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Автор: Grafiati
Опубліковано: 11 вересня 2023

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Статті в журналах з теми "WEB USAGE DATA"

1

Thakur, Bhawesh Kumar, Syed Qamar Abbas, and Mohd Rizwan Beg. "Web Personalization Using Clustering of Web Usage Data." International Journal in Foundations of Computer Science & Technology 4, no. 5 (September 30, 2014): 69–84. http://dx.doi.org/10.5121/ijfcst.2014.4507.

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2

Garcia, Jorge Esparteiro, and Ana C. R. Paiva. "Maintaining Requirements Using Web Usage Data." Procedia Computer Science 100 (2016): 626–33. http://dx.doi.org/10.1016/j.procs.2016.09.204.

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3

Patel, Ketul, and Dr A. R. Patel. "Process of Web Usage Mining to find Interesting Patterns from Web Usage Data." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 2012): 144–48. http://dx.doi.org/10.24297/ijct.v3i1c.2767.

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Анотація:
The traffic on World Wide Web is increasing rapidly and huge amount of data is generated due to users’ numerous interactions with web sites. Web Usage Mining is the application of data mining techniques to discover the useful and interesting patterns from web usage data. It supports to know frequently accessed pages, predict user navigation, improve web site structure etc. In order to apply Web Usage Mining, various steps are performed. This paper discusses the process of Web Usage Mining consisting steps: Data Collection, Pre-processing, Pattern Discovery and Pattern Analysis. It has also presented Web Usage Mining applications and some Web Mining software.
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4

Jarukasemratana, Sorn, and Tsuyoshi Murata. "Web Caching Replacement Algorithm Based on Web Usage Data." New Generation Computing 31, no. 4 (October 2013): 311–29. http://dx.doi.org/10.1007/s00354-013-0404-z.

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5

Malik, Varun, Vikas Rattan, Jaiteg Singh, Ruchi Mittal, and Urvashi Tandon. "Performance Comparison of Data Mining Classifiers on Web Log Data." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 5113–16. http://dx.doi.org/10.1166/jctn.2020.9349.

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Анотація:
Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified instances and identify the algorithms that gives better instances accuracy.
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6

PADMAKUMAR, SUJATHA, Dr PUNITHAVALLI Dr.PUNITHAVALLI, and Dr RANJITH Dr.RANJITH. "A Web Usage Mining Approach to User Navigation Pattern and Prediction in Web Log Data." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 92–94. http://dx.doi.org/10.15373/22778179/apr2014/34.

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7

Thiyagarajan, V. S. "Web Data mining-A Research area in Web usage mining." IOSR Journal of Computer Engineering 13, no. 1 (2013): 22–26. http://dx.doi.org/10.9790/0661-1312226.

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8

Sandhyarani, Ramancha. "Construction of Community Web Directories based on Web usage Data." Advanced Computing: An International Journal 3, no. 2 (March 31, 2012): 41–48. http://dx.doi.org/10.5121/acij.2012.3205.

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9

Pierrakos, Dimitrios, and George Paliouras. "Personalizing Web Directories with the Aid of Web Usage Data." IEEE Transactions on Knowledge and Data Engineering 22, no. 9 (September 2010): 1331–44. http://dx.doi.org/10.1109/tkde.2009.173.

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10

Birukou, Aliaksandr, Enrico Blanzieri, Vincenzo DAndrea, Paolo Giorgini, and Natallia Kokash. "Improving Web Service Discovery with Usage Data." IEEE Software 24, no. 6 (November 2007): 47–54. http://dx.doi.org/10.1109/ms.2007.169.

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Більше джерел

Дисертації з теми "WEB USAGE DATA"

1

Winblad, Emanuel. "Visualization of web site visit and usage data." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110576.

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Анотація:
This report documents the work and results of a master’s thesis in Media Tech- nology that has been carried out at the Department of Science and Technology at Linköping University with the support of Sports Editing Sweden AB (SES). Its aim is to create a solution which aids the users of SES’ web CMS products in gaining insight into web site visit and usage statistics. The resulting solu- tion is the concept and initial version of a web based service. This service has been developed through an agile process with user centered design in mind and provides a graphical user interface which makes high use of visualizations to achieve the project goal.
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2

Khalil, Faten. "Combining web data mining techniques for web page access prediction." University of Southern Queensland, Faculty of Sciences, 2008. http://eprints.usq.edu.au/archive/00004341/.

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Анотація:
[Abstract]: Web page access prediction gained its importance from the ever increasing number of e-commerce Web information systems and e-businesses. Web page prediction, that involves personalising the Web users’ browsing experiences, assists Web masters in the improvement of the Web site structure and helps Web users in navigating the site and accessing the information they need. The most widely used approach for this purpose is the pattern discovery process of Web usage mining that entails many techniques like Markov model, association rules and clustering. Implementing pattern discovery techniques as such helps predict the next page tobe accessed by theWeb user based on the user’s previous browsing patterns. However, each of the aforementioned techniques has its own limitations, especiallywhen it comes to accuracy and space complexity. This dissertation achieves better accuracy as well as less state space complexity and rules generated by performingthe following combinations. First, we combine low-order Markov model and association rules. Markov model analysis are performed on the data sets. If the Markov model prediction results in a tie or no state, association rules are used for prediction. The outcome of this integration is better accuracy, less Markov model state space complexity and less number of generated rules than using each of the methods individually. Second, we integrate low-order Markov model and clustering. The data sets are clustered and Markov model analysis are performed oneach cluster instead of the whole data sets. The outcome of the integration is better accuracy than the first combination with less state space complexity than higherorder Markov model. The last integration model involves combining all three techniques together: clustering, association rules and low-order Markov model. The data sets are clustered and Markov model analysis are performed on each cluster. If the Markov model prediction results in close accuracies for the same item, association rules are used for prediction. This integration model achievesbetter Web page access prediction accuracy, less Markov model state space complexity and less number of rules generated than the previous two models.
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3

Bayir, Murat Ali. "A New Reactive Method For Processing Web Usage Data." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12607323/index.pdf.

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Анотація:
In this thesis, a new reactive session reconstruction method '
Smart-SRA'
is introduced. Web usage mining is a type of web mining, which exploits data mining techniques to discover valuable information from navigations of Web users. As in classical data mining, data processing and pattern discovery are the main issues in web usage mining. The first phase of the web usage mining is the data processing phase including session reconstruction. Session reconstruction is the most important task of web usage mining since it directly affects the quality of the extracted frequent patterns at the final step, significantly. Session reconstruction methods can be classified into two categories, namely '
reactive'
and '
proactive'
with respect to the data source and the data processing time. If the user requests are processed after the server handles them, this technique is called as &lsquo
reactive&rsquo
, while in &lsquo
proactive&rsquo
strategies this processing occurs during the interactive browsing of the web site. Smart-SRA is a reactive session reconstruction techique, which uses web log data and the site topology. In order to compare Smart-SRA with previous reactive methods, a web agent simulator has been developed. Our agent simulator models behavior of web users and generates web user navigations as well as the log data kept by the web server. In this way, the actual user sessions will be known and the successes of different techniques can be compared. In this thesis, it is shown that the sessions generated by Smart-SRA are more accurate than the sessions constructed by previous heuristics.
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4

Wu, Hao-cun, and 吳浩存. "A multidimensional data model for monitoring web usage and optimizing website topology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29528215.

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5

Wang, Long. "X-tracking the usage interest on web sites." Phd thesis, Universität Potsdam, 2011. http://opus.kobv.de/ubp/volltexte/2011/5107/.

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Анотація:
The exponential expanding of the numbers of web sites and Internet users makes WWW the most important global information resource. From information publishing and electronic commerce to entertainment and social networking, the Web allows an inexpensive and efficient access to the services provided by individuals and institutions. The basic units for distributing these services are the web sites scattered throughout the world. However, the extreme fragility of web services and content, the high competence between similar services supplied by different sites, and the wide geographic distributions of the web users drive the urgent requirement from the web managers to track and understand the usage interest of their web customers. This thesis, "X-tracking the Usage Interest on Web Sites", aims to fulfill this requirement. "X" stands two meanings: one is that the usage interest differs from various web sites, and the other is that usage interest is depicted from multi aspects: internal and external, structural and conceptual, objective and subjective. "Tracking" shows that our concentration is on locating and measuring the differences and changes among usage patterns. This thesis presents the methodologies on discovering usage interest on three kinds of web sites: the public information portal site, e-learning site that provides kinds of streaming lectures and social site that supplies the public discussions on IT issues. On different sites, we concentrate on different issues related with mining usage interest. The educational information portal sites were the first implementation scenarios on discovering usage patterns and optimizing the organization of web services. In such cases, the usage patterns are modeled as frequent page sets, navigation paths, navigation structures or graphs. However, a necessary requirement is to rebuild the individual behaviors from usage history. We give a systematic study on how to rebuild individual behaviors. Besides, this thesis shows a new strategy on building content clusters based on pair browsing retrieved from usage logs. The difference between such clusters and the original web structure displays the distance between the destinations from usage side and the expectations from design side. Moreover, we study the problem on tracking the changes of usage patterns in their life cycles. The changes are described from internal side integrating conceptual and structure features, and from external side for the physical features; and described from local side measuring the difference between two time spans, and global side showing the change tendency along the life cycle. A platform, Web-Cares, is developed to discover the usage interest, to measure the difference between usage interest and site expectation and to track the changes of usage patterns. E-learning site provides the teaching materials such as slides, recorded lecture videos and exercise sheets. We focus on discovering the learning interest on streaming lectures, such as real medias, mp4 and flash clips. Compared to the information portal site, the usage on streaming lectures encapsulates the variables such as viewing time and actions during learning processes. The learning interest is discovered in the form of answering 6 questions, which covers finding the relations between pieces of lectures and the preference among different forms of lectures. We prefer on detecting the changes of learning interest on the same course from different semesters. The differences on the content and structure between two courses leverage the changes on the learning interest. We give an algorithm on measuring the difference on learning interest integrated with similarity comparison between courses. A search engine, TASK-Moniminer, is created to help the teacher query the learning interest on their streaming lectures on tele-TASK site. Social site acts as an online community attracting web users to discuss the common topics and share their interesting information. Compared to the public information portal site and e-learning web site, the rich interactions among users and web content bring the wider range of content quality, on the other hand, provide more possibilities to express and model usage interest. We propose a framework on finding and recommending high reputation articles in a social site. We observed that the reputation is classified into global and local categories; the quality of the articles having high reputation is related with the content features. Based on these observations, our framework is implemented firstly by finding the articles having global or local reputation, and secondly clustering articles based on their content relations, and then the articles are selected and recommended from each cluster based on their reputation ranks.
Wegen des exponentiellen Ansteigens der Anzahl an Internet-Nutzern und Websites ist das WWW (World Wide Web) die wichtigste globale Informationsressource geworden. Das Web bietet verschiedene Dienste (z. B. Informationsveröffentlichung, Electronic Commerce, Entertainment oder Social Networking) zum kostengünstigen und effizienten erlaubten Zugriff an, die von Einzelpersonen und Institutionen zur Verfügung gestellt werden. Um solche Dienste anzubieten, werden weltweite, vereinzelte Websites als Basiseinheiten definiert. Aber die extreme Fragilität der Web-Services und -inhalte, die hohe Kompetenz zwischen ähnlichen Diensten für verschiedene Sites bzw. die breite geographische Verteilung der Web-Nutzer treiben einen dringenden Bedarf für Web-Manager und das Verfolgen und Verstehen der Nutzungsinteresse ihrer Web-Kunden. Die Arbeit zielt darauf ab, dass die Anforderung "X-tracking the Usage Interest on Web Sites" erfüllt wird. "X" hat zwei Bedeutungen. Die erste Bedeutung ist, dass das Nutzungsinteresse von verschiedenen Websites sich unterscheidet. Außerdem stellt die zweite Bedeutung dar, dass das Nutzungsinteresse durch verschiedene Aspekte (interne und externe, strukturelle und konzeptionelle) beschrieben wird. Tracking zeigt, dass die Änderungen zwischen Nutzungsmustern festgelegt und gemessen werden. Die Arbeit eine Methodologie dar, um das Nutzungsinteresse gekoppelt an drei Arten von Websites (Public Informationsportal-Website, E-Learning-Website und Social-Website) zu finden. Wir konzentrieren uns auf unterschiedliche Themen im Bezug auf verschieden Sites, die mit Usage-Interest-Mining eng verbunden werden. Education Informationsportal-Website ist das erste Implementierungsscenario für Web-Usage-Mining. Durch das Scenario können Nutzungsmuster gefunden und die Organisation von Web-Services optimiert werden. In solchen Fällen wird das Nutzungsmuster als häufige Pagemenge, Navigation-Wege, -Strukturen oder -Graphen modelliert. Eine notwendige Voraussetzung ist jedoch, dass man individuelle Verhaltensmuster aus dem Verlauf der Nutzung (Usage History) wieder aufbauen muss. Deshalb geben wir in dieser Arbeit eine systematische Studie zum Nachempfinden der individuellen Verhaltensweisen. Außerdem zeigt die Arbeit eine neue Strategie, dass auf Page-Paaren basierten Content-Clustering aus Nutzungssite aufgebaut werden. Der Unterschied zwischen solchen Clustern und der originalen Webstruktur ist der Abstand zwischen Zielen der Nutzungssite und Erwartungen der Designsite. Darüber hinaus erforschen wir Probleme beim Tracking der Änderungen von Nutzungsmustern in ihrem Lebenszyklus. Die Änderungen werden durch mehrere Aspekte beschrieben. Für internen Aspekt werden konzeptionelle Strukturen und Funktionen integriert. Der externe Aspekt beschreibt physische Eigenschaften. Für lokalen Aspekt wird die Differenz zwischen zwei Zeitspannen gemessen. Der globale Aspekt zeigt Tendenzen der Änderung entlang des Lebenszyklus. Eine Plattform "Web-Cares" wird entwickelt, die die Nutzungsinteressen findet, Unterschiede zwischen Nutzungsinteresse und Website messen bzw. die Änderungen von Nutzungsmustern verfolgen kann. E-Learning-Websites bieten Lernmaterialien wie z.B. Folien, erfaßte Video-Vorlesungen und Übungsblätter an. Wir konzentrieren uns auf die Erfoschung des Lerninteresses auf Streaming-Vorlesungen z.B. Real-Media, mp4 und Flash-Clips. Im Vergleich zum Informationsportal Website kapselt die Nutzung auf Streaming-Vorlesungen die Variablen wie Schauzeit und Schautätigkeiten während der Lernprozesse. Das Lerninteresse wird erfasst, wenn wir Antworten zu sechs Fragen gehandelt haben. Diese Fragen umfassen verschiedene Themen, wie Erforschung der Relation zwischen Teilen von Lehrveranstaltungen oder die Präferenz zwischen den verschiedenen Formen der Lehrveranstaltungen. Wir bevorzugen die Aufdeckung der Veränderungen des Lerninteresses anhand der gleichen Kurse aus verschiedenen Semestern. Der Differenz auf den Inhalt und die Struktur zwischen zwei Kurse beeinflusst die Änderungen auf das Lerninteresse. Ein Algorithmus misst die Differenz des Lerninteresses im Bezug auf einen Ähnlichkeitsvergleich zwischen den Kursen. Die Suchmaschine „Task-Moniminer“ wird entwickelt, dass die Lehrkräfte das Lerninteresse für ihre Streaming-Vorlesungen über das Videoportal tele-TASK abrufen können. Social Websites dienen als eine Online-Community, in den teilnehmenden Web-Benutzern die gemeinsamen Themen diskutieren und ihre interessanten Informationen miteinander teilen. Im Vergleich zur Public Informationsportal-Website und E-Learning Website bietet diese Art von Website reichhaltige Interaktionen zwischen Benutzern und Inhalten an, die die breitere Auswahl der inhaltlichen Qualität bringen. Allerdings bietet eine Social-Website mehr Möglichkeiten zur Modellierung des Nutzungsinteresses an. Wir schlagen ein Rahmensystem vor, die hohe Reputation für Artikel in eine Social-Website empfiehlt. Unsere Beobachtungen sind, dass die Reputation in globalen und lokalen Kategorien klassifiziert wird. Außerdem wird die Qualität von Artikeln mit hoher Reputation mit den Content-Funktionen in Zusammenhang stehen. Durch die folgenden Schritte wird das Rahmensystem im Bezug auf die Überwachungen implementiert. Der erste Schritt ist, dass man die Artikel mit globalen oder lokalen Reputation findet. Danach werden Artikel im Bezug auf ihre Content-Relationen in jeder Kategorie gesammelt. Zum Schluß werden die ausgewählten Artikel aus jedem basierend auf ihren Reputation-Ranking Cluster empfohlen.
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6

Norguet, Jean-Pierre. "Semantic analysis in web usage mining." Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210890.

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Анотація:
With the emergence of the Internet and of the World Wide Web, the Web site has become a key communication channel in organizations. To satisfy the objectives of the Web site and of its target audience, adapting the Web site content to the users' expectations has become a major concern. In this context, Web usage mining, a relatively new research area, and Web analytics, a part of Web usage mining that has most emerged in the corporate world, offer many Web communication analysis techniques. These techniques include prediction of the user's behaviour within the site, comparison between expected and actual Web site usage, adjustment of the Web site with respect to the users' interests, and mining and analyzing Web usage data to discover interesting metrics and usage patterns. However, Web usage mining and Web analytics suffer from significant drawbacks when it comes to support the decision-making process at the higher levels in the organization.

Indeed, according to organizations theory, the higher levels in the organizations need summarized and conceptual information to take fast, high-level, and effective decisions. For Web sites, these levels include the organization managers and the Web site chief editors. At these levels, the results produced by Web analytics tools are mostly useless. Indeed, most of these results target Web designers and Web developers. Summary reports like the number of visitors and the number of page views can be of some interest to the organization manager but these results are poor. Finally, page-group and directory hits give the Web site chief editor conceptual results, but these are limited by several problems like page synonymy (several pages contain the same topic), page polysemy (a page contains several topics), page temporality, and page volatility.

Web usage mining research projects on their part have mostly left aside Web analytics and its limitations and have focused on other research paths. Examples of these paths are usage pattern analysis, personalization, system improvement, site structure modification, marketing business intelligence, and usage characterization. A potential contribution to Web analytics can be found in research about reverse clustering analysis, a technique based on self-organizing feature maps. This technique integrates Web usage mining and Web content mining in order to rank the Web site pages according to an original popularity score. However, the algorithm is not scalable and does not answer the page-polysemy, page-synonymy, page-temporality, and page-volatility problems. As a consequence, these approaches fail at delivering summarized and conceptual results.

An interesting attempt to obtain such results has been the Information Scent algorithm, which produces a list of term vectors representing the visitors' needs. These vectors provide a semantic representation of the visitors' needs and can be easily interpreted. Unfortunately, the results suffer from term polysemy and term synonymy, are visit-centric rather than site-centric, and are not scalable to produce. Finally, according to a recent survey, no Web usage mining research project has proposed a satisfying solution to provide site-wide summarized and conceptual audience metrics.

In this dissertation, we present our solution to answer the need for summarized and conceptual audience metrics in Web analytics. We first described several methods for mining the Web pages output by Web servers. These methods include content journaling, script parsing, server monitoring, network monitoring, and client-side mining. These techniques can be used alone or in combination to mine the Web pages output by any Web site. Then, the occurrences of taxonomy terms in these pages can be aggregated to provide concept-based audience metrics. To evaluate the results, we implement a prototype and run a number of test cases with real Web sites.

According to the first experiments with our prototype and SQL Server OLAP Analysis Service, concept-based metrics prove extremely summarized and much more intuitive than page-based metrics. As a consequence, concept-based metrics can be exploited at higher levels in the organization. For example, organization managers can redefine the organization strategy according to the visitors' interests. Concept-based metrics also give an intuitive view of the messages delivered through the Web site and allow to adapt the Web site communication to the organization objectives. The Web site chief editor on his part can interpret the metrics to redefine the publishing orders and redefine the sub-editors' writing tasks. As decisions at higher levels in the organization should be more effective, concept-based metrics should significantly contribute to Web usage mining and Web analytics.


Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

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7

Luczak-Rösch, Markus [Verfasser]. "Usage-dependent maintenance of structured Web data sets / Markus Luczak-Rösch." Berlin : Freie Universität Berlin, 2014. http://d-nb.info/1068253827/34.

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8

Vollino, Bruno Winiemko. "Descoberta de perfis de uso de web services." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2013. http://hdl.handle.net/10183/83669.

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Анотація:
Durante o ciclo de vida de um web service, diversas mudanças são feitas na sua interface, eventualmente causando incompatibilidades em relação aos seus clientes e ocasionando a quebra de suas aplicações. Os provedores precisam tomar decisões sobre mudanças em seus serviços frequentemente, muitas vezes sem um bom entendimento a respeito do efeito destas mudanças sobre seus clientes. Os trabalhos e ferramentas existentes não fornecem ao provedor um conhecimento adequado a respeito do uso real das funcionalidades da interface de um serviço, considerando os diferentes tipos de consumidores, o que impossibilita avaliar o impacto das mudanças. Este trabalho apresenta um framework para a descoberta de perfis de uso de serviços web, os quais constituem um modelo descritivo dos padrões de uso dos diferentes grupos de clientes do serviço, com relação ao uso das funcionalidades em sua interface. O framework auxilia no processo de descoberta de conhecimento através de tarefas semiautomáticas e parametrizáveis para a preparação e análise de dados de uso, minimizando a necessidade de intervenção do usuário. O framework engloba o monitoramento de interações de web services, a carga de dados de uso pré-processados em uma base de dados unificada, e a geração de perfis de uso. Técnicas de mineração de dados são utilizadas para agrupar clientes de acordo com seus padrões de uso de funcionalidades, e esses grupos são utilizados na construção de perfis de uso de serviços. Todo o processo é configurado através de parâmetros, permitindo que o usuário determine o nível de detalhe das informações sobre o uso incluídas nos perfis e os critérios para avaliar a similaridade entre clientes. A proposta é validada por meio de experimentos com dados sintéticos, simulados de acordo com características esperadas no comportamento de clientes de um serviço real. Os resultados dos experimentos demonstram que o framework proposto permite a descoberta de perfis de uso de serviço úteis, e fornecem evidências a respeito da parametrização adequada do framework.
During the life cycle of a web service, several changes are made in its interface, which possibly are incompatible with regard to current usage and may break client applications. Providers must make decisions about changes on their services, most often without insight on the effect these changes will have over their customers. Existing research and tools fail to input provider with proper knowledge about the actual usage of the service interface’s features, considering the distinct types of customers, making it impossible to assess the actual impact of changes. This work presents a framework for the discovery of web service usage profiles, which constitute a descriptive model of the usage patterns found in distinct groups of clients, concerning the usage of service interface features. The framework supports a user in the process of knowledge discovery over service usage data through semi-automatic and configurable tasks, which assist the preparation and analysis of usage data with the minimum user intervention possible. The framework performs the monitoring of web services interactions, loads pre-processed usage data into a unified database, and supports the generation of usage profiles. Data mining techniques are used to group clients according to their usage patterns of features, and these groups are used to build service usage profiles. The entire process is configured via parameters, which allows the user to determine the level of detail of the usage information included in the profiles, and the criteria for evaluating the similarity between client applications. The proposal is validated through experiments with synthetic data, simulated according to features expected in the use of a real service. The experimental results demonstrate that the proposed framework allows the discovery of useful service usage profiles, and provide evidences about the proper parameterization of the framework.
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9

Özakar, Belgin Püskülcü Halis. "Finding And Evaluating Patterns In Wes Repository Using Database Technology And Data Mining Algorithms/." [s.l.]: [s.n.], 2002. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000130.pdf.

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10

Karlsson, Sophie. "Datainsamling med Web Usage Mining : Lagringsstrategier för loggning av serverdata." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-9467.

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Webbapplikationers komplexitet och mängden avancerade tjänster ökar. Loggning av aktiviteter kan öka förståelsen över användares beteenden och behov, men används i för stor mängd utan relevant information. Mer avancerade system medför ökade krav för prestandan och loggning blir än mer krävande för systemen. Det finns behov av smartare system, utveckling inom tekniker för prestandaförbättringar och tekniker för datainsamling. Arbetet kommer undersöka hur svarstider påverkas vid loggning av serverdata, enligt datainsamlingsfasen i web usage mining, beroende på lagringsstrategier. Hypotesen är att loggning kan försämra svarstider ytterligare. Experiment genomförs där fyra olika lagringsstrategier används för att lagra serverdata med olika tabell- och databasstrukturer, för att se vilken strategi som påverkar svarstiderna minst. Experimentet påvisar statistiskt signifikant skillnad mellan lagringsstrategierna enligt ANOVA. Lagringsstrategi 4 påvisar bäst effekt för prestandans genomsnittliga svarstid, jämfört med lagringsstrategi 2 som påvisar mest negativ effekt för den genomsnittliga svarstiden. Framtida arbete vore intressant för att stärka resultaten.
Web applications complexity and the amount of advanced services increases. Logging activities can increase the understanding of users behavior and needs, but is used too much without relevant information. More advanced systems brings increased requirements for performance and logging becomes even more demanding for the systems. There is need of smarter systems, development within the techniques for performance improvements and techniques for data collection. This work will investigate how response times are affected when logging server data, according to the data collection phase in web usage mining, depending on storage strategies. The hypothesis is that logging may degrade response times even further. An experiment was conducted in which four different storage strategies are used to store server data with different table- and database structures, to see which strategy affects the response times least. The experiment proves statistically significant difference between the storage strategies with ANOVA. Storage strategy 4 proves the best effect for the performance average response time compared with storage strategy 2, which proves the most negative effect for the average response time. Future work would be interesting for strengthening the results.
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Книги з теми "WEB USAGE DATA"

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Web data mining: Exploring hyperlinks, contents, and usage data. 2nd ed. Heidelberg: Springer, 2011.

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author, Roghani Ali, ed. Big data analytics for beginners. [India]: Crux Tech Limited, 2014.

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Taniar, David, and Lukman Hakim Iwan. Exploring advances in interdisciplinary data mining and analytics: New trends. Hershey, PA: Information Science Reference, 2012.

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Zaïane, Osmar R., Jaideep Srivastava, Myra Spiliopoulou, and Brij Masand, eds. WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b11784.

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Dēta mainingu to shūgōchi: Kiso kara web, sōsharu media made = Data mining and collective intelligence from basics to web and social media. Tōkyō: Kyōritsu Shuppan, 2012.

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Developments in data extraction, management, and analysis. Hershey, PA: Information Science Reference, 2012.

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Abraham, Kandel, ed. Search engines, link analysis, and user's Web behavior: [a unifying Web mining approach]. Berlin: Springer, 2008.

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service), ScienceDirect (Online, ed. Cult of analytics: Driving online marketing strategies using Web analytics. Amsterdam: Elsevier/Butterworth-Heinemann, 2009.

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Understanding user-Web interactions via Web analytics. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2009.

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Advanced Web metrics with Google Analytics. 2nd ed. Indianapolis, Ind: Wiley, 2010.

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Частини книг з теми "WEB USAGE DATA"

1

Liu, Bing, Bamshad Mobasher, and Olfa Nasraoui. "Web Usage Mining." In Web Data Mining, 527–603. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19460-3_12.

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Ganibardi, Amine, and Chérif Arab Ali. "Web Usage Data Cleaning." In Big Data Analytics and Knowledge Discovery, 193–203. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98539-8_15.

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da Silva, Alzennyr, Yves Lechevallier, Fabrice Rossi, and Francisco de Carvalho. "Clustering Dynamic Web Usage Data." In Innovative Applications in Data Mining, 71–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88045-5_4.

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L’Huillier, Gaston, and Juan D. Velásquez. "Web Usage Data Pre-processing." In Advanced Techniques in Web Intelligence-2, 11–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33326-2_2.

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Tan, Pang-Ning, and Vipin Kumar. "Discovery of Indirect Associations from Web Usage Data." In Web Intelligence, 128–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05320-1_7.

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Lu, Lin, Margaret Dunham, and Yu Meng. "Mining Significant Usage Patterns from Clickstream Data." In Advances in Web Mining and Web Usage Analysis, 1–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11891321_1.

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Rossi, Fabrice, Francisco De Carvalho, Yves Lechevallier, and Alzennyr Da Silva. "Dissimilarities for Web Usage Mining." In Studies in Classification, Data Analysis, and Knowledge Organization, 39–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-34416-0_5.

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Zaïane, Osmar R., Jiyang Chen, and Randy Goebel. "Mining Research Communities in Bibliographical Data." In Advances in Web Mining and Web Usage Analysis, 59–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00528-2_4.

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Li, Xiang-ying. "Data Preprocessing in Web Usage Mining." In The 19th International Conference on Industrial Engineering and Engineering Management, 257–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38391-5_27.

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Grčar, Miha, Dunja Mladenič, Blaž Fortuna, and Marko Grobelnik. "Data Sparsity Issues in the Collaborative Filtering Framework." In Advances in Web Mining and Web Usage Analysis, 58–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11891321_4.

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Тези доповідей конференцій з теми "WEB USAGE DATA"

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Madiraju, Praveen, and Yanqing Zhang. "Web usage data mining agent." In AeroSense 2002, edited by Belur V. Dasarathy. SPIE, 2002. http://dx.doi.org/10.1117/12.460231.

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Mehra, Jayanti. "Web Personalization Using Web Session for Web Usage Mining." In 2020 2nd International Conference on Data, Engineering and Applications (IDEA). IEEE, 2020. http://dx.doi.org/10.1109/idea49133.2020.9170665.

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Baeza-Yates, Ricardo, and Yoelle Maarek. "(Big) usage data in web search." In the 35th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2348283.2348531.

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Baeza-Yates, Ricardo, and Yoelle Maarek. "(big) usage data in web search." In the sixth ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2433396.2433501.

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Sudheer Reddy, K., G. Partha Saradhi Varma, and S. Sai Satyanarayana Reddy. "Understanding the scope of web usage mining & applications of web data usage patterns." In 2012 International Conference on Computing, Communication and Applications (ICCCA). IEEE, 2012. http://dx.doi.org/10.1109/iccca.2012.6179230.

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Kumari, Prachi, Alexander Pretschner, Jonas Peschla, and Jens-Michael Kuhn. "Distributed data usage control for web applications." In the first ACM conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1943513.1943526.

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RAJU, G. T., P. S. SATHYANARAYANA, and L. M. PATNAK. "KNOWLEDGE DISCOVERY FROM WEB USAGE DATA: SURVEY." In Proceedings of the 3rd Asian Applied Computing Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781860948534_0026.

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Suter, Philippe, and Erik Wittern. "Inferring Web API Descriptions from Usage Data." In 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb). IEEE, 2015. http://dx.doi.org/10.1109/hotweb.2015.19.

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Dhandi, Monika, and Rajesh Kumar Chakrawarti. "A comprehensive study of web usage mining." In 2016 Symposium on Colossal Data Analysis and Networking (CDAN). IEEE, 2016. http://dx.doi.org/10.1109/cdan.2016.7570889.

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Chaudhary, Kamika, and Santosh Kumar Gupta. "Prioritizing web links based on web usage and content data." In 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT). IEEE, 2014. http://dx.doi.org/10.1109/icicict.2014.6781340.

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Звіти організацій з теми "WEB USAGE DATA"

1

McDougall, Robert, and Nico van Leeuwen. International MariBunkers: An Attempt to Assign its Usage to the Right Countries. GTAP Research Memoranda, September 2010. http://dx.doi.org/10.21642/gtap.rm20.

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In recent GTAP data releases, in transforming energy volumes data from the IEA extended energy balances to an input-output format, we record inflows into the energy balances flow "international marine bunkers" as exports, but record no corresponding imports. Here, we revise the energy module to balance the trade flows by recording international marine bunker usage as imports into the country of residence of the ship operator, and as usage by that country’s transport industry. We allocate usage across countries in proportion to the money value of their water transport services exports.
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Emery, Benjamin. National Sediment Placement Data Viewer users guide. Engineer Research and Development Center (U.S.), July 2022. http://dx.doi.org/10.21079/11681/44700.

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This US Army Corps of Engineers (USACE) Regional Sediment Management (RSM) technical note serves as a user’s guide for the RSM National Sediment Placement Data Viewer. This application was created utilizing over 20 yr* of detailed and verified USACE dredging data, giving users an interactive web-based tool that takes these datasets and displays them on a national map, viewable at the district or project scale. The Data Viewer will quantify the total cubic yards dredged, disposed, and/or beneficially used based on the user selected parameters. Detailed information on the datasets utilized and the verification processes followed to create this application can be found in ERDC/TN RSM-22-XX, USACE Navigation Sediment Placement: An RSM Program Database (1998 – 2019) (Elko et al. 2022). This technical note attempts to define each of the inputs/outputs given from the Data Viewer and then provide a step-by-step example of utilizing the Data Viewer, accessed here: https://www.arcgis.com/apps/MapSeries/index.html?appid=0ea8fc0a956f46068428c862e7497233
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Boone, Jonathan, Bobby Sells, Matthew Davis, and Dan McDonald. Alternative analysis for construction progress data spatial visualization. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42166.

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The U.S. Army Corps of Engineers (USACE) construction projects have multiple stakeholders that collaborate with project delivery team members during the execution of these projects. Many of these stakeholders are located across the U.S., which makes virtual interactions a common communication method for these teams. These interactions often lack spatial visualization, which can add complications to the progress reports provided and how the information is received/interpreted. The visualization of project progress and documents would be invaluable to the stakeholders on critical projects constructed by the USACE. This research was conducted to determine alternatives for migrating Resident Management System (RMS) data into a portal web viewer. This report provides proposed solutions to creating these links in efforts to better harmonize data management and improve project presentation.
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Elko, Nicole, Katherine Brutsché, Quin Robertson, Michael Hartman, and Zhifei Dong. USACE Navigation Sediment Placement : An RSM Program Database (1998 – 2019). Engineer Research and Development Center (U.S.), July 2022. http://dx.doi.org/10.21079/11681/44703.

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This US Army Corps of Engineers, Regional Sediment Management, technical note describes a geodatabase of federal coastal and inland navigation projects developed to determine the extent to which RSM goals have been implemented across the USACE at the project and district levels. The effort 1) quantified the volume of sediment dredged from federal navigation channels by both contract and USACE-owned dredges and 2) identified the placement type and whether sediment was placed beneficially. The majority of the dredging data used to populate the geodatabase were based on the USACE Dredging Information System DIS database, but when available, the geodatabase was expanded to include more detailed USACE district-specific data that were not included in the DIS database. Two datasets were developed in this study: the National Dataset and the District-Specific and Quality-Checked Dataset. The National Dataset is based on statistics extracted from the combined DIS Contract and Government Plant data. This database is a largely unedited database that combined two available USACE datasets. Due to varying degrees of data completeness in these two datasets, this study undertook a data refinement process to improve the information. This was done through interviews with the districts, literature search, and the inclusion of additional district-specific data provided by individual districts that often represent more detailed information on dredging activities. The District-Specific and Quality-Checked Database represents a customized database generated by this study. An interactive web-based tool was developed that accesses both datasets and displays them on a national map that can be viewed at the district or project scale.
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Kraushaar, Judith, and Sabine Bohnet-Joschko. Prevalence and patterns of mobile device usage among physicians in clinical practice: a systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0087.

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Review question / Objective: The aim of this review is to systematically analyze quantitative data extracted from studies on the use of mobile devices by physicians in clinical practice in order to be able to derive concrete statements on the prevalence, patterns, and trends of usage. Condition being studied: Prevalence, patterns, and trends of mobile device usage by physicians in clinical practice. Main outcome(s): With this review, we want to open a new perspective on the use of mobile devices. Together with the information from qualitative reviews, the particular relevance of mobile devices for KM strategies in hospitals can be viewed in its entirety.
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Hlushak, Oksana M., Svetlana O. Semenyaka, Volodymyr V. Proshkin, Stanislav V. Sapozhnykov, and Oksana S. Lytvyn. The usage of digital technologies in the university training of future bachelors (having been based on the data of mathematical subjects). [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3860.

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This article demonstrates that mathematics in the system of higher education has outgrown the status of the general education subject and should become an integral part of the professional training of future bachelors, including economists, on the basis of intersubject connection with special subjects. Such aspects as the importance of improving the scientific and methodological support of mathematical training of students by means of digital technologies are revealed. It is specified that in order to implement the task of qualified training of students learning econometrics and economic and mathematical modeling, it is necessary to use digital technologies in two directions: for the organization of electronic educational space and in the process of solving applied problems at the junction of the branches of economics and mathematics. The advantages of using e-learning courses in the educational process are presented (such as providing individualization of the educational process in accordance with the needs, characteristics and capabilities of students; improving the quality and efficiency of the educational process; ensuring systematic monitoring of the educational quality). The unified structures of “Econometrics”, “Economic and mathematical modeling” based on the Moodle platform are the following ones. The article presents the results of the pedagogical experiment on the attitude of students to the use of e-learning course (ELC) in the educational process of Borys Grinchenko Kyiv University and Alfred Nobel University (Dnipro city). We found that the following metrics need improvement: availability of time-appropriate mathematical materials; individual approach in training; students’ self-expression and the development of their creativity in the e-learning process. The following opportunities are brought to light the possibilities of digital technologies for the construction and research of econometric models (based on the problem of dependence of the level of the Ukrainian population employment). Various stages of building and testing of the econometric model are characterized: identification of variables, specification of the model, parameterization and verification of the statistical significance of the obtained results.
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Jung, Jacob, Richard Fischer, Chester McConnell, and Pam Bates. The use of US Army Corps of Engineers reservoirs as stopover sites for the Aransas–Wood Buffalo population of whooping crane. Engineer Research and Development Center (U.S.), August 2022. http://dx.doi.org/10.21079/11681/44980.

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This technical report summarizes the use of US Army Corps of Engineers (USACE) reservoirs as spring and fall migration stopover sites for the endangered Aransas–Wood Buffalo population of whooping cranes (WHCR), which proved much greater than previously known. We assessed stopover use within the migration flyway with satellite transmitter data on 68 WHCR during 2009–2018 from a study by the US Geological Survey (USGS) and collaborators, resulting in over 165,000 location records, supplemented by incidental observations from the US Fish and Wildlife Ser-vice (USFWS) and the USGS Biodiversity Information Serving Our Nation (BISON) databases. Significant stopover use was observed during both spring and fall migration, and one reservoir served as a wintering location in multiple years. Future efforts should include (a) continued monitoring for WHCR at USACE reservoirs within the flyway; (b) reservoir-specific management plans at all projects with significant WHCR stopover; (c) a USACE-specific and range-wide Endangered Species Act Section 7(a)(1) conservation plan that specifies proactive conservation actions; (d) habitat management plans that include potential pool-level modifications during spring and fall to optimize stopover habitat conditions; and (e) continued evaluation of habitat conditions at USACE reservoirs.
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Bakhshaei, Mahsa, Angela Hardy, Aubrey Francisco, Sierra Noakes, and Judi Fusco. Fostering Powerful Use of Technology Through Instructional Coaching. Digital Promise, 2018. http://dx.doi.org/10.51388/20.500.12265/48.

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Research findings suggest that instructional technology coaching may be a critical lever in closing the gap in the usage of technology, sometimes referred to as the digital use divide. In the 2017-2018 school year, we provided 50 schools in 20 school districts across five states, with a grant to support an onsite, full-time instructional technology coach (called a DLP coach). Our data shows that after one year of working with their DLP coach, teachers are using technology more frequently and in more powerful ways. DLP teachers report significant increases in using technology for both teaching content and pedagogy—in other words, teachers are using technology to support what they are teaching, as well as how they are teaching it.
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Bernad, Ludovic, Yves Nsengiyumva, Benjamin Byinshi, Naphtal Hakizimana, and Fabrizio Santoro. Digital Merchant Payments as a Medium of Tax Compliance. Institute of Development Studies, March 2023. http://dx.doi.org/10.19088/ictd.2023.011.

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Consumers in Africa increasingly pay for their purchases through mobile money, especially since the pandemic. These transactions are known as digital merchant payments. Rwandan consumers can choose between using standard mobile money services or a specific service only for digital merchant payments – MoMo Pay. Digital payments of any kind have the potential to improve tax compliance, because they imply digital data trails and better record keeping. How far is this potential being realised in Rwanda? In collaboration with the Rwanda Revenue Authority, we collected survey data from 1,100 merchants country-wide and were able to correlate this with tax administrative data, i.e. the tax records of the interviewees held by the revenue authority. We also conducted focus group discussions with 15 merchants. We found that the great majority of payments are still made in cash. Larger, more knowledgeable and financially included merchants opt for MoMo Pay as opposed to standard mobile money, the latter being preferred by female and less educated and equipped merchants. At the start of the pandemic, in March 2020, for a period of 18 months, all fees on MoMo Pay transactions were waived to foster digital payments through the service. In September 2021, fees were then reintroduced. The waiver led to a significant rise in the use of MoMo Pay relative to cash. When the MoMo Pay fee was reintroduced, there was a significant shift back to cash from both MoMo Pay and standard mobile money services, even if the latter were not affected by the fee. Lastly, we measure whether the adoption of digital payments correlates with merchants’ tax perceptions and compliance behaviour. First, we show that merchants using MoMo Pay tend to disagree with the obligation of paying taxes in order to receive public services, a measure of fiscal reciprocity. Such negative correlation is probably due to the fee imposed on MoMo Pay. Furthermore, standard mobile money usage improves the perceived ease of complying with taxes, while that is not the case for MoMo Pay. Again, the fact that fees on MoMo Pay are not clearly identifiable in MoMo Pay statements complicates merchants’ reporting and reconciliation of their activity for tax purposes. When it comes to compliance behaviour with VAT, the adoption of digital payments by merchants only improves their reported VAT sales and inputs, and only in the short term, while final VAT liability does not change. This hints at perverse compensating strategies to avoid taxes. We recommend that the tax administration better understand the adoption patterns of digital payments and incentivise usage among less equipped categories of taxpayers. The tax administration would also benefit from getting access to mobile money data to better monitor and enforce merchants’ compliance.
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Mascagni, Giulia, Roel Dom, and Fabrizio Santoro. The VAT in Practice: Equity, Enforcement and Complexity. Institute of Development Studies (IDS), January 2021. http://dx.doi.org/10.19088/ictd.2021.002.

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Анотація:
The value added tax (VAT) is supposed to be a tax on consumption that achieves greater economic efficiency than alternative indirect taxes. It is also meant to facilitate enforcement through the ‘self-enforcing mechanism’ – based on opposed incentives for buyers and sellers, and because of the paper trail it creates. Being a rather sophisticated tax, however, the VAT is complex to administer and costly to comply with, especially in lower-income countries. This paper takes a closer look at how the VAT system functions in practice in Rwanda. Using a mixed-methods approach, which combines qualitative information from focus group discussions with the analysis of administrative and survey data, we document and explain a number of surprising inconsistencies in the filing behaviour of VAT-remitting firms, which lead to suboptimal usage of electronic billing machines, as well as failure to claim legitimate VAT credits. The consequence of these inconsistencies is twofold. It makes it difficult for the Rwanda Revenue Authority to exploit its VAT data to the fullest, and leads to firms, particularly smaller ones, bearing a higher VAT burden than larger ones. There are several explanations for these inconsistencies. They appear to lie in a combination of taxpayer confusion, fear of audit, and constraints in administrative capacity.
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