Results 251 to 260 of about 28,374,038 (313)

A Demonstration of KGLac: A Data Discovery and Enrichment Platform for Data Science

Proceedings of the VLDB Endowment, 2021
Data science growing success relies on knowing where a relevant dataset exists, understanding its impact on a specific task, finding ways to enrich a dataset, and leveraging insights derived from it.
A. Helal, M. Helali, Khaled Ammar, Essam Mansour   +3 more
semanticscholar   +1 more source

DICE: Data Discovery by Example

Proceedings of the VLDB Endowment, 2021
In order to conduct analytical tasks, data scientists often need to find relevant data from an avalanche of sources (e.g., data lakes, large organizational databases).
E. Rezig, Anshul Bhandari, Anna Fariha, Benjamin Price, Allan Vanterpool, V. Gadepally, M. Stonebraker   +6 more
semanticscholar   +1 more source

Data Discovery

Encyclopedia of Big Data, 2018
A. Prabhu
openaire   +2 more sources

Streamlining data discovery

Network Security, 2018
C. Tankard
openaire   +2 more sources

How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation

International Conference on Human Factors in Computing Systems, 2019
With the rise of big data, there has been an increasing need for practitioners in this space and an increasing opportunity for researchers to understand their workflows and design new tools to improve it.
Michael J. Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Liao, Casey Dugan, T. Erickson   +7 more
semanticscholar   +1 more source

Knowledge Discovery in Simulation Data

ACM Transactions on Modeling and Computer Simulation, 2020
This article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and analyzing large-scale simulation experiments with a broad coverage of possible system behavior targeted ...
Niclas Feldkamp, Soeren Bergmann, Steffen Strassburger   +2 more
openaire   +2 more sources

Knowledge discovery from numerical data

Knowledge-Based Systems, 1998
One of the authors previously presented an algorithm for discovering understandable propositions from numerical data. The algorithm consists of normalization, multiple regression analysis and the approximation of multilinear functions by continuous Boolean functions. Continuous Boolean functions are included in the space of multilinear functions.
Chie Morita, Hiroshi Tsukimoto
openaire   +1 more source

Scientists' data discovery and reuse behavior: (Meta)data fitness for use and the FAIR data principles

ASIS&T Annual Meeting, 2019
Environmental science researchers frequently must work at regional and global scales that require them to search for, evaluate, and reuse data collected by others.
B. Bishop, Carolyn Hank, Joel Webster, Rebecca Howard   +3 more
semanticscholar   +1 more source

Knowledge Discovery from Geographical Data

2008
During the last decade, data miners became aware of geographical data. Today, knowledge discovery from geographic data is still an open research field but promises to be a solid starting point for developing solutions for mining spatiotemporal patterns in a knowledge-rich territory.
S. RINZIVILLO, TURINI, FRANCO, V. BOGORNY, C. KOERNER, B. KUIJPERS, M. MAY   +5 more
openaire   +5 more sources

Discovering discovery: Data discovery best practices

Applied Marketing Analytics: The Peer-Reviewed Journal, 2015
Data discovery is the art of going beyond answering specific questions. Given the goals of the organisation and a clean and reliable dataset, the ‘data detective’ is at his or her best in formulating intriguing questions. This paper looks at a variety of ways to stimulate the creative analytics process through the study of anomalies, the employment of ...
openaire   +1 more source