Data Enrichment Using Web APIs

2012 ◽  
Author(s):  
Karthik Gomadam ◽  
Peter Z. Yeh ◽  
Kunal Verma ◽  
John A. Miller
Keyword(s):  
Data Enrichment

scholarly journals Data Enrichment in the Information Graphs Environment Based on a Specialized Architecture of Information Channels

2021 ◽  
Vol 190 ◽  
pp. 492-499
Author(s):  
Sergey Kosikov ◽  
Larisa Ismailova ◽  
Viacheslav Wolfengagen
Keyword(s):  
Data Enrichment ◽  
Information Channels

A Manifold Learning Data Enrichment Methodology for Homicide Prediction

2020 ◽  
Author(s):  
Juan S. Moreno Pabon ◽  
Mateo Dulce Rubio ◽  
Yor Castano ◽  
Alvaro J. Riascos ◽  
Paula Rodriguez Diaz
Keyword(s):  
Manifold Learning ◽  
Data Enrichment ◽  
Learning Data

scholarly journals A Study on the Data Fusion for Data Enrichment

2004 ◽  
Vol 17 (3) ◽  
pp. 605-617
Keyword(s):  
Data Fusion ◽  
Data Enrichment

A Multiagent Approach to Database Migration for Big Data Systems

2017 ◽  
Vol 13 (02) ◽  
pp. 159-180 ◽  
Author(s):  
Mihai Horia Zaharia
Keyword(s):  
Big Data ◽  
Intelligent Agents ◽  
Data Systems ◽  
Data Repositories ◽  
Migration System ◽  
Data Enrichment ◽  
Final Goal ◽  
Data Architecture ◽  
Web Of Data ◽  
Big Data Systems

Presented in this paper is a possible solution for speeding up the integration of various data in the big data mainstream. The data enrichment and convergence of all possible sources is still at the beginning. As a result, existing techniques must be retooled in order to increase the integration of already existing databases or of the ones specific to Internet of Things in order to use the advantages of the big data to fulfill the final goal of web of data creation. In this paper, semantic web-specific solutions are used to design a system based on intelligent agents. It tries to solve some problems specific to automation of the database migration system with the final goal of creating a common ontology over various data repositories or producers in order to integrate them into systems based on big data architecture.

Evaluating Elements of Web-Based Data Enrichment for Pseudo-relevance Feedback Retrieval

2021 ◽  
pp. 53-64
Author(s):  
Timo Breuer ◽  
Melanie Pest ◽  
Philipp Schaer
Keyword(s):  
Relevance Feedback ◽  
Web Based ◽  
Data Enrichment ◽  
Pseudo Relevance Feedback

Using Meta-analysis for Data Enrichment-optimal Families of Estimation Strategies

2005 ◽  
Vol 5 (9) ◽  
pp. 1575-1581
Author(s):  
Ashok Sahai ◽  
M. Mahibbur Rahman . ◽  
Rameshwar P. Jaju .
Keyword(s):  
Meta Analysis ◽  
Data Enrichment

SmPFT: Social media based profile fusion technique for data enrichment

2019 ◽  
Vol 158 ◽  
pp. 123-131 ◽  
Author(s):  
Amit Agarwal ◽  
Durga Toshniwal
Keyword(s):  
Social Media ◽  
Fusion Technique ◽  
Data Enrichment

Ontology-based analysis of the large collection of historical Hebrew manuscripts

2019 ◽  
Author(s):  
Maayan Zhitomirsky-Geffet ◽  
Gila Prebor ◽  
Isaac Miller
Keyword(s):  
Quantitative Analysis ◽  
Large Scale ◽  
Scale Analysis ◽  
Large Collection ◽  
Data Enrichment ◽  
Large Scale Analysis ◽  
The World ◽  
Library Catalogue ◽  
Event Based ◽  
New Framework

Abstract In this paper, we present a new semi-automatic methodology for construction of event-based ontology from the library catalogue of the largest collection in the world of metadata records of historical Hebrew manuscripts. Based on the constructed ontology, we developed and implemented a new framework for catalogue data enrichment, correction, and its systematic quantitative analysis. Finally, we demonstrate the results of the proposed large-scale analysis of three most prominent event types in the corpus, as well as a few cross-event relations and trends.

Towards the Russian Linked Culture Cloud: Data Enrichment and Publishing

2015 ◽  
pp. 637-651 ◽  
Author(s):  
Dmitry Mouromtsev ◽  
Peter Haase ◽  
Eugene Cherny ◽  
Dmitry Pavlov ◽  
Alexey Andreev ◽  
...  
Keyword(s):  
Cloud Data ◽  
Data Enrichment

Data Enrichment

2017 ◽  
pp. 317-317
Keyword(s):  
Data Enrichment
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