3rd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores: Graph Edition
Co-located with ICDE 2024 (, Utrecht, Netherlands)
SEA Graph workshop proposes a unique international venue for researchers and practitioners willing to share their insights, experience, and solutions in the management and analysis of heterogeneous Graph data.
Companies, governments, and organizations are now producing and collecting data from multiple heterogeneous sources, such as transactional data, internet traffic, logs, IoT applications, knowledge bases, and much more. The unprecedented pace in which we produce and consume data calls for methods that organize, retrieve, and analyze such data appropriately. While traditionally data were organized into homogeneous datastores and formats, the current data collection from multiple different sources makes such datastores impractical. Even within the same organization, data dwells in independent silos, each one with a distinct data model and serving a specific application.
In today data management systems, there is a need to expose more flexible and expressive data and query models. Consequently, graphs have attracted considerable attention from the community for their flexibility in modelling, organizing, managing, and querying heterogeneous data. Among these, knowledge graphs have demonstrated high effectiveness as holistic data integration models.
Motivated by the growing relevance of the graph data model (recently added even to the SQL standard) and the recent efforts in the research community, the SEA Graph workshop proposes a unique international venue for researchers and practitioners willing to share their insights, experience, and solutions in the management and analysis of heterogeneous graph data.
The SEA Graph workshop will provide a forum for researchers and practitioners to exchange ideas, results, and visions on challenges in adopting graphs to handle data management, information extraction, exploration, and analysis of heterogeneous data and multiple data models at once.