In this study, we design an innovative method for answering students' or scholars' academic questions (for a specific scientific publication) by automatically recommending e‐learning resources in a cyber‐infrastructure‐enabled learning environment to enhance the learning experiences of students and scholars. By using information retrieval and metasearch methodologies, different types of referential metadata (related Wikipedia pages, data sets, source code, video lectures, presentation slides, and online tutorials) for an assortment of publications and scientific topics will be automatically retrieved, associated, and ranked (via the language model and the inference network model) to provide easily understandable cyberlearning resources to answer students' questions. We also designed an experimental system to automatically answer students' questions for a specific academic publication and then evaluated the quality of the answers (the recommended resources) using mean reciprocal rank and normalized discounted cumulative gain. After examining preliminary evaluation results and student feedback, we found that cyberlearning resources can provide high‐quality and straightforward answers for students' and scholars' questions concerning the content of academic publications.

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications. We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm. Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents. CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications. Video Link The video component of this article can be found at https://www.jove.com/video/59108/ , is very efficient compared to the traditional methods of data management and computation because of its functional document management called Text-Cube 2,3,4 , which distributes the documents while maintaining underlying hierarchy and neighbourhoods. It has been applied in biomedical research 5 to study entity-category association. The CaseOLAP platform consists of six major steps including download and extraction of data, parsing, indexing, Text-Cube creation, entity count, and CaseOLAP score calculation; which is the main focus of the protocol (Figure 1, Figure 2, Table 1).

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With the rapid growth of the smartphone and tablet market, mobile application (App) industry that provides a variety of functional devices is also growing at a striking speed. Product life cycle (PLC) theory, which has a long history, has been applied to a great number of industries and products and is widely used in the management domain. In this study, we apply classical PLC theory to mobile Apps on Apple smartphone and tablet devices (Apple App Store). Instead of trying to utilize often-unavailable sales or download volume data, we use open-access App daily download rankings as an indicator to characterize the normalized dynamic market popularity of an App. We also use this ranking information to generate an App life cycle model. By using this model, we compare paid and free Apps from 20 different categories. Our results show that Apps across various categories have different kinds of life cycles and exhibit various unique and unpredictable characteristics. Furthermore, as large-scale heterogeneous data (e.g., user App ratings, App hardware/software requirements, or App version updates) become available and are attached to each target App, an important contribution of this paper is that we perform in-depth studies to explore how such data correlate and affect the App life cycle. Using different regression techniques (i.e., logistic, ordinary least squares, and partial least squares), we built different models to investigate these relationships. The results indicate that some explicit and latent independent variables are more important than others for the characterization of App life cycle. In addition, we find that life cycle analysis for different App categories requires different tailored regression models, confirming that inner-category App life cycles are more predictable and comparable than App life cycles across different categories.