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Добірка наукової літератури з теми "Data management and data science"

Автор: Grafiati

Опубліковано: 10 січня 2023

Оновлено: 28 січня 2023

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Статті в журналах з теми "Data management and data science"

Schäffer, Utz, and Jürgen Weber. "Management von Data Science." Controlling & Management Review 65, no. 8 (November 2021): 3. http://dx.doi.org/10.1007/s12176-021-0423-4.

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Sévigny, Alex. "Data science and communications management." Journal of Professional Communication 5, no. 2 (October 12, 2018): 3–6. http://dx.doi.org/10.15173/jpc.v5i2.3745.

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In this editorial for issue two of volume five of the Journal of ProfessionalCommunication, the author discusses how data science is changingthe communications landscape. He suggests that advances intechnology are making it easier to learn about and communicate withpublics. The author challenges communciations professionals to makebetter use of this new technology in their own work.

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Lagoze, Carl, William C. Block, Jeremy Williams, John Abowd, and Lars Vilhuber. "Data Management of Confidential Data." International Journal of Digital Curation 8, no. 1 (June 14, 2013): 265–78. http://dx.doi.org/10.2218/ijdc.v8i1.259.

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Social science researchers increasingly make use of data that is confidential because it contains linkages to the identities of people, corporations, etc. The value of this data lies in the ability to join the identifiable entities with external data, such as genome data, geospatial information, and the like. However, the confidentiality of this data is a barrier to its utility and curation, making it difficult to fulfil US federal data management mandates and interfering with basic scholarly practices, such as validation and reuse of existing results. We describe the complexity of the relationships among data that span a public and private divide. We then describe our work on the CED2AR prototype, a first step in providing researchers with a tool that spans this divide and makes it possible for them to search, access and cite such data.

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George, Gerard, Ernst C. Osinga, Dovev Lavie, and Brent A. Scott. "Big Data and Data Science Methods for Management Research." Academy of Management Journal 59, no. 5 (October 2016): 1493–507. http://dx.doi.org/10.5465/amj.2016.4005.

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Timinsky, Alexander, Anna Kolomiiets, and Olga Mezentseva. "Project management models to create IT company in the field of Data Science." Advanced Information Technology, no. 1 (1) (2021): 86–94. http://dx.doi.org/10.17721/ait.2021.1.11.

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The basis for the emergence of projects to create IT companies in the field of Data Science is considered. The relevance of such projects is substantiated. The feasibility of their study using models and methods of project management is proven. Literary sources in three directions are analyzed. Such areas include classical project management, flexible approaches to project management, value-oriented management. Insufficient research of the described subject is proved. Groups of models and methods are described that describe the relevant areas of project management knowledge that will be necessary and minimally sufficient to develop the scientific basis of the IT company creation project in the field of Data Science. Namely: project team management, project value management, project concept development, project content management, project communications management, flexible project management tools. In the analysis of each area, the relevant models and methods are highlighted. Their applicability to the researched project is analyzed. A model for selecting a set of models and methods within the identified key knowledge areas for the research project in the form of a convolution of criteria is proposed. SWOT-analysis of the proposed approach was performed. The strengths, weaknesses, opportunities and threats associated with the proposed approach are highlighted. Conclusions to the study are formulated. Prospects for further research in the chosen direction are outlined.

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Maienschein, Jane, John N. Parker, Manfred Laubichler, and Edward J. Hackett. "Data Management and Data Sharing in Science and Technology Studies." Science, Technology, & Human Values 44, no. 1 (September 18, 2018): 143–60. http://dx.doi.org/10.1177/0162243918798906.

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This paper presents reports on discussions among an international group of science and technology studies (STS) scholars who convened at the US National Science Foundation (January 2015) to think about data sharing and open STS. The first report, which reflects discussions among members of the Society for Social Studies of Science (4S), relates the potential benefits of data sharing and open science for STS. The second report, which reflects discussions among scholars from many professional STS societies (i.e., European Association for the Study of Science and Technology [ EASST], 4S, Society for the History of Technology [ SHOT], History of Science Society [ HSS], and Philosophy of Science Association [ PSA]), focuses on practical and conceptual issues related to managing, storing, and curating STS data. As is the case for all reports of such open discussions, a scholar’s presence at the meeting does not necessarily mean that they agree with all aspects of the text to follow.

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Abedjan, Ziawasch. "Enabling data-centric AI through data quality management and data literacy." it - Information Technology 64, no. 1-2 (February 18, 2022): 67–70. http://dx.doi.org/10.1515/itit-2021-0048.

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Abstract Data is being produced at an intractable pace. At the same time, there is an insatiable interest in using such data for use cases that span all imaginable domains, including health, climate, business, and gaming. Beyond the novel socio-technical challenges that surround data-driven innovations, there are still open data processing challenges that impede the usability of data-driven techniques. It is commonly acknowledged that overcoming heterogeneity of data with regard to syntax and semantics to combine various sources for a common goal is a major bottleneck. Furthermore, the quality of such data is always under question as the data science pipelines today are highly ad-hoc and without the necessary care for provenance. Finally, quality criteria that go beyond the syntactical and semantic correctness of individual values but also incorporate population-level constraints, such as equal parity and opportunity with regard to protected groups, play a more and more important role in this process. Traditional research on data integration was focused on post-merger integration of companies, where customer or product databases had to be integrated. While this is often hard enough, today the challenges aggravate because of the fact that more stakeholders are using data analytics tools to derive domain-specific insights. I call this phenomenon the democratization of data science, a process, which is both challenging and necessary. Novel systems need to be user-friendly in a way that not only trained database admins can handle them but also less computer science savvy stakeholders. Thus, our research focuses on scalable example-driven techniques for data preparation and curation. Furthermore, we believe that it is important to educate the breadth of society on implications of a data-driven world and actively promote the concept of data literacy as a fundamental competence.

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Bailo, Daniele, Keith G. Jeffery, Kuvvet Atakan, Luca Trani, Rossana Paciello, Valerio Vinciarelli, Jan Michalek, and Alessandro Spinuso. "Data integration and FAIR data management in Solid Earth Science." Annals of Geophysics 65, no. 2 (April 29, 2022): DM210. http://dx.doi.org/10.4401/ag-8742.

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Integrated use of multidisciplinary data is nowadays a recognized trend in scientific research, in particular in the domain of solid Earth science where the understanding of a physical process is improved and made complete by different types of measurements – for instance, ground acceleration, SAR imaging, crustal deformation – describing a physical phenomenon. FAIR principles are recognized as a means to foster data integration by providing a common set of criteria for building data stewardship systems for Open Science. However, the implementation of FAIR principles raises issues along dimensions like governance and legal beyond, of course, the technical one. In the latter, in particular, the development of FAIR data provision systems is often delegated to Research Infrastructures or data providers, with support in terms of metrics and best practices offered by cluster projects or dedicated initiatives. In the current work, we describe the approach to FAIR data management in the European Plate Observing System (EPOS), a distributed research infrastructure in the solid Earth science domain that includes more than 250 individual research infrastructures across 25 countries in Europe. We focus in particular on the technical aspects, but including also governance, policies and organizational elements, by describing the architecture of the EPOS delivery framework both from the organizational and technical point of view and by outlining the key principles used in the technical design. We describe how a combination of approaches, namely rich metadata and service-based systems design, are required to achieve data integration. We show the system architecture and the basic features of the EPOS data portal, that integrates data from more than 220 services in a FAIR way. The construction of such a portal was driven by the EPOS FAIR data management approach, that by defining a clear roadmap for compliance with the FAIR principles, produced a number of best practices and technical approaches for complying with the FAIR principles. Such a work, that spans over a decade but concentrates the key efforts in the last 5 years with the EPOS Implementation Phase project and the establishment of EPOS-ERIC, was carried out in synergy with other EU initiatives dealing with FAIR data. On the basis of the EPOS experience, future directions are outlined, emphasizing the need to provide i) FAIR reference architectures that can ease data practitioners and engineers from the domain communities to adopt FAIR principles and build FAIR data systems; ii) a FAIR data management framework addressing FAIR through the entire data lifecycle, including reproducibility and provenance; and iii) the extension of the FAIR principles to policies and governance dimensions. .

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CAMPBELL, W., P. SMITH, R. PRICE, and L. ROELOFS. "Advancements in land science data management Pilot Land Data System." Science of The Total Environment 56 (November 15, 1986): 31–44. http://dx.doi.org/10.1016/0048-9697(86)90311-6.

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Vilar, Polona, and Vlasta Zabukovec. "Research data management and research data literacy in Slovenian science." Journal of Documentation 75, no. 1 (January 14, 2019): 24–43. http://dx.doi.org/10.1108/jd-03-2018-0042.

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PurposeThe purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level.Design/methodology/approachPurposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate methods (clustering) were used.FindingsThe authors identified three perception-related and four behavior-related connections; this gave three clusters per area. First, perceptions – skeptical group, mainly social (SocS) and natural sciences (NatS): no clear RDM and ethical issues standpoints, do not agree that every university needs a data management plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS, engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an ethical question, is positive toward institutional DMP’s. Second, behaviors – sparse group, mainly from MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets with metadata, do not use file-naming conventions/standards. Frequent group – many ENG, SocS, moderate numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/standards, version-control systems, have experience with public-domain data, are reluctant to use metadata with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no MedHeS: often use public-domain data, other three activities are rare.Research limitations/implicationsResearch could be expanded to a wider population, include other stakeholders and use qualitative methods.Practical implicationsResults are useful for international comparisons but also give foundations and recommendations on institutional and national RDM and RDL policies, implementations, and how to bring academic libraries into the picture. Identified differences suggest that different educational, awareness-raising and participatory approaches are needed for each group.Originality/valueThe findings offer valuable insight into RDM and RDL of Slovenian scientists, which have not yet been investigated in Slovenia.

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Дисертації з теми "Data management and data science"

Yang, Ying. "Interactive Data Management and Data Analysis." Thesis, State University of New York at Buffalo, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10288109.

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Everyone today has a big data problem. Data is everywhere and in different formats, they can be referred to as data lakes, data streams, or data swamps. To extract knowledge or insights from the data or to support decision-making, we need to go through a process of collecting, cleaning, managing and analyzing the data. In this process, data cleaning and data analysis are two of the most important and time-consuming components.

One common challenge in these two components is a lack of interaction. The data cleaning and data analysis are typically done as a batch process, operating on the whole dataset without any feedback. This leads to long, frustrating delays during which users have no idea if the process is effective. Lacking interaction, human expert effort is needed to make decisions on which algorithms or parameters to use in the systems for these two components.

We should teach computers to talk to humans, not the other way around. This dissertation focuses on building systems --- Mimir and CIA --- that help user conduct data cleaning and analysis through interaction. Mimir is a system that allows users to clean big data in a cost- and time-efficient way through interaction, a process I call on-demand ETL. Convergent inference algorithms (CIA) are a family of inference algorithms in probabilistic graphical models (PGM) that enjoys the benefit of both exact and approximate inference algorithms through interaction.

Mimir provides a general language for user to express different data cleaning needs. It acts as a shim layer that wraps around the database making it possible for the bulk of the ETL process to remain within a classical deterministic system. Mimir also helps users to measure the quality of an analysis result and provides rankings for cleaning tasks to improve the result quality in a cost efficient manner. CIA focuses on providing user interaction through the process of inference in PGMs. The goal of CIA is to free users from the upfront commitment to either approximate or exact inference, and provide user more control over time/accuracy trade-offs to direct decision-making and computation instance allocations. This dissertation describes the Mimir and CIA frameworks to demonstrate that it is feasible to build efficient interactive data management and data analysis systems.

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Dedge, Parks Dana M. "Defining Data Science and Data Scientist." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/7014.

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The world’s data sets are growing exponentially every day due to the large number of devices generating data residue across the multitude of global data centers. What to do with the massive data stores, how to manage them and defining who are performing these tasks has not been adequately defined and agreed upon by academics and practitioners. Data science is a cross disciplinary, amalgam of skills, techniques and tools which allow business organizations to identify trends and build assumptions which lead to key decisions. It is in an evolutionary state as new technologies with capabilities are still being developed and deployed. The data science tasks and the data scientist skills needed in order to be successful with the analytics across the data stores are defined in this document. The research conducted across twenty-two academic articles, one book, eleven interviews and seventy-eight surveys are combined to articulate the convergence on the terms data science. In addition, the research identified that there are five key skill categories (themes) which have fifty-five competencies that are used globally by data scientists to successfully perform the art and science activities of data science. Unspecified portions of statistics, technology programming, development of models and calculations are combined to determine outcomes which lead global organizations to make strategic decisions every day. This research is intended to provide a constructive summary about the topics data science and data scientist in order to spark the dialogue for us to formally finalize the definitions and ultimately change the world by establishing set guidelines on how data science is performed and measured.

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Wason, Jasmin Lesley. "Automating data management in science and engineering." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396143.

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Wang, Yi. "Data Management and Data Processing Support on Array-Based Scientific Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436157356.

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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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Fernández, Moctezuma Rafael J. "A Data-Descriptive Feedback Framework for Data Stream Management Systems." PDXScholar, 2012. https://pdxscholar.library.pdx.edu/open_access_etds/116.

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Data Stream Management Systems (DSMSs) provide support for continuous query evaluation over data streams. Data streams provide processing challenges due to their unbounded nature and varying characteristics, such as rate and density fluctuations. DSMSs need to adapt stream processing to these changes within certain constraints, such as available computational resources and minimum latency requirements in producing results. The proposed research develops an inter-operator feedback framework, where opportunities for run-time adaptation of stream processing are expressed in terms of descriptions of substreams and actions applicable to the substreams, called feedback punctuations. Both the discovery of adaptation opportunities and the exploitation of these opportunities are performed in the query operators. DSMSs are also concerned with state management, in particular, state derived from tuple processing. The proposed research also introduces the Contracts Framework, which provides execution guarantees about state purging in continuous query evaluation for systems with and without inter-operator feedback. This research provides both theoretical and design contributions. The research also includes an implementation and evaluation of the feedback techniques in the NiagaraST DSMS, and a reference implementation of the Contracts Framework.

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Nguyen, Benjamin. "Privacy-Centric Data Management." Habilitation à diriger des recherches, Université de Versailles-Saint Quentin en Yvelines, 2013. http://tel.archives-ouvertes.fr/tel-00936130.

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This document will focus on my core computer science research since 2010, covering the topic of data management and privacy. More speci cally, I will present the following topics : - A new paradigm, called Trusted Cells for privacy-centric personal data management based on the Asymmetric Architecture composed of trusted or open (low power) distributed hardware devices acting as personal data servers and a highly powerful, highly available supporting server, such as a cloud. (Chapter 2). - Adapting aggregate data computation techniques to the Trusted Cells environment, with the example of Privacy-Preserving Data Publishing (Chapter 3). - Minimizing the data that leaves a Trusted Cell, i.e. enforcing the general privacy principle of Limited Data Collection (Chapter 4). This document contains only results that have already been published. As such, rather than focus on the details and technicalities of each result, I have tried to provide an easy way to have a global understanding of the context behind the work, explain the problematic of the work, and give a summary of the main scienti c results and impact.

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Tran, Viet-Trung. "Scalable data-management systems for Big Data." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00920432.

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Big Data can be characterized by 3 V's. * Big Volume refers to the unprecedented growth in the amount of data. * Big Velocity refers to the growth in the speed of moving data in and out management systems. * Big Variety refers to the growth in the number of different data formats. Managing Big Data requires fundamental changes in the architecture of data management systems. Data storage should continue being innovated in order to adapt to the growth of data. They need to be scalable while maintaining high performance regarding data accesses. This thesis focuses on building scalable data management systems for Big Data. Our first and second contributions address the challenge of providing efficient support for Big Volume of data in data-intensive high performance computing (HPC) environments. Particularly, we address the shortcoming of existing approaches to handle atomic, non-contiguous I/O operations in a scalable fashion. We propose and implement a versioning-based mechanism that can be leveraged to offer isolation for non-contiguous I/O without the need to perform expensive synchronizations. In the context of parallel array processing in HPC, we introduce Pyramid, a large-scale, array-oriented storage system. It revisits the physical organization of data in distributed storage systems for scalable performance. Pyramid favors multidimensional-aware data chunking, that closely matches the access patterns generated by applications. Pyramid also favors a distributed metadata management and a versioning concurrency control to eliminate synchronizations in concurrency. Our third contribution addresses Big Volume at the scale of the geographically distributed environments. We consider BlobSeer, a distributed versioning-oriented data management service, and we propose BlobSeer-WAN, an extension of BlobSeer optimized for such geographically distributed environments. BlobSeer-WAN takes into account the latency hierarchy by favoring locally metadata accesses. BlobSeer-WAN features asynchronous metadata replication and a vector-clock implementation for collision resolution. To cope with the Big Velocity characteristic of Big Data, our last contribution feautures DStore, an in-memory document-oriented store that scale vertically by leveraging large memory capability in multicore machines. DStore demonstrates fast and atomic complex transaction processing in data writing, while maintaining high throughput read access. DStore follows a single-threaded execution model to execute update transactions sequentially, while relying on a versioning concurrency control to enable a large number of simultaneous readers.

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Nyström, Dag. "Data Management in Vehicle Control-Systems." Doctoral thesis, Mälardalen University, Department of Computer Science and Electronics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-66.

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As the complexity of vehicle control-systems increases, the amount of information that these systems are intended to handle also increases. This thesis provides concepts relating to real-time database management systems to be used in such control-systems. By integrating a real-time database management system into a vehicle control-system, data management on a higher level of abstraction can be achieved. Current database management concepts are not sufficient for use in vehicles, and new concepts are necessary. A case-study at Volvo Construction Equipment Components AB in Eskilstuna, Sweden presented in this thesis, together with a survey of existing database platforms confirms this. The thesis specifically addresses data access issues by introducing; (i) a data access method, denoted database pointers, which enables data in a real-time database management system to be accessed efficiently. Database pointers, which resemble regular pointers variables, permit individual data elements in the database to be directly pointed out, without risking a violation of the database integrity. (ii) two concurrency-control algorithms, denoted 2V-DBP and 2V-DBP-SNAP which enable critical (hard real-time) and non-critical (soft real-time) data accesses to co-exist, without blocking of the hard real-time data accesses or risking unnecessary abortions of soft real-time data accesses. The thesis shows that 2V-DBP significantly outperforms a standard real-time concurrency control algorithm both with respect to lower response-times and minimized abortions. (iii) two concepts, denoted substitution and subscription queries that enable service- and diagnostics-tools to stimulate and monitor a control-system during run-time. The concepts presented in this thesis form a basis on which a data management concept suitable for embedded real-time systems, such as vehicle control-systems, can be built.

Ett modernt fordon är idag i princip helt styrt av inbyggda datorer. I takt med att funktionaliteten i fordonen ökar, blir programvaran i dessa datorer mer och mer komplex. Komplex programvara är svår och kostsam att konstruera. För att hantera denna komplexitet och underlätta konstruktion, satsar nu industrin på att finna metoder för att konstruera dessa system på en högre abstraktionsnivå. Dessa metoder syftar till att strukturera programvaran idess olika funktionella beståndsdelar, till exempel genom att använda så kallad komponentbaserad programvaruutveckling. Men, dessa metoder är inte effektiva vad gäller att hantera den ökande mängden information som följer med den ökande funktionaliteten i systemen. Exempel på information som skall hanteras är data från sensorer utspridda i bilen (temperaturer, tryck, varvtal osv.), styrdata från föraren (t.ex. rattutslag och gaspådrag), parameterdata, och loggdata som används för servicediagnostik. Denna information kan klassas som säkerhetskritisk eftersom den används för att styra beteendet av fordonet. På senare tid har dock mängden icke säkerhetskritisk information ökat, exempelvis i bekvämlighetssystem som multimedia-, navigations- och passagerarergonomisystem.

Denna avhandling syftar till att visa hur ett datahanteringssystem för inbyggda system, till exempel fordonssystem, kan konstrueras. Genom att använda ett realtidsdatabashanteringssystem för att lyfta upp datahanteringen på en högre abstraktionsnivå kan fordonssystem tillåtas att hantera stora mängder information på ett mycket enklare sätt än i nuvarande system. Ett sådant datahanteringssystem ger systemarkitekterna möjlighet att strukturera och modellera informationen på ett logiskt och överblickbart sätt. Informationen kan sedan läsas och uppdateras genom standardiserade gränssnitt anpassade förolika typer av funktionalitet. Avhandlingen behandlar specifikt problemet hur information i databasen, med hjälp av en concurrency-control algoritm, skall kunna delas av både säkerhetskritiska och icke säkerhetskritiska systemfunktioner i fordonet. Vidare avhandlas hur information kan distribueras både mellan olika datorsystem i fordonet, men också till diagnostik- och serviceverktyg som kan kopplas in i fordonet.

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Karras, Panagiotis. "Data structures and algorithms for data representation in constrained environments." Thesis, Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38897647.

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Книги з теми "Data management and data science"

Data with semantics: Data models and data management. New York: Van Nostrand Reinhold, 1989.

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Data management and data description. Aldershot, Hants, England: Ashgate, 1992.

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Data management and data description. Aldershot, Hants, England: Ashgate, 1992.

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Lawrence, Dubov, ed. Master data management and data governance. 2nd ed. New York: McGraw-Hill, 2011.

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Ghosh, Sakti P. Data base organization for data management. 2nd ed. Orlando (Fla.): Academic Press, 1986.

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Data base organization for data management. 2nd ed. Orlando, Fla: Academic Press, 1986.

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Liu, Qing. Data Provenance and Data Management in eScience. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Data sharing using a common data architecture. New York: Wiley, 1994.

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Polkowski, Zdzislaw, Sambit Kumar Mishra, and Julian Vasilev. Data Science in Engineering and Management. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003216278.

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Borah, Samarjeet, Sambit Kumar Mishra, Brojo Kishore Mishra, Valentina Emilia Balas, and Zdzislaw Polkowski, eds. Advances in Data Science and Management. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5685-9.

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Частини книг з теми "Data management and data science"

Zanin, Massimiliano, Andrew Cook, and Seddik Belkoura. "Data Science." In Complexity Science in Air Traffic Management, 105–29. Burlington, VT : Ashgate, [2016] |: Routledge, 2016. http://dx.doi.org/10.4324/9781315573205-7.

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Weik, Martin H. "management data." In Computer Science and Communications Dictionary, 971. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_10996.

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Weik, Martin H. "data management." In Computer Science and Communications Dictionary, 352. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_4318.

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Gadatsch, Andreas, and Dirk Schreiber. "Management von Big Data Projekten." In Data Science, 41–62. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33403-1_3.

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Busulwa, Richard, and Nina Evans. "Data, data management, data analytics, and data science technologies." In Digital Transformation in Accounting, 183–96. Abingdon, Oxon ; New York, NY : Routledge, 2021. | Series: Business & digital transformation: Routledge, 2021. http://dx.doi.org/10.4324/9780429344589-18.

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Kampakis, Stylianos. "Data Management." In The Decision Maker's Handbook to Data Science, 23–29. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5494-3_2.

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Papp, Stefan, and Bernhard Ortner. "Data Management." In The Handbook of Data Science and AI, 131–51. München: Carl Hanser Verlag GmbH & Co. KG, 2022. http://dx.doi.org/10.3139/9781569908877.005.

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Spengler, Sylvia. "Data Scientists, Data Management and Data Policy." In Lecture Notes in Computer Science, 490. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22351-8_32.

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Dhaya, R., M. Devi, R. Kanthavel, and Fahad AlGarni. "Big Data Analysis and Management in Healthcare." In Data Science, 127–57. Boca Raton : CRC Press, [2020]: CRC Press, 2019. http://dx.doi.org/10.1201/9780429263798-6.

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Vermeulen, Andreas François. "Three Management Layers." In Practical Data Science, 119–45. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3054-1_6.

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Тези доповідей конференцій з теми "Data management and data science"

Getoor, Lise. "Responsible Data Science." In SIGMOD/PODS '19: International Conference on Management of Data. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3299869.3314117.

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Parashar, Manish. "Data-Management for Extreme Science." In HPDC '22: The 31st International Symposium on High-Performance Parallel and Distributed Computing. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3502181.3537771.

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Stoyanovich, Julia. "Teaching Responsible Data Science." In SIGMOD/PODS '22: International Conference on Management of Data. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3531072.3535318.

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Baunsgaard, Sebastian, Matthias Boehm, Ankit Chaudhary, Behrouz Derakhshan, Stefan Geißelsöder, Philipp M. Grulich, Michael Hildebrand, et al. "ExDRa: Exploratory Data Science on Federated Raw Data." In SIGMOD/PODS '21: International Conference on Management of Data. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3448016.3457549.

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Kumar, Arun. "Automation of Data Prep, ML, and Data Science." In SIGMOD/PODS '21: International Conference on Management of Data. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3448016.3457537.

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Zhan, Zheng, Zheng Xiaojing, Taiyuanyuan, Zhao Wei, and Cai Tianqi. "Complexity science management and big data." In 2014 IEEE International Conference on Granular Computing (GrC). IEEE, 2014. http://dx.doi.org/10.1109/grc.2014.6982867.

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Fisler, Kathi. "Data-Centricity: Rethinking Introductory Computing to Support Data Science." In SIGMOD/PODS '22: International Conference on Management of Data. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3531072.3535317.

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Rossi, Rogério, and Kechi Hirama. "Characterizing Big Data Management." In InSITE 2015: Informing Science + IT Education Conferences: USA. Informing Science Institute, 2015. http://dx.doi.org/10.28945/2192.

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[The final form of this paper was published in the journal Issues in Informing Science and Information Technology.] Considering that big data is a reality for an increasing number of organizations in many areas, its management represents a set of challenges involving big data modeling, storage and retrieval, analysis and visualization. However, technological resources, people and processes are crucial dimensions to facilitate the management of big data in any organization, allowing information and knowledge from a large volume of data to support decision-making. Big data management must be supported by technology, people and processes; hence, this article discusses these three dimensions: the technologies for storage, analysis and visualization of big data; the human aspects of big data; and, in addition, the process management involved in a technological and business approach for big data management.

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Handley, Thomas H., Y. P. Li, and Mark R. Rubin. "DataHub: knowledge-based science data management for exploratory data analysis." In Recent Advances in Sensors, Radiometric Calibration, and Processing of Remotely Sensed Data. SPIE, 1993. http://dx.doi.org/10.1117/12.161564.

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Govind, Yash, Pradap Konda, Paul Suganthan G.C., Philip Martinkus, Palaniappan Nagarajan, Han Li, Aravind Soundararajan, et al. "Entity Matching Meets Data Science." In SIGMOD/PODS '19: International Conference on Management of Data. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3299869.3314042.

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Звіти організацій з теми "Data management and data science"

Mount, Richard P. The Office of Science Data-Management Challenge. Office of Scientific and Technical Information (OSTI), October 2005. http://dx.doi.org/10.2172/878079.

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Maltzahn, Carlos. Science-Driven Data Management for Multi-Tiered Storage. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1594174.

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Parashar, Manish. SIRIUS: Science-Driven Data Management for Multi-Tiered Storage. Office of Scientific and Technical Information (OSTI), November 2018. http://dx.doi.org/10.2172/1736017.

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Ancion, Zoé, Francis Andre, Sarah Cadorel, Romain Feret, Odile Hologne, Kenneth Maussang, Marine Moguen-Toursel, and Véronique Stoll. Data Management Plan - Recommendations to the ANR. Ministère de l'enseignement supérieur et de la recherche, June 2019. http://dx.doi.org/10.52949/23.

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In this note, the Committee for Open Science provides 15 recommendations to the French National Agency for Research (ANR) for the implementation of a data management plan. The committee draws attention to a step-by-step approach would encourage community adoption and better adaptation to changing practices.

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Kolda, T., D. Brown, J. Corones, T. Critchlow, T. Eliassi-Rad, L. Getoor, B. Hendrickson, et al. Data Sciences Technology for Homeland Security Information Management and Knowledge Discovery. Office of Scientific and Technical Information (OSTI), January 2005. http://dx.doi.org/10.2172/917886.

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Semerikov, Serhiy O., Vladyslav S. Pototskyi, Kateryna I. Slovak, Svitlana M. Hryshchenko, and Arnold E. Kiv. Automation of the Export Data from Open Journal Systems to the Russian Science Citation Index. [б. в.], November 2018. http://dx.doi.org/10.31812/123456789/2651.

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It is shown that the calculation of scientometric indicators of the scientist and also the scientific journal continues to be an actual problem nowadays. It is revealed that the leading scientometric databases have the capabilities of automated metadata collection from the scientific journal website by the use of specialized electronic document management systems, in particular Open Journal Systems. It is established that Open Journal Systems successfully exports metadata about an article from scientific journals to scientometric databases Scopus, Web of Science and Google Scholar. However, there is no standard method of export from Open Journal Systems to such scientometric databases as the Russian Science Citation Index and Index Copernicus, which determined the need for research. The aim of the study is to develop the plug-in to the Open Journal Systems for the export of data from this system to scientometric database Russian Science Citation Index. As a result of the study, an infological model for exporting metadata from Open Journal Systems to the Russian Science Citation Index was proposed. The SirenExpo plug-in was developed to export data from Open Journal Systems to the Russian Science Citation Index by the use of the Articulus release preparation system.

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Moulton, David, Dean Williams, Deb Agarwal, Tom Boden, Roelof Versteeg, Charlie Koven, Tim Scheibe, et al. Building a Cyberinfrastructure for Environmental System Science: Modeling Frameworks, Data Management, and Scientific Workflows, Workshop Report, Potomac, Maryland, April 30-May 1, 2015. Office of Scientific and Technical Information (OSTI), November 2015. http://dx.doi.org/10.2172/1471414.

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Soenen, Karen, Dana Gerlach, Christina Haskins, Taylor Heyl, Danie Kinkade, Sawyer Newman, Shannon Rauch, et al. How can BCO-DMO help with your oceanographic data? How can BCO-DMO help with your oceanographic data?, December 2021. http://dx.doi.org/10.1575/1912/27803.

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BCO-DMO curates a database of research-ready data spanning the full range of marine ecosystem related measurements including in-situ and remotely sensed observations, experimental and model results, and synthesis products. We work closely with investigators to publish data and information from research projects supported by the National Science Foundation (NSF), as well as those supported by state, private, and other funding sources. BCO-DMO supports all phases of the data life cycle and ensures open access of well-curated project data and information. We employ F.A.I.R. Principles that comprise a set of values intended to guide data producers and publishers in establishing good data management practices that will enable effective reuse.

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Shapovalov, Yevhenii B., Viktor B. Shapovalov, and Vladimir I. Zaselskiy. TODOS as digital science-support environment to provide STEM-education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3250.

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The amount of scientific information has been growing exponentially. It became more complicated to process and systemize this amount of unstructured data. The approach to systematization of scientific information based on the ontological IT platform Transdisciplinary Ontological Dialogs of Object-Oriented Systems (TODOS) has many benefits. It has been proposed to select semantic characteristics of each work for their further introduction into the IT platform TODOS. An ontological graph with a ranking function for previous scientific research and for a system of selection of journals has been worked out. These systems provide high performance of information management of scientific information.

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