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Автор: Grafiati
Опубліковано: 10 січня 2023
Оновлено: 29 січня 2023

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Статті в журналах з теми "4605 Data management and data science":

1

"Paul" Zhang, Xihui, Ming Wang, M. Shane Banks, Qiunan Zhang, and Colin G. Onita. "Design and Delivery of an Online Information Systems Management Course for MBA Programs." Journal of Information Technology Education: Innovations in Practice 19 (2020): 047–74. http://dx.doi.org/10.28945/4600.

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Aim/Purpose: In this paper, we present our experience in design and delivery of a graduate Information Systems Management (ISM) course in an online MBA program. Also presented are a detailed examination of the design and delivery of the online course, survey results of students’ perceptions and backgrounds, course evaluation results, best practices and lessons learned, and potential changes and future actions. Background: This graduate ISM course needs to not only cover a broad range of dynamic technology and business topics, but also strike a balance between the width and depth of the content. Effective course design and delivery are critical to improved teaching and learning, especially when the course is delivered online. Methodology: We provided a comprehensive review of the related literature to develop guidelines for the design and delivery of our ISM course; we collected survey data to evaluate the students’ backgrounds and their perceptions of the course; we used data analysis and content analysis methods to assess the course evaluation results. Contribution: A review of the related literature indicates that IS researchers and educators have not adequately studied online graduate education. Given the importance of the graduate ISM course in most MBA programs, and the lack of attention from the IS community, it is critical to address this gap in the research. We believe we have done so with this paper. Findings: The paper’s major findings are embedded in a detailed examination of the design and delivery of the online course, survey results of students’ perceptions and backgrounds, course evaluation results, best practices and lessons learned, and potential changes and future actions. Recommendations for Practitioners: Even though our experience may not be fully applicable to other institutions, we hope our IS colleagues can learn from the design and delivery of this online course, as well as our best practices and lessons learned to improve the teaching and learning effectiveness in IS online graduate education, in general. Furthermore, we provide instructors with an actionable framework onto which they can map their current course offering, and compare their current pedagogical offering to literature driven best practices for ISM courses, in particular. Recommendation for Researchers: It is our hope that the design and delivery of this online course, and our best practices and lessons learned can inspire our IS colleagues to search for innovative ways to improve the teaching and learning effectiveness in IS online graduate education. In addition, we distill a literature driven framework for ISM courses design and delivery that can help researchers frame their pedagogical research questions. Impact on Society: The online course in this study prepares students for more efficiently and effectively delivering IT systems in organizations. Many MBA students work for non-profits and other socially-focused organizations and are able to use the skills learned in the course for the betterment of society. Future Research: We will continue to monitor the impact of the changes on student learning effectiveness and attempt to identify additional innovative ways to improve the design and delivery of this online ISM course.
2

Utli, Hediye, and Birgül Vural Doğru. "The relationship between patient activation level and self-care management in elderly patients with chronic illness in the southeastern anatolian region of Turkey." Progress in Health Sciences 12, no. 1 (April 12, 2022): 14–21. http://dx.doi.org/10.5604/01.3001.0015.8874.

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Purpose: High level of patient activation is important for better patient outcomes in chronic illnesses. The purpose of the study was to determine the relationship between patient activation level and self-care management among elderly patients with chronic illness. Materials and methods: This descriptive and relational study was conducted with 503 patients aged 65 and older. "Personal Information Form", "Self-care Management Scale in Chronic Illness" and " Patient Activation Measure" was used to collect data. The Chi-squared test, Mann Whitney U,Kruskal Wallis tests and Spearman correlation test were used to evaluate the data. Results: The mean age of the elderly patients with chronic illness was 75.8±7.6. The mean Patient Activation Measure score was 51.3±14.8, and the mean Self-care Management Scale in Chronic Illness score was 99.1 ±10.7. 46.5% of the participants had a low level of activation. A positive and statistically significant correlation was found between the Patient Activation Measure and Self-care Management scale scores. Conclusion: There was a poor association between patient activation and self-care management in these elderly patients with chronic illness. As the activity of the elderly patients participating in this study increased, their self-care management levels also increased. It is thought that the evaluation of activity and self-care levels in order to improve the health outcomes of elderly patients with chronic diseases is important in terms of determining the interventions that should be applied individually. Keywords: Chronic illness; elderly; patient activation; self-care; self-management
3

Ranjbarfard, Mina, and Zeynab Hatami. "Critical Success Factors for Implementing Business Intelligence Projects (A BI Implementation Methodology Perspective)." Interdisciplinary Journal of Information, Knowledge, and Management 15 (2020): 175–202. http://dx.doi.org/10.28945/4607.

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Aim/Purpose: The purpose of this paper is to identify Critical Success Factors (CSFs) for Business Intelligence (BI) implementation projects by studying the existing BI project implementation methodologies and to compare these methodologies based on the identified CSFs. Background: The implementation of BI project has become one of the most important technological and organizational innovations in modern organizations. The BI project implementation methodology provides a framework for demonstrating knowledge, ideas and structural techniques. It is defined as a set of instructions and rules for implementing BI projects. Identifying CSFs of BI implementation project can help the project team to concentrate on solving prior issues and needed resources. Methodology: Firstly, the literature review was conducted to find the existing BI project implementation methodologies. Secondly, the content of the 13 BI project implementation methodologies was analyzed by using thematic analysis method. Thirdly, for examining the validation of the 20 identified CSFs, two questionnaires were distributed among BI experts. The gathered data of the first questionnaire was analyzed by content validity ratio (CVR) and 11 of 20 CSFs were accepted as a result. The gathered data of the second questionnaire was analyzed by fuzzy Delphi method and the results were the same as CVR. Finally, 13 raised BI project implementation methodologies were compared based on the 11 validated CSFs. Contribution: This paper contributes to the current theory and practice by identifying a complete list of CSFs for BI projects implementation; comparison of existing BI project implementation methodologies; determining the completeness degree of existing BI project implementation methodologies and introducing more complete ones; and finding the new CSF “Expert assessment of business readiness for successful implementation of BI project” that was not expressed in previous studies. Findings: The CSFs that should be considered in a BI project implementation include: “Obvious BI strategy and vision”, “Business requirements definition”, “Business readiness assessment”, “BI performance assessment”, “Establishing BI alignment with business goals”, “Management support”, “IT support for BI”, “Creating data resources and source data quality”, “Installation and integration BI programs”, “BI system testing”, and “BI system support and maintenance”. Also, all the 13 BI project implementation methodologies can be divided into four groups based on their completeness degree. Recommendations for Practitioners: The results can be used to plan BI project implementation and help improve the way of BI project implementation in the organizations. It can be used to reduce the failure rate of BI implementation projects. Furthermore, the 11 identified CSFs can give a better understanding of the BI project implementation methodologies. Recommendation for Researchers: The results of this research helped researchers and practitioners in the field of business intelligence to better understand the methodology and approaches available for the implementation and deployment of BI systems and thus use them. Some methodologies are more complete than other studied methodologies. Therefore, organizations that intend to implement BI in their organization can select these methodologies according to their goals. Thus, Findings of the study can lead to reduce the failure rate of implementation projects. Future Research: Future researchers may add other BI project implementation methodologies and repeat this research. Also, they can divide CSFs into three categories including required before BI project implementation, required during BI project implementation and required after BI project implementation. Moreover, researchers can rank the BI project implementation CSFs. As well, Critical Failure Factors (CFFs) need to be explored by studying the failed implementations of BI projects. The identified CSFs probably affect each other. So, studying the relationship between them can be a topic for future research.
4

BAKHSH, A., I. BASHIR, H. U. FARID, and S. A. WAJID. "USING CERES-WHEAT MODEL TO SIMULATE GRAIN YIELD PRODUCTION FUNCTION FOR FAISALABAD, PAKISTAN, CONDITIONS." Experimental Agriculture 49, no. 3 (February 26, 2013): 461–75. http://dx.doi.org/10.1017/s0014479713000185.

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SUMMARYUsing computer simulation model as a management tool requires model calibration and validation against field data. A three-year (2008–2009 to 2010–2011) field study was conducted at the Postgraduate Agricultural Research Station of the University of Agriculture, Faisalabad, Pakistan, to simulate wheat grain yield production as a function of urea fertilizer applications using Crop Environment REsource Synthesis (CERES)-Wheat model. The model was calibrated using yield data for treatment of urea fertilizer application at the rate of 247 kg-urea ha−1 during growing season 2009–2010 and was validated against independent data sets of yield of two years (2008–2009 and 2010–2011) for a wide variety of treatments ranging from no urea application to 247 kg-urea ha−1 application. The model simulations were found to be acceptable for calibration as well as validation period, as the model evaluation indicators showed a mean difference of 8.9%, ranging from 0.05 to 15.38%, root mean square error of 356 having its range from 242 to 471 kg ha−1, against all observed grain yield data. The scenario simulations showed maximum grain yield of 4100 kg ha−1 for 350 kg-urea ha−1 in 2008–2009; 4600 kg ha−1 for 300 kg-urea ha−1 in 2009–2010 and 5200 kg ha−1 for 340 kg-urea ha−1 in 2010–2011. Any further increase in urea application resulted in decline of grain yield function. These results show that model has the ability to simulate effects of urea fertilizer applications on wheat yield; however, the simulated maximum grain yield data need field-based verification.
5

Ramirez, Jorge C. G., Diane J. Cook, Lynn L. Peterson, and Dolores M. Peterson. "An event set approach to sequence discovery in medical data." Intelligent Data Analysis 4, no. 6 (December 22, 2000): 513–30. http://dx.doi.org/10.3233/ida-2000-4605.

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6

Fitzgerald, Nurgul, and Shailja Mathur. "Assessment of Dietary Intake Among South Asian Adults in the United States." Current Developments in Nutrition 5, Supplement_2 (June 2021): 124. http://dx.doi.org/10.1093/cdn/nzab035_032.

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Abstract Objectives To examine the dietary intake patterns of South Asian adults by using three different assessment methods. Methods The participants were a convenience sample of 62 adults from South Asian descent, who lived in the United States and participated in a community-based diabetes self-management program. Dietary intake data were collected through Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24), self-administered Diet History Questionnaire III (self-DHQ), and researcher-administered DHQ III (res-DHQ) (National Cancer Institute). Thirty-seven participants completed ASA24 and self-DHQ back-to-back during in-person sessions, and 25 participants completed res-DHQ through video conferencing sessions with the researcher. Group level data were examined using IBM SPSS Statistics software. Results On average, participants’ daily energy intake levels were estimated to be 805.8 ± 551.3, 1686.4 ± 985.9 and 1469.7 ± 887.5 kcal/d by self-DHQ, ASA24, and res-DHQ, respectively. Self-DHQ produced the lowest of the estimates (mean ± SD) for daily protein (28.9 ± 18.8 vs 63.1 ± 35.2, and 53.1 ± 27.9 g/d), carbohydrate (106.4 ± 68.0 vs 224.9 ± 128.4 and 199.9 ± 119.7 g/d), and total fat (31.7 ± 29.2 vs. 63.5 ± 46.5 and 56.2 ± 40.9 g/d) intakes in comparison to ASA24 and res-DHQ, respectively. Conclusions In this study, self-administered DHQ produced substantially lower estimates of daily macronutrient and energy intake levels. The ASA24 or researcher-administered DHQ were relatively more reliable methods of dietary assessment in this sample of South Asian adults. Funding Sources NJ Department of Health, Office of Minority and Multicultural Health.
7

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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8

Kayumba, Ephrem, and Claude Rusibana. "Employee Turnover and Operational Performance of Commercial Banks in Rwanda." Journal of Advance Research in Business Management and Accounting (ISSN: 2456-3544) 7, no. 5 (May 31, 2021): 01–09. http://dx.doi.org/10.53555/nnbma.v7i5.990.

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Employee turnover was the movement through which an organization hired and missed its employees. This might be voluntary, involuntary, internal transfer, and retirement turnover. The objective of this study was to analyse the employee turnover and operational performance of commercial banks in Rwanda, a case of KCB Bank Rwanda located in Nyarugenge District, Rwanda. The specific objectives were to analyse the effect of employee compensation on operational performance, to determine the impact of employee overscheduling on operational performance, and to find out the impact of employee favouritism on the operational performance of KCB Bank Rwanda. This quantitative research used the descriptive research survey design with questionnaire as research instrument where 80 questionnaires were distributed to 80 employees by using both physical and digital approaches forms due to situations of COVID-19. The data collection took six months and consisted of 15 Microsoft forms, 40 physical forms, and 25 emails responses. The data analysis was done by using Statistical Package for Social Science (SPSS) version 20 through which the census method was applied, and the descriptive method was used to make the conclusion and has been applied to determine the reliability and validity at 0.8%. This research contributed to the management of employee turnover to improve operational performance of commercial banks in Rwanda. It indicated that KCB Bank Rwanda recognized a considerable rate of employee turnover at a percentage of 46.5% since its creation in 2008 year to December 2020 where the low number of recruited employees compared to the number of employees who exited. This was caused by factors including poor employee compensation, employee overscheduling and employee favouritism. The study discovered that the research objectives were major causes of employee turnover that affected the operational performance of KCB Bank Rwanda at a percentage of 13.8%. Data analysis showed that compensation affected the bank’s operations at a percentage of 73.8% (see table 4.9.), overscheduling at 50.1% (see table 4.7), and favouritism at 56.3% (see table 4.8). The study discovered that the most concern of KCB Bank Rwanda was not the relevance of number of employees who left but the quality of those employee and the targets they had set during the set and submission of the annual balanced scorecard, which affects the operational performance review. The research recommended that the management should review the compensation policy to match the operational performance, reduce favouritism by approaching marginal employees, and reduce overscheduling by re-examining the job descriptions and visiting employee’s office to discover added and non-corresponding duties that attracted the employee turnover in the KCB Bank Rwanda.
9

Chen, Jinchuan, Yueguo Chen, Xiaoyong Du, Cuiping Li, Jiaheng Lu, Suyun Zhao, and Xuan Zhou. "Big data challenge: a data management perspective." Frontiers of Computer Science 7, no. 2 (April 2013): 157–64. http://dx.doi.org/10.1007/s11704-013-3903-7.

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10

Wygant, Robert M. "Data file management." Computers & Industrial Engineering 11, no. 1-4 (January 1986): 367–71. http://dx.doi.org/10.1016/0360-8352(86)90113-0.

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Статті в журналах Дисертації Книги

Дисертації з теми "4605 Data management and data science":

1

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.

2

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.
3

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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4

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.
5

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

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6

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 : -ˆ 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). ˆ- 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.
7

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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8

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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9

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.

10

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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Книги з теми "4605 Data management and data science":

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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7

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

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8

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

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9

Loomis, Mary E. S. Data management and file processing. Englewood Cliffs(N.J.): Prentice-Hall, 1986.

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10

Kunii, Hideko S. Graph data model and its data language. Tokyo: Springer-Verlag, 1990.

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Частини книг з теми "4605 Data management and data science":

1

Kekre, Sunder, Tridas Mukhopadhyay, and Kannan Srinivasan. "Modeling Impacts of Electronic Data Interchange Technology." In International Series in Operations Research & Management Science, 359–79. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4949-9_12.

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2

Daley, D. J., and L. D. Servi. "Estimating Customer Loss Rates from Transactional Data." In International Series in Operations Research & Management Science, 313–32. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5191-1_20.

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3

Wojtkowski, Wita, and W. Gregory Wojtkowski. "Resource Object Data Manager: Structured Approach to Systems and Network Management." In Systems Science, 475–80. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4615-2862-3_84.

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4

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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5

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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6

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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7

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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8

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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9

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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10

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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Тези доповідей конференцій з теми "4605 Data management and data science":

1

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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2

Handley, Thomas H., and Y. Philip Li. "DataHub: Knowledge-based data management for data discovery." In The earth and space science information system (ESSIS). AIP, 1993. http://dx.doi.org/10.1063/1.44479.

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3

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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4

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.
5

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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6

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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7

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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8

Xiao-dan, Wu, Yue Dian-min, Liu Feng-li, Wang Yun-feng, and Chu Chao-Hsien. "Privacy Preserving Data Mining Algorithms by Data Distortion." In 2006 International Conference on Management Science and Engineering. IEEE, 2006. http://dx.doi.org/10.1109/icmse.2006.313871.

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9

Chard, Ryan, Kyle Chard, Steve Tuecke, and Ian Foster. "Software Defined Cyberinfrastructure for Data Management." In 2017 IEEE 13th International Conference on e-Science (e-Science). IEEE, 2017. http://dx.doi.org/10.1109/escience.2017.69.

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10

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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Звіти організацій з теми "4605 Data management and data science":

1

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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2

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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3

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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4

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.
5

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.
6

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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7

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.
8

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.
9

Woods, Mel, Saskia Coulson, Raquel Ajates, Angelos Amditis, Andy Cobley, Dahlia Domian, Gerid Hager, et al. Citizen Science Projects: How to make a difference. WeObserve, 2020. http://dx.doi.org/10.20933/100001193.

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Citizen Science Projects: How to make a difference, is a massive open online course (MOOC). It was developed by the H2020 WeObserve project and ran on the FutureLearn platform from 2019. The course was designed to assist learners from all backgrounds and geographical locations to discover how to build their own citizen science project to address global challenges and create positive change. It also helped learners with interpreting the information they collected and using their findings to educate others about important local and global concerns. The main learning objectives for the course were: * Discover what citizen science and citizen observatories are * Engage with the general process of a citizen science project, the tools used and where they can be accessed * Collect and analyse data on relevant issues such as environmental challenges and disaster management, and discuss the results of their findings * Explore projects happening around the world, what the aims of these projects are and how learners could get involved * Model the steps to create their own citizen science project * Evaluate the potential of citizen science in bringing about change This course also provided five open-source, downloadable tools which have been tested in previous citizen science projects and created for the use of a wider range of projects. These tools are listed below and available in the research repository: * Empathy timeline tool * Community-level indicators tool * Data postcards tool * Future newspaper tool * Co-evaluation tool
10

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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