This paper presents a novel framework for constructing a diabetes-specific knowledge graph (KG) using a streamlined multi-agent system powered by Gemini-based Large Language Models (LLMs). Leveraging insights from the 2016 National Diabetes Survey (NNDS) conducted by the National Diabetes Education Program (NDEP), the framework extracts critical variables related to diagnosis, risk perception, medical advice, and self-management practices across diverse U.S. populations. By processing data from the NNDS’s extensive 94-question survey, the methodology performs adaptive ontology mapping using APIs for six major medical standards (e.g., SNOMED CT, ICD-11), ensuring semantic interoperability. Relationships between variables are identified and structured using RDF, RDFS, and OWL standards. The integration of LLMs with ontology tools like Protégé enhances automation and scalability. Results demonstrate the framework’s effectiveness in generating contextually rich and clinically relevant knowledge graphs, providing a robust foundation for advancing healthcare informatics and personalized diabetes management.

There are increasingly leveraging public data with cities increasingly interested in driving both responsiveness to citizen demands and cost savings through data analytics. As public managers seek to augment existing data sources, such as 311 complaints, with existing secondary data, such as US Census products, severe challenges exist. This paper considers the problems inherent in data being collected at divergent geographic levels over different time horizons. An inductive analytical methodology is developed to create units of analysis that are both useful and analytically appropriate for public managers and policy leaders in urban areas. A big data analytics framework for public data, called BDAP, was presented predictive analytics for community need considering data the spatial and temporal location while addressing the data issues such as missing values, privacy-preserving, and predictive modeling. The findings illustrate the power of inductive data curation and privacy-preserving leading to benefits to the big data community. An application for the Open Data Platform was developed using KCMO’s 311 data, crime data and census data.

In this paper, we propose a new framework, DeepLite, for real-time deep learning on the edge. In DeepLite, a network of multiple deep learning models is designed to conduct the context-aware inferencing using real-time deep learning technologies. Comprehensively, DeepLite has several innovative concepts as follows: 1) an inference network of deep learning models/containers, 2) invocation of a new model based on the output of the previous model's inferencing, 3) intelligent containers for models, 4) plug-and-play model/container, and 5) microservice architecture on the edge. DeepLite has been evaluated via a case study, NeighborNets which was based on an inference network of several deep learning models deployed to edge devices for computer vision, e.g., object detection algorithms to compute a diverse range of aspects, such as house types, level of greenery, house age, traffic conditions, and the types of recreational facilities.

Understanding software evolution is an imperative prerequisite for software related activities such as testing, debugging, and maintenance. As a software system evolves, it increases in size and complexity, introducing new challenges of understating the inner system interactions and subsequently hinders the overall system comprehension. While tools that construct and visualize call graphs have been used to facilitate software comprehension, they are still limited to capturing the functionality of a single software system at a time. However, understanding the similarities and differences across multiple releases becomes an imperative and challenging task during software evolution. To this end, we present a tool, named GraphEvo, that focuses on automating the process of quantifying and visualizing the changes across multiple releases of a software system based on an information-theoretic approach to compare the call graphs. Specifically, GraphEvo can automatically (1) construct and visualize the call graph for one or more software releases, (2) calculate and display a set of graph-based metrics, and (3) construct color-coded call graphs to visualize system evolution. The main goal of GraphEvo is to assist software developers and testers in exploring and tracking software changes over time. We demonstrate the functionality of GraphEvo by analyzing and studying five real software systems throughout their entire lifespan. The tool, evaluation results, and a video demo are available at https://goo.gl/8edZ64.