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Relevant bibliographies by topics / Relational database system

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Academic literature on the topic 'Relational database system'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Relational database system"

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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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Keivani, Negin, Abdelsalam M. Maatuk, Shadi Aljawarneh, and Muhammad Akhtar Ali. "Towards the Maturity of Object-Relational Database Technology: Promises and Reality." International Journal of Technology Diffusion 6, no. 4 (October 2015): 1–19. http://dx.doi.org/10.4018/ijtd.2015100101.

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Object-relational technology provides a significant increase in scalability and flexibility over the traditional relational databases. The additional object-relational features are particularly satisfying for advanced database applications that relational database systems have experienced difficulties. The key factor to the success of object-relational database systems is their performance. This paper aims to review the promises of Object-Relational database systems, examine the reality, and how their promises may be fulfilled through unification with the relational technology. To investigate the performance implications of using object-relational relative to relational technology, the query-oriented BUCKY benchmark has been previously applied to an early object-relational database system, i.e., Illustra 97. This paper presents the results obtained from implementing and running the BUCKY benchmark on Oracle 10g. The results acquired from the work described in this paper are compared with the results obtained in BUCKY benchmark. This study throws light on the functionality of object-relational databases, where object-relational technology has made improvements but some limitations are identified as well. In general, the performance of relational supersedes that of object-relational database system.

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Albert, Arnauld, and Cristiano Bozza. "The relational database system of KM3NeT." EPJ Web of Conferences 116 (2016): 07004. http://dx.doi.org/10.1051/epjconf/201611607004.

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Glass, Alan J., and Neal M. Holtz. "A relational database for building structures." Canadian Journal of Civil Engineering 13, no. 5 (October 1, 1986): 528–34. http://dx.doi.org/10.1139/l86-080.

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This paper looks at some of the requirements of a database for building structure data and the role of the building structure database in an integrated design system. A method of representing the necessary building structure data in a relational database is discussed. The basic data manipulation commands for relational databases are briefly explained. A demonstration database for a steel structure implemented on a microcomputer using a commercial relational database is presented. Key words: computer-aided design (CAD), database, database management system (DBMS), relational database, engineering database, building description.

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Tho, Do Xuan, and Dinh Thi Ngoc Thanh. "TN-82 — a relational database management system." Journal of Computer Science and Cybernetics 1, no. 3 (August 6, 2015): 13–21. http://dx.doi.org/10.15625/1813-9663/1/3/6689.

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Using the relational approach of database systems, the TN-82 has been developed and implemented on a minicomputer. The system consists of four subsystems which have relatively independent functions. These subsystems can be modified and extended, if it is necessary to obtain a new one, which adapts another practical problem with few costs. The system TN-82 is a structured system and controlled by hierarchical principles. The method based on module programming has been used. Data independence, data consistency, some problems of data security and integrity are guaranteed. Many blocks of information stored in the system can be unified. The system has been constructed in such a way that the redundant information is eliminated as much as possible. With a simple but powerful Vietnamese query manguage, a user is able to easily formulate his non-procedural queries. The query processing is based on implementing in the system of almost operations of relational algebra (such as selection, projection, join, union, difference, intersection). Some problems of query optimizing are resolved and applied in this system, such as analyzing and choosing the orders of executed operations, selecting access paths,… The system TN-82 can be easily realized on the other more powerful computers.

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Matallah, Houcine, Ghalem Belalem, and Karim Bouamrane. "Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database." International Journal of Software Science and Computational Intelligence 13, no. 3 (July 2021): 38–63. http://dx.doi.org/10.4018/ijssci.2021070104.

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NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.

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Ahmad, Khaleel, Mohammad Shoaib Alam, and Nur Izura Udzir. "Security of NoSQL Database Against Intruders." Recent Patents on Engineering 13, no. 1 (February 8, 2019): 5–12. http://dx.doi.org/10.2174/1872212112666180731114714.

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Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.

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Berezky, O. M., O. Yo Pitsun, S. O. Verbovyi, and T. V. Datsko. "RELATIONAL DATABASE OF INTELLIGENT AUTOMATED MICROSCOPY SYSTEM." Scientific Bulletin of UNFU 27, no. 5 (August 29, 2017): 125–29. http://dx.doi.org/10.15421/40270525.

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Scotti, Alex, Mark Hannum, Michael Ponomarenko, Dorin Hogea, Akshat Sikarwar, Mohit Khullar, Adi Zaimi, et al. "Comdb2 bloomberg's highly available relational database system." Proceedings of the VLDB Endowment 9, no. 13 (September 2016): 1377–88. http://dx.doi.org/10.14778/3007263.3007275.

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Logan, Paula M., Clifford W. Hale, and John M. Atkins. "An academic relational algebra database management system." ACM SIGCSE Bulletin 22, no. 3 (August 1990): 25–28. http://dx.doi.org/10.1145/101085.101093.

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Dissertations / Theses on the topic "Relational database system"

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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.

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Ulibarri, Desirea Duarte. "Volunteer system project Regis University Networking Lab Practicum /." [Denver, Colo.] : Regis University, 2006. http://165.236.235.140/lib/DUlibarriPartI2006.pdf.

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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.
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Long, J. A. "Implementing a heterogeneous relational database node." Thesis, University of Bristol, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355094.

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Ma, Xuesong 1975. "Data mining using relational database management system." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98757.

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With the wide availability of huge amounts of data and the imminent demands to transform the raw data into useful information and knowledge, data mining has become an important research field both in the database area and the machine learning areas. Data mining is defined as the process to solve problems by analyzing data already present in the database and discovering knowledge in the data. Database systems provide efficient data storage, fast access structures and a wide variety of indexing methods to speed up data retrieval. Machine learning provides theory support for most of the popular data mining algorithms. Weka-DB combines properties of these two areas to improve the scalability of Weka, which is an open source machine learning software package. Weka implements most of the machine learning algorithms using main memory based data structure, so it cannot handle large datasets that cannot fit into main memory. Weka-DB is implemented to store the data into and access the data from DB2, so it achieves better scalability than Weka. However, the speed of Weka-DB is much slower than Weka because secondary storage access is more expensive than main memory access. In this thesis we extend Weka-DB with a buffer management component to improve the performance of Weka-DB. Furthermore, we increase the scalability of Weka-DB even further by putting further data structures into the database, which uses a buffer to access the data in database. Furthermore, we explore another method to improve the speed of the algorithms, which takes advantage of the data access properties of machine learning algorithms.

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NAZARETH, LEANDRO DOS SANTOS. "SYSTEM FOR KEYWORD SEARCH IN RELATIONAL DATABASE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13816@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Esta dissertação descreve um sistema desenvolvido que permite a criação e execução de consultas a partir de palavras-chave sobre um banco de dados relacional. O sistema recebe palavras-chave quaisquer e tenta criar consultas que podem ser executadas em um Sistema de Gerenciamento de Banco de dados (SGBD). Para realizar esta geração automática de consultas o sistema utiliza os dados do catálogo da base de origem. O sistema permite assim efetuar consultas em um SGBD sem o conhecimento de uma linguagem de consultas, como SQL, e sem conhecimento do modelo do banco de dados.
This dissertation describes a developed system that allows the creation and execution of searches from keywords in a relational database. The system receives any keywords and tries to create queries that can be executed in database. To perform this automatic generation of queries the system uses the information of the catalogue of the source database. The system allows to make queries in a database without the knowledge of a language queries, like SQL, and the model from the database.

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Clemens, David W. "Relational database design of a shipboard ammunition inventory, requisitioning, and reporting system." Thesis, Monterey, California : Naval Postgraduate School, 1990. http://handle.dtic.mil/100.2/ADA231559.

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Thesis (M.S. in Information Systems)--Naval Postgraduate School, June 1990.
Thesis Advisor(s): Kamel, Magdi N. Second Reader: Bhargava, Hemant K. "June 1990." Description based on signature page as viewed on October 19, 2009. Author(s) subject terms: Ammunition, database design, relational database. Includes bibliographical references (p. 163-166). Also available online.

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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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Mnushkin, Ellen. "Inheritance in a relational object-oriented database system." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61083.

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This thesis shows that major object-oriented features can be implemented in a relational database to improve its ease of use. Such object-oriented features as class hierarchy, inheritance of attributes and methods, polymorphism, and collections of classes, are captured by three meta-relations in which the relationships between classes, association of methods with classes, and the composition of collections, are recorded. Each of these meta-relations implies a modification of the relational algebra operators so that they can be applied both to the independent relations and to the relations which are part of the class hierarchy.
We describe the relational algebra implementation of all the modifications to the relational operators necessary to accommodate the object-oriented features mentioned above. New syntax is suggested when these modifications are not sufficient.

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Books on the topic "Relational database system"

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IBM Data Management Solutions Education Services. Relational database design: Version 2. [United States?]: IBM, 2001.

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Relational database management with Oracle. 2nd ed. Wokingham, England: Addison-Wesley Pub. Co., 1992.

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Relational database management with Oracle. Wokingham, England: Addison-Wesley Pub., 1989.

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Hellerstein, Joseph M. Architecture of a database system. Boston: now Publishers Inc., 2007.

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Lowry, Stephen J. Relational database management system for prime ECG. [s.l: The Author], 2003.

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Using the new DB2: IBM's object-relational database system. San Francisco, Cslif: Morgan Kaufmann Publishers, 1996.

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Sawyer, Gregory R. Managing sound in a relational multimedia database system. Monterey, Calif: Naval Postgraduate School, 1988.

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Zabidin, Zaini Amri. Image storage and retrieval in a relational database system. London: University of East London, 1996.

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Spear, Ronald L. A relational/object-oriented database management system: R/OODBMS. Monterey, Calif: Naval Postgraduate School, 1992.

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Stevens, Al. C++ database development: Featuring PARODY, the Persistent Almost-Relational Object Database Management System. New York, N.Y: MIS:Press, 1992.

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Book chapters on the topic "Relational database system"

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Foster, Elvis C., and Shripad V. Godbole. "Relational System — a Closer Look." In Database Systems, 163–68. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0877-9_9.

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Watson, Paul, and Paul Townsend. "The EDS parallel relational database system." In Lecture Notes in Computer Science, 149–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54132-2_55.

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Takagi, Toshihisa, Fumihiro Matsuo, Shooichi Futamura, and Kazuo Ushijima. "Implementation of Inferential Relational Database System." In Foundations of Data Organization, 539–46. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1881-1_45.

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Grossman, David A., and James R. Driscoll. "Structuring Text within a Relational System." In Database and Expert Systems Applications, 72–77. Vienna: Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-7557-6_13.

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Zeidler, H. Ch. "RDBM — A Relational Database Machine Based on a Dedicated Multiprocessor System." In Database Machines, 15–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-82937-6_2.

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Scheyder, Elizabeth C. "Relational Database Applications in Manufacturing System Design." In Database and Expert Systems Applications, 96–100. Vienna: Springer Vienna, 1990. http://dx.doi.org/10.1007/978-3-7091-7553-8_15.

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Nørvåg, Kjetil, Marit Limstrand, and Lene Myklebust. "TeXOR: Temporal XML Database on an Object-Relational Database System." In Lecture Notes in Computer Science, 520–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-39866-0_50.

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Soffer, Aya, and Hanan Samet. "Two Data Organizations for Storing Symbolic Images in a Relational Database System." In Database Semantics, 435–56. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-0-387-35561-0_24.

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Inuzuka, Nobuhiro, and Toshiyuki Makino. "Multi-Relational Pattern Mining System for General Database Systems." In Knowledge-Based and Intelligent Information and Engineering Systems, 72–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15393-8_9.

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Davidson, Louis, and Jessica Moss. "Architecting Your System." In Pro SQL Server Relational Database Design and Implementation, 657–704. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1973-7_13.

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Conference papers on the topic "Relational database system"

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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.

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Kitsuregawa, Masaru, Miyuki Nakano, Lilian Harada, and Mikio Takagi. "Functional disk system for relational database." In 1984 IEEE First International Conference on Data Engineering. IEEE, 1987. http://dx.doi.org/10.1109/icde.1987.7272361.

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Albert, Arnauld. "The relational database system of KM3NeT." In 6TH INTERNATIONAL WORKSHOP ON VERY LARGE VOLUME NEUTRINO TELESCOPES: VLVvT 13. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4902798.

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Gupta, Abhinav, Sankar Subramanian, Srikanth Bellamkonda, Tolga Bozkaya, Nathan Folkert, Lei Sheng, and Andrew Witkowski. "Data densification in a relational database system." In the 2004 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1007568.1007668.

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Ngoc Chau, Vo, and Suphamit Chittayasothorn. "A temporal compatible object relational database system." In Proceedings 2007 IEEE SoutheastCon. IEEE, 2007. http://dx.doi.org/10.1109/secon.2007.342862.

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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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Afonin, Sergey, Alexander Kozitsyn, and Ivan Astapov. "SQLReports - Yet Another Relational Database Reporting System." In 9th International Conference on Software Engineering and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005114205290534.

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Kim, Dongkyu, Adil Alaoui, Betty Levine, and Kevin Cleary. "Database schema models of integrated biomedical information in relational database system." In SPIE Medical Imaging, edited by Brent J. Liu and William W. Boonn. SPIE, 2010. http://dx.doi.org/10.1117/12.846369.

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Silberberg, David. "Migrating databases via distributed relational database operations—The Hubble Space Telescope approach." In The earth and space science information system (ESSIS). AIP, 1993. http://dx.doi.org/10.1063/1.44462.

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Rattanaprateep, Chuleerat, and Suphamit Chittayasothorn. "A Frame-based Object Relational Expert Database System." In AFRICON 2007. IEEE, 2007. http://dx.doi.org/10.1109/afrcon.2007.4401490.

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Reports on the topic "Relational database system"

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Hodges, Julia, Shekar Ramanathan, and Susan Bridges. Re-Engineering a Relational Database System to Produce a Prototype Object-Oriented Geophysical Database System. Fort Belvoir, VA: Defense Technical Information Center, June 1995. http://dx.doi.org/10.21236/ada296539.

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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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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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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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Ortiz, Agustin, and Jr. Development and Implementation of an Expert System for Remotely Accessing a Relational Database. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada295400.

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Timothy R. Carr. NATIONAL CARBON SEQUESTRATION DATABASE AND GEOGRAPHIC INFORMATION SYSTEM (NATCARB) FORMER TITLE-MIDCONTINENT INTERACTIVE DIGITAL CARBON ATLAS AND RELATIONAL DATABASE (MIDCARB). Office of Scientific and Technical Information (OSTI), July 2004. http://dx.doi.org/10.2172/826358.

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Hargrove, James P. Design and Implementation of an Interface Editor for the Amadeus Multi- Relational Database Front-end System. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada267403.

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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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Tidd, Alexander N., Richard A. Ayers, Grant P. Course, and Guy R. Pasco. Scottish Inshore Fisheries Integrated Data System (SIFIDS): work package 6 final report development of a pilot relational data resource for the collation and interpretation of inshore fisheries data. Edited by Mark James and Hannah Ladd-Jones. Marine Alliance for Science and Technology for Scotland (MASTS), 2019. http://dx.doi.org/10.15664/10023.23452.

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Abstract:

[Extract from Executive Summary] The competition for space from competing sectors in the coastal waters of Scotland has never been greater and thus there is a growing a need for interactive seascape planning tools that encompass all marine activities. Similarly, the need to gather data to inform decision makers, especially in the fishing industry, has become essential to provide advice on the economic impact on fishing fleets both in terms of alternative conservation measures (e.g. effort limitations, temporal and spatial closures) as well as the overlap with other activities, thereby allowing stakeholders to derive a preferred option. The SIFIDS project was conceived to allow the different relevant data sources to be identified and to allow these data to be collated in one place, rather than as isolated data sets with multiple data owners. The online interactive tool developed as part of the project (Work Package 6) brought together relevant data sets and developed data storage facilities and a user interface to allow various types of user to view and interrogate the data. Some of these data sets were obtained as static layers which could sit as background data e.g. substrate type, UK fishing limits; whilst other data came directly from electronic monitoring systems developed as part of the SIFIDS project. The main non-static data source was Work Package 2, which was collecting data from a sample of volunteer inshore fishing vessels (<12m). This included data on location; time; vessel speed; count, time and position of deployment of strings of creels (or as fleets and pots as they are also known respectively); and a count of how many creels were hauled on these strings. The interactive online tool allowed all the above data to be collated in a specially designed database and displayed in near real time on the web-based application.

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