@article {772,
	title = {Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain},
	journal = {Big Data and Cognitive Computing},
	volume = {4},
	year = {2020},
	pages = {7},
	abstract = {Advancements in cultural informatics have significantly influenced the way we perceive, analyze, communicate and understand culture. New data sources, such as social media, digitized cultural content, and Internet of Things (IoT) devices, have allowed us to enrich and customize the cultural experience, but at the same time have created an avalanche of new data that needs to be stored and appropriately managed in order to be of value. Although data management plays a central role in driving forward the cultural heritage domain, the solutions applied so far are fragmented, physically distributed, require specialized IT knowledge to deploy, and entail significant IT experience to operate even for trivial tasks. In this work, we present Hydria, an online data lake that allows users without any IT background to harvest, store, organize, analyze and share heterogeneous, multi-faceted cultural heritage data. Hydria provides a zero-administration, zero-cost, integrated framework that enables researchers, museum curators and other stakeholders within the cultural heritage domain to easily (i) deploy data acquisition services (like social media scrapers, focused web crawlers, dataset imports, questionnaire forms), (ii) design and manage versatile customizable data stores, (iii) share whole datasets or horizontal/vertical data shards with other stakeholders, (iv) search, filter and analyze data via an expressive yet simple-to-use graphical query engine and visualization tools, and (v) perform user management and access control operations on the stored data. To the best of our knowledge, this is the first solution in the literature that focuses on collecting, managing, analyzing, and sharing diverse, multi-faceted data in the cultural heritage domain and targets users without an IT background.},
	keywords = {analytics and visualization, big data management, Cultural heritage, data lake, data store, open source},
	doi = {10.3390/bdcc4020007},
	url = {https://www.mdpi.com/2504-2289/4/2/7},
	author = {Kimon Deligiannis and Paraskevi Raftopoulou and Christos Tryfonopoulos and Nikos Platis and Costas Vassilakis}
}
