Search results for "Web"
showing 10 items of 2018 documents
Emergent Web Intelligence: Advanced Semantic Technologies
2010
The future of the World Wide Web depended on its ability to understand and automatically process content to enable computers and people to work in cooperation. New advanced techniques and intelligent approaches are required more than ever to transform the Web into a universal reasoning and semantic-driven computing machine. The Web intelligence discipline attempts to deal with this challenge by exploits information technologies and artificial intelligence approaches to design next generation of web-empowered systems and services. The Emergent Web Intelligence: Advanced Semantic Technologies" book provides valuable references and cuttingedge technologies for: undergraduate and postgraduate s…
Web 2.0 patterns: A model-driven engineering approach
2012
Web Engineering methods, specifically Model-driven Engineering (MDE) ones, are not yet ready to cope with user involvement features that Web 2.0 is demanding. In this work, Web 2.0 patterns are introduced using conceptual models that represent both interaction and functionality, since in Web 2.0 applications both features are intertwined to support the user involvement concern. This work also drafts how Web 2.0 patterns can be integrated into models of a model-driven Web Engineering method.
Applying the ReMiP to Web Site Migration
2007
Web sites serve to publish information, both locally in intranets as well as on a global scale. Like all software systems, they have to cope with changing requirements and evolving technologies. The reference process model for software migration, ReMiP, provides a generic process model for software migration in general. The paper introduces ReMiP and summarises the application of a tailored ReMiP towards migrating a static HTML-based Web site to a content management system.
Ontosmartresource: an industrial resource generation in semantic web
2005
Semantic Web is a logical evolution of the existing Web. It was meant to serve for machines as today's Web does for humans. The term "machines" according to the existing semantic Web's vocabulary mostly means "computers". However industry needs such applications, which consider machines also as embedded computational entities within field devices, personal devices, microwave ovens, etc. In other words, now we should involve the real (industrial) world objects as resources into semantic Web. Still the main object of such a world will be a human, which becoming a resource (not just a user of resources) in the distributed environment. In this paper we introduce an extension of the semantic Web…
Proactively Composing Web Services as Tasks by Semantic Web Agents
2011
This chapter presents the framework for agent-enabled dynamic composition of Semantic Web services. The approach and the framework have been developed in several research and development projects by ISRG and IOG. The core of the methodology is the new understanding of a Semantic Web service as a capability of an intelligent software agent supplied with the proper ontological description. It is demonstrated how diverse Web services may be composed and mediated by dynamic coalitions of software agents collaboratively performing tasks for service requestors. Middle agent layer is introduced to conduct the transformation of a Web service request to the corresponding task, agent-enabled cooperat…
Lifestyle changes and mental health during the COVID-19 pandemic: A repeated, cross-sectional web survey
2021
VBM acknowledges the national grant PI16/01770 from the Instituto de Salud Carlos III, ISCIII (The PROBILIFE study). EV thanks the support of the Spanish Ministry of Science and Innovation (PI15/00283, PI18/00805) integrated into the Plan Nacional de I+D+I and co-financed by the ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER); the Instituto de Salud Carlos III; the CIBER of Mental Health (CIBERSAM) (...)
Venture capital syndication and its causal relationship with performance outcomes
2010
Venture capital firms benefit from frequent and diverse syndication ties; these are causal of increased IPO generation.
Application of neural network to predict purchases in online store
2016
A key ability of competitive online stores is effective prediction of customers’ purchase intentions as it makes it possible to apply personalized service strategy to convert visitors into buyers and increase sales conversion rates. Data mining and artificial intelligence techniques have proven to be successful in classification and prediction tasks in complex real-time systems, like e-commerce sites. In this paper we proposed a back-propagation neural network model aiming at predicting purchases in active user sessions in a Web store. The neural network training and evaluation was performed using a set of user sessions reconstructed from server log data. The proposed neural network was abl…
Using association rules to assess purchase probability in online stores
2016
The paper addresses the problem of e-customer behavior characterization based on Web server log data. We describe user sessions with the number of session features and aim to identify the features indicating a high probability of making a purchase for two customer groups: traditional customers and innovative customers. We discuss our approach aimed at assessing a purchase probability in a user session depending on categories of viewed products and session features. We apply association rule mining to real online bookstore data. The results show differences in factors indicating a high purchase probability in session for both customer types. The discovered association rules allow us to formu…
Identifying legitimate Web users and bots with different traffic profiles — an Information Bottleneck approach
2020
Abstract Recent studies reported that about half of Web users nowadays are intelligent agents (Web bots). Many bots are impersonators operating at a very high sophistication level, trying to emulate navigational behaviors of legitimate users (humans). Moreover, bot technology continues to evolve which makes bot detection even harder. To deal with this problem, many advanced methods for differentiating bots from humans have been proposed, a large part of which relies on supervised machine learning techniques. In this paper, we propose a novel approach to identify various profiles of bots and humans which combines feature selection and unsupervised learning of HTTP-level traffic patterns to d…