ArchaeoDAL: A Data Lake for Archaeological Data Management and Analytics

TL;DR

This paper introduces ArchaeoDAL, a comprehensive data lake architecture designed to efficiently manage, process, and analyze diverse archaeological data types, addressing current challenges in data lifecycle coverage and heterogeneity.

Contribution

It presents a novel, flexible data lake architecture with an advanced metadata management system tailored for archaeological data, covering the entire data lifecycle.

Findings

Effective handling of heterogeneous archaeological data.

Implementation demonstrates improved data management and analysis.

Metadata system enhances data discoverability and quality control.

Abstract

With new emerging technologies, such as satellites and drones, archaeologists collect data over large areas. However, it becomes difficult to process such data in time. Archaeological data also have many different formats (images, texts, sensor data) and can be structured, semi-structured and unstructured. Such variety makes data difficult to collect, store, manage, search and analyze effectively. A few approaches have been proposed, but none of them covers the full data lifecycle nor provides an efficient data management system. Hence, we propose the use of a data lake to provide centralized data stores to host heterogeneous data, as well as tools for data quality checking, cleaning, transformation, and analysis. In this paper, we propose a generic, flexible and complete data lake architecture. Our metadata management system exploits goldMEDAL, which is the most complete metadata model currently available. Finally, we detail the concrete implementation of this architecture dedicated to an archaeological project.