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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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Статті в журналах з теми "Warehouses Management Data processing"

1

Aufaure, Marie-Aude, Alfredo Cuzzocrea, Cécile Favre, Patrick Marcel, and Rokia Missaoui. "An Envisioned Approach for Modeling and Supporting User-Centric Query Activities on Data Warehouses." International Journal of Data Warehousing and Mining 9, no. 2 (April 2013): 89–109. http://dx.doi.org/10.4018/jdwm.2013040105.

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Анотація:
In this vision paper, the authors discuss models and techniques for integrating, processing and querying data, information and knowledge within data warehouses in a user-centric manner. The user-centric emphasis allows us to achieve a number of clear advantages with respect to classical data warehouse architectures, whose most relevant ones are the following: (i) a unified and meaningful representation of multidimensional data and knowledge patterns throughout the data warehouse layers (i.e., loading, storage, metadata, etc); (ii) advanced query mechanisms and guidance that are capable of extracting targeted information and knowledge by means of innovative information retrieval and data mining techniques. Following this main framework, the authors first outline the importance of knowledge representation and management in data warehouses, where knowledge is expressed by existing ontology or patterns discovered from data. Then, the authors propose a user-centric architecture for OLAP query processing, which is the typical applicative interface to data warehouse systems. Finally, the authors propose insights towards cooperative query answering that make use of knowledge management principles and exploit the peculiarities of data warehouses (e.g., multidimensionality, multi-resolution, and so forth).
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2

Mujiono, Mujiono, and Aina Musdholifah. "Pengembangan Data Warehouse Menggunakan Pendekatan Data-Driven untuk Membantu Pengelolaan SDM." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 1 (January 31, 2016): 1. http://dx.doi.org/10.22146/ijccs.11184.

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The basis of bureaucratic reform is the reform of human resources management. One supporting factor is the development of an employee database. To support the management of human resources required including data warehouse and business intelligent tools. The data warehouse is an integrated concept of reliable data storage to provide support to all the needs of the data analysis. In this study developed a data warehouse using the data-driven approach to the source data comes from SIMPEG, SAPK and electronic presence. Data warehouses are designed using the nine steps methodology and unified modeling language (UML) notation. Extract transform load (ETL) is done by using Pentaho Data Integration by applying transformation maps. Furthermore, to help human resource management, the system is built to perform online analytical processing (OLAP) to facilitate web-based information. In this study generated BI application development framework with Model-View-Controller (MVC) architecture and OLAP operations are built using the dynamic query generation, PivotTable, and HighChart to present information about PNS, CPNS, Retirement, Kenpa and Presence
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3

Kartanova, A. Dzh, and T. I. Imanbekov. "OVERVIEW OF OPTIMIZATION METHODS FOR PRODUCTIVITY OF THE ETL PROCESS." Heralds of KSUCTA, №1, 2022, no. 1-2022 (March 14, 2022): 64–70. http://dx.doi.org/10.35803/1694-5298.2022.1.64-70.

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One of the important aspects in management and acceleration of processes, operations in databases and data warehouses is ETL processes, the process of extracting, transforming and loading data. These processes without optimizing, a realization data warehouse project is costly, complex, and time-consuming. This paper provides an overview and research of methods for optimizing the performance of ETL processes; that the most important indicator of ETL system's operation is the time and speed of data processing is shown. The issues of the generalized structure of ETL process flows are considered, the architecture of ETL process optimization is proposed, and the main methods of parallel data processing in ETL systems are presented, those methods can improve its performance. The most relevant today of the problem is performance of ETL processes for data warehouses is considered in detail.
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4

Kartanova, A. Dzh, and T. I. Imanbekov. "OVERVIEW OF OPTIMIZATION METHODS FOR PRODUCTIVITY OF THE ETL PROCES." Heralds of KSUCTA, №4, 2021, no. 4-2021 (December 27, 2021): 556–63. http://dx.doi.org/10.35803/1694-5298.2021.4.556-563.

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Анотація:
One of the important aspects in management and acceleration of processes, operations in databases and data warehouses is ETL processes, the process of extracting, transforming and loading data. These processes without optimizing, a realization data warehouse project is costly, complex, and time-consuming. This paper provides an overview and research of methods for optimizing the performance of ETL processes; that the most important indicator of ETL system's operation is the time and speed of data processing is shown. The issues of the generalized structure of ETL process flows are considered, the architecture of ETL process optimization is proposed, and the main methods of parallel data processing in ETL systems are presented, those methods can improve its performance. The most relevant today of the problem is performance of ETL processes for data warehouses is considered in detail.
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5

Kihel, Yousra. "Digital Transition Methodology of a Warehouse in the Concept of Sustainable Development with an Industrial Case Study." Sustainability 14, no. 22 (November 17, 2022): 15282. http://dx.doi.org/10.3390/su142215282.

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Анотація:
Logistics is one of the sectors that is evolving in parallel with Industry 4.0, which refers to the integration of new technologies, information, and agents, with the common goal of improving the efficiency and responsiveness of a logistics management system. The warehouse is an essential link in logistics management, a factor of competitiveness, and a link between the partners of the entire logistics chain. It has become essential to manage warehouses effectively and to allocate their resources efficiently. The digitalization of warehouses is currently one of the research topics of Logistics 4.0. This work presents a methodology of the digital transition of warehouse management, which consists of four main steps: the diagnosis of a warehouse to identify the different processes, the degree of involvement of the employees, a calculation of the degree of maturity to identify the new technology and means of data transfer, and the associated software for the collection of information and the methods of data processing. This digital transition methodology was applied to an industrial company. The results obtained allowed for the improvement of all the indicators measuring the performance of the warehouse on economic, social, and environmental levels.
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6

Ribeiro de Almeida, Damião, Cláudio de Souza Baptista, Fabio Gomes de Andrade, and Amilcar Soares. "A Survey on Big Data for Trajectory Analytics." ISPRS International Journal of Geo-Information 9, no. 2 (February 1, 2020): 88. http://dx.doi.org/10.3390/ijgi9020088.

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Анотація:
Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.
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Rao, M. Venkata Krishna, Ch Suresh, K. Kamakshaiah, and M. Ravikanth. "Prototype Analysis for Business Intelligence Utilization in Data Mining Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 29, 2017): 30. http://dx.doi.org/10.23956/ijarcsse.v7i7.93.

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Анотація:
Tremendous increase of high availability of more disparate data sources than ever before have raised difficulties in simplifying frequent utility report across multiple transaction systems apart from an integration of large historical data. It is main focusing concept in data exploration with high transactional data systems in real time data processing. This problem mainly occurs in data warehouses and other data storage proceedings in Business Intelligence (BI) for knowledge management and business resource planning. In this phenomenon, BI consists software construction of data warehouse query processing in report generation of high utility data mining in transactional data systems. The growth of a huge voluminous data in the real world is posing challenges to the research and business community for effective data analysis and predictions. In this paper, we analyze different data mining techniques and methods for Business Intelligence in data analysis of transactional databases. For that, we discuss what the key issues are by performing in-depth analysis of business data which includes database applications in transaction data source system analysis. We also discuss different integrated techniques in data analysis in business operational process for feasible solutions in business intelligence
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Husin, Albert Eddy, and Eko Arif Budianto. "Influential factors in the application of the Lean Six Sigma and time-cost trade-off method in the construction of the ammunition warehouse." SINERGI 26, no. 1 (February 1, 2022): 81. http://dx.doi.org/10.22441/sinergi.2022.1.011.

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Анотація:
Construction projects have developed so rapidly, one of which is the construction of warehouses. The warehouse discussed in this study is an ammunition warehouse. The construction of the ammunition warehouse has a deadline in accordance with the contract agreed between the owner, contractor and consultant. But in the implementation in the field, there was a delay in the work of the concrete structure. This study aims to obtain the dominant factors causing delays in the ammunition warehouse project by applying the Lean Six Sigma method and time-cost trade-off in solving the problem. Data processing used statistical analysis SPSS (Statistical Package for the Social Sciences), which was obtained from questionnaires filled out by experts. From this processing, the highest ten factors were obtained, namely 1. Inadequate planning and scheduling, 2. Implementation of work plans, 3. Delay in drawing up and approval of drawings, 4. Cost reduction, 5. Relationship between management and labor, 6. Relationship design internal team, 7. Lack of skilled manpower, 8. Flexibility, 9. Errors during construction and 10. Inaccurate prediction of craftsman production levels. This research is useful and beneficial for readers and can be developed again.
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Datta, Anindya, and Helen Thomas. "The cube data model: a conceptual model and algebra for on-line analytical processing in data warehouses." Decision Support Systems 27, no. 3 (December 1999): 289–301. http://dx.doi.org/10.1016/s0167-9236(99)00052-4.

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Toews, M. D., F. H. Arthur та J. F. Campbell. "Monitoring Tribolium castaneum (Herbst) in pilot-scale warehouses treated with β-cyfluthrin: are residual insecticides and trapping compatible?" Bulletin of Entomological Research 99, № 2 (24 жовтня 2008): 121–29. http://dx.doi.org/10.1017/s0007485308006172.

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Анотація:
AbstractIntegrated pest management strategies for cereal processing facilities often include both pheromone-baited pitfall traps and crack and crevice applications of a residual insecticide such as the pyrethroid cyfluthrin. In replicated pilot-scale warehouses, a 15-week-long experiment was conducted comparing population trends suggested by insect captures in pheromone-baited traps to direct estimates obtained by sampling the food patches in untreated and cyfluthrin-treated warehouses. Warehouses were treated, provisioned with food patches and then infested with all life stages of Tribolium castaneum (Herbst). Food patches, both those initially infested and additional uninfested, were surrounded by cyfluthrin bands to evaluate if insects would cross the bands. Results show that insect captures correlated with population trends determined by direct product samples in the untreated warehouses, but not the cyfluthrin-treated warehouses. However, dead insects recovered from the floor correlated with the insect densities observed with direct samples in the cyfluthrin-treated warehouses. Initially, uninfested food patches were exploited immediately and after six weeks harbored similar infestation densities to the initially infested food patches. These data show that pest management professionals relying on insect captures in pheromone-baited traps in cyfluthrin-treated structures could be deceived into believing that a residual insecticide application was suppressing population growth, when the population was actually increasing at the same rate as an untreated population.
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Більше джерел

Дисертації з теми "Warehouses Management Data processing"

1

Issa, Carla Mounir. "Data warehouse applications in modern day business." CSUSB ScholarWorks, 2002. https://scholarworks.lib.csusb.edu/etd-project/2148.

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Анотація:
Data warehousing provides organizations with strategic tools to achieve the competitive advantage that organazations are constantly seeking. The use of tools such as data mining, indexing and summaries enables management to retrieve information and perform thorough analysis, planning and forcasting to meet the changes in the market environment. in addition, The data warehouse is providing security measures that, if properly implemented and planned, are helping organizations ensure that their data quality and validity remain intact.
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2

Ponelis, S. R. (Shana Rachel). "Data marts as management information delivery mechanisms: utilisation in manufacturing organisations with third party distribution." Thesis, University of Pretoria, 2002. http://hdl.handle.net/2263/27061.

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Анотація:
Customer knowledge plays a vital part in organisations today, particularly in sales and marketing processes, where customers can either be channel partners or final consumers. Managing customer data and/or information across business units, departments, and functions is vital. Frequently, channel partners gather and capture data about downstream customers and consumers that organisations further upstream in the channel require to be incorporated into their information systems in order to allow for management information delivery to their users. In this study, the focus is placed on manufacturing organisations using third party distribution since the flow of information between channel partner organisations in a supply chain (in contrast to the flow of products) provides an important link between organisations and increasingly represents a source of competitive advantage in the marketplace. The purpose of this study is to determine whether there is a significant difference in the use of sales and marketing data marts as management information delivery mechanisms in manufacturing organisations in different industries, particularly the pharmaceuticals and branded consumer products. The case studies presented in this dissertation indicates that there are significant differences between the use of sales and marketing data marts in different manufacturing industries, which can be ascribed to the industry, both directly and indirectly.
Thesis (MIS(Information Science))--University of Pretoria, 2002.
Information Science
MIS
unrestricted
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3

Rosa, Luiz Henrique Leite. "Sistema de apoio à gestão de utilidades e energia: aplicação de conceitos de sistemas de informação e de apoio à tomada de decisão." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/3/3143/tde-03082007-165825/.

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Este trabalho trata da especificação, desenvolvimento e utilização do Sistema de Apoio à Gestão de Utilidades e Energia - SAGUE, um sistema concebido para auxiliar na análise de dados coletados de sistemas de utilidades como ar comprimido, vapor, sistemas de bombeamento, sistemas para condicionamento ambiental e outros, integrados com medições de energia e variáveis climáticas. O SAGUE foi desenvolvido segundo conceitos presentes em sistemas de apoio à decisão como Data Warehouse e OLAP - Online Analytical Processing - com o intuito de transformar os dados oriundos de medições em informações que orientem diretamente as ações de conservação e uso racional de energia. As principais características destes sistemas, que influenciaram na especificação e desenvolvimento do SAGUE, são tratadas neste trabalho. Além disso, este texto aborda a gestão energética e os sistemas de gerenciamento de energia visando apresentar o ambiente que motivou o desenvolvimento do SAGUE. Neste contexto, é apresentado o Sistema de Gerenciamento de Energia Elétrica - SISGEN, um sistema de informação para suporte à gestão de energia elétrica e de contratos de fornecimento, cujos dados coletados podem ser analisados através do SAGUE. A aplicação do SAGUE é tratada na forma de um estudo de caso no qual se analisa a correlação existente entre o consumo de energia elétrica da CUASO - Cidade Universitária Armando de Sales Oliveira, obtido através do SISGEN, e as medições de temperatura ambiente, fornecidas pelo IAG - Instituto de Astronomia, Geofísica e Ciências Atmosféricas da USP.
This work deals with specification, development and utilization of the Support System for Utility and Energy Management - SAGUE, a system created to assist in analysis of data collected from utilities systems as compressed air, vapor, water pumping systems, environmental conditioning systems and others, integrated with energy consumption and climatic measurements. The development of SAGUE was based on concepts and methodologies from Decision Support System as Data Warehouse and OLAP - Online Analytical Processing - in order to transform data measurements in information that guide the actions for energy conservation and rational utilization. The main characteristics of Data Warehouse and OLAP tools that influenced in the specifications and development of SAGUE are described in this work. In addition, this text deals with power management and energy management systems in order to present the environment that motivated the SAGUE development. Within this context, it is presented the Electrical Energy Management System - SISGEN, a system for energy management support, whose electrical measurements can be analyzed by SAGUE. The SAGUE utilization is presented in a case study that discusses the relation between electrical energy consumption of CUASO - Cidade Universitária Armando de Sales Oliveira, obtained throughout SISGEN, and the local temperature measurements supplied by IAG - Institute of Astronomic and Atmospheric Science of USP.
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Strand, Mattias. "External Data Incorporation into Data Warehouses." Doctoral thesis, Kista : Skövde : Dept. of computer and system sciences, Stockholm University : School of humanities and informatics, University of Skövde, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-660.

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Belcin, Andrei. "Smart Cube Predictions for Online Analytic Query Processing in Data Warehouses." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/41956.

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Анотація:
A data warehouse (DW) is a transformation of many sources of transactional data integrated into a single collection that is non-volatile and time-variant that can provide decision support to managerial roles within an organization. For this application, the database server needs to process multiple users’ queries by joining various datasets and loading the result in main memory to begin calculations. In current systems, this process is reactionary to users’ input and can be undesirably slow. In previous studies, it was shown that a personalization scheme of a single user’s query patterns and loading the smaller subset into main memory the query response time significantly shortened the query response time. The LPCDA framework developed in this research handles multiple users’ query demands, and the query patterns are subject to change (so-called concept drift) and noise. To this end, the LPCDA framework detects changes in user behaviour and dynamically adapts the personalized smart cube definition for the group of users. Numerous data mart (DM)s, as components of the DW, are subject to intense aggregations to assist analytics at the request of automated systems and human users’ queries. Subsequently, there is a growing need to properly manage the supply of data into main memory that is in closest proximity to the CPU that computes the query in order to reduce the response time from the moment a query arrives at the DW server. As a result, this thesis proposes an end-to-end adaptive learning ensemble for resource allocation of cuboids within a a DM to achieve a relevant and timely constructed smart cube before the time in need, as a way of adopting the just-in-time inventory management strategy applied in other real-world scenarios. The algorithms comprising the ensemble involve predictive methodologies from Bayesian statistics, data mining, and machine learning, that reflect the changes in the data-generating process using a number of change detection algorithms. Therefore, given different operational constraints and data-specific considerations, the ensemble can, to an effective degree, determine the cuboids in the lattice of a DM to pre-construct into a smart cube ahead of users submitting their queries, thereby benefiting from a quicker response than static schema views or no action at all.
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6

Sobati, Moghadam Somayeh. "Contributions to Data Privacy in Cloud Data Warehouses." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE2020.

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Анотація:
Actuellement, les scénarios d’externalisation de données deviennent de plus en plus courants avec l’avènement de l’infonuagique. L’infonuagique attire les entreprises et les organisations en raison d’une grande variété d’avantages fonctionnels et économiques.De plus, l’infonuagique offre une haute disponibilité, le passage d’échelle et une reprise après panne efficace. L’un des services plus notables est la base de données en tant que service (Database-as-a-Service), où les particuliers et les organisations externalisent les données, le stockage et la gestion `a un fournisseur de services. Ces services permettent de stocker un entrepôt de données chez un fournisseur distant et d’exécuter des analysesen ligne (OLAP).Bien que l’infonuagique offre de nombreux avantages, elle induit aussi des problèmes de s´sécurité et de confidentialité. La solution usuelle pour garantir la confidentialité des données consiste à chiffrer les données localement avant de les envoyer à un serveur externe. Les systèmes de gestion de base de données sécurisés utilisent diverses méthodes de cryptage, mais ils induisent un surcoût considérable de calcul et de stockage ou révèlent des informations sur les données.Dans cette thèse, nous proposons une nouvelle méthode de chiffrement (S4) inspirée du partage secret de Shamir. S4 est un système homomorphique additif : des additions peuvent être directement calculées sur les données cryptées. S4 trait les points faibles des systèmes existants en réduisant les coûts tout en maintenant un niveau raisonnable de confidentialité. S4 est efficace en termes de stockage et de calcul, ce qui est adéquat pour les scénarios d’externalisation de données qui considèrent que l’utilisateur dispose de ressources de calcul et de stockage limitées. Nos résultats expérimentaux confirment l’efficacité de S4 en termes de surcoût de calcul et de stockage par rapport aux solutions existantes.Nous proposons également de nouveaux schémas d’indexation qui préservent l’ordre des données, OPI et waOPI. Nous nous concentrons sur le problème de l’exécution des requêtes exacts et d’intervalle sur des données chiffrées. Contrairement aux solutions existantes, nos systèmes empêchent toute analyse statistique par un adversaire. Tout en assurant la confidentialité des données, les schémas proposés présentent de bonnes performances et entraînent un changement minimal dans les logiciels existants
Nowadays, data outsourcing scenarios are ever more common with the advent of cloud computing. Cloud computing appeals businesses and organizations because of a wide variety of benefits such as cost savings and service benefits. Moreover, cloud computing provides higher availability, scalability, and more effective disaster recovery rather than in-house operations. One of the most notable cloud outsourcing services is database outsourcing (Database-as-a-Service), where individuals and organizations outsource data storage and management to a Cloud Service Provider (CSP). Naturally, such services allow storing a data warehouse (DW) on a remote, untrusted CSP and running on-line analytical processing (OLAP).Although cloud data outsourcing induces many benefits, it also brings out security and in particular privacy concerns. A typical solution to preserve data privacy is encrypting data locally before sending them to an external server. Secure database management systems use various encryption schemes, but they either induce computational and storage overhead or reveal some information about data, which jeopardizes privacy.In this thesis, we propose a new secure secret splitting scheme (S4) inspired by Shamir’s secret sharing. S4 implements an additive homomorphic scheme, i.e., additions can be directly computed over encrypted data. S4 addresses the shortcomings of existing approaches by reducing storage and computational overhead while still enforcing a reasonable level of privacy. S4 is efficient both in terms of storage and computing, which is ideal for data outsourcing scenarios that consider the user has limited computation and storage resources. Experimental results confirm the efficiency of S4 in terms of computation and storage overhead with respect to existing solutions.Moreover, we also present new order-preserving schemes, order-preserving indexing (OPI) and wrap-around order-preserving indexing (waOPI), which are practical on cloud outsourced DWs. We focus on the problem of performing range and exact match queries over encrypted data. In contrast to existing solutions, our schemes prevent performing statistical and frequency analysis by an adversary. While providing data privacy, the proposed schemes bear good performance and lead to minimal change for existing software
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7

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

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8

Vijayakumar, Nithya Nirmal. "Data management in distributed stream processing systems." [Bloomington, Ind.] : Indiana University, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3278228.

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Анотація:
Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2007.
Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6093. Adviser: Beth Plale. Title from dissertation home page (viewed May 9, 2008).
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9

Brito, Jaqueline Joice. "Processamento de consultas SOLAP drill-across e com junção espacial em data warehouses geográficos." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18022013-090739/.

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Um data warehouse geográco (DWG) é um banco de dados multidimensional, orientado a assunto, integrado, histórico, não-volátil e geralmente organizado em níveis de agregação. Além disso, também armazena dados espaciais em uma ou mais dimensões ou em pelo menos uma medida numérica. Visando oferecer suporte à tomada de decisão, é possível realizar em DWGs consultas SOLAP (spatial online analytical processing ), isto é, consultas analíticas multidimensionais (e.g., drill-down, roll-up, drill-across ) com predicados espaciais (e.g., intersecta, contém, está contido) denidos para range queries e junções espaciais. Um desafio no processamento dessas consultas é recuperar, de forma eficiente, dados espaciais e convencionais em DWGs muito volumosos. Na literatura, existem poucos índices voltados à indexação de DWGs, e ainda assim nenhum desses índices dedica-se a indexar consultas SOLAP drill-across e com junção espacial. Esta dissertação visa suprir essa limitação, por meio da proposta de estratégias para o processamento dessas consultas complexas. Para o processamento de consultas SOLAP drill-across foram propostas duas estratégias, Divide e Única, além da especicação de um conjunto de diretrizes que deve ser seguido para o projeto de um esquema de DWG que possibilite a execução dessas consultas e da especicação de classes de consultas. Para o processamento de consultas SOLAP com junção espacial foi proposta a estratégia SJB, além da identicação de quais características o esquema de DWG deve possuir para possibilitar a execução dessas consultas e da especicação do formato dessas consultas. A validação das estratégias propostas foi realizada por meio de testes de desempenho considerando diferentes congurações, sendo que os resultados obtidos foram contrastados com a execução de consultas do tipo junção estrela e o uso de visões materializadas. Os resultados mostraram que as estratégias propostas são muito eficientes. No processamento de consultas SOLAP drill-across, as estratégias Divide e Única mostraram uma redução no tempo de 82,7% a 98,6% com relação à junção estrela e ao uso de visões materializadas. No processamento de consultas SOLAP com junção espacial, a estratégia SJB garantiu uma melhora de desempenho na grande maioria das consultas executadas. Para essas consultas, o ganho de desempenho variou de 0,3% até 99,2%
A geographic data warehouse (GDW) is a special kind of multidimensional database. It is subject-oriented, integrated, historical, non-volatile and usually organized in levels of aggregation. Furthermore, a GDW also stores spatial data in one or more dimensions or at least in one numerical measure. Aiming at decision support, GDWs allow SOLAP (spatial online analytical processing) queries, i.e., multidimensional analytical queries (e.g., drill-down, roll-up, drill-across) extended with spatial predicates (e.g., intersects, contains, is contained) dened for range and spatial join queries. A challenging issue related to the processing of these complex queries is how to recover spatial and conventional data stored in huge GDWs eciently. In the literature, there are few access methods dedicated to index GDWs, and none of these methods focus on drill-across and spatial join SOLAP queries. In this master\'s thesis, we propose novel strategies for processing these complex queries. We introduce two strategies for processing SOLAP drill-across queries (namely, Divide and Unique), dene a set of guidelines for the design of a GDW schema that enables the execution of these queries, and determine a set of classes of these queries to be issued over a GDW schema that follows the proposed guidelines. As for the processing of spatial join SOLAP queries, we propose the SJB strategy, and also identify the characteristics of a DWG schema that enables the execution of these queries as well as dene the format of these queries. We validated the proposed strategies through performance tests that compared them with the star join computation and the use of materialized views. The obtained results showed that our strategies are very ecient. Regarding the SOLAP drill-across queries, the Divide and Unique strategies showed a time reduction that ranged from 82,7% to 98,6% with respect to star join computation and the use of materialized views. Regarding the SOLAP spatial join queries, the SJB strategy guaranteed best results for most of the analyzed queries. For these queries, the performance gain of the SJB strategy ranged from 0,3% to 99,2% over the star join computation and the use of materialized view
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Abril, Raul Mario. "The inner and inter construct associations of the quality of data warehouse customer relationship data for problem enactment." Thesis, Brunel University, 2005. http://bura.brunel.ac.uk/handle/2438/7912.

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The literature identifies perceptions of data quality as a key factor influencing a wide range of attitudes and behaviors related to data in organizational settings (e.g. decision confidence). In particular, there is an overwhelming consensus that effective customer relationship management, CRM, depends on the quality of customer data. Data warehouses, if properly implemented, enable data integration which is a key attribute of data quality. The literature highlights the relevance of formulating problem statements because this will determine the course of action. CRM managers formulate problem statements through a cognitive process known as enactment. The literature on data quality is very fragmented. It posits that this construct is of a high order nature (it is dimensional), it is contextual and situational, and it is closely linked to a utilitarian value. This study addresses all these disperse views of the nature of data quality from a holistic perspective. Social cognitive theory, SCT, is the backbone for studying data quality in terms of information search behavior and enhancements in formulating problem statements. The main objective of this study is to explore the nature of a data warehouse's customer relationship data quality in situations where there is a need for understanding a customer relationship problem. The research question is What are the inner and inter construct associations of the quality of data warehouse customer relationship data for problem enactment? To reach this objective, a positivistic approach was adopted complemented with qualitative interventions along the research process. Observations were gathered with a survey. Scales were adjusted using a construct-based approach. Research findings confirm that data quality is a high order construct with a contextual dimension and a situational dimension. Problem sense making enhancements is a dependent variable of data quality in a confirmed positive association between both constructs. Problem sense making enhancements is also a high order construct with a mastering experience dimension and a self-efficacy dimension. Behavioral patterns for information search mode (scanning mode orientation vs. focus mode orientation) and for information search heuristic (template heuristic orientation vs. trial-and-error heuristic orientation) have been identified. Focus is the predominant information search mode orientation and template is the predominant information search heuristic orientation. Overall, the research findings support the associations advocated by SCT. The self-efficacy dimension in problem sense making enhancements is a discriminant for information search mode orientation (focus mode orientation vs. scanning mode orientation). The contextual dimension in data quality (i.e. data task utility) is a discriminant for information search heuristic (template heuristic orientation vs. trial-and-error heuristic orientation). A data quality cognitive metamodel and a data quality for problem enactment model are suggested for research in the areas of data quality, information search behavior, and cognitive enhancements.
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Книги з теми "Warehouses Management Data processing"

1

missing], [name. Fundamentals of data warehouses. 2nd ed. Berlin: Springer, 2003.

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Computerizing warehouse operations. Englewood Cliffs, N.J: Prentice-Hall, Business & Professional Division, 1985.

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Obal, Philip. What to look for in warehouse management systems software. Webbers Falls, OK: Industrial Data & Information, 1998.

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Co, Business Communications. Global markets for warehouse management systems and related services. Norwalk, CT: Business Communications Co., 2002.

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Kannapan, Balaji. Warehouse management with SAP EWM. Bonn: Rheinwerk Publishing, 2015.

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Inmon, William H. Building the data warehouse. 2nd ed. New York: Wiley Computer Pub., 1996.

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Inmon, William H. Building the data warehouse. Boston: QED Technical Pub. Group, 1992.

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Whelan, Barry. Critical factors in implementing a data warehouse. Dublin: University College Dublin, Graduate School of Business, 1998.

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Data warehouse design: Modern principles and methodologies. San Francisco: McGraw-Hill Companies, 2009.

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Quality management with SAP. Bonn: Rheinwerk Publishing, 2015.

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Частини книг з теми "Warehouses Management Data processing"

1

Dorndorf, Ulrich, and Erwin Pesch. "Data Warehouses." In Handbook on Data Management in Information Systems, 387–430. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24742-5_9.

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Ayala-Bush, Mary, John Jordan, and Walter Kuketz. "Web-Enabled Data Warehouses." In Data Management, 711–19. 3rd ed. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9780429114878-65.

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Jarke, Matthias, Maurizio Lenzerini, Yannis Vassiliou, and Panos Vassiliadis. "Query Processing and Optimization." In Fundamentals of Data Warehouses, 107–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04138-3_6.

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Jarke, Matthias, Maurizio Lenzerini, Yannis Vassiliou, and Panos Vassiliadis. "Query Processing and Optimization." In Fundamentals of Data Warehouses, 107–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05153-5_6.

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Lehner, Wolfgang. "Query Processing in Data Warehouses." In Encyclopedia of Database Systems, 1–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_298-3.

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Lehner, Wolfgang. "Query Processing in Data Warehouses." In Encyclopedia of Database Systems, 2297–301. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_298.

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Lehner, Wolfgang. "Query Processing in Data Warehouses." In Encyclopedia of Database Systems, 3039–46. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_298.

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Kargın, Yağız, Holger Pirk, Milena Ivanova, Stefan Manegold, and Martin Kersten. "Instant-On Scientific Data Warehouses." In Lecture Notes in Business Information Processing, 60–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39872-8_5.

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Gorawski, Marcin, and Pawel Marks. "Resumption of Data Extraction Process in Parallel Data Warehouses." In Parallel Processing and Applied Mathematics, 478–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_58.

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Märtens, Holger, Erhard Rahm, and Thomas Stöhr. "Dynamic Query Scheduling in Parallel Data Warehouses." In Euro-Par 2002 Parallel Processing, 321–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45706-2_43.

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Тези доповідей конференцій з теми "Warehouses Management Data processing"

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PAJIC, ANA, and ELENA MILOVANOVIC. "Comparing Graph and Relational Database Management Systems for Querying Data Warehouses." In Third International Conference on Advances in Information Processing and Communication Technology - IPCT 2015. Institute of Research Engineers and Doctors, 2015. http://dx.doi.org/10.15224/978-1-63248-077-4-120.

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Alias, Cyril, Udo Salewski, Viviana Elizabeth Ortiz Ruiz, Frank Eduardo Alarcón Olalla, José do Egypto Neirão Reymão, and Bernd Noche. "Adapting Warehouse Management Systems to the Requirements of the Evolving Era of Industry 4.0." In ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/msec2017-2611.

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With global megatrends like automation and digitization changing societies, economies, and ultimately businesses, shift is underway, disrupting current business plans and entire industries. Business actors have accordingly developed an instinctive fear of economic decline and realized the necessity of taking adequate measures to keep up with the times. Increasingly, organizations find themselves in an evolve-or-die race with their success depending on their capability of recognizing the requirements for serving a specific market and adopting those requirements accurately into their own structure. In the transportation and logistics sector, emerging technological and information challenges are reflected in fierce competition from within and outside. Especially, processes and supporting information systems are put to the test when technological innovation start to spread among an increasing number of actors and promise higher performance or lower cost. As to warehousing, technological innovation continuously finds its way into the premises of the heterogeneous warehouse operators, leading to modifications and process improvements. Such innovation can be at the side of the hardware equipment or in the form of new software solutions. Particularly, the fourth industrial revolution is globally underway. Same applies to Future Internet technologies, a European term for innovative software technologies and the research upon them. On the one hand, new hardware solutions using robotics, cyber-physical systems and sensors, and advanced materials are constantly put to widespread use. On the other one, software solutions based on intensified digitization including new and more heterogeneous sources of information, higher volumes of data, and increasing processing speed are also becoming an integral part of popular information systems for warehouses, particularly for warehouse management systems. With a rapidly and dynamically changing environment and new legal and business requirements towards processes in the warehouses and supporting information systems, new performance levels in terms of quality and cost of service are to be obtained. For this purpose, new expectations of the functionality of warehouse management systems need to be derived. While introducing wholly new solutions is one option, retrofitting and adapting existing systems to the new requirements is another one. The warehouse management systems will need to deal with more types of data from new and heterogeneous data sources. Also, it will need to connect to innovative machines and represent their respective operating principles. In both scenarios, systems need to satisfy the demand for new features in order to remain capable of processing information and acting and, thereby, to optimize logistics processes in real time. By taking a closer look at an industrial use case of a warehouse management system, opportunities of incorporating such new requirements are presented as the system adapts to new data types, increased processing speed, and new machines and equipment used in the warehouse. Eventually, the present paper proves the adaptability of existing warehouse management systems to the requirements of the new digital world, and viable methods to adopt the necessary renovation processes.
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Wrembel, Robert. "Session details: Data warehouse processing." In CIKM07: Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2007. http://dx.doi.org/10.1145/3259158.

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Baghal, Ahmad, Shaymaa Al-Shukri, and Annu Kumari. "Agile Natural Language Processing Model for Pathology Knowledge Extraction and Integration with Clinical Enterprise Data Warehouse." In 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS). IEEE, 2019. http://dx.doi.org/10.1109/snams.2019.8931828.

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Li, Ke, and Shanfeng Yang. "Inland Water Transport Decision Support System for Sustainable Development Based on Data Warehouse and On-Line Analytical Processing." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5304400.

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Du, T. C., J. Wong, and M. Lee. "Designing data warehouses for supply chain management." In Proceedings. IEEE International Conference on e-Commerce Technology, 2004. CEC 2004. IEEE, 2004. http://dx.doi.org/10.1109/icect.2004.1319731.

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Eavis, Todd. "Session details: Spatio-temporal data warehouses and data mining." In CIKM07: Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2007. http://dx.doi.org/10.1145/3259159.

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Sellam, Thibault, Emmanuel Müller, and Martin Kersten. "Semi-Automated Exploration of Data Warehouses." In CIKM'15: 24th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2806416.2806538.

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Paul, Arindam, Varuni Ganesan, Jagat Sesh Challa, and Yashvardhan Sharma. "HADCLEAN: A hybrid approach to data cleaning in data warehouses." In 2012 International Conference on Information Retrieval & Knowledge Management (CAMP). IEEE, 2012. http://dx.doi.org/10.1109/infrkm.2012.6205022.

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Rizvi, Sanam Shahla, and Tae-Sun Chung. "Flash Memory SSD Based Data Management for Data Warehouses and Data Marts." In 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/iccit.2009.124.

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Звіти організацій з теми "Warehouses Management Data processing"

1

Clark, R. E. Waste receiving and processing facility module 1 data management system software project management plan. Office of Scientific and Technical Information (OSTI), November 1994. http://dx.doi.org/10.2172/10105127.

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Rosnick, C. K. Waste Receiving and Processing Facility Module 1 Data Management System software requirements specification. Office of Scientific and Technical Information (OSTI), April 1996. http://dx.doi.org/10.2172/341303.

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Brann, E. C. II. Waste Receiving and Processing Facility Module 1 Data Management System Software Requirements Specification. Office of Scientific and Technical Information (OSTI), September 1994. http://dx.doi.org/10.2172/10186533.

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Palmer, M. E. W-026, Waste Receiving and Processing Facility data management system validation and verification report. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/345061.

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PALMER, M. E. Waste Receiving & Processing (WRAP) Facility Test Report For Data Management System (DMS) Security Test DMS-F81. Office of Scientific and Technical Information (OSTI), May 2001. http://dx.doi.org/10.2172/807106.

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PALMER, M. E. Waste Receiving and Processing (WRAP) Facility PMS Test Report For Data Management System (DMS) Security Test DMS-Y2K. Office of Scientific and Technical Information (OSTI), September 1999. http://dx.doi.org/10.2172/798056.

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Ke, Jian-yu, Fynnwin Prager, Jose Martinez, and Chris Cagle. Achieving Excellence for California’s Freight System: Developing Competitiveness and Performance Metrics; Incorporating Sustainability, Resilience, and Workforce Development. Mineta Transportation Institute, December 2021. http://dx.doi.org/10.31979/mti.2021.2023.

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This study explores the question of whether California's freight system is staying competitive with other US regions. A novel analytical framework compares supply chain performance metrics across multiple US states and regions for seaports, airports, highways, freight rail service, and distribution centers by combining the Performance Evaluation Matrix (PEM), Competitive Position Matrix (CPM), and Business Process Management (BPM) approaches. Analysis of industry data and responses from structured interviews with 30 freight industry experts across 5 transportation sectors suggests that California's freight system is competitive for seaports, airports, and freight rail; however, highways and distribution centers have room for improvement with respect to travel time reliability and operation costs, and California should prioritize infrastructure investments here. To stay competitive with the Texas and North East regions, state investments could also expand seaport container terminals and air cargo handling facilities, improve intermodal port connections and management of flows of chassis, container trucks, empty containers to ameliorate cargo backlogs and congestion on highways, at the ports, and at warehouses. The state could also invest in inland ports, transporting goods by rail directly from seaports to the Inland Empire or Central Valley.
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Avis, William. Drivers, Barriers and Opportunities of E-waste Management in Africa. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/k4d.2022.016.

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Population growth, increasing prosperity and changing consumer habits globally are increasing demand for consumer electronics. Further to this, rapid changes in technology, falling prices and consumer appetite for better products have exacerbated e-waste management challenges and seen millions of tons of electronic devices become obsolete. This rapid literature review collates evidence from academic, policy focussed and grey literature on e-waste management in Africa. This report provides an overview of constitutes e-waste, the environmental and health impacts of e-waste, of the barriers to effective e-waste management, the opportunities associated with effective e-waste management and of the limited literature available that estimate future volumes of e-waste. Africa generated a total of 2.9 million Mt of e-waste, or 2.5 kg per capita, the lowest regional rate in the world. Africa’s e-waste is the product of Local and imported Sources of Used Electronic and Electrical Equipment (UEEE). Challenges in e-waste management in Africa are exacerbated by a lack of awareness, environmental legislation and limited financial resources. Proper disposal of e-waste requires training and investment in recycling and management technology as improper processing can have severe environmental and health effects. In Africa, thirteen countries have been identified as having a national e-waste legislation/policy.. The main barriers to effective e-waste management include: Insufficient legislative frameworks and government agencies’ lack of capacity to enforce regulations, Infrastructure, Operating standards and transparency, illegal imports, Security, Data gaps, Trust, Informality and Costs. Aspirations associated with energy transition and net zero are laudable, products associated with these goals can become major contributors to the e-waste challenge. The necessary wind turbines, solar panels, electric car batteries, and other "green" technologies require vast amounts of resources. Further to this, at the end of their lifetime, they can pose environmental hazards. An example of e-waste associated with energy transitions can be gleaned from the solar power sector. Different types of solar power cells need to undergo different treatments (mechanical, thermal, chemical) depending on type to recover the valuable metals contained. Similar issues apply to waste associated with other energy transition technologies. Although e-waste contains toxic and hazardous metals such as barium and mercury among others, it also contains non-ferrous metals such as copper, aluminium and precious metals such as gold and copper, which if recycled could have a value exceeding 55 billion euros. There thus exists an opportunity to convert existing e-waste challenges into an economic opportunity.
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Apiyo, Eric, Zita Ekeocha, Stephen Robert Byrn, and Kari L. Clase. Improving Pharmacovigilliance Quality Management System in the Pharmacy and Poisions Board of Kenya. Purdue University, December 2021. http://dx.doi.org/10.5703/1288284317444.

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The purpose of this study was to explore ways of improving the pharmacovigilance quality system employed by the Pharmacy and Poisons Board of Kenya. The Pharmacy and Poisons Board of Kenya employs a hybrid system of pharmacovigilance that utilizes an online system of reporting pharmacovigilance incidences and a physical system, where a yellow book is physically filled by the healthcare worker and sent to the Pharmacy and Poisons Board for onward processing. This system, even though it has been relatively effective compared to other systems employed in Africa, has one major flaw. It is a slow and delayed system that captures the data much later after the fact and the agency will always be behind the curve in controlling the adverse incidents and events. This means that the incidences might continue to arise or go out of control. This project attempts to develop a system that would be more proactive in the collection of pharmacovigilance data and more predictive of pharmacovigilance incidences. The pharmacovigilance system should have the capacity to detect and analyze subtle changes in reporting frequencies and in patterns of clinical symptoms and signs that are reported as suspected adverse drug reactions. The method involved carrying out a thorough literature review of the latest trends in pharmacovigilance employed by different regulatory agencies across the world, especially the more stringent regulatory authorities. A review of the system employed by the Pharmacy and Poisons Board of Kenya was also done. Pharmacovigilance data, both primary and secondary, were collected and reviewed. Media reports on adverse drug reactions and poor-quality medicines over the period were also collected and reviewed. An appropriate predictive pharmacovigilance tool was also researched and identified. It was found that the Pharmacy and Poisons Board had a robust system of collecting historical pharmacovigilance data both from the healthcare workers and the general public. However, a more responsive data collection and evaluation system is proposed that will help the agency achieve its pharmacovigilance objectives. On analysis of the data it was found that just above half of all the product complaints, about 55%, involved poor quality medicines; 15% poor performance, 13% presentation, 8% adverse drug reactions, 7% market authorization, 2% expired drugs and 1% adulteration complaints. A regulatory pharmacovigilance prioritization tool was identified, employing a risk impact analysis was proposed for regulatory action.
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KHAIRALLAH, Sara, and EL HARROUDI Tijani. Delayed coloanal anastomosis technique in the management of low-lying rectal cancer: systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0002.

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Review question / Objective: Is there a difference in terms of post-operative events between delayed and immediate coloanal anastomoses in the management of rectum carcinoma? Condition being studied: Rectal carcinoma. Eligibility criteria: We defined the lower rectum as any rectal tumor located within 6cm of the anal margin or within 2cm of the upper edge of the sphincter ring.- All scientific articles published or not published between 01/1985 and 09/2021 that aim to demonstrate the postoperative, oncological and functional results of ACAD in the curative treatment of adenocarcinoma of the lower rectum or rectal cancer including the lower rectum.- Scientific articles that discuss case series treated with ACAD in different benign or malignant pathologies, but where patient data and results of this procedure are well individualized in patients operated on rectal adenocarcinoma. - Abstracts of conference sessions, theses or unpublished articles (grey literature) with complete data, allowing their extraction and processing in our review.Translated with http://www.DeepL.com/Translator (free version).
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