Relevant bibliographies by topics / Relational database management systems

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Relational database management systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Relational database management systems"

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Sacco, Giovanni Maria, and Mario Schkolnick. "Buffer management in relational database systems." ACM Transactions on Database Systems 11, no. 4 (December 1986): 473–98. http://dx.doi.org/10.1145/7239.7336.

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Mohan, C., H. Pirahesh, W. G. Tang, and Y. Wang. "Parallelism in relational database management systems." IBM Systems Journal 33, no. 2 (1994): 349–71. http://dx.doi.org/10.1147/sj.332.0349.

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Hull, MEC, FF Cai, and DA Bell. "Buffer management algorithms for relational database management systems." Information and Software Technology 30, no. 2 (March 1988): 66–80. http://dx.doi.org/10.1016/0950-5849(88)90067-5.

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McDermid, Lindsay. "Evaluation of two relational database management systems." ACM SIGSMALL/PC Notes 12, no. 2 (May 1986): 27–98. http://dx.doi.org/10.1145/382158.383032.

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Jackson, Denise F., and Kelechuku Okike. "Relational database management systems and industrial engineering." Computers & Industrial Engineering 23, no. 1-4 (November 1992): 479–82. http://dx.doi.org/10.1016/0360-8352(92)90165-g.

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Parimala, N. "Relational database management with Oracle." Information and Software Technology 34, no. 1 (January 1992): 70. http://dx.doi.org/10.1016/0950-5849(92)90105-x.

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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.

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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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Atay, Canan Eren. "An Implementation of Bitemporal Relational Database Management Systems." Pamukkale University Journal of Engineering Sciences 20, no. 2 (2014): 54–62. http://dx.doi.org/10.5505/pajes.2014.25743.

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Wan, Jiangling. "Mapping relational database management systems to hypertext (abstract)." ACM SIGWEB Newsletter 5, no. 2 (June 1996): 7. http://dx.doi.org/10.1145/231738.232571.

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Nilakanta, S. "Controlling user authorization in relational database management systems." Information and Software Technology 31, no. 6 (July 1989): 290–94. http://dx.doi.org/10.1016/0950-5849(89)90130-4.

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Dissertations / Theses on the topic "Relational database management systems"

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Alkahtani, Mufleh M. "Modeling relational database management systems." Virtual Press, 1993. http://liblink.bsu.edu/uhtbin/catkey/865955.

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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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Zou, Beibei 1974. "Data mining with relational database management systems." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82456.

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With the increasing demands of transforming raw data into information and knowledge, data mining becomes an important field to the discovery of useful information and hidden patterns in huge datasets. Both machine learning and database research have made major contributions to the field of data mining. However, there is still little effort made to improve the scalability of algorithms applied in data raining tasks. Scalability is crucial for data mining algorithms, since they have to handle large datasets quite often. In this thesis we take a step in this direction by extending a popular machine learning software, Weka3.4, to handle large datasets that can not fit into main memory by relying on relational database technology. Weka3.4-DB is implemented to store the data into and access the data from DB2 with a loose coupling approach in general. Additionally, a semi-tight coupling is applied to optimize the data manipulation methods by implementing core functionalities within the database. Based on the DB2 storage implementation, Weka3.4-DB achieves better scalability, but still provides a general interface for developers to implement new algorithms without the need of database or SQL knowledge.
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Moolman, G. Chris. "A relational database management systems approach to system design /." This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-07102009-040421/.

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Moolman, George Christiaan. "A relational database management systems approach to system design." Thesis, Virginia Tech, 1992. http://hdl.handle.net/10919/43628.

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Systems are developed to fulfill certain requirements. Several system design configurations usually can fulfill the technical requirements, but at different equivalent life-cycle costs. The problem is how to manipulate and evaluate different system configurations so that the required system effectiveness can be achieved at a minimum equivalent cost. It is also important to have a good definition of all the major consequences of each design configuration. For each alternative configuration considered, it is useful to know the number of units to deploy, the inventory and other logistic requirements, as well as the sensitivity of the system to changes in input variable values. An intelligent relational database management system is defined to solve the problem described. Table structures are defined to maintain the required data elements and algorithms are constructed to manipulate the data to provide the necessary information. The methodology is as follows: Customer requirements are analyzed in functional terms. Feasible design alternatives are considered and defined as system design configurations. The reliability characteristics of each system configuration are determined, initially from a system-level allocation, and later determined from test and evaluation data. A maintenance analysis is conducted to determine the inventory requirements (using reliability data) and the other logistic requirements for each design configuration. A vector of effectiveness measures can be developed for each customer, depending on objectives, constraints, and risks. These effectiveness measures, consisting of a combination of performance and cost measures, are used to aid in objectively deciding which alternative is preferred. Relationships are defined between the user requirements, the reliability and maintainability of the system, the number of units deployed, the inventory level, and other logistic characteristics of the system. A heuristic procedure is developed to interactively manipulate these parameters to obtain a good solution to the problem with technical performance and cost measures as criteria. Although it is not guaranteed that the optimal solution will be found, a feasible solution close to the optimal will be found. Eventually the user will have, at any time, the ability to change the value of any parameter modeled. The impact on the total system will subsequently be made visible.
Master of Science
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Ibrahim, Karim. "Management of Big Annotations in Relational Database Management Systems." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/272.

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Annotations play a key role in understanding and describing the data, and annotation management has become an integral component in most emerging applications such as scientific databases. Scientists need to exchange not only data but also their thoughts, comments and annotations on the data as well. Annotations represent comments, Lineage of data, description and much more. Therefore, several annotation management techniques have been proposed to efficiently and abstractly handle the annotations. However, with the increasing scale of collaboration and the extensive use of annotations among users and scientists, the number and size of the annotations may far exceed the size of the original data itself. However, current annotation management techniques don’t address large scale annotation management. In this work, we propose three chapters to that tackle the Big annotations from three different perspectives (1) User-Centric Annotation Propagation, (2) Proactive Annotation Management and (3) InsightNotes Summary-Based Querying. We capture users' preferences in profiles and personalizes the annotation propagation at query time by reporting the most relevant annotations (per tuple) for each user based on time plan. We provide three Time-Based plans, support static and dynamic profiles for each user. We support a proactive annotation management which suggests data tuples to be annotated in case new annotation has a reference to a data value and user doesn’t annotate the data precisely. Moreover, we provide an extension on the InsightNotes: Summary-Based Annotation Management in Relational Databases by adding query language that enable the user to query the annotation summaries and add predicates on the annotation summaries themselves. Our system is implemented inside PostgreSQL.
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Kitinya, Sylivano Chiluli Nonga. "Relational multimedia databases." Thesis, Durham University, 1987. http://etheses.dur.ac.uk/1242/.

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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.
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Sayli, Ayla. "Semantic query optimization in relational database management systems." Thesis, University of Essex, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284608.

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Lepinioti, Konstantina. "Data mining and database systems : integrating conceptual clustering with a relational database management system." Thesis, Bournemouth University, 2011. http://eprints.bournemouth.ac.uk/17765/.

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Many clustering algorithms have been developed and improved over the years to cater for large scale data clustering. However, much of this work has been in developing numeric based algorithms that use efficient summarisations to scale to large data sets. There is a growing need for scalable categorical clustering algorithms as, although numeric based algorithms can be adapted to categorical data, they do not always produce good results. This thesis presents a categorical conceptual clustering algorithm that can scale to large data sets using appropriate data summarisations. Data mining is distinguished from machine learning by the use of larger data sets that are often stored in database management systems (DBMSs). Many clustering algorithms require data to be extracted from the DBMS and reformatted for input to the algorithm. This thesis presents an approach that integrates conceptual clustering with a DBMS. The presented approach makes the algorithm main memory independent and supports on-line data mining.
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Floratos, Sofoklis. "High Performance Iterative Processing in Relational Database Management Systems." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1605909940057503.

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Kolonko, Kamil. "Performance comparison of the most popular relational and non-relational database management systems." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16112.

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Context. Database is an essential part of any software product. With an emphasis on application performance, database efficiency becomes one of the key factors to analyze in the process of technology selection. With a development of new data models and storage technologies, the necessity for a comparison between relational and non-relational database engines is especially evident in the software engineering domain. Objectives. This thesis investigates current knowledge on database performance measurement methods, popularity of relational and non-relational database engines, defines characteristics of databases, approximates their average values and compares the performance of two selected database engines.Methods. In this study a number of research methods are used, including literature review, a review of Internet sources, and an experiment. Literature datasets used in the research incorporate over 100 sources including IEEE Xplore and ACM Digital Library. YCSB Benchmark was used as a direct performance comparison method in an experiment to compare OracleDB’s and MongoDB’s performance. Results. A list of database performance measurement methods has been defined as a result of the literature review. Two most popular database management engines, one relational and one non-relational has been identified. A set of database characteristics and a database performance comparison methodology has been identified. Performance of two selected database engines has been measured and compared. Conclusions. Performance comparison between two selected database engines indicated superior results for MongoDB under the experimental conditions. This database proved to be more efficient in terms of average operation latency and throughput for each of the measured workloads. OracleDB however, presented stable results in each of the category leaving the final choice of database to the specifics of a software engineering project. Activities required for the definition of database performance comparison methodology proved to be challenging and require study extension.
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Books on the topic "Relational database management systems"

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Delobel, Claude. Relational database systems. Amsterdam: North-Holland, 1985.

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Ramakrishnan, Raghu. Database management systems. New York: McGraw-Hill, 1998.

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Johannes, Gehrke, ed. Database management systems. 2nd ed. Boston, MA: McGraw-Hill, 2000.

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Ramakrishnan, Raghu. Database management systems. New York: McGraw-Hill, 1996.

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Ramakrishnan, Raghu. Database management systems. Boston, Mass: WCB/McGraw-Hill, 1998.

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Krishnan, Venkatraman. Benchmarking relational database management systems. Toronto: University of Toronto, Dept. of Computer Science, 1985.

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1954-, Valder W., ed. Relational database management: A systems programming approach. New York: Prentice Hall, 1989.

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Howe, D. R. Relational properties of database management systems. Leicester: Leicester Polytechnic, Centre forApplied Research in Information Systems, School of Mathematics, Computing and Statistics, 1987.

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Sumathi, S., and S. Esakkirajan. Fundamentals of Relational Database Management Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-48399-1.

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Valduriez, Patrick. Analysis and comparison of relational database systems. Reading, Mass: Addison-Wesley Pub. Co., 1989.

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Book chapters on the topic "Relational database management systems"

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Fernandez, Ignatius. "Relational Database Management Systems." In Beginning Oracle Database 12c Administration, 3–24. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0193-0_1.

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Yannakoudakis, E. J., and C. P. Cheng. "Database Management Systems." In Standard Relational and Network Database Languages, 3–17. London: Springer London, 1988. http://dx.doi.org/10.1007/978-1-4471-3287-5_1.

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Sperko, Richard. "Understanding Relational Database Management Systems." In Java Persistence for Relational Databases, 17–33. Berkeley, CA: Apress, 2003. http://dx.doi.org/10.1007/978-1-4302-0816-7_2.

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Dasararaju, Hemanth Kumar, and Peeyush Taori. "Data Management—Relational Database Systems (RDBMS)." In International Series in Operations Research & Management Science, 41–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-68837-4_3.

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de Lima, Murilo R., Marcos S. Sunyé, Eduardo C. de Almeida, and Alexandre I. Direne. "Distributed Benchmarking of Relational Database Systems." In Advances in Data and Web Management, 544–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00672-2_49.

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Abel, D. J. "A spatial database shell for relational database management systems." In Foundations of Data Organization and Algorithms, 257–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51295-0_136.

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Takahashi, Yoshikane. "A Fuzzy Query Language for Relational Databases." In Fuzziness in Database Management Systems, 365–84. Heidelberg: Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-7908-1897-0_16.

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Zou, Beibei, Xuesong Ma, Bettina Kemme, Glen Newton, and Doina Precup. "Data Mining Using Relational Database Management Systems." In Advances in Knowledge Discovery and Data Mining, 657–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_75.

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Jaekel, Tobias. "The Role Concept for Relational Database Management Systems." In Lecture Notes in Computer Science, 277–86. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14139-8_29.

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Jomphrom, Chawarnwan, and Kriengkrai Porkaew. "Version Management of Hierarchical Data in Relational Database." In Advances in Intelligent Systems and Computing, 275–84. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19024-2_28.

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Conference papers on the topic "Relational database management systems"

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Bertino, Elisa, Pierangela Samarati, and Sushil Jajodia. "Authorizations in relational database management systems." In the 1st ACM conference. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/168588.168605.

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Rys, Michael, Don Chamberlin, and Daniela Florescu. "XML and relational database management systems." In the 2005 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1066157.1066298.

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Rys, Michael. "XML and relational database management systems." In the 2005 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1066157.1066301.

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Malecha, Gregory, Greg Morrisett, Avraham Shinnar, and Ryan Wisnesky. "Toward a verified relational database management system." In the 37th annual ACM SIGPLAN-SIGACT symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1706299.1706329.

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Zhang, Chun, Jeffrey Naughton, David DeWitt, Qiong Luo, and Guy Lohman. "On supporting containment queries in relational database management systems." In the 2001 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2001. http://dx.doi.org/10.1145/375663.375722.

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Muthurajkumar, S., S. Murugesan, and A. Kannan. "Secured Storage for Cloud Based Relational Database Management Systems." In International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/pecteam-18.2018.12.

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Poljak, R., P. Poscic, and D. Jaksic. "Comparative analysis of the selected relational database management systems." In 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2017. http://dx.doi.org/10.23919/mipro.2017.7973658.

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Voitsekhovskaya, Olga K., and C. V. Kusnetsov. "Relational database management system for high resolution spectroscopy." In High Resolution Molecular Spectroscopy: 11th Symposium and School, edited by Alexander I. Nadezhdinskii, Yu V. Ponomarev, and Leonid N. Sinitsa. SPIE, 1994. http://dx.doi.org/10.1117/12.166255.

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Kim, Won-Young, Kyu-Young Whang, Byung Suk Lee, Young-Koo Lee, and Ji-Woong Chang. "Partial rollback in object-oriented/object-relational database management systems." In the eleventh international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/584792.584844.

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McDermid, Lindsay. "A comparison of relational database management systems UNIFY and iDB." In the 1986 ACM SIGSMALL/PC symposium. New York, New York, USA: ACM Press, 1986. http://dx.doi.org/10.1145/317559.322768.

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Reports on the topic "Relational database management systems"

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Purvy, B., A. Daniel, M. Sinykin, and J. Smith. Relational Database Management System (RDBMS) Management Information Base (MIB) using SMIv2. Edited by D. Brower. RFC Editor, August 1994. http://dx.doi.org/10.17487/rfc1697.

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Herbschleb, J., and P. M. Maurenbrecher. The engineering geology 'INGEOBASE' relational database management system for Amsterdam. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/193959.

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Robson, P. G., and K. A. M. Adlam. A menu aided retrieval system (MARS) for use with a relational database management system. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/193969.

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Graham, S. L., and Timoleon K. Sellis. Optimization of Extended Relational Database Systems. Fort Belvoir, VA: Defense Technical Information Center, July 1986. http://dx.doi.org/10.21236/ada179308.

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Wright, Peggy. Intelligent Access to Multiple Relational Database Systems. Fort Belvoir, VA: Defense Technical Information Center, November 1992. http://dx.doi.org/10.21236/ada261198.

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Dabrowski, Christopher E., Elizabeth N. Fong, and Deyuan Yang. Object database management systems. Gaithersburg, MD: National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.sp.500-179.

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Dabrowski, Christopher, and Elizabeth Fong. Database Management Systems in engineering. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4987.

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Mears, Brad. Advanced Traffic Management Systems (ATMS) Research Analysis Database System. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada388177.

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Wolery, T., and M. Sutton. Generic Natural Systems Evaluation - Thermodynamic Database Development and Data Management. Office of Scientific and Technical Information (OSTI), September 2011. http://dx.doi.org/10.2172/1035958.

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Rogers, N., and B. Brodaric. Spatially linked relational database management of petrology and geochemistry using Fieldlog v3.0: a worked example from the Bathurst mining camp, New Brunswick. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1996. http://dx.doi.org/10.4095/207893.

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