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Relevant bibliographies by topics / Data management system

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Academic literature on the topic 'Data management system'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 June 2025

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Journal articles on the topic "Data management system"

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Moreno, P., M. Ruiz, and F. J. Gorines. "TBM Process Data Management System." International Journal of Engineering and Technology 7, no. 5 (2015): 431–34. http://dx.doi.org/10.7763/ijet.2015.v7.832.

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Kaku, Kazuya, Takashi Yamazaki, Toshiaki Hashimoto, and Ryo Tanabe. "Data management/system." Geocarto International 12, no. 4 (1997): 79–85. http://dx.doi.org/10.1080/10106049709354620.

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Ju, Seung-Hwan, and Hee-Suk Seo. "Data Quality Test Method for Factory Energy Management System." Webology 19, no. 1 (2022): 4420–27. http://dx.doi.org/10.14704/web/v19i1/web19291.

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Because data is an important factor in the software industry, how to reliably test data is important. This is even more essential for building Industry 4.0 and smart industrial complexes. This study prepares ISO/IEC 25024-based test methods and guidelines and uses them for energy management at the industrial complex level. In order to provide services by collecting energy data from industrial complexes, it is necessary to verify data quality based on data reliability and compatibility of each plant. Data quality technology needs to conform to ISO TC184/SC4/WG13 (industrial data quality standard) based technology. The study defines the data quality evaluation matrix for the energy management system of industrial parks and factories. It defines five categories and maps detailed indicators to each. The category has three detailed items, which are evaluation items for core requirements, interoperability, and conformity to standards. Each data requirement category covers functionality and reliability, usability and efficiency, and portability as data requirements in the system. Core requirements for system operation such as data consistency are basic evaluation items, and interoperability, which is the semantic compatibility of data for integrated operation of multiple sites, is verified. In addition, data quality is evaluated by verifying standard conformance. Through this evaluation system, the requirements for linking the factory energy management system data with the industrial complex energy management system can be evaluated. This can be used to monitor data quality and develop improvement technologies by developing a master data quality management technology standard suitable for industrial sites.

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McCorkell, SJ. "Data-base management system." American Journal of Roentgenology 144, no. 3 (1985): 649. http://dx.doi.org/10.2214/ajr.144.3.649-a.

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Martins, Diego De Oliveira, Gabriel Jurado Martins de Oliveira, Francisco Roza de Moraes, Irineu Da Silva, and André Luiz Cunha. "Geomatics data management system." Revista Brasileira de Geomática 8, no. 1 (2020): 056. http://dx.doi.org/10.3895/rbgeo.v8n1.10141.

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Shaffer, Robert E., and Jonathan S. Greiner. "SEWER SYSTEM DATA MANAGEMENT." Proceedings of the Water Environment Federation 2000, no. 4 (2000): 442–51. http://dx.doi.org/10.2175/193864700785140926.

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Veseli, Siniša, Nicholas Schwarz, and Collin Schmitz. "APS Data Management System." Journal of Synchrotron Radiation 25, no. 5 (2018): 1574–80. http://dx.doi.org/10.1107/s1600577518010056.

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As the capabilities of modern X-ray detectors and acquisition technologies increase, so do the data rates and volumes produced at synchrotron beamlines. This brings into focus a number of challenges related to managing data at such facilities, including data transfer, near real-time data processing, automated processing pipelines, data storage, handling metadata and remote user access to data. The Advanced Photon Source Data Management System software is designed to help beamlines deal with these issues. This paper presents the system architecture and describes its components and functionality; the system's current usage is discussed, examples of its use have been provided and future development plans are outlined.

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Bell, Lansford C., and Charles P. Markert. "Microcomputer Data Management System." Journal of Transportation Engineering 111, no. 3 (1985): 251–57. http://dx.doi.org/10.1061/(asce)0733-947x(1985)111:3(251).

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Ma, Qiumei, and Yao Zhang. "BESIII Data Management System." EPJ Web of Conferences 214 (2019): 04001. http://dx.doi.org/10.1051/epjconf/201921404001.

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The BESIII experiment has got about 1PB raw data and 1PB DST data from 2009 to date, so how to manage these data and condition data well is very important. The BESIII data managment system has run successfully for ten years, which has offered a full-featured, time-tested approach to BESIII offline and physics users. We designed an almost perfect structure for the system and had good backup and maintance strategy.

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Lee, Fook‐Hou, Thiam‐Soon Tan, G. P. Karunaratne, and Seng‐Lip Lee. "Geotechnical Data Management System." Journal of Computing in Civil Engineering 4, no. 3 (1990): 239–54. http://dx.doi.org/10.1061/(asce)0887-3801(1990)4:3(239).

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Dissertations / Theses on the topic "Data management system"

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Anumalla, Kalyani. "DATA PREPROCESSING MANAGEMENT SYSTEM." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1196650015.

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Wang, Yanchao. "Protein Structure Data Management System." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/20.

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With advancement in the development of the new laboratory instruments and experimental techniques, the protein data has an explosive increasing rate. Therefore how to efficiently store, retrieve and modify protein data is becoming a challenging issue that most biological scientists have to face and solve. Traditional data models such as relational database lack of support for complex data types, which is a big issue for protein data application. Hence many scientists switch to the object-oriented databases since object-oriented nature of life science data perfectly matches the architecture of object-oriented databases, but there are still a lot of problems that need to be solved in order to apply OODB methodologies to manage protein data. One major problem is that the general-purpose OODBs do not have any built-in data types for biological research and built-in biological domain-specific functional operations. In this dissertation, we present an application system with built-in data types and built-in biological domain-specific functional operations that extends the Object-Oriented Database (OODB) system by adding domain-specific additional layers Protein-QL, Protein Algebra Architecture and Protein-OODB above OODB to manage protein structure data. This system is composed of three parts: 1) Client API to provide easy usage for different users. 2) Middleware including Protein-QL, Protein Algebra Architecture and Protein-OODB is designed to implement protein domain specific query language and optimize the complex queries, also it capsulates the details of the implementation such that users can easily understand and master Protein-QL. 3) Data Storage is used to store our protein data. This system is for protein domain, but it can be easily extended into other biological domains to build a bio-OODBMS. In this system, protein, primary, secondary, and tertiary structures are defined as internal data types to simplify the queries in Protein-QL such that the domain scientists can easily master the query language and formulate data requests, and EyeDB is used as the underlying OODB to communicate with Protein-OODB. In addition, protein data is usually stored as PDB format and PDB format is old, ambiguous, and inadequate, therefore, PDB data curation will be discussed in detail in the dissertation.

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Okkonen, O. (Olli). "RESTful clinical data management system." Master's thesis, University of Oulu, 2015. http://urn.fi/URN:NBN:fi:oulu-201505291735.

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Elämme digitalisaation aikakautta, jolloin tietokoneiden avulla on mahdollista saavuttaa kustannustehokkuutta ja automaatio tiedonhallintaan. Kliiniset tutkimukset eivät ole onnistuneet pysymään mukana teknologiakehityksessä vaan turvautuvat yhä perinteisiin paperisiin keinoihin, mikä hidastaa ja vaikeuttaa tiedon hallintaa suurina määrinä viivästyttäen tutkimuksen loppuanalyysia. Suurimmat syyt tähän ovat olleen kehnot ohjelmistojen laadut, tietotaidon puute sekä epäonnistuneet käyttöönotot organisaation tasolla. Tämä diplomityö esittelee Genesiksen, web-pohjaisen kliinisen tiedonhallintajärjestelmän tukemaan LIRA-tutkimuksen tarpeita Suomessa sekä Ruotsissa. Työssä esitellään kuinka Genesiksen kehityksessä on huomioitu tietoturva ja ketterän kehityksen periaatteet tarjoamaan ohjelmistolle vaivatonta käyttöä sekä lisäarvoa uudelleenkäytettävyydellä. Uudelleenkäytettävyyttä on tavoitettu ohjelmiston helpolla siirrettävyydellä tuleviin tutkimuksiin sekä yhteensopivuutta web-pohjaisten laitteiden kanssa yhtenäisellä rajapinnalla. Lisäksi, työssä esitellään Genesiksen toteutus ja pohditaan järjestelmän tulevaisuutta. Alustavasti Genesiksen hyödyntämisestä on kiinnostunut myös maailman suurin tyypin-1 diabetes tutkimus<br>In the era of digitalization, clinical trials have often been left behind in adoption of automation and cost-efficiency offered by computerized systems. Poor implementations, lack of technical experience, and inertia caused by overlapping old and new procedures have failed to prove the business value of data management systems. This has led into settling for inadequate tools for data management, leaving many studies struggling with traditional approaches involving heavy paper usage further complicating the management and drastically slowing preparations for final analysis. This Master’s Thesis presents Genesis, a web-based clinical data management system development for the LIRA-study, which will take place in Finland and Sweden. Genesis has been developed to address the aforementioned obstacles with adopting information technology solutions in an agile manner with the integration of security concerns. Furthermore, Genesis has been designed to offer the long term value through reusability in terms of effortless portability for upcoming studies and interconnectability with web-enabled legacy system and handheld devices via a uniform interface. In addition to representing the design, implementation and evaluation of Genesis, the future prospects of Genesis are discussed, noting the preliminary interest of utilizing Genesis in additional studies, including the world’s largest type-1 diabetes study

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Tong, J. "A graphical process data management system." Thesis, Swansea University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.639249.

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This thesis aims to investigate aspects of data management and program integration in process design. It proposes a new method and model for data storage, manipulation, and representation, on which a new process design environment may be based. Several aspects of process design database systems are examined, including data models, data handling, graphical user interfaces, and application integration. Process design is an increasingly complex activity. Its complexity is compounded by a great amount of data, the variety of applications involved, and data transfer between applications. There is clear evidence that better data management is needed to support the design activity. The main focus of this research is the use of ODBC technology in a graphical process data management system (GPDMS). Since ODBC is employed, GPDMS uses ACCESS as the underlying DBMS. The GPDMS database consists of TABLSs or ACCESS, which are familiar to most software engineers. This approach benefits those who develop applications sharing data with GPDMS. This research also studies the strengths and weaknesses of existing data models for engineering usage. This leads to the Process Object Model (POM), a user-level graphical data model for handling complex process objects. A new application integration method is proposed by using MACROs to build links and exchange data between the GPDMS database and the integrated applications.

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Huml, Kathy Pederson. "Intelligent Data Object Management System (IDOMS)." Thesis, Kansas State University, 1986. http://hdl.handle.net/2097/9918.

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Tatarinov, Igor. "Semantic data sharing with a peer data management system /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/6942.

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Ofori-Duodu, Michael Samuel. "Exploring Data Security Management Strategies for Preventing Data Breaches." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7947.

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Insider threat continues to pose a risk to organizations, and in some cases, the country at large. Data breach events continue to show the insider threat risk has not subsided. This qualitative case study sought to explore the data security management strategies used by database and system administrators to prevent data breaches by malicious insiders. The study population consisted of database administrators and system administrators from a government contracting agency in the northeastern region of the United States. The general systems theory, developed by Von Bertalanffy, was used as the conceptual framework for the research study. The data collection process involved interviewing database and system administrators (n = 8), organizational documents and processes (n = 6), and direct observation of a training meeting (n = 3). By using methodological triangulation and by member checking with interviews and direct observation, efforts were taken to enhance the validity of the findings of this study. Through thematic analysis, 4 major themes emerged from the study: enforcement of organizational security policy through training, use of multifaceted identity and access management techniques, use of security frameworks, and use of strong technical control operations mechanisms. The findings of this study may benefit database and system administrators by enhancing their data security management strategies to prevent data breaches by malicious insiders. Enhanced data security management strategies may contribute to social change by protecting organizational and customer data from malicious insiders that could potentially lead to espionage, identity theft, trade secrets exposure, and cyber extortion.

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Lam, Lawrence G. "Digital Health-Data platforms : biometric data aggregation and their potential impact to centralize Digital Health-Data." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/106235.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, School of Engineering, System Design and Management Program, Engineering and Management Program, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 81).<br>Digital Health-Data is being collected at unprecedented rates today as biometric micro sensors continue to diffuse into our lives in the form of smart devices, wearables, and even clothing. From this data, we hope to learn more about preventative health so that we can spend less money on the doctor. To help users aggregate this perpetual growth of biometric "big" data, Apple HealthKit, Google Fit, and Samsung SAMI were each created with the hope of becoming the dominant design platform for Digital Health-Data. The research for this paper consists of citings from technology strategy literature and relevant journalism articles regarding recent and past developments that pertain to the wearables market and the digitization movement of electronic health records (EHR) and protected health information (PHI) along with their rules and regulations. The culmination of these citations will contribute to my hypothesis where the analysis will attempt to support my recommendations for Apple, Google, and Samsung. The ending chapters will encompass discussions around network effects and costs associated with multi-homing user data across multiple platforms and finally ending with my conclusion based on my hypothesis.<br>by Lawrence G. Lam.<br>S.M. in Engineering and Management

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Quintero, Michael C. "Constructing a Clinical Research Data Management System." Thesis, University of South Florida, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10640886.

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<p> Clinical study data is usually collected without knowing what kind of data is going to be collected in advance. In addition, all of the possible data points that can apply to a patient in any given clinical study is almost always a superset of the data points that are actually recorded for a given patient. As a result of this, clinical data resembles a set of sparse data with an evolving data schema. To help researchers at the Moffitt Cancer Center better manage clinical data, a tool was developed called GURU that uses the Entity Attribute Value model to handle sparse data and allow users to manage a database entity’s attributes without any changes to the database table definition. The Entity Attribute Value model’s read performance gets faster as the data gets sparser but it was observed to perform many times worse than a wide table if the attribute count is not sufficiently large. Ultimately, the design trades read performance for flexibility in the data schema.</p><p>

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Quintero, Michael C. "Constructing a Clinical Research Data Management System." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7081.

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Clinical study data is usually collected without knowing what kind of data is going to be collected in advance. In addition, all of the possible data points that can apply to a patient in any given clinical study is almost always a superset of the data points that are actually recorded for a given patient. As a result of this, clinical data resembles a set of sparse data with an evolving data schema. To help researchers at the Moffitt Cancer Center better manage clinical data, a tool was developed called GURU that uses the Entity Attribute Value model to handle sparse data and allow users to manage a database entity’s attributes without any changes to the database table definition. The Entity Attribute Value model’s read performance gets faster as the data gets sparser but it was observed to perform many times worse than a wide table if the attribute count is not sufficiently large. Ultimately, the design trades read performance for flexibility in the data schema.

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Books on the topic "Data management system"

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United States. Environmental Protection Agency. Office of Research and Development, ed. GIS\Key Environmental Data Management System. U.S. Environmental Protection Agency, Office of Research and Development, 1994.

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Organization, World Meteorological, ed. CLICOM project: (Climate data management system). World Meteorlogical Organization, 1989.

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Ferrigno, Carmelo F. A data-management system for detailed areal interpretive data. U.S. Dept. of the Interior, Geological Survey, 1986.

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Ferrigno, Carmelo F. A data-management system for detailed areal interpretive data. U.S. Dept. of the Interior, Geological Survey, 1986.

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Ferrigno, Carmelo F. A data-management system for detailed areal interpretive data. U.S. Dept. of the Interior, Geological Survey, 1986.

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Ferrigno, Carmelo F. A data-management system for detailed areal interpretive data. U.S. Dept. of the Interior, Geological Survey, 1986.

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Ferrigno, Carmelo F. A data-management system for detailed areal interpretive data. U.S. Dept. of the Interior, Geological Survey, 1986.

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Ferrigno, Carmelo F. A data-management system for detailed areal interpretive data. U.S. Dept. of the Interior, Geological Survey, 1986.

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Awani, Alfred O. Data processing project management. Petrocelli Books, 1986.

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Global Climate Observing System. Data and Information Management Panel. Data and information management plan. Joint Planning Office, Global Climate Observing System, 1995.

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Book chapters on the topic "Data management system"

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Weik, Martin H. "data management system." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_4319.

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Majumder, Soumi, and Nilanjan Dey. "Knowledge Management System." In Studies in Big Data. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0316-8_4.

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Ceruti, Marion G. "A Review of Database System Terminology." In Data Management, 3rd ed. Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878-4.

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Cudré-Mauroux, Philippe. "Peer Data Management System." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_1233-2.

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Cudré-Mauroux, Philippe. "Peer Data Management System." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_1233.

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Cudré-Mauroux, Philippe. "Peer Data Management System." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_1233.

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Strekalova, Yulia A., and Mustapha Bouakkaz. "Content Management System (CMS)." In Encyclopedia of Big Data. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_43.

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Strekalova, Yulia A., and Mustapha Bouakkaz. "Content Management System (CMS)." In Encyclopedia of Big Data. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-32001-4_43-1.

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Burnette, Gerald A. "Relational Database Management System Basics." In Managing Environmental Data. CRC Press, 2021. http://dx.doi.org/10.1201/9781003131953-5.

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Stuart, Gloria J., Bruce I. Blum, and Raymond E. Lenhard. "Clinical Data Management." In A Clinical Information System for Oncology. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3638-2_4.

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Conference papers on the topic "Data management system"

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Warner, J. W. "Environmental Data Management System." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 1993. http://dx.doi.org/10.2118/26363-ms.

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Fu, Lingli, Sheng Ding, and Tao Chen. "Clinical Data Management System." In 2010 International Conference on Biomedical Engineering and Computer Science (ICBECS). IEEE, 2010. http://dx.doi.org/10.1109/icbecs.2010.5462386.

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Vinter, Brian, Jonas Bardino, Martin Rehr, Klaus Birkelund, and Mads Ohm Larsen. "Imaging Data Management System." In EuroSys '17: Twelfth EuroSys Conference 2017. ACM, 2017. http://dx.doi.org/10.1145/3068126.3071061.

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Teague, Ralph. "Surveillance data management system." In Optomechatronic Systems III, edited by Toru Yoshizawa. SPIE, 2002. http://dx.doi.org/10.1117/12.469989.

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Yao, Leehter, Chien-Chi Lai, and Wei Hong Lim. "Home Energy Management System Based on Photovoltaic System." In 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS). IEEE, 2015. http://dx.doi.org/10.1109/dsdis.2015.42.

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Raza, Basit, Abdul Mateen, Muhammad Sher, Mian Muhammad Awais, and Tauqeer Hussain. "Autonomicity in Oracle Database Management System." In 2010 International Conference on Data Storage and Data Engineering (DSDE). IEEE, 2010. http://dx.doi.org/10.1109/dsde.2010.72.

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Tsilingeridis, Odysseas, and Alexandros Karakasidis. "MILMS: A Microservices-based Learning Management System." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378285.

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Tang, Xuejiao, Jiong Qiu, Ruijun Chen, et al. "A Data-driven Human Responsibility Management System." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378484.

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Coole, Matthew, Paul Rayson, and John Mariani. "lexiDB: A scalable corpus database management system." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7841062.

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Dash, Debasis, Juthika Dash, Younghee Park, and Jerry Gao. "Integrated Data Management System for Data Center." In The 28th International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc. and Knowledge Systems Institute Graduate School, 2016. http://dx.doi.org/10.18293/seke2016-128.

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Reports on the topic "Data management system"

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McCullouch, Bob. Automated Construction Data Management System. Purdue University, 1991. http://dx.doi.org/10.5703/1288284314186.

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McCullouch, Bob. Automated Construction Data Management System. Purdue University Press, 1991. http://dx.doi.org/10.5703/1288284313424.

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Kerrigan, W. Analytical Data Management System (ADMS). Office of Scientific and Technical Information (OSTI), 1987. http://dx.doi.org/10.2172/6845581.

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Wilson, D. Data management plan for HANDI 2000 business management system. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/362441.

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Thomas E. McDermott. Standard Data Exchanges for Distribution System Management. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/917256.

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Narva, Adele. Mobilization Data Base Management System (MOBDABS) Documentation. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada200181.

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Fowler, Kimberly M., Christopher J. Anderson, and Benjamin E. Ford. Energy Data Management System Commercial Product Summary. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1400350.

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McCullouch, Bob. Automated Construction Data Management System : Executive Summary. Purdue University, 1991. http://dx.doi.org/10.5703/1288284314187.

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Bierman, A., and M. Bjorklund. A YANG Data Model for System Management. RFC Editor, 2014. http://dx.doi.org/10.17487/rfc7317.

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Wolfe, James, David J. Carney, and Audrey A. Hook. Defense Logistics Agency Data System Center Forms Management System (FMS). Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada196633.

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