evaluating parallel relational databases for medical data analysis. Office of Scientific and Technical Information (OSTI), March 2012. http://dx.doi.org/10.2172/1039004.
Chen, Tennyson, Martin Meyer, Nanthini Ganapathi, Shuangquan Liu, and Jonathan Cirella. Improving Data Quality in Relational Databases: Overcoming Functional Entanglements. Research Triangle Park, NC: RTI Press, May 2011. http://dx.doi.org/10.3768/rtipress.2011.op.0004.1105.
Pan, Zhengxiang, and Jeff Heflin. DLDB: Extending Relational Databases to Support Semantic Web Queries. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada451847.
Lauret, Jerome, and Michael Furey. CWS4DB: A Customizable Web Service for Efficient Access to Distributed Nuclear Physics Relational Databases. Office of Scientific and Technical Information (OSTI), June 2013. http://dx.doi.org/10.2172/1083469.
Marstaller, J. Comparative performance measures of relational and object-oriented databases using High Energy Physics data. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/94015.
Ontology-based data access (OBDA) generalizes query answering in relational databases. It allows to query a database by using the language of an ontology, abstracting from the actual relations of the database. For ontologies formulated in Description Logics of the DL-Lite family, OBDA can be realized by rewriting the query into a classical first-order query, e.g. an SQL query, by compiling the information of the ontology into the query. The query is then answered using classical database techniques. In this report, we consider a temporal version of OBDA. We propose a temporal query language that combines a linear temporal logic with queries over DL-Litecore-ontologies. This language is well-suited for expressing temporal properties of dynamical systems and is useful in context-aware applications that need to detect specific situations. Using a first-order rewriting approach, we transform our temporal queries into queries over a temporal database. We then present three approaches to answering the resulting queries, all having different advantages and drawbacks.
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Borgwardt, Stefan, and Veronika Thost. Temporal Query Answering in DL-Lite with Negation. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.221.
Ontology-based query answering augments classical query answering in databases by adopting the open-world assumption and by including domain knowledge provided by an ontology. We investigate temporal query answering w.r.t. ontologies formulated in DL-Lite, a family of description logics that captures the conceptual features of relational databases and was tailored for efficient query answering. We consider a recently proposed temporal query language that combines conjunctive queries with the operators of propositional linear temporal logic (LTL). In particular, we consider negation in the ontology and query language, and study both data and combined complexity of query entailment.
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Borgida, Alex, and Ralf Küsters. What's not in a name? Initial Explorations of a Structural Approach to Integrating Large Concept Knowledge-Bases. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.101.
Aus der Einleitung: Given two ontologies/terminologies collections of terms and their 'meanings' as used in some universe of discourse (UofD), our general task is to integrate them into a single ontology, which captures the meanings of the original terms and their inter-relationships. This problem is motivated by several application scenarios: • First, such ontologies have been and are being developed independently by multiple groups for knowledge-based and other applications. Among others, medicine is an area in which such ontologies already abound [RZStGC, CCHJ94, SCC97]. • Second, a traditional step in database design has been so-called 'view integration': taking the descriptions of the database needs of different parts of an organization (called 'external views'), and coming up with a unified central schema (called the 'logical schema') for the database [BLN86]. Although the database views might be expressed in some low-level formalism, such as the relational data model, one can express the semantics (meta-data) in a more expressive notation, which can be thought of as an ontology. Then the integration of the ontologies can guide the integration of the views. • Finally, databases and semistructured data on the internet provide many examples where there are multiple, existing heterogeneous information sources, for which uniform access is desired. To achieve this goal, it is necessary to relate the contents of the various information sources. The approach of choice has been the development of a single, integrated ontology, starting from separate ontologies capturing the semantics of the heterogeneous sources[Kas97, CDGL+98]. Of course, we could just take the union of the two ontologies, and return the result as the integration. However, except for the case when the ontologies had absolutely nothing to do with each other, this seems inappropriate. Therefore part of our task will to be explore what it means to 'integrate' two ontologies. To help in this, we will in fact assume here that the ontologies are describing exactly the same aspects of the universe of discourse (UofD), leaving for a separate paper the issue of dealing with partially overlapping ontologies.
