Relevant bibliographies by topics / Data warehousing Case studies

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Academic literature on the topic 'Data warehousing Case studies'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Data warehousing Case studies"

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Kembro, Joakim Hans, and Andreas Norrman. "Warehouse configuration in omni-channel retailing: a multiple case study." International Journal of Physical Distribution & Logistics Management 50, no. 5 (December 20, 2019): 509–33. http://dx.doi.org/10.1108/ijpdlm-01-2019-0034.

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Purpose The purpose of this study is to explore warehouse configuration in omni-channel retailing. Design/methodology/approach A multiple case study is conducted with six large omni-channel retailers from three different sectors. Findings The study shows an increase in the number, variation and frequency of flows passing through omni-channel warehouses. Along with an increased variety of stock keeping units (including singles vs multipacks), there is an increase in the complexity of planning and coordination of order fulfillment. Retailers test a mix of different solutions for storage and picking and partly shift focus to advanced sorting operations. The companies already have or plan to invest in substantial automation systems, which emphasize the importance of capturing and using accurate master data. Research limitations/implications The study highlights the need to understand the interrelations and co-development of configuration elements in omni-channel warehousing. The findings also suggest that a successful transformation requires increased collaboration with upstream and downstream partners. Conceptual models are developed to illustrate strategies and development paths in omni-channel warehousing, and suggestions for future research are summarized in a research agenda. A research limitation is the focus on Swedish retailers in three sectors (fashion, consumer electronics and DIY/construction material). Future studies can include additional sectors, extend the geographical scope and explore cross-regional differences. Practical implications As one of the few deeper case studies on omni-channel warehousing, practitioners will find new configurations described and analyzed here. Along with conceptual models, a synthesis of challenges and potential solutions are presented to support retailers’ practical analysis and decision making. Originality/value This is one of the first multiple case studies that go deeper into omni-channel warehouse configuration, which is of increasing importance to both scholars and practitioners in the field.
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Azzi, Anna, Daria Battini, Maurizio Faccio, Alessandro Persona, and Fabio Sgarbossa. "Inventory holding costs measurement: a multi-case study." International Journal of Logistics Management 25, no. 1 (May 6, 2014): 109–32. http://dx.doi.org/10.1108/ijlm-01-2012-0004.

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Purpose – Logisticians in the worldwide industry are frequently faced with the problem of measuring the total cost of holding inventories with simple and easy-to-use methodologies. The purpose of this paper is to look at the problem, and in particular illustrate the inventory holding cost rate computation, when different kind of warehousing systems are applied. Design/methodology/approach – A multiple case study analysis is here developed and supported by a methodological framework directly derived from the working group discussions and brainstorming activities. Two different field of application are considered: one related to five companies with manual warehousing systems operating with traditional fork lift trucks; the other is among five companies operating with automated storage/retrieval systems (AS/RS) to store inventories. Findings – The multi-case study helps to understand how the holding cost parameter is currently computed by industrial managers and how much the difference between manual and automated/automatic warehousing systems impacts on the inventory cost structure definition. The insights from the ten case studies provide evidence that the kind of storage system adopted inside the factory can impact on the holding cost rate computation and permit to derive important considerations. Practical implications – The final aim of this work is to help industrial engineers and logisticians in correctly understanding the inventory costs involved in their systems and their cost structure. In addition, the multi-case analysis leads to considerations, to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the presented methodology, and with aid from the data from this paper. Originality/value – The relevance of this work is to help industrial engineers and logisticians in understanding correctly the inventory costs involved in their logistics systems and their cost structure. In addition, the multi-case analysis lead to interesting final considerations, easily to be applied in different industrial contexts. As other industrial applications are identified, they may be analyzed by using the methodology and extrapolating the data from this paper.
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You, Jane, Qin Li, and Jinghua Wang. "On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 4 (October 2010): 18–36. http://dx.doi.org/10.4018/jcini.2010100102.

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This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing. It also provides an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features. Experimental results confirm that the new approach is feasible for content-based image retrieval.
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Ubaid, Ayesha, Farookh Hussain, and Muhammad Saqib. "Container Shipment Demand Forecasting in the Australian Shipping Industry: A Case Study of Asia–Oceania Trade Lane." Journal of Marine Science and Engineering 9, no. 9 (September 6, 2021): 968. http://dx.doi.org/10.3390/jmse9090968.

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Demand forecasting has a pivotal role in making informed business decisions by predicting future sales using historical data. Traditionally, demand forecasting has been widely used in the management of production, staffing and warehousing for sales and marketing data. However, the use of demand forecasting has little been studied in the container shipping industry. Improved visibility into the demand for container shipments has been a long-held objective of industry stakeholders. This paper addresses the shortcomings of both short-term and long-term shipment demand forecasting for the Australian container shipping industry. In this study, we compare three forecasting models, namely, the seasonal auto-regressive integrated moving average (SARIMA), Holt–Winters’ seasonal method and Facebook’s Prophet, to find the best fitting model for short-term and long-term import demand forecasting in the Australian shipping industry. Demand data from three years, i.e., 2016–2018, is used for the Asia–Oceania trade lane. The mean absolute percentage error (MAPE), root mean squared error (RMSE) and 2-fold walk-forward cross-validation are used for the model evaluation. The experiment results observed from the selected metrics suggest that Prophet outperforms the other models in its comparison for container shipment demand forecasting.
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Azanha, Adrialdo, Mauro Vivaldini, Silvio R. I. Pires, and João Batista de Camargo Junior. "Voice picking: analysis of critical factors through a case study in Brazil and the United States." International Journal of Productivity and Performance Management 65, no. 5 (June 13, 2016): 723–39. http://dx.doi.org/10.1108/ijppm-11-2015-0163.

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Purpose – The purpose of this paper is to identify and analyse the difficulties encountered in the implementation of a voice picking system at a large multinational company of the tractor industrial segment, outlining a comparison related to the main critical factors concerning the system implementation at a Brazilian and a USA plant. Design/methodology/approach – The methodology utilised was qualitative and exploratory conducted through case studies in the two plants of the multinational company. The main data were collected through interviews with key managers directly involved in the project of voice picking system implementation. Findings – The results indicate that the picking processes in the two plants were similar, since both were designed for the production line and conducted using bar-code readers and paper lists. Nevertheless, the internal warehousing process in the USA was more mature and computerised, whereas the Brazilian process still had opportunity for improvement, such as, the visual storage process, where the operator was responsible for locating an empty position. Research limitations/implications – Since this research is an exploratory case study, its results cannot be generalised. Practical implications – The paper provides relevant practical information and experiences to managers interested in implementing voice picking systems, as well as interested in improving the accuracy and productivity of logistics processes within warehouses. Originality/value – The voice picking systems are more widespread in the USA than in Brazil, and therefore, companies around the world can use this studied case to better understand about the voice picking systems implementation process in both emerging and mature marketplaces.
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He, Wenjun, Katie G. Kirchoff, Royce R. Sampson, Kimberly K. McGhee, Andrew M. Cates, Jihad S. Obeid, and Leslie A. Lenert. "Research Integrated Network of Systems (RINS): a virtual data warehouse for the acceleration of translational research." Journal of the American Medical Informatics Association 28, no. 7 (March 17, 2021): 1440–50. http://dx.doi.org/10.1093/jamia/ocab023.

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Abstract Objective Integrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier—the Research Master Identifier (RMID)—for tracking research studies across disparate systems and a data warehouse-inspired model—the Research Integrated Network of Systems (RINS)—for integrating data from those systems. Materials and Methods In 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes. Results Within 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patient-level reporting. Discussion Barriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart. Conclusion By applying data warehousing principles to federate data at the “study” level, the RINS project reduced data fragmentation and promoted research systems integration.
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Filippi, Amanda Cristina Gaban, Patricia Guarnieri, José Márcio Carvalho, Silvia Araújo Reis, and Cleyzer Adrian da Cunha. "New configurations in Brazilian agribusiness: rural warehouse condominiums." Journal of Agribusiness in Developing and Emerging Economies 10, no. 1 (December 30, 2019): 41–63. http://dx.doi.org/10.1108/jadee-12-2018-0178.

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Purpose The purpose of this paper is to analyze the development of rural warehouses condominiums (or “condos”) as new rural configurations in the Brazilian agribusiness from the perspective of the theory of collective action. Design/methodology/approach A systematic literature review was complemented with multiple case studies based on interviews and direct observation. Content analysis was used to interpret the secondary and primary data. Findings Rural warehouse condos can be characterized as a collective action which is a new business model in Brazilian agribusiness. This new initiative has several advantages: reduction of the warehouse deficit and other logistical bottlenecks; strategic commercialization of production; reduction of logistical costs; inclusion of smallholders in the economy and, consequently, the generation of income for smallholders and their participation in the market. Research limitations/implications The study used a qualitative approach and findings and discussion are inherently interpretative. Social implications Understanding the organization of rural warehouse condos supports the inclusion of smallholding farmers and income generation. This creates a social benefit through rural growth and economic development. Some constraints related to the lack of public policies, specific credit lines in financial institutions, the problem of non-regularized rural areas, lack of transparency and conflicts among the members can limit expansion. Originality/value Rural warehouse condos are expanding mainly in the south region of Brazil, where smallholders are concentrated. In this new configuration, they can engage in an association to obtain several benefits, such as reduced logistics costs, better access to financing and increased bargaining power with suppliers. As this research dealt with a relatively new and poorly studied topic, it contributes to the development of new studies to map innovative warehousing practices.
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Ahmad, Shafiq, Muhammad Arfan Hadyait, and Muhammad Mohsin Rashid. "Effect Of Supply Chain Risk Management On Organization Performance: A Case Study Of National Foods Manooabad Muridke District Sheikhupura." International Journal of social Sciences and Economic Review 1, no. 1 (May 19, 2019): 1–7. http://dx.doi.org/10.36923/ijsser.v1i1.22.

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Purpose: Previously there are many studies that focused on risk management in general context and little attention was paid on how supply chain risks management effects an organization profit and performance. The main aim of our study to explore the effect of supply chain risk management on organization performance in national foods Manooabad Muridke district Sheikhupura. Methodology: This study conducted in National foods Manooabad Muridke. Data were collected by using questioner from the employees of National foods Manooabad Muridke. SPSS 22 (Statistical package for social sciences) was used to analyze the collected data. Main Findings: This study is an attempt to check the effects of supply chain risk management variables to the performance of the organization. Three main supply chain risk management variables were identified namely, supply chain risk identification, supply chain risk sources and supply chain risk mitigation. We find that risk register method is mainly used for the identification of risks in the supply chain at any level. The most important risks of our study that are supplier risks, environmental risks, political risks, markets risks, warehousing risks, and financial risks. In mitigation strategies that are good to mitigate the risk in supply chain risk avoidance measures, risk control measures and risk cooperation measures. Applications: We concluded that it is important for an organization to identify risks proactively as early as possible in the decision-making process. Different types of risks need to be identified in all process of supply chains. After all the identification of risks, the organization needs to be mitigated these risks by using different mitigate strategies plan. Originality: In the future, this study will be helpful for many organizations which deal with food items consumed by people. As in this study, we worked on supply chain risks and their management before many studies were conducted generally on risk management so this study especially focused on supply chain risks. There is a need to conduct more study in this regard in different industries so better information available to people to control risks in supply chain management.
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Akkad, Mohammad Zaher, Sameh Haidar, and Tamás Bányai. "Design of Cyber-Physical Waste Management Systems Focusing on Energy Efficiency and Sustainability." Designs 6, no. 2 (April 13, 2022): 39. http://dx.doi.org/10.3390/designs6020039.

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Industry 4.0 tools, such as the Internet of Things, artificial intelligence, digital twinning, and cloud computing, create a technological revolution that accelerates efforts to optimize the efficiency of cyber-physical operations and services. The waste management system requires a critical share of city logistics optimization, especially when using cyber-physical systems. Modern tools reduce the required municipal waste handling, such as loading and unloading, transportation, and warehousing, which leads to an increase in efficiency and flexibility, saving energy and time, and protecting the environment. In this paper, we present a cyber-physical waste management system solution by providing a cyber-physical model design and description, mathematical modeling, and two cases to investigate the impact on energy consumption and emissions. After an introduction and literature review, we describe the design of the cyber-physical model and tackle the first echelon. The designed system incorporates the IoT, smart bins with multi-percentage sensors, data and information analysis, vehicles’ actual routes, energy and emissions optimization, multi-echelon systems, time windows, and flexibility. Mathematical modeling equations for the optimized total energy consumption are presented. Thirty and twenty smart bins located in VIII District in Budapest are detailed as two case studies, where solutions for the optimized real routes and energy consumption are found using three metaheuristic algorithms: genetic, particle swarm, and simulated annealing optimization algorithms. The accrued emissions of CO, NMHC, CH4, NOx, and PM for the optimized solutions are calculated. Finally, the results are compared with a random traditional solution to measure the effectiveness.
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Nag, Barin, Chaodong Han, and Dong-qing Yao. "Mapping supply chain strategy: an industry analysis." Journal of Manufacturing Technology Management 25, no. 3 (April 1, 2014): 351–70. http://dx.doi.org/10.1108/jmtm-06-2012-0062.

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Purpose – In manufacturing industries, the levels of inventories at all stages (i.e. raw material, work-in-process and finished goods inventories) indicate the firm's competitive positioning, strategies, internal processes and relationships with suppliers and downstream customers. The authors identify patterns of manufacturing industries based on levels of raw material and finished goods inventories to classify inbound and outbound supply chain strategies. Design/methodology/approach – The authors review literature on supply chain inventory strategy and perform cluster analysis to analyze patterns of manufacturing industries based on manufacturing industry data collected from US Census of Bureau. Following Porter's Five Forces Model, the authors perform in-depth case studies of four representative industries to analyze factors driving supply chain strategies, including industry intensity of rivalry, threat of new entrants, threat of substitutes, bargaining power of suppliers, and bargaining power of buyers. Findings – This study identifies three streams of research on supply chain strategy: Fisher's model and its variations, lean and agile paradigms, and push/pull systems. It finds that whether an industry shows low or high raw materials or finished goods inventories depending on its products, processes, and the dynamics of all forces described in the Five Forces Model. Research limitations/implications – This study is not able to include supplier selection, production strategies, warehousing and distribution, and even product design into the analysis of supply chain strategy due to data limitation. This study classifies industries based on average inventory levels of raw materials and finished goods, while inventory levels and supply chain strategies for specific firms may vary significantly within each industry. Originality/value – This study contributes to the supply chain management literature by providing a parsimonious framework of mapping inbound and outbound supply chain inventory strategies, and the results based on the analyses of all US manufacturing industries provide a baseline picture for supply chain management professionals with manufacturing firms.
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Dissertations / Theses on the topic "Data warehousing Case studies"

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Mathew, Avin D. "Asset management data warehouse data modelling." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/19310/1/Avin_Mathew_Thesis.pdf.

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Data are the lifeblood of an organisation, being employed by virtually all business functions within a firm. Data management, therefore, is a critical process in prolonging the life of a company and determining the success of each of an organisation’s business functions. The last decade and a half has seen data warehousing rising in priority within corporate data management as it provides an effective supporting platform for decision support tools. A cross-sectional survey conducted by this research showed that data warehousing is starting to be used within organisations for their engineering asset management, however the industry uptake is slow and has much room for development and improvement. This conclusion is also evidenced by the lack of systematic scholarly research within asset management data warehousing as compared to data warehousing for other business areas. This research is motivated by the lack of dedicated research into asset management data warehousing and attempts to provide original contributions to the area, focussing on data modelling. Integration is a fundamental characteristic of a data warehouse and facilitates the analysis of data from multiple sources. While several integration models exist for asset management, these only cover select areas of asset management. This research presents a novel conceptual data warehousing data model that integrates the numerous asset management data areas. The comprehensive ethnographic modelling methodology involved a diverse set of inputs (including data model patterns, standards, information system data models, and business process models) that described asset management data. Used as an integrated data source, the conceptual data model was verified by more than 20 experts in asset management and validated against four case studies. A large section of asset management data are stored in a relational format due to the maturity and pervasiveness of relational database management systems. Data warehousing offers the alternative approach of structuring data in a dimensional format, which suggests increased data retrieval speeds in addition to reducing analysis complexity for end users. To investigate the benefits of moving asset management data from a relational to multidimensional format, this research presents an innovative relational vs. multidimensional model evaluation procedure. To undertake an equitable comparison, the compared multidimensional are derived from an asset management relational model and as such, this research presents an original multidimensional modelling derivation methodology for asset management relational models. Multidimensional models were derived from the relational models in the asset management data exchange standard, MIMOSA OSA-EAI. The multidimensional and relational models were compared through a series of queries. It was discovered that multidimensional schemas reduced the data size and subsequently data insertion time, decreased the complexity of query conceptualisation, and improved the query execution performance across a range of query types. To facilitate the quicker uptake of these data warehouse multidimensional models within organisations, an alternate modelling methodology was investigated. This research presents an innovative approach of using a case-based reasoning methodology for data warehouse schema design. Using unique case representation and indexing techniques, the system also uses a business vocabulary repository to augment case searching and adaptation. The system was validated through a case-study where multidimensional schema design speed and accuracy was measured. It was found that the case-based reasoning system provided a marginal benefit, with a greater benefits gained when confronted with more difficult scenarios.
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Mathew, Avin D. "Asset management data warehouse data modelling." Queensland University of Technology, 2008. http://eprints.qut.edu.au/19310/.

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Data are the lifeblood of an organisation, being employed by virtually all business functions within a firm. Data management, therefore, is a critical process in prolonging the life of a company and determining the success of each of an organisation’s business functions. The last decade and a half has seen data warehousing rising in priority within corporate data management as it provides an effective supporting platform for decision support tools. A cross-sectional survey conducted by this research showed that data warehousing is starting to be used within organisations for their engineering asset management, however the industry uptake is slow and has much room for development and improvement. This conclusion is also evidenced by the lack of systematic scholarly research within asset management data warehousing as compared to data warehousing for other business areas. This research is motivated by the lack of dedicated research into asset management data warehousing and attempts to provide original contributions to the area, focussing on data modelling. Integration is a fundamental characteristic of a data warehouse and facilitates the analysis of data from multiple sources. While several integration models exist for asset management, these only cover select areas of asset management. This research presents a novel conceptual data warehousing data model that integrates the numerous asset management data areas. The comprehensive ethnographic modelling methodology involved a diverse set of inputs (including data model patterns, standards, information system data models, and business process models) that described asset management data. Used as an integrated data source, the conceptual data model was verified by more than 20 experts in asset management and validated against four case studies. A large section of asset management data are stored in a relational format due to the maturity and pervasiveness of relational database management systems. Data warehousing offers the alternative approach of structuring data in a dimensional format, which suggests increased data retrieval speeds in addition to reducing analysis complexity for end users. To investigate the benefits of moving asset management data from a relational to multidimensional format, this research presents an innovative relational vs. multidimensional model evaluation procedure. To undertake an equitable comparison, the compared multidimensional are derived from an asset management relational model and as such, this research presents an original multidimensional modelling derivation methodology for asset management relational models. Multidimensional models were derived from the relational models in the asset management data exchange standard, MIMOSA OSA-EAI. The multidimensional and relational models were compared through a series of queries. It was discovered that multidimensional schemas reduced the data size and subsequently data insertion time, decreased the complexity of query conceptualisation, and improved the query execution performance across a range of query types. To facilitate the quicker uptake of these data warehouse multidimensional models within organisations, an alternate modelling methodology was investigated. This research presents an innovative approach of using a case-based reasoning methodology for data warehouse schema design. Using unique case representation and indexing techniques, the system also uses a business vocabulary repository to augment case searching and adaptation. The system was validated through a case-study where multidimensional schema design speed and accuracy was measured. It was found that the case-based reasoning system provided a marginal benefit, with a greater benefits gained when confronted with more difficult scenarios.
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Haneuse, Sebastian J. P. A. "Ecological studies using supplemental case-control data /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/9595.

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Bhansali, Neera, and nbhansali@yahoo com. "Strategic Alignment in Data Warehouses Two Case Studies." RMIT University. Business Information Technology, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080108.150431.

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This research investigates the role of strategic alignment in the success of data warehouse implementation. Data warehouse technology is inherently complex, requires significant capital investment and development time. Many organizations fail to realize the full benefits from it. While failure to realize benefits has been attributed to numerous causes, ranging from technical to organizational reasons, the underlying strategic alignment issues have not been studied. This research confirms, through two case studies, that the successful adoption of the data warehouse depends on its alignment to the business plans and strategy. The research found that the factors that are critical to the alignment of data warehouses to business strategy and plans are (a) joint responsibility between data warehouse and business managers, (b) alignment between data warehouse plan and business plan, (c) business user satisfaction, (d) flexibility in data warehouse planning and (e) technical integration of the data warehouse. In the case studies, the impact of strategic alignment was visible both at implementation and use levels. The key findings from the case studies are that a) Senior management commitment and involvement are necessary for the initiation of the data warehouse project. The awareness and involvement of data warehouse managers in corporate strategies and a high level of joint responsibility between business and data warehouse managers is critical to strategic alignment and successful adoption of the data warehouse. b) Communication of the strategic direction between the business and data warehouse managers is important for the strategic alignment of the data warehouse. Significant knowledge sharing among the stakeholders and frequent communication between the iv data warehouse managers and users facilitates better understanding of the data warehouse and its successful adoption. c) User participation in the data warehouse project, perceived usefulness of the data warehouse, ease of use and data quality (accuracy, consistency, reliability and timelines) were significant factors in strategic alignment of the data warehouse. d) Technology selection based on its ability to address business and user requirements, and the skills and response of the data warehousing team led to better alignment of the data warehouse to business plans and strategies. e) The flexibility to respond to changes in business needs and flexibility in data warehouse planning is critical to strategic alignment and successful adoption of the data warehouse. Alignment is seen as a process requiring continuous adaptation and coordination of plans and goals. This research provides a pathway for facilitating successful adoption of data warehouse. The model developed in this research allows data warehouse professionals to ensure that their project when implemented, achieve the strategic goals and business objectives of the organization.
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Godes, David Bradley. "Use of heterogeneous data sources : three case studies." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/61057.

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Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1989.
Title as it appears in the M.I.T. Graduate List, June 1989: Integration of heterogeneous data sources--three case studies.
Includes bibliographical references (leaf 159).
by David Bradley Godes.
M.S.
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Perl, Henning [Verfasser]. "Security and Data Analysis - Three Case Studies / Henning Perl." Bonn : Universitäts- und Landesbibliothek Bonn, 2017. http://d-nb.info/1149154179/34.

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Connelly, Roxanne. "Social stratification and education : case studies analysing social survey data." Thesis, University of Stirling, 2013. http://hdl.handle.net/1893/18590.

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Social Stratification is an enduring influence in contemporary societies which shapes many outcomes over the lifecourse. Social Stratification is also a key mechanism by which social inequalities are transmitted from one generation to the next. This thesis presents a set of inter-related case studies which explore social stratification in contemporary Britain. This thesis focuses on the analysis of an appropriate set of large scale social survey datasets, which contain detailed micro-level data. The thesis begins with a detailed review of one area of social survey research practice which has been neglected, namely the measurement and operationalisation of ‘key variables’. Three case studies are then presented which undertake original analyses using five different large-scale social survey resources. Throughout this thesis detailed consideration of the operationalisation of variables is made and a range of statistical modelling approaches are employed to address middle range theories regarding the processes of social stratification. Case study one focuses on cognitive inequalities in the early years of childhood. This case study builds on research which has indicated that social stratification impacts on the cognitive performance of young children. This chapter makes the original contribution of charting the extent of social inequalities on childhood cognitive abilities between three British birth cohorts. There are clear patterns of social inequality within each cohort. Between the cohorts there is also evidence that the association between socio-economic advantage and childhood cognitive capability have remained largely stable over the post-war period, in spite of the raft of policy measures that have been floated to tackle social inequality. Case study two investigates the recent sociological idea that there is a ‘middle’ group of young people who are absent in sociological inquiries. This chapter sets out to explore the existence of a ‘middle’ group based on their socio-economic characteristics. This case study focuses on school GCSE examination performance, and finds that performance is highly stratified by parental occupational positions. The analysis provided no persuasive evidence of the existence of a ‘middle’, mediocre or ordinary group of young people. The analytical benefits of studying the full attainment spectrum are emphasised, over a priori categorisation. Case study three combines the analysis of intra-generational and inter-generational status attainment perspectives by studying the influences of social origins, educational attainment and cognitive abilities across the occupational lifecourse. This case study tests theoretical ideas regarding the importance of these three areas of influence over time. This case study therefore presents a detailed picture of social stratification processes. The results highlight that much more variation in occupational positions is observed between individuals, rather than across an individual’s lifecourse. The influence of social origins, educational attainment and cognitive ability on occupational positions appear to decrease across an individual’s occupational lifecourse. A brief afterword that showcases a sensitivity analysis is presented at the end of the thesis. This brief exposition is provided to illustrate the potential benefit of undertaking sensitivity analyses when developing research which operationalises key variables in social stratification. It is argued that such an activity is beneficial and informative and should routinely be undertaken within sociological analyses of social surveys. The thesis concludes with a brief reflection on large-scale survey research and statistical modelling and comments on potential areas for future research.
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Lewis, Taariq, and Bryan Long. "Case analysis studies of diffusion models on E-commerce transaction data." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/49772.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 2009.
Includes bibliographical references (p. 50).
As online merchants compete in the growing e-commerce markets for customers, attention to data generated from merchant and customer website interactions continues to drive ongoing online analytical innovation. However, successful online sales forecasting arising from historical transaction data still proves elusive for many online retailers. Although there are numerous software and statistical models used in online retail, not many practitioners claim success creating accurate online inventory management or marketing effectiveness forecast models. Thus, online retailers with both online and offline strategies express frustration that although they are able to predict sales in their offline properties, even with substantial online data, they are not as successful with their online-stores. This paper attempts to test two analytical approaches to determine whether reliable forecasting can be developed using already established statistical models. Firstly, we use the original Bass Model of Diffusion and modify it for analysis of online retail data. Then, we test the model's forecasting effectiveness to extrapolate expected sales in the following year. As a second method, we use statistical cluster analysis to categorize groups of products into distinct product performance groups. We then analyze those groups for distinct characteristics and then test whether we can forecast new product performance based on the identified group characteristics.
(cont.) We partnered with a medium-sized online retail e-commerce firm with both online and offline retail channels to provide us with online transaction data. Using a modified Bass Diffusion Model, we were able to fit a sales forecast curve to a sample of products. We then used k-means cluster analysis to partition products into similar groups of sales transaction-behavior, over the period of 1 year. For each group, we tried to identify characteristics which we could use to forecast new product launch behavior. However, lack of accurate, characteristic mapping of products made it difficult to establish confidence in cluster forecasting for some groups with similar curves. With more accurate characteristic mapping of products, we're hopeful that cluster analysis can reasonably forecast new product performance in online retail catalogs.
by Taariq Lewis [and] Bryan Long.
M.B.A.
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Kang, Sangwook Cai Jianwen. "Statistical methods for case-control and case-cohort studies with possibly correlated failure time data." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1244.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.
Title from electronic title page (viewed Mar. 26, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Biostatistics, School of Public Health." Discipline: Biostatistics; Department/School: Public Health.
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Koylu, Caglar. "A Case Study In Weather Pattern Searching Using A Spatial Data Warehouse Model." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609573/index.pdf.

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Data warehousing and Online Analytical Processing (OLAP) technology has been used to access, visualize and analyze multidimensional, aggregated, and summarized data. Large part of data contains spatial components. Thus, these spatial components convey valuable information and must be included in exploration and analysis phases of a spatial decision support system (SDSS). On the other hand, Geographic Information Systems (GISs) provide a wide range of tools to analyze spatial phenomena and therefore must be included in the analysis phases of a decision support system (DSS). In this regard, this study aims to search for answers to the problem how to design a spatially enabled data warehouse architecture in order to support spatio-temporal data analysis and exploration of multidimensional data. Consequently, in this study, the concepts of OLAP and GISs are synthesized in an integrated fashion to maximize the benefits generated from the strengths of both systems by building a spatial data warehouse model. In this context, a multidimensional spatio-temporal data model is proposed as a result of this synthesis. This model addresses the integration problem of spatial, non-spatial and temporal data and facilitates spatial data exploration and analysis. The model is evaluated by implementing a case study in weather pattern searching.
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Books on the topic "Data warehousing Case studies"

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Gentleman, Jane F., and G. A. Whitmore, eds. Case Studies in Data Analysis. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2688-8.

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Shipman, Alan. Data protection: Risk assessment case studies. London: BSI Business Information, 2004.

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Case studies in business data bases. New York: Holt, Rinehart and Winston, 1988.

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Vilhuber, Lars. Adjusting imperfect data: Overview and case studies. Cambridge, MA: National Bureau of Economic Research, 2007.

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Meimei, Ma J., ed. Concepts and case studies in data management. Cary, NC: SAS Institute, 1996.

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Clancy, Michael. Designing Pascalsolutions: Case studies using data structures. New York: Computer Science Press, 1996.

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Mengersen, Kerrie L., Pierre Pudlo, and Christian P. Robert, eds. Case Studies in Applied Bayesian Data Science. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42553-1.

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O'Leary, Dianne P. Scientific Computing with Case Studies. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, 2009.

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Akram, Khan Muhammad. Data analysis in performance auditing. Lahore: Directorate-General of Research and Development, Dept. of the Auditor-General of Pakistan, 1991.

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N, Damask Jay, and Damask John B, eds. Injury causation analyses: Case studies and data sources. Charlottesville, Va: Michie Co., 1990.

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Book chapters on the topic "Data warehousing Case studies"

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Agarwal, Sonali, Murli Dhar Tiwari, and Iti Tiwari. "Government Data Mining Case Studies on Education and Health." In E Governance Data Center, Data Warehousing and Data Mining, 155–201. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003357254-8.

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Frie, Thorsten, and Ralph Wellmann. "Der Business Case im Kontext des Data Warehousing." In Data Warehousing Strategie, 21–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58350-6_2.

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List, Beate, Josef Schiefer, and A. Min Tjoa. "Use Case Driven Requirements Analysis for Data Warehouse Systems." In Data Warehousing 2000, 23–39. Heidelberg: Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-642-57681-2_2.

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Liu, Han, and Mihaela Cocea. "Case Studies." In Studies in Big Data, 77–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70058-8_8.

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Liu, Han, Alexander Gegov, and Mihaela Cocea. "Case Studies." In Studies in Big Data, 81–95. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23696-4_8.

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Benker, Thomas, and Carsten Jürck. "A Case Study on Model-Driven Data Warehouse Development." In Data Warehousing and Knowledge Discovery, 54–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32584-7_5.

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Pitoura, Evaggelia, and George Samaras. "Case Studies." In Data Management for Mobile Computing, 111–26. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5527-8_6.

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Maimon, Oded, and Mark Last. "Case Studies." In Knowledge Discovery and Data Mining, 71–103. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3296-2_6.

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Abdul Kadhar, K. Mohaideen, and G. Anand. "Case Studies." In Data Science with Raspberry Pi, 171–228. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6825-4_9.

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Skillicorn, David B. "Case studies." In Cyberspace, Data Analytics, and Policing, 181–228. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003126225-8.

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Conference papers on the topic "Data warehousing Case studies"

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Kulkarni, Manashree, Meiliu Lu, and Du Zhang. "A case-based data warehousing courseware." In Integration (2010 IRI). IEEE, 2010. http://dx.doi.org/10.1109/iri.2010.5558932.

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Lao, Guoling, and Ying Tang. "The application of data warehousing in e-business environment and case study." In the 7th international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1089551.1089707.

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Mansingh, Gunjan, Lila Rao-Graham, Kweku-Muata Osei-Bryson, and Annette Mills. "Using data mining process standards: A case study - Profiling Internet Banking Users in Jamaica." In Annual International Academic Conference on Business Intelligence and Data Warehousing. Global Science and Technology Forum, 2010. http://dx.doi.org/10.5176/978-981-08-6308-1_36.

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Meunier, J. "Land PRM Case Studies." In EAGE Workshop on Permanent Reservoir Monitoring (PRM) - Using Seismic Data 2011. Netherlands: EAGE Publications BV, 2011. http://dx.doi.org/10.3997/2214-4609.20145210.

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"Social Science Data Archives: Case Studies in Data Sustainability." In iConference 2014 Proceedings: Breaking Down Walls. Culture - Context - Computing. iSchools, 2014. http://dx.doi.org/10.9776/14287.

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Paszto, Vit. "SPATIAL ANALYSES OF TWITTER DATA � CASE STUDIES." In 14th SGEM GeoConference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING. Stef92 Technology, 2014. http://dx.doi.org/10.5593/sgem2014/b21/s8.101.

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Rosenthal, Stephanie, Scott McMillan, and Matthew E. Gaston. "Developer toolchains for large-scale analytics: Two case studies." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363887.

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Lindman, Juho, Tomi Kinnari, and Matti Rossi. "Industrial Open Data: Case Studies of Early Open Data Entrepreneurs." In 2014 47th Hawaii International Conference on System Sciences (HICSS). IEEE, 2014. http://dx.doi.org/10.1109/hicss.2014.99.

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Dossis, Michael, Dimitrios Amanatidis, and Ifigeneia Mylona. "Mining Twitter Data: Case Studies with Trending Hashtags." In The 4th Virtual International Conference on Advanced Research in Scientific Areas. Publishing Society, 2015. http://dx.doi.org/10.18638/arsa.2015.4.1.751.

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Arms, W. Y., S. Aya, M. Calimlim, J. Cordes, J. Deneva, P. Dmitriev, J. Gehrke, et al. "Three Case Studies of Large-Scale Data Flows." In 22nd International Conference on Data Engineering Workshops (ICDEW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdew.2006.148.

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Reports on the topic "Data warehousing Case studies"

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Mazzella, Andrew J., Delorey Jr., Larson Dennis E., Dickson Kevin P., and Jr Peter. Case Studies in Data Analysis. Fort Belvoir, VA: Defense Technical Information Center, June 1989. http://dx.doi.org/10.21236/ada215342.

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Vilhuber, Lars. Adjusting Imperfect Data: Overview and Case Studies. Cambridge, MA: National Bureau of Economic Research, March 2007. http://dx.doi.org/10.3386/w12977.

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Mittleman, Murray, Elizabeth Mostofsky, and Long Ngo. Improving Methods for Analyzing Data from Case-Only Studies. Patient-Centered Outcomes Research Institute® (PCORI), February 2022. http://dx.doi.org/10.25302/02.2022.me.150731028.

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Xu, Tengfang, and Steve Greenberg. Data Center Energy Benchmarking: Part 5 - Case Studies on aCorporate Data Center (No. 22). Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/926604.

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Xu, Tengfang, and Steve Greenberg. Data Center Energy Benchmarking Part 1 - Case Studies on TwoCo-location Data Centers (No. 16 and 17). Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/926603.

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Cai, Hao, Xinyi Wang, Jarod Kelly, and Michael Wang. Building Life-Cycle Analysis with the GREET Building Module: Methodology, Data, and Case Studies. Office of Scientific and Technical Information (OSTI), October 2021. http://dx.doi.org/10.2172/1823607.

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Xu, Tengfang, and Steve Greenberg. Data Center Energy Benchmarking: Part 2 - Case Studies on TwoCo-location Network Data Centers (No. 18 and 19). Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/928722.

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Staenz, K., R. A. Neville, J. Lévesque, T. Szeredi, V. Singhroy, G. A. Borstad, and P. Hauff. Evaluation of casi and SFSI hyperspectral data for environmental and geological applications - two case studies. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219197.

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Ganda, Francesco, and Giovanni Maronati. Economic Data and Modeling Support for the Two Regional Case Studies: Nuclear-Renewable Hybrid Energy Systems: Analysis of Technical & Economic Issues. Office of Scientific and Technical Information (OSTI), August 2018. http://dx.doi.org/10.2172/1483989.

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Chainey, Jennie, Debbie Wong, Elizabeth Cassity, and Hilary Hollingsworth. Teacher development multi-year studies. Using case studies to investigate and understand teaching quality and student learning: Initial lessons learned. Australian Council for Educational Research, 2022. http://dx.doi.org/10.37517/978-1-74286-679-6.

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This paper presents some initial lessons learned about the use of case studies as a key form of evidence regarding teaching quality and student learning in a multi-year teacher development study series. This study series, commissioned by the Australian Government’s Department of Foreign Affairs and Trade (DFAT), involves the investigation of teacher development initiatives in Lao People’s Democratic Republic (Laos), Timor-Leste and Vanuatu. The overall aim of the study series is to understand the extent to which the Australian investment has improved teaching quality and student learning. This paper discusses the processes used to design, implement, analyse and report case study data, and key lessons learned about these that could be applied to other contexts and programs. These processes include: design, implementation, and analysis and reporting.
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