Relevant bibliographies by topics / Big quality

Contents

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

Academic literature on the topic 'Big quality'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Big quality"

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Moore, Alison. "Thinking big on quality." Nursing Standard 30, no. 3 (September 16, 2015): 22–23. http://dx.doi.org/10.7748/ns.30.3.22.s23.

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Batini, Carlo, Anisa Rula, Monica Scannapieco, and Gianluigi Viscusi. "From Data Quality to Big Data Quality." Journal of Database Management 26, no. 1 (January 2015): 60–82. http://dx.doi.org/10.4018/jdm.2015010103.

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This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation between them and quality dimensions is provided. Finally, Big Data quality issues are considered in a conceptual framework suitable to map the evolution of the quality paradigm according to three core coordinates that are significant in the context of the Big Data phenomenon: the data type considered, the source of data, and the application domain. Thus, the framework allows ascertaining the relevant changes in data quality emerging with the Big Data phenomenon, through an integrative and theoretical literature review.
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ParkJooSeok, Hocheol Ryu, Jangho Lee, 이준용, 김승현, and 이준기. "Applying Service Quality to Big Data Quality." Korea Journal of BigData 2, no. 2 (December 2017): 87–93. http://dx.doi.org/10.36498/kbigdt.2017.2.2.87.

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Ge, Mouzhi, and Vlastislav Dohnal. "Quality Management in Big Data." Informatics 5, no. 2 (April 16, 2018): 19. http://dx.doi.org/10.3390/informatics5020019.

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Virgolino, Zirvaldo Z., Osvaldo Resende, Douglas N. Gonçalves, Kaique A. F. Marçal, and Juliana de F. Sales. "Physiological quality of soybean seeds artificially cooled and stored in different packages." Revista Brasileira de Engenharia Agrícola e Ambiental 20, no. 5 (May 2016): 473–80. http://dx.doi.org/10.1590/1807-1929/agriambi.v20n5p473-480.

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ABSTRACT The aeration of seeds with artificially cooled air and their storage in big-bag packages aim to lengthen the shelf life while maintaining the quality. Thus, the objective was to study the effects of cooling before storage on germination and vigor of soybean seeds stored in trifoliate kraft paper bag and big bag in non-air-conditioned environment. Seeds of soybean cultivar NA 7337 RR were mechanically harvested in March 2013, holding an average moisture content of 18% w.b. In June, 16,000 kg of seeds were processed and cooled to 18 °C. Equal amount of non-cooled seeds was used as a control. Equally divided in kraft paper and in big bags, and combining cooling and packaging, the seeds were stored for three months and analyzed for moisture content, germination and vigor. Cooling to room temperature and different packaging types had similar effects on seed quality. Big bags packing showed better efficiency in retaining the moisture content of cooled seeds. No direct effects of cooling could be identified prior to storage on the germination and vigor of soybean seeds.
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Park, Sorah. "Audit Quality And Accrual Quality: Do Big 4 Auditors Indeed Enhance Accrual Quality Of ‘Powerful’ Clients?" Journal of Applied Business Research (JABR) 33, no. 2 (March 1, 2017): 343–50. http://dx.doi.org/10.19030/jabr.v33i2.9908.

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External auditors are considered ‘watchdogs’ which closely monitor corporate financial reporting process and provide guidelines for investors and financial institutions. However, recent accounting scandals in Korea indicate that external auditors may cater their audit reports to their clients’ needs. Based on a sample of listed companies on the Korea Stock Exchange from 2001 to 2010, this study finds the evidence consistent with such conjecture. First, large business conglomerates in Korea (so called ‘chaebols’) audited by Big 4 have lower accrual quality than the others, indicating that Big 4 auditors may not serve as watchdogs to enhance accrual quality of ‘powerful’ clients. However, powerful clients who pay greater non-audit service fees to Big 4 auditors have higher accrual quality than the others. This result suggests that non-audit services provided by Big 4 may not necessarily harm the quality of accounting information, contrary to the traditional view in the literature.
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Che, Limei, Ole-Kristian Hope, and John Christian Langli. "How Big-4 Firms Improve Audit Quality." Management Science 66, no. 10 (October 2020): 4552–72. http://dx.doi.org/10.1287/mnsc.2019.3370.

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This paper studies whether and how Big-4 firms provide higher-quality audits than non-Big-4 firms. Specifically, we first examine a Big-4 effect and then explore three sources of the Big-4 effect. To test the Big-4 effect, we use a unique data set of individual audit partners for a large sample of private companies and a novel research design exploiting the fact that auditees may follow the auditor who switches affiliation from a non-Big-4 firm to a Big-4 firm. Thus, we compare audit quality and audit fees of the same partner–auditee pairs before and after the switch. The results show that the Big-4 effect exists in the private-firm segment. More important, we find evidence for three sources of the Big-4 effect. First, Big-4 firms are able to recruit non-Big-4 partners who deliver higher audit quality than other non-Big-4 partners in the preswitch period. Second, enhanced learning has taken place after the switch. Third, the increased audit quality can also be attributed to stronger incentives/monitoring. These are new findings to the literature. This paper was accepted by Suraj Srinivasan, accounting.
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Escobar, Carlos A., Jeffrey A. Abell, Marcela Hernández-de-Menéndez, and Ruben Morales-Menendez. "Process-Monitoring-for-Quality — Big Models." Procedia Manufacturing 26 (2018): 1167–79. http://dx.doi.org/10.1016/j.promfg.2018.07.153.

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Singer, Peter, Pavan Sukhdev, Hon-Lam Li, Madhu Suri Prakash, Hellmuth Lange, Susanna Baltscheffsky, and ElIzabeth Peredo. "The Big Question: Quality of Life." World Policy Journal 28, no. 2 (2011): 3–6. http://dx.doi.org/10.1177/0740277511415049.

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Goodchild, Michael F. "The quality of big (geo)data." Dialogues in Human Geography 3, no. 3 (November 2013): 280–84. http://dx.doi.org/10.1177/2043820613513392.

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Dissertations / Theses on the topic "Big quality"

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Blahová, Leontýna. "Big Data Governance." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-203994.

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This master thesis is about Big Data Governance and about software, which is used for this purposes. Because Big Data are huge opportunity and also risk, I wanted to map products which can be easily use for Data Quality and Big Data Governance in one platform. This thesis is not only on theoretical knowledge level, but also evaluates five key products (from my point of view). I defined requirements for every kind of domain and then I set up the weights and points. The main objective is to evaluate software capabilities and compere them.
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Serra-Diaz, Josep M., Brian J. Enquist, Brian Maitner, Cory Merow, and Jens-C. Svenning. "Big data of tree species distributions: how big and how good?" SPRINGER HEIDELBERG, 2018. http://hdl.handle.net/10150/626611.

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Background: Trees play crucial roles in the biosphere and societies worldwide, with a total of 60,065 tree species currently identified. Increasingly, a large amount of data on tree species occurrences is being generated worldwide: from inventories to pressed plants. While many of these data are currently available in big databases, several challenges hamper their use, notably geolocation problems and taxonomic uncertainty. Further, we lack a complete picture of the data coverage and quality assessment for open/public databases of tree occurrences. Methods: We combined data from five major aggregators of occurrence data (e.g. Global Biodiversity Information Facility, Botanical Information and Ecological Network v.3, DRYFLOR, RAINBIO and Atlas of Living Australia) by creating a workflow to integrate, assess and control data quality of tree species occurrences for species distribution modeling. We further assessed the coverage - the extent of geographical data - of five economically important tree families (Arecaceae, Dipterocarpaceae, Fagaceae, Myrtaceae, Pinaceae). Results: Globally, we identified 49,206 tree species (84.69% of total tree species pool) with occurrence records. The total number of occurrence records was 36.69 M, among which 6.40 M could be considered high quality records for species distribution modeling. The results show that Europe, North America and Australia have a considerable spatial coverage of tree occurrence data. Conversely, key biodiverse regions such as South-East Asia and central Africa and parts of the Amazon are still characterized by geographical open-public data gaps. Such gaps are also found even for economically important families of trees, although their overall ranges are covered. Only 15,140 species (26.05%) had at least 20 records of high quality. Conclusions: Our geographical coverage analysis shows that a wealth of easily accessible data exist on tree species occurrences worldwide, but regional gaps and coordinate errors are abundant. Thus, assessment of tree distributions will need accurate occurrence quality control protocols and key collaborations and data aggregation, especially from national forest inventory programs, to improve the current publicly available data.
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Palmqvist, Simon. "Validating the Quality of a Big Data Java Corpus." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75410.

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Recent research within the field of Software Engineering have used GitHub, the largest hub for open source projects with almost 20 million users and 57 million repositories, to mine large amounts of source code to get more trustworthy results when developing machine and deep learning models. Mining GitHub comes with many challenges since the dataset is large and the data does not only contain quality software projects. In this project, we try to mine projects from GitHub based on earlier research by others and try to validate the quality by comparing the projects with a small subset of quality projects with the help of software complexity metrics.
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Yu, Dong Michael. "The effect of big four office size on audit quality." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4827.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on October 15, 2007) Vita. Includes bibliographical references.
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Tian, Chao. "Towards effective analysis of big graphs : from scalability to quality." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29578.

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This thesis investigates the central issues underlying graph analysis, namely, scalability and quality. We first study the incremental problems for graph queries, which aim to compute the changes to the old query answer, in response to the updates to the input graph. The incremental problem is called bounded if its cost is decided by the sizes of the query and the changes only. No matter how desirable, however, our first results are negative: for common graph queries such as graph traversal, connectivity, keyword search and pattern matching, their incremental problems are unbounded. In light of the negative results, we propose two new characterizations for the effectiveness of incremental computation, and show that the incremental computations above can still be effectively conducted, by either reducing the computations on big graphs to small data, or incrementalizing batch algorithms by minimizing unnecessary recomputation. We next study the problems with regards to improving the quality of the graphs. To uniquely identify entities represented by vertices in a graph, we propose a class of keys that are recursively defined in terms of graph patterns, and are interpreted with subgraph isomorphism. As an application, we study the entity matching problem, which is to find all pairs of entities in a graph that are identified by a given set of keys. Although the problem is proved to be intractable, and cannot be parallelized in logarithmic rounds, we provide two parallel scalable algorithms for it. In addition, to catch numeric inconsistencies in real-life graphs, we extend graph functional dependencies with linear arithmetic expressions and comparison predicates, referred to as NGDs. Indeed, NGDs strike a balance between expressivity and complexity, since if we allow non-linear arithmetic expressions, even of degree at most 2, the satisfiability and implication problems become undecidable. A localizable incremental algorithm is developed to detect errors using NGDs, where the cost is determined by small neighbors of nodes in the updates instead of the entire graph. Finally, a rule-based method to clean graphs is proposed. We extend graph entity dependencies (GEDs) as data quality rules. Given a graph, a set of GEDs and a block of ground truth, we fix violations of GEDs in the graph by combining data repairing and object identification. The method finds certain fixes to errors detected by GEDs, i.e., as long as the GEDs and the ground truth are correct, the fixes are assured correct as their logical consequences. Several fundamental results underlying the method are established, and an algorithm is developed to implement the method. We also parallelize the method and guarantee to reduce its running time with the increase of processors.
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Rizk, Raya. "Big Data Validation." Thesis, Uppsala universitet, Informationssystem, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353850.

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With the explosion in usage of big data, stakes are high for companies to develop workflows that translate the data into business value. Those data transformations are continuously updated and refined in order to meet the evolving business needs, and it is imperative to ensure that a new version of a workflow still produces the correct output. This study focuses on the validation of big data in a real-world scenario, and implements a validation tool that compares two databases that hold the results produced by different versions of a workflow in order to detect and prevent potential unwanted alterations, with row-based and column-based statistics being used to validate the two versions. The tool was shown to provide accurate results in test scenarios, providing leverage to companies that need to validate the outputs of the workflows. In addition, by automating this process, the risk of human error is eliminated, and it has the added benefit of improved speed compared to the more labour-intensive manual alternative. All this allows for a more agile way of performing updates on the data transformation workflows by improving on the turnaround time of the validation process.
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TANNEEDI, NAREN NAGA PAVAN PRITHVI. "Customer Churn Prediction Using Big Data Analytics." Thesis, Blekinge Tekniska Högskola, Institutionen för kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13518.

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Customer churn is always a grievous issue for the Telecom industry as customers do not hesitate to leave if they don’t find what they are looking for. They certainly want competitive pricing, value for money and above all, high quality service. Customer churning is directly related to customer satisfaction. It’s a known fact that the cost of customer acquisition is far greater than cost of customer retention, that makes retention a crucial business prototype. There is no standard model which addresses the churning issues of global telecom service providers accurately. BigData analytics with Machine Learning were found to be an efficient way for identifying churn. This thesis aims to predict customer churn using Big Data analytics, namely a J48 decision tree on a Java based benchmark tool, WEKA. Three different datasets from various sources were considered; first includes Telecom operator’s six month aggregate active and churned users’ data usage volumes, second includes globally surveyed data and third dataset comprises of individual weekly data usage analysis of 22 android customers along with their average quality, annoyance and churn scores by accompanying theses. Statistical analyses and J48 Decision trees were drawn for three different datasets. From the statistics of normalized volumes, autocorrelations were small owing to reliable confidence intervals, but confidence intervals were overlapping and close by, therefore no much significance could be noticed, henceforth no strong trends could be observed. From decision tree analytics, decision trees with 52%, 70% and 95% accuracies were achieved for three different data sources respectively.      Data preprocessing, data normalization and feature selection have shown to be prominently influential. Monthly data volumes have not shown much decision power. Average Quality, Churn Risk and to some extent, Annoyance scores may point out a probable churner. Weekly data volumes with customer’s recent history and necessary attributes like age, gender, tenure, bill, contract, data plan, etc., are pivotal for churn prediction.
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Sonsa-ardjit, Pitchaya, and Ramon Vejaratpimol. "Clients’ Perspectives Toward Audit Service Quality of the Big 4 inThailand." Thesis, Karlstad University, Faculty of Economic Sciences, Communication and IT, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-6198.

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Purpose

The purpose of this thesis is, firstly, to investigate clients’ perspective toward the Big 4’s financial audit service quality. Secondly, the gaps between clients’ perceptions and expectations of audit service quality provided by the Big 4 audit firms will be studied. Finally, factors influencing clients’ expectations of audit service quality will be categorised.

Method

A combination of qualitative and quantitative approach is used in the form of a web-based self-completion questionnaire. A qualitative approach is used in one section of the questionnaire which is an open-ended question asking about the

clients’ perception toward audit service quality. A quantitative approach is used in the rest of the 2 sections of the questionnaire; firstly, asking the respondents to score the level of perception and expectation of audit service quality; secondly, asking for types of clients’ industries. The respondents are 25 clients who have direct experience with the Big 4 audit firms located in Thailand.

Finding 

Clients strongly expect assurance, reliability, and responsiveness while strongly perceive assurance and reliability of the Big4’s audit service quality. However, it is obvious that clients’ perception of all 5 dimensions is less than those of expectation; assurance, reliability and responsiveness are significantly different at .05 level. Moreover, eight factors from given expectation score are re-categorised in order from the most important issue to the least important as follows; Factor 1: Trust & Confidence, Factor 2: Responsiveness & Accuracy, Factor 3: Knowledge and skills in clients’ industry, Caring and Independence, Factor 4: Understanding of Clients, Factor 5: Timing/Scheduling & Right Service, Factor 6: Physical Facilities, Factor 7: Professional appearance & Professional Procedures, and Factor 8: Information & Communication Channels and Materials.

Conclusion 

In conclusion, the factors that are not satisfied by the clients; assurance, reliability, responsiveness, should be taken account of by the Big 4. Not only the Big 4 operating in Thailand have to be aware of their service quality, the other audit firms both international brands and local brands should also be aware of their service quality in order to satisfy their clients and to avoid damages of the firms and markets from audit failure. Both the audit firms and the clients together can help in audit quality improvement.

Recommendation 

To improve audit service quality, it is not only the Big4 audit firms’ responsibility but also the good cooperation from the clients could be the crucial support, and the ongoing policies are needed because it takes some time to see the consequences. When the quality level of audit service becomes a win-win situation, both audit firms and clients receive mutual benefits. Moreover, the Big 4 are the big actors in the audit industry in Thailand with promptly financial and human resource, they should support non-Big 4 to improve audit service quality. Because it means the overall image of audit service in Thailand would be improve somehow.

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Santos, Lúcio Fernandes Dutra. "Similaridade em big data." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07022018-104929/.

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Os volumes de dados armazenados em grandes bases de dados aumentam em ritmo sempre crescente, pressionando o desempenho e a flexibilidade dos Sistemas de Gerenciamento de Bases de Dados (SGBDs). Os problemas de se tratar dados em grandes quantidades, escopo, complexidade e distribuição vêm sendo tratados também sob o tema de big data. O aumento da complexidade cria a necessidade de novas formas de busca - representar apenas números e pequenas cadeias de caracteres já não é mais suficiente. Buscas por similaridade vêm se mostrando a maneira por excelência de comparar dados complexos, mas até recentemente elas não estavam disponíveis nos SGBDs. Agora, com o início de sua disponibilidade, está se tornando claro que apenas os operadores de busca por similaridade fundamentais não são suficientes para lidar com grandes volumes de dados. Um dos motivos disso é que similaridade\' é, usualmente, definida considerando seu significado quando apenas poucos estão envolvidos. Atualmente, o principal foco da literatura em big data é aumentar a eficiência na recuperação dos dados usando paralelismo, existindo poucos estudos sobre a eficácia das respostas obtidas. Esta tese visa propor e desenvolver variações dos operadores de busca por similaridade para torná-los mais adequados para processar big data, apresentando visões mais abrangentes da base de dados, aumentando a eficácia das respostas, porém sem causar impactos consideráveis na eficiência dos algoritmos de busca e viabilizando sua execução escalável sobre grandes volumes de dados. Para alcançar esse objetivo, este trabalho apresenta quatro frentes de contribuições: A primeira consistiu em um modelo de diversificação de resultados que pode ser aplicado usando qualquer critério de comparação e operador de busca por similaridade. A segunda focou em definir técnicas de amostragem e de agrupamento de dados com o modelo de diversificação proposto, acelerando o processo de análise dos conjuntos de resultados. A terceira contribuição desenvolveu métodos de avaliação da qualidade dos conjuntos de resultados diversificados. Por fim, a última frente de contribuição apresentou uma abordagem para integrar os conceitos de mineração visual de dados e buscas por similaridade com diversidade em sistemas de recuperação por conteúdo, aumentando o entendimento de como a propriedade de diversidade pode ser aplicada.
The data being collected and generated nowadays increase not only in volume, but also in complexity, requiring new query operators. Health care centers collecting image exams and remote sensing from satellites and from earth-based stations are examples of application domains where more powerful and flexible operators are required. Storing, retrieving and analyzing data that are huge in volume, structure, complexity and distribution are now being referred to as big data. Representing and querying big data using only the traditional scalar data types are not enough anymore. Similarity queries are the most pursued resources to retrieve complex data, but until recently, they were not available in the Database Management Systems. Now that they are starting to become available, its first uses to develop real systems make it clear that the basic similarity query operators are not enough to meet the requirements of the target applications. The main reason is that similarity is a concept formulated considering only small amounts of data elements. Nowadays, researchers are targeting handling big data mainly using parallel architectures, and only a few studies exist targeting the efficacy of the query answers. This Ph.D. work aims at developing variations for the basic similarity operators to propose better suited similarity operators to handle big data, presenting a holistic vision about the database, increasing the effectiveness of the provided answers, but without causing impact on the efficiency on the searching algorithms. To achieve this goal, four mainly contributions are presented: The first one was a result diversification model that can be applied in any comparison criteria and similarity search operator. The second one focused on defining sampling and grouping techniques with the proposed diversification model aiming at speeding up the analysis task of the result sets. The third contribution concentrated on evaluation methods for measuring the quality of diversified result sets. Finally, the last one defines an approach to integrate the concepts of visual data mining and similarity with diversity searches in content-based retrieval systems, allowing a better understanding of how the diversity property is applied in the query process.
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Grillo, Aderibigbe. "Developing a data quality scorecard that measures data quality in a data warehouse." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17137.

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The main purpose of this thesis is to develop a data quality scorecard (DQS) that aligns the data quality needs of the Data warehouse stakeholder group with selected data quality dimensions. To comprehend the research domain, a general and systematic literature review (SLR) was carried out, after which the research scope was established. Using Design Science Research (DSR) as the methodology to structure the research, three iterations were carried out to achieve the research aim highlighted in this thesis. In the first iteration, as DSR was used as a paradigm, the artefact was build from the results of the general and systematic literature review conduct. A data quality scorecard (DQS) was conceptualised. The result of the SLR and the recommendations for designing an effective scorecard provided the input for the development of the DQS. Using a System Usability Scale (SUS), to validate the usability of the DQS, the results of the first iteration suggest that the DW stakeholders found the DQS useful. The second iteration was conducted to further evaluate the DQS through a run through in the FMCG domain and then conducting a semi-structured interview. The thematic analysis of the semi-structured interviews demonstrated that the stakeholder's participants' found the DQS to be transparent; an additional reporting tool; Integrates; easy to use; consistent; and increases confidence in the data. However, the timeliness data dimension was found to be redundant, necessitating a modification to the DQS. The third iteration was conducted with similar steps as the second iteration but with the modified DQS in the oil and gas domain. The results from the third iteration suggest that DQS is a useful tool that is easy to use on a daily basis. The research contributes to theory by demonstrating a novel approach to DQS design This was achieved by ensuring the design of the DQS aligns with the data quality concern areas of the DW stakeholders and the data quality dimensions. Further, this research lay a good foundation for the future by establishing a DQS model that can be used as a base for further development.
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Books on the topic "Big quality"

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East Dakota Conservancy Sub-district (S.D.). The Big Sioux aquifer water quality study. [Brookings, S.D: East Dakota Conservancy Sub-district, 1988.

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Hacid, Hakim, Quan Z. Sheng, Tetsuya Yoshida, Azadeh Sarkheyli, and Rui Zhou, eds. Data Quality and Trust in Big Data. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19143-6.

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Mason, Paul. How big is your water footprint? New York: Marshall Cavendish Benchmark, 2009.

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How big is your water footprint? New York: Marshall Cavendish Benchmark, 2009.

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AABB, ed. Romancing the big Q: Dancing with the quality gorilla. Bethesda, Md: AABB Press, 2012.

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Walters, Lisa M. Romancing the big Q: Dancing with the quality gorilla. Edited by AABB. Bethesda, Md: AABB Press, 2012.

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Williams, Mike. Upper Big Sioux River watershed project continuation. Watertown, S.D: City of Watertown, 2005.

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Bahls, Loren L. Use support in Big Spring Creek based on periphyton composition and community structure. Helena, Mont: [Montana Dept. of Environmental Quality], 1999.

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Williams, Mike. Continuation of the Upper Big Sioux River Watershed project: Final report. Watertown, S.D: City of Watertown, 2008.

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Bahls, Loren L. Streamflow and water quality in the lower Big Hole River, Summer 1977. Helena?]: Water Quality Bureau, Environmental Sciences Division, Montana Dept. of Health and Environmental Sciences, 1987.

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Book chapters on the topic "Big quality"

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Thota, Subash. "Big Data Quality." In Encyclopedia of Big Data, 1–5. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32001-4_240-1.

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Mukherjee, Shyama Prasad. "Quality: A Big Canvas." In India Studies in Business and Economics, 1–20. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1271-7_1.

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Kuiler, Erik W. "Data Quality Management." In Encyclopedia of Big Data, 1–4. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32001-4_317-1.

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Hoeren, Thomas. "Big Data and Data Quality." In Big Data in Context, 1–12. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62461-7_1.

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Floridi, Luciano. "Big Data and Information Quality." In The Philosophy of Information Quality, 303–15. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07121-3_15.

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Stuchfield, Nic, James Angel, William Harts, David Krell, Andreas Preuss, James Ross, and Larry Tabb. "Market Quality, The Big Picture." In Technology and Regulation, 65–74. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-0480-5_6.

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Steidl, Monika, Ruth Breu, and Benedikt Hupfauf. "Challenges in Testing Big Data Systems." In Software Quality: Quality Intelligence in Software and Systems Engineering, 13–27. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35510-4_2.

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Taleb, Ikbal, Mohamed Adel Serhani, and Rachida Dssouli. "Big Data Quality: A Data Quality Profiling Model." In Services – SERVICES 2019, 61–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23381-5_5.

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Mildenberger, Peter. "IT Innovation and Big Data." In Quality and Safety in Imaging, 159–70. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/174_2017_144.

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Wierzchoń, Sławomir T., and Mieczysław A. Kłopotek. "Cluster Quality Versus Choice of Parameters." In Studies in Big Data, 163–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69308-8_4.

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Conference papers on the topic "Big quality"

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Becker, David, Trish Dunn King, and Bill McMullen. "Big data, big data quality problem." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7364064.

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Abdallah, Mohammad. "Big Data Quality Challenges." In 2019 International Conference on Big Data and Computational Intelligence (ICBDCI). IEEE, 2019. http://dx.doi.org/10.1109/icbdci.2019.8686099.

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Arruda, Darlan, and Nazim H. Madhavji. "QualiBD: A Tool for Modelling Quality Requirements for Big Data Applications." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006294.

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Taleb, Ikbal, Hadeel T. El Kassabi, Mohamed Adel Serhani, Rachida Dssouli, and Chafik Bouhaddioui. "Big Data Quality: A Quality Dimensions Evaluation." In 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld). IEEE, 2016. http://dx.doi.org/10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0122.

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Fu, Qian, and John M. Easton. "Understanding data quality: Ensuring data quality by design in the rail industry." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258380.

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Taleb, Ikbal, Mohamed Adel Serhani, and Rachida Dssouli. "Big Data Quality: A Survey." In 2018 IEEE International Congress on Big Data (BigData Congress). IEEE, 2018. http://dx.doi.org/10.1109/bigdatacongress.2018.00029.

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Keller, Christoph A., Mathew J. Evans, J. Nathan Kutz, and Steven Pawson. "Machine learning and air quality modeling." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258500.

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Rao, Dhana, Venkat N. Gudivada, and Vijay V. Raghavan. "Data quality issues in big data." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7364065.

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Conti, Christopher J., Aparna S. Varde, and Weitian Wang. "Task quality optimization in collaborative robotics." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378498.

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Domova, Veronika, and Shiva Sander-Tavallaey. "Visualization for Quality Healthcare: Patient Flow Exploration." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006351.

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Reports on the topic "Big quality"

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Bad Bear, D. J., and D. Hooker. Little Big Horn River Water Quality Project. Office of Scientific and Technical Information (OSTI), October 1995. http://dx.doi.org/10.2172/224632.

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Maurer, M. A. Water quality study of Richardson Clearwater Creek near Big Delta, Alaska. Alaska Division of Geological & Geophysical Surveys, 1999. http://dx.doi.org/10.14509/1904.

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Becker, Dave, Trish D. King, Bill McMullen, Lisa D. Lais, David Bloom, Ali Obaidi, and Donna Fickett. Big Data Quality Case Study Preliminary Findings, U.S. Army MEDCOM MODS. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada595983.

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Albouy, David. Are Big Cities Bad Places to Live? Estimating Quality of Life across Metropolitan Areas. Cambridge, MA: National Bureau of Economic Research, November 2008. http://dx.doi.org/10.3386/w14472.

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CORPS OF ENGINEERS OMAHA NE. Water Quality Conditions Monitored at the Corps' Big Bend Project in South Dakota during the 3-Year Period 2008 through 2010. Fort Belvoir, VA: Defense Technical Information Center, November 2011. http://dx.doi.org/10.21236/ada581200.

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Mendell, Mark J., and Mike G. Apte. Balancing energy conservation and occupant needs in ventilation rate standards for Big Box stores and other commercial buildings in California. Issues related to the ASHRAE 62.1 Indoor Air Quality Procedure. Office of Scientific and Technical Information (OSTI), October 2010. http://dx.doi.org/10.2172/1213550.

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Apte, Michael G., Mark J. Mendell, Michael D. Sohn, Spencer M. Dutton, Pam M. Berkeley, and Michael Spears. Final Report Balancing energy conservation and occupant needs in ventilation rate standards for Big Box stores in California. Predicted indoor air quality and energy consumption using a matrix of ventilation scenarios. Office of Scientific and Technical Information (OSTI), February 2011. http://dx.doi.org/10.2172/1223009.

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McKillip, Michael. Coupling the Hydrodynamic and Water Quality Model CE-QUAL-W2 With a Multi-Trophic Fish Bio-Energetics Model for Lake Roosevelt, Washington. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.3073.

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Carney, Nancy, Tamara Cheney, Annette M. Totten, Rebecca Jungbauer, Matthew R. Neth, Chandler Weeks, Cynthia Davis-O'Reilly, et al. Prehospital Airway Management: A Systematic Review. Agency for Healthcare Research and Quality (AHRQ), June 2021. http://dx.doi.org/10.23970/ahrqepccer243.

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Abstract:
Objective. To assess the comparative benefits and harms across three airway management approaches (bag valve mask [BVM], supraglottic airway [SGA], and endotracheal intubation [ETI]) by emergency medical services in the prehospital setting, and how the benefits and harms differ based on patient characteristics, techniques, and devices. Data sources. We searched electronic citation databases (Ovid® MEDLINE®, CINAHL®, the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews, and Scopus®) from 1990 to September 2020 and reference lists, and posted a Federal Register notice request for data. Review methods. Review methods followed Agency for Healthcare Research and Quality Evidence-based Practice Center Program methods guidance. Using pre-established criteria, studies were selected and dual reviewed, data were abstracted, and studies were evaluated for risk of bias. Meta-analyses using profile-likelihood random effects models were conducted when data were available from studies reporting on similar outcomes, with analyses stratified by study design, emergency type, and age. We qualitatively synthesized results when meta-analysis was not indicated. Strength of evidence (SOE) was assessed for primary outcomes (survival, neurological function, return of spontaneous circulation [ROSC], and successful advanced airway insertion [for SGA and ETI only]). Results. We included 99 studies (22 randomized controlled trials and 77 observational studies) involving 630,397 patients. Overall, we found few differences in primary outcomes when airway management approaches were compared. • For survival, there was moderate SOE for findings of no difference for BVM versus ETI in adult and mixed-age cardiac arrest patients. There was low SOE for no difference in these patients for BVM versus SGA and SGA versus ETI. There was low SOE for all three comparisons in pediatric cardiac arrest patients, and low SOE in adult trauma patients when BVM was compared with ETI. • For neurological function, there was moderate SOE for no difference for BVM compared with ETI in adults with cardiac arrest. There was low SOE for no difference in pediatric cardiac arrest for BVM versus ETI and SGA versus ETI. In adults with cardiac arrest, neurological function was better for BVM and ETI compared with SGA (both low SOE). • ROSC was applicable only in cardiac arrest. For adults, there was low SOE that ROSC was more frequent with SGA compared with ETI, and no difference for BVM versus SGA or BVM versus ETI. In pediatric patients there was low SOE of no difference for BVM versus ETI and SGA versus ETI. • For successful advanced airway insertion, low SOE supported better first-pass success with SGA in adult and pediatric cardiac arrest patients and adult patients in studies that mixed emergency types. Low SOE also supported no difference for first-pass success in adult medical patients. For overall success, there was moderate SOE of no difference for adults with cardiac arrest, medical, and mixed emergency types. • While harms were not always measured or reported, moderate SOE supported all available findings. There were no differences in harms for BVM versus SGA or ETI. When SGA was compared with ETI, there were no differences for aspiration, oral/airway trauma, and regurgitation; SGA was better for multiple insertion attempts; and ETI was better for inadequate ventilation. Conclusions. The most common findings, across emergency types and age groups, were of no differences in primary outcomes when prehospital airway management approaches were compared. As most of the included studies were observational, these findings may reflect study design and methodological limitations. Due to the dynamic nature of the prehospital environment, the results are susceptible to indication and survival biases as well as confounding; however, the current evidence does not favor more invasive airway approaches. No conclusion was supported by high SOE for any comparison and patient group. This supports the need for high-quality randomized controlled trials designed to account for the variability and dynamic nature of prehospital airway management to advance and inform clinical practice as well as emergency medical services education and policy, and to improve patient-centered outcomes.
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Geohydrology and ground-water quality, Big Elk Creek Basin, Chester County, Pennsylvania, and Cecil County, Maryland. US Geological Survey, 2002. http://dx.doi.org/10.3133/wri024057.

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