Data Management

Simplify Data Science by providing high-level abstractions, creating systems and tools to efficiently execute various tasks of the Data Science lifecycle

Data Management 1 2 3

Research Areas

1Declarative, Large-scale Machine Learning and Data Science

We are building SystemDS, an open source ML system for the end-to-end Data Science lifecycle (integration, cleaning, preparation, training, debugging, serving).

2Data Integration, Cleaning & Validation for ML Workloads

We aim to investigate language abstractions for data integration, data cleaning, anomaly detection, as well as data validation and debugging for ML workloads.

3Domain-Specific Data Management

We intend to complement our systems-oriented research by applied research on data management and advanced analytics for specific domains and non-relational data models like time series, graphs, and semi-structured documents.

Our Competencies

  • ML Systems for Data Science Lifecycle
  • Distributed Data Management
  • Cloud Data Platforms
  • Data Integration and Data Cleaning
  • Specialized Data Systems
  • System Architecture

Area Management