5,720 research outputs found
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
Profile Management System in Ubiquitous Healthcare Cloud Computing Environment
A shift from the doctor-centric model to a patient-centric model is required to face the challenges of the healthcare sector. The vision of patient-centric model can be materialized integrating ubiquitous healthcare and the notion of personalization in services. Cloud computing can be the underlying technology for ubiquitous healthcare. The use of profiles enables the personalization in healthcare services and the use of profile management systems facilitates the deployment of these services. In this paper, we propose a profile management system in ubiquitous healthcare cloud computing environment. The proposed system exploits the cloud computing technology and the smart card technology to increase the efficiency and the quality of the provided healthcare services in the context of the patient-centric model. Furthermore, we propose generic healthcare profile structures corresponding to the main classes of the participating entities in a ubiquitous healthcare cloud computing environment
Active Analytics: Suggesting Navigational Links to Users Based on Temporal Analytics Data
Front-end developers are tasked with keeping websites up-to-date while optimizing user experiences and interactions. Tools and systems have been developed to give these individuals granular analytic insight into who, with what, and how users are interacting with their sites. These systems maintain a historical record of user interactions that can be leveraged for design decisions. Developing a framework to aggregate those historical usage records and using it to anticipate user interactions on a webpage could automate the task of optimizing web pages. In this research a system called Active Analytics was created that takes Google Analytics historical usage data and provides a dynamic front-end system for automatically updating web page navigational elements. The previous year’s data is extracted from Google Analytics and transformed into a summarization of top navigation steps. Once stored, a responsive front-end system selects from this data a timespan of three weeks from the previous year: current, previous and next. The most frequently reached pages, or their parent pages, will have their navigational UI elements highlighted on a top-level or landing page to attempt to reduce the effort to reach those pages. The Active Analytics framework was evaluated by eliciting volunteers by randomly assigning two versions of a site, one with the framework, one without. It was found that users of the framework-enabled site were able to navigate a site more easily than the original
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Leveraging Smart Technology for User Experience Personalization – A Comparative Case Study of Innovative Payment Systems
Background: This study seeks to understand how the attributes of smart technology (SMT) can be leveraged to enable personalized services and optimize unique user experiences to attract and retain customers. Based on Kang et al.’s (2020) study of SMT attributes and quality effects and Liang et al.’s (2006) study on personalized recommendation and user satisfaction, we constructed a SMT personalization model to analyze how the SMT attributes of smart functionality and smart content enable personalization in different ways and create unique customer experiences throughout the user journey.
Method: Two representative payment systems were selected to depict how they integrated the strengths of personalized smart functionalities and contents to innovate their business models, optimize user experiences, and sustain business growth.
Results: Based on the comparative analysis of the two payment cases, the functionality and content attributes of smart chips and omni-channel platforms were explored, and the tailored advisory and responsive support for customers both offline and online were validated.
Conclusion: The life-enriching service innovations provide valuable insights for leveraging SMT for personalization. It is hoped that the SMT personalization model can be extended to other types of SMT applications and can be used as a framework for designing innovative services
Visualized Architecture Knowledge Management Collaboration Services
Software (system) architecture knowledge is a critical element in making effective design/ implementation decisions for Information Technology departments within companies. This knowledge can be codified and/ or personalized so as to harness the advantages and avoid the missed steps of implementers before us. In research of architecture knowledge enablement, there have been a few ventures, including but not limited to, Processcentric Architecture Knowledge Management Environment (PAKME) [3] and Architecture Design Decision Support System (ADDSS) [4]. In study of these ventures, we find modest attempts at focusing on dissecting types of architecture knowledge and enabling access to details through web tools. The purpose of this paper is to document the design and features of a web tool, namely Visualized Architecture Knowledge Management Collaboration Services (VAKMCS) and its approach in providing an innovative way at accessing and interacting with architecture information to make sound investment decision on IT projects
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel
with the witnessed advancements in both internet of things (IoT) and artificial
intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI,
multiple forms of data are incorporated in these systems, e.g. social,
implicit, local and personal information, which can help in improving
recommender systems' performance and widen their applicability to traverse
different disciplines. On the other side, energy efficiency in the building
sector is becoming a hot research topic, in which recommender systems play a
major role by promoting energy saving behavior and reducing carbon emissions.
However, the deployment of the recommendation frameworks in buildings still
needs more investigations to identify the current challenges and issues, where
their solutions are the keys to enable the pervasiveness of research findings,
and therefore, ensure a large-scale adoption of this technology. Accordingly,
this paper presents, to the best of the authors' knowledge, the first timely
and comprehensive reference for energy-efficiency recommendation systems
through (i) surveying existing recommender systems for energy saving in
buildings; (ii) discussing their evolution; (iii) providing an original
taxonomy of these systems based on specified criteria, including the nature of
the recommender engine, its objective, computing platforms, evaluation metrics
and incentive measures; and (iv) conducting an in-depth, critical analysis to
identify their limitations and unsolved issues. The derived challenges and
areas of future implementation could effectively guide the energy research
community to improve the energy-efficiency in buildings and reduce the cost of
developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl
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