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Developing Portals
Published in Shailesh Kumar Shivakumar, and User Experience Platforms, 2015
We will try to understand the main terms we frequently use in the wider context of portal and the ones we use throughout this book: Portal: A portal is a web-based application that provides features such as personalization, content aggregation, single sign-on, and collaboration for presenting a unified and personalized view of functionality, data, and information. It has specialized web application characteristics: Aggregates data, functionality, and content from disparate sourcesProvides a personalized view for each userEnables users to customize the pagesEnables collaboration featuresEnables multichannel supportProvides single sign-on features.
Sharing Information
Published in Kirk Hausman, Sustainable Enterprise Architecture, 2011
In addition to commercial products such as Microsoft SharePoint, Lotus Domino, and IBM’s WebSphere Portal, a wide variety of FOSS portal solutions exist as well. Before investing in commercial solutions that fully integrate with user productivity suites, architects should consider creating a test portal using a FOSS solution such as DotNetNuke. This will enable users to provide input on desirable content, interface layout, and content organization while IT support staff members develop policies and protocols for document storage, archival, and information aggregation.
Data Governance Model To Enhance Data Quality In Financial Institutions
Published in Information Systems Management, 2023
The Data Quality Control (DQC) monitoring tool serves as a central repository to implement and monitor data quality controls. The proposed Data Quality Control monitoring tool consists of a monitoring engine and reporting portal. Reporting portal is a graphical user interface where the DQC results are published and visualized as a dashboard. Each error in data, found by the Data Quality Control monitoring tool, is categorized based on its criticality and type. Error criticality specifies the urgency of dealing with errors. Error criticality is evaluated by the color range signalization. The following colors are used: red – criticality is very high, identified errors require immediate data corrections,orange – criticality is medium, identified errors require data correction within one month or till next calculation period,green – criticality is low, result of the DQC does not require any action.
Deep E-Learning RecommendNet: An Acute E-Learning Recommendation System with Meta-Heuristic-Based Hybrid Deep Learning Architecture
Published in Cybernetics and Systems, 2022
Pradnya Vaibhav Kulkarni, Rajneeshkaur Sachdeo, Sunil Rai, Rohini Kale
Fung, Lam, and Tam (2014) have attempted to develop an E-learning recommendation system for augmenting web search engines in order to get the personalized recommendations based on E-learning by integrating student’s behaviors and learning competencies over the network. The web interface was the gateway between the organization web portal and the Google search engine. This E-learning recommendation system has been functioned by using the content re-ranking module and the dynamic profiling module of the students. All the records related to the students were recorded in the dynamic profiling module. The re-ranking module has been utilized to prioritize the five most suitable links over the Google search space. The experimental results from this system suggested that the proposed approach has been provided high performance and satisfaction for the students.
Taiwan depository receipts forecasting along a novel regular Markov chain model
Published in Journal of the Chinese Institute of Engineers, 2020
Wen-Tso Huang, Gary Yu-Hsin Chen, Ping-Shun Chen
Figure 1 illustrates the flowchart of constructing a regular Markov chain model. Step (a) models the regular Markov chain model’s quality: Rising more than average, rising less than average, and falling, and in a total of 9 (= 3 * 3) states. Step (b) establishes a probability matrix that uses five equations. Step (c) defines a graphical representation for the regular model’s probability matrix, P’. Step (d) collects the data of TDR from Dow Jones in the United States through the Yahoo portal site. Step (e) calculates the initial probability distribution of the TDR. Step (f) verifies the proposed regular Markov chain model by raising its transition matrix to a higher power. Step (g) obtains the steady states of the proposed regular Markov chain model. Step (h) discusses the insights of the proposed regular Markov chain model.