Data Integration for the Relational Web

By Michael J. Cafarella, Alon Y. Halevy, Nodira Khoussainova. 2009

275 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Information Systems

In Management Science and Operations Research

Citations in the corpus

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, methodology): “Finally, techniques for enhancing the tables [30], [31] were proposed where entities or attribute values are added tomake the tablesmore complete.”
Recovering Semantics of Tables on the Web (2011, background): “Several works have considered how to extract and manage data tables found on the Web (e.g., [5, 11, 15]), but did not consider the annotation or search problems.”
InfoGather: entity augmentation and attribute discovery by holistic matching with web tables (2012, methodology): “The state-of-the-art entity augmentation technique, namely Octopus, implements a variant of this technique using the search engine API [4].”
TEGRA: Table Extraction by Global Record Alignment (2015, background): “including table search [1, 2], table integration [9, 27], and knowledge discovery [26] among many others.”
Big Data: A Survey (2014, unknown): “flow processing engines and search engines [30, 67].”
Aurum: A Data Discovery System (2018, methodology): “For example, systems such as Goods [1], Infogather [2] and Octopus [3] do not provide a ﬂexible way to support different discovery queries than those they were designed to solve.”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “OCTOPUS [87] further incorporates data cleansing with table classification tasks to filter informative tables.”
A Demo of the Data Civilizer System (2017, background): “For example, work in data integration and schema mapping has focused on matching the data and meta data of di ↵ erent data sources [2], while data cleaning solutions have focused primarily on cleaning speciﬁc data sources with respect to a certain type of errors [5].”
Ten Years of WebTables (2018, background): “In Augmentation by attribute name (ABA) (also called EXTEND() in [6]), the system ﬁlls in attribute values given an explicit user-provided attribute.”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, methodology): “In general, data integration methods are better intertwined with the streaming processing engines [34] and search engines [104].”
The Data Civilizer System (2017, result): “This is similar to the extend operator of Octopus [7], or the ABA operation of Infogather [23].”
Data Lake Management: Challenges and Opportunities (2019, background): “In query-driven discovery, a user starts search with a query (dataset or keywords) and the goal is to ﬁnd similar datasets to the query [6, 4] or datasets that can be integrated (joined or unioned) with the query [27, 38].”