author = "Vasconcelos, Leandro Guarino de and Baldochi, Laercio Augusto and 
                         Santos, Rafael Duarte Coelho dos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Itajub{\'a} (UNIFEI)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "An approach to support the construction of adaptive Web 
              journal = "International Journal of Web Information Systems",
                 year = "2020",
               volume = "16",
               number = "2",
                pages = "171--199",
             keywords = "Web Mining, Adaptive Web applications, User behavior analysis.",
             abstract = "Purpose This paper aims to presents Real-time Usage Mining (RUM), 
                         an approach that exploits the rich information provided by client 
                         logs to support the construction of adaptive Web applications. The 
                         main goal of RUM is to provide useful information about the 
                         behavior of users that are currently browsing a Web application. 
                         By consuming this information, the application is able to adapt 
                         its user interface in real-time to enhance the user experience. 
                         RUM provides two types of services as follows: support for the 
                         detection of struggling users; and user profiling based on the 
                         detection of behavior patterns. Design/methodology/approach RUM 
                         leverages the previous study on usability evaluation to provide a 
                         service that evaluates the usability of tasks performed by users 
                         while they browse applications. This evaluation is based on a 
                         metric that allows the detection of struggling users, making it 
                         possible to identify these users as soon as few logs from their 
                         interaction are processed. RUM also exploits log mining techniques 
                         to detect usage patterns, which are then associated with user 
                         profiles previously defined by the application specialist. After 
                         associating usage patterns to user profiles, RUM is able to 
                         classify users as they browse applications, allowing the 
                         application developer to tailor the user interface according to 
                         the users needs and preferences. Findings The proposed approach 
                         was exploited to improve user experience in real-world Web 
                         applications. Experiments showed that RUM was effective to provide 
                         support for struggling users to complete tasks. Moreover, it was 
                         also effective to detect usage patterns and associate them with 
                         user profiles. Originality/value Although the literature reports 
                         studies that explore client logs to support both the detection of 
                         struggling users and the user profiling based on usage patterns, 
                         no existing solutions provide support for detecting users from 
                         specific profiles or struggling users, in real-time, while they 
                         are browsing Web applications. RUM also provides a toolkit that 
                         allows the approach to be easily deployed in any Web 
                  doi = "10.1108/IJWIS-12-2018-0089",
                  url = "http://dx.doi.org/10.1108/IJWIS-12-2018-0089",
                 issn = "1744-0084",
                label = "lattes: 8626122636195184 1 VasconcelosBALDSant:2020:ApSuCo",
             language = "en",
           targetfile = "vasconcelos_approach.pdf",
        urlaccessdate = "13 abr. 2021"