TY - GEN
T1 - User-Friendly Exploration of Highly Heterogeneous Data Lakes
AU - Barret, Nelly
AU - Ebel, Simon
AU - Galizzi, Théo
AU - Manolescu, Ioana
AU - Mohanty, Madhulika
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - The proliferation of digital data sources and formats has led to the apparition of data lakes, systems where numerous data sources coexist, with less (or no) control and coordination among the sources, than previously practised in enterprise databases and data warehouses. While most data lakes are designed for very large number of tables, ConnectionLens [2, 3] is a data lake system for structured, semi-structured, and unstructured data, which it integrates into a single graph; the graph can be explored via graph queries with keyword search [4] and entity path enumeration [5]. In this paper, we describe ConnectionStudio, a user-friendly platform leveraging ConnectionLens, and integrating feedback from non-expert users, in particular, journalists. Our main insights are: (i) improve and entice exploration by giving a first global view; (ii) facilitate tabular exports from the integrated graph; (iii) provide interactive means to improve the graph constructions. The insights can be used to further advance the exploration and usage of data lakes for non-IT users.
AB - The proliferation of digital data sources and formats has led to the apparition of data lakes, systems where numerous data sources coexist, with less (or no) control and coordination among the sources, than previously practised in enterprise databases and data warehouses. While most data lakes are designed for very large number of tables, ConnectionLens [2, 3] is a data lake system for structured, semi-structured, and unstructured data, which it integrates into a single graph; the graph can be explored via graph queries with keyword search [4] and entity path enumeration [5]. In this paper, we describe ConnectionStudio, a user-friendly platform leveraging ConnectionLens, and integrating feedback from non-expert users, in particular, journalists. Our main insights are: (i) improve and entice exploration by giving a first global view; (ii) facilitate tabular exports from the integrated graph; (iii) provide interactive means to improve the graph constructions. The insights can be used to further advance the exploration and usage of data lakes for non-IT users.
KW - Data exploration
KW - Data lake
KW - Heterogeneous data
U2 - 10.1007/978-3-031-46846-9_30
DO - 10.1007/978-3-031-46846-9_30
M3 - Conference contribution
AN - SCOPUS:85175961206
SN - 9783031468452
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 488
EP - 496
BT - Cooperative Information Systems - 29th International Conference, CoopIS 2023, Proceedings
A2 - Sellami, Mohamed
A2 - Gaaloul, Walid
A2 - Vidal, Maria-Esther
A2 - van Dongen, Boudewijn
A2 - Panetto, Hervé
PB - Springer Science and Business Media Deutschland GmbH
T2 - 29th International Conference on Cooperative Information Systems, CoopIS 2023
Y2 - 30 October 2023 through 3 November 2023
ER -