Information Systems最新文献

Concept maps are visual tools for organizing knowledge, commonly used in education and design. The process often involves reading and developing conceptual models, where feedback is crucial. Learners (e.g., students, designers) often refer to reading materials, and receive feedback from instructors (e.g., teachers, stakeholders) based on the maps they create. However, annotations made by learners, like highlights, are usually not visible to instructors, limiting tailored feedback. We propose incorporating annotation practices into concept mapping. Learners could highlight text and link these highlights to existing or newly created concepts in their concept map. This way, instructors can access both the concept map and the relevant readings for better feedback. This vision is realized through Concept&Go, a plug-in for the editor CmapCloud. This extension aims at the interplay between mapping, reading, and feedback during concept mapping. The effectiveness of this approach is demonstrated through a focus group (n=5) and a UTAUT evaluation (n=12). Concept&Go is publicly available.

Numerous challenges and open problems have appeared with the dawn of multi-model data. In most cases, single-model solutions cannot be straightforwardly extended, and new, efficient approaches must be found. In addition, since there are no standards related to combining and managing multiple models, the situation is even more complicated and confusing for users.

This paper deals with the most important aspect of data management — querying. To enable the user to grasp all the popular models, we base our solution on the abstract categorical representation of multi-model data, which can be viewed as a graph. To unify the querying of multi-model data, we enable the user to query the categorical graph using a SPARQL-based model-agnostic query language called MMQL. The query is then decomposed and translated into languages of the underlying systems. The intermediate results are then combined into the final categorical result that can be expressed in any selected format. The support for cross-model redundancy enables one to create distinct query plans and choose the optimal one. We also introduce a proof-of-concept implementation of our solution called MM-quecat.

ClinicalTrials.gov hosts an online database with over 440,000 medical studies (as of 2023) evaluating drugs, supplements, medical devices, and behavioral treatments. Target users include scientists, medical researchers, pharmaceutical companies, and other public and private institutions. Although ClinicalTrials has some filtering ability, it does not provide visualization tools, reporting tools or historical data; only the most recent state of each trial is visible to users. To fill these functionality gaps, we present Tri-AL: an open-source data platform for clinical trial visualization, information extraction, historical analysis, and reporting. This paper describes the design and functionality of Tri-AL, including a programmable module to incorporate machine learning models and extract disease-specific data from unstructured trial reports, which we demonstrate using Alzheimer’s disease reporting as a case study. We also highlight the use of Tri-AL for trial participation analysis in terms of sex, gender, race and ethnicity. The source code is publicly available at https://github.com/pouyan9675/Tri-AL.