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An approach to support the construction of adaptive Web applications
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 brow...
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| Published in: | International journal of Web information systems 2020-06, Vol.16 (2), p.171-199 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c317t-4ec4fda839f19d65b31b60f9d868c55e1bd928ae12c471ac846a80a554f197123 |
| container_end_page | 199 |
| container_issue | 2 |
| container_start_page | 171 |
| container_title | International journal of Web information systems |
| container_volume | 16 |
| creator | Vasconcelos, Leandro Guarino Baldochi, Laercio Augusto Santos, Rafael Duarte Coelho |
| description | 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 application. |
| doi_str_mv | 10.1108/IJWIS-12-2018-0089 |
| format | article |
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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 application.</description><identifier>ISSN: 1744-0084</identifier><identifier>EISSN: 1744-0092</identifier><identifier>DOI: 10.1108/IJWIS-12-2018-0089</identifier><language>eng</language><publisher>Bingley: Emerald Group Publishing Limited</publisher><subject>Applications programs ; Automation ; Browsing ; Evaluation ; Experiments ; Multimedia communications ; Real time ; Servers ; Usability ; Use statistics ; User behavior ; User experience ; User interface ; User interfaces ; User needs ; User profiles</subject><ispartof>International journal of Web information systems, 2020-06, Vol.16 (2), p.171-199</ispartof><rights>Emerald Publishing Limited 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c317t-4ec4fda839f19d65b31b60f9d868c55e1bd928ae12c471ac846a80a554f197123</citedby><cites>FETCH-LOGICAL-c317t-4ec4fda839f19d65b31b60f9d868c55e1bd928ae12c471ac846a80a554f197123</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2499019794/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2499019794?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>315,786,790,21409,21422,27338,27957,27958,33646,33941,34170,43768,43927,74578,74766</link.rule.ids></links><search><creatorcontrib>Vasconcelos, Leandro Guarino</creatorcontrib><creatorcontrib>Baldochi, Laercio Augusto</creatorcontrib><creatorcontrib>Santos, Rafael Duarte Coelho</creatorcontrib><title>An approach to support the construction of adaptive Web applications</title><title>International journal of Web information systems</title><description>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 application.</description><subject>Applications programs</subject><subject>Automation</subject><subject>Browsing</subject><subject>Evaluation</subject><subject>Experiments</subject><subject>Multimedia communications</subject><subject>Real time</subject><subject>Servers</subject><subject>Usability</subject><subject>Use statistics</subject><subject>User behavior</subject><subject>User experience</subject><subject>User interface</subject><subject>User interfaces</subject><subject>User needs</subject><subject>User profiles</subject><issn>1744-0084</issn><issn>1744-0092</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ALSLI</sourceid><sourceid>CNYFK</sourceid><sourceid>F2A</sourceid><recordid>eNo9kF1LwzAUhoMoOKd_wKuA19WcJG2TyzF1TgZeqOwypGnKOmYTk1Tw35s68eq8cJ7zwYPQNZBbACLu1s_b9WsBtKAEREGIkCdoBjXnOUt6-p8FP0cXMe4JqQQDOUP3iwFr74PTZoeTw3H03oWE085i44aYwmhS7wbsOqxb7VP_ZfHWNtPQoTd66sVLdNbpQ7RXf3WO3h8f3pZPxeZltV4uNoVhUKeCW8O7VgsmO5BtVTYMmop0shWVMGVpoWklFdoCNbwGbQSvtCC6LHnma6Bsjm6Oe_O_n6ONSe3dGIZ8UlEuJcmU5JmiR8oEF2OwnfKh_9DhWwFRky31a0sBVZMtNdliPxKAXUY</recordid><startdate>20200603</startdate><enddate>20200603</enddate><creator>Vasconcelos, Leandro Guarino</creator><creator>Baldochi, Laercio Augusto</creator><creator>Santos, Rafael Duarte Coelho</creator><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7XB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CNYFK</scope><scope>DWQXO</scope><scope>E3H</scope><scope>F2A</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M1O</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20200603</creationdate><title>An approach to support the construction of adaptive Web applications</title><author>Vasconcelos, Leandro Guarino ; Baldochi, Laercio Augusto ; Santos, Rafael Duarte Coelho</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c317t-4ec4fda839f19d65b31b60f9d868c55e1bd928ae12c471ac846a80a554f197123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Applications programs</topic><topic>Automation</topic><topic>Browsing</topic><topic>Evaluation</topic><topic>Experiments</topic><topic>Multimedia communications</topic><topic>Real time</topic><topic>Servers</topic><topic>Usability</topic><topic>Use statistics</topic><topic>User behavior</topic><topic>User experience</topic><topic>User interface</topic><topic>User interfaces</topic><topic>User needs</topic><topic>User profiles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vasconcelos, Leandro Guarino</creatorcontrib><creatorcontrib>Baldochi, Laercio Augusto</creatorcontrib><creatorcontrib>Santos, Rafael Duarte Coelho</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Social Science Premium Collection</collection><collection>Advanced Technologies & Aerospace Database (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Library & Information Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Library Science Database</collection><collection>ProQuest Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>International journal of Web information systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vasconcelos, Leandro Guarino</au><au>Baldochi, Laercio Augusto</au><au>Santos, Rafael Duarte Coelho</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An approach to support the construction of adaptive Web applications</atitle><jtitle>International journal of Web information systems</jtitle><date>2020-06-03</date><risdate>2020</risdate><volume>16</volume><issue>2</issue><spage>171</spage><epage>199</epage><pages>171-199</pages><issn>1744-0084</issn><eissn>1744-0092</eissn><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 application.</abstract><cop>Bingley</cop><pub>Emerald Group Publishing Limited</pub><doi>10.1108/IJWIS-12-2018-0089</doi><tpages>29</tpages></addata></record> |
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| identifier | ISSN: 1744-0084 |
| ispartof | International journal of Web information systems, 2020-06, Vol.16 (2), p.171-199 |
| issn | 1744-0084 1744-0092 |
| language | eng |
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| source | Library & Information Science Abstracts (LISA); Social Science Premium Collection; Emerald:Jisc Collections:Emerald Subject Collections HE and FE 2024-2026:Emerald Premier (reading list); Library & Information Science Collection |
| subjects | Applications programs Automation Browsing Evaluation Experiments Multimedia communications Real time Servers Usability Use statistics User behavior User experience User interface User interfaces User needs User profiles |
| title | An approach to support the construction of adaptive Web applications |
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