Research
The UMBC DAMS Research Group focuses on four main areas of research.
Data Management
Data management for the scale and heterogeneity of sensor data: query processing over sensor observations, benchmarking the middleware that moves the data around, and systems that turn raw readings into something applications can actually query.
Knowledge Representation & Reasoning
Semantic models, ontologies, and knowledge graphs that describe smart spaces, the devices in them, and the people who use them — so systems can reason about high-level concepts such as occupancy or comfort instead of raw sensor output.
Privacy
Understanding what connected devices reveal about people, and building technology that lets individuals express and enforce their privacy preferences — from analyzing privacy policies and regulation to privacy-aware data management.
Internet of Things
Building, deploying, and measuring real systems in real spaces — smart campus buildings, smart homes, and instrumented testbeds — and studying how devices behave once they are actually out there.
The global IoT market size is projected to reach $1,000B by 2026 with dozens of billions of smart devices connected to the Internet by then. The expectations of individuals with respect to their privacy are also increasing and new legislation to protect individuals' privacy (such as Europe’s GPDR, California’s CCPA, Brazil’s LGPD, and India’s PDPB) is emerging worldwide. The large amounts of highly heterogeneous data captured by those devices will require further processing (e.g., using machine learning algorithms) to become useful, but sometimes sensitive, inferences that applications and people can use. In the DAMS group we deal with upcoming challenges in IoT data management, especially due to the massive scale and heterogeneity of data and strong privacy requirements. In particular, we aim to:
- Bridge the gap that exists between raw data (e.g., data captured by sensors) and semantically meaningful data that is easily understood by people (e.g., inferences extracted from sensor observations).
- Design innovative data management solutions to automate the translation of low-level data to higher-level insights.
- Incorporate semantics and privacy-awareness to data management to design smarter and more responsible systems.
- Deal with upcoming challenges in data management in the context of the Internet of Things.
- Develop prototypes of the approaches/systems designed as part of the research tasks and deploy them in the real world.
Funding
Our research is supported by the following grants.
External Funding
CAREER: Towards a Sensorless Internet of Things through Sensor Data Management Abstraction
SFS AI + Cybersecurity: Preparing the Next Generation of Artificial Intelligence and Cybersecurity Professionals
Travel: Fellowships for Students from U.S. Universities to Attend ISWC 2024
International Digital Trust Framework (Seed Funding)
Internal Funding
Optimizing AI Workflows for Reliable, Low-Cost Agentic Systems
SPACES: Learning by Making UMBC Smarter — A Reusable Framework for Interdisciplinary, Service-Learning Projects
Cybersecurity Graduate Fellows program
A Multidisciplinary Approach to Developing a Community-driven Framework for Sensorization in Assisted Living Communities
Evaluation of standards and methods that claim to prove the authenticity of electronic audio-visual content
Supplement for Undergraduate Research Experiences (SURE) award
Projects
