Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Ihab F. Ilyas"'
Autor:
Ihab F. Ilyas, Theodoros Rekatsinas
Publikováno v:
Journal of Data and Information Quality. 14:1-11
The last few years witnessed significant advances in building automated or semi-automated data quality, data cleaning and data integration systems powered by machine learning (ML). In parallel, large deployment of ML systems in business, science, env
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3445e4cefb316f4ca6b9583e5c3b260d
https://doi.org/10.1145/3506712
https://doi.org/10.1145/3506712
Publikováno v:
Proceedings of the VLDB Endowment. 14:1886-1899
Organizations are increasingly relying on data to support decisions. When data contains private and sensitive information, the data owner often desires to publish a synthetic database instance that is similarly useful as the true data, while ensuring
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75d2a204f467b8fdb96b2de86afef776
https://doi.org/10.14778/3467861.3467876
https://doi.org/10.14778/3467861.3467876
Autor:
Ihab F. Ilyas, Theodoros Rekatsinas, Vishnu Konda, Jeffrey Pound, Xiaoguang Qi, Mohamed Soliman
We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremental design to continuously integrate billions of facts about real-worl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::287fccf5e8ecd65735876239290e02ad
Publikováno v:
Proceedings of the VLDB Endowment. 13:184-196
Data profiling is an important task to understand data semantics and is an essential pre-processing step in many tools. Due to privacy constraints, data is often partitioned into silos, with different access control. Discovering functional dependenci
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::53a9edc6820cea2765a44fb38cecb7f7
https://doi.org/10.14778/3364324.3364332
https://doi.org/10.14778/3364324.3364332
Publikováno v:
Proceedings of the VLDB Endowment. 12:1624-1636
We analyze the problem of discovering dependencies from distributed big data. Existing (non-distributed) algorithms focus on minimizing computation by pruning the search space of possible dependencies. However, distributed algorithms must also optimi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6003798312e193d05e6ef1216919a151
https://doi.org/10.14778/3342263.3342638
https://doi.org/10.14778/3342263.3342638
Structured data, or data that adheres to a pre-defined schema, can suffer from fragmented context: information describing a single entity can be scattered across multiple datasets or tables tailored for specific business needs, with no explicit linki
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0cc48280d72073109d805a135b1caf0
The problem of mining integrity constraints from data has been extensively studied over the past two decades for commonly used types of constraints including the classic Functional Dependencies (FDs) and the more general Denial Constraints (DCs). In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5641916ad2f571e2a07871745954cad3
Autor:
Miller J. Miller, Tamraparni Dasu, Felix Naumann, Juliana Freire, Ihab F. Ilyas, Xiaofang Zhou, Sebastian Link, Shazia Sadiq, Xin Luna Dong, Divesh Srivastava
Publikováno v:
ACM SIGMOD Record. 46:35-43
We outline a call to action for promoting empiricism in data quality research. The action points result from an analysis of the landscape of data quality research. The landscape exhibits two dimensions of empiricism in data quality research relating
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7eff8a10626a7202b0a57ef6dabd3a93
https://doi.org/10.1145/3186549.3186559
https://doi.org/10.1145/3186549.3186559
Publikováno v:
CIKM
To help databases users who have just started learning SQL or are not familiar with their database, we propose ExplIQuE, an exploration interface with query extensions. Its purpose is to assist users to smoothly dive into data exploration, and to be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad3d493c3ab82553f769d8f37a798208
https://doi.org/10.1145/3357384.3357847
https://doi.org/10.1145/3357384.3357847
Autor:
Xu Chu, Ihab F. Ilyas
Publikováno v:
Data Cleaning ISBN: 9781450371520
Data Cleaning
Data Cleaning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a1e83ed4f8bb43b66ff08e04e8abcc0
https://doi.org/10.1145/3310205.3310213
https://doi.org/10.1145/3310205.3310213