A spatial query language for multiple data sources based on σ-operator sequences

1998 ◽  
pp. 143-158
Author(s):  
S. -K. Change ◽  
Erland Jungert
Keyword(s):  
Query Language ◽  
Data Sources ◽  
Spatial Query ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Spatial Query Language

Querying Multiple Data Sources via an Object-Oriented Spatial Query Interface and Framework

2001 ◽  
Vol 12 (1) ◽  
pp. 37-60 ◽  
Author(s):  
MIYI CHUNG ◽  
RUTH WILSON ◽  
KEVIN SHAW ◽  
FREDERICK E. PETRY ◽  
MARIA A. COBB
Keyword(s):  
Object Oriented ◽  
Data Sources ◽  
Spatial Query ◽  
Query Interface ◽  
Multiple Data Sources ◽  
Multiple Data

A SPATIAL/TEMPORAL QUERY LANGUAGE FOR MULTIPLE DATA SOURCES IN A HETEROGENEOUS INFORMATION SYSTEM ENVIRONMENT

1998 ◽  
Vol 07 (02n03) ◽  
pp. 167-186 ◽  
Author(s):  
SHI-KUO CHANG ◽  
ERLAND JUNGERT
Keyword(s):  
Information System ◽  
Virtual World ◽  
Query Language ◽  
Data Sources ◽  
Heterogeneous Information ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Temporal Query ◽  
Operator Sequence ◽  
Temporal Query Language

To support the retrieval, fusion and discovery of multimedia information, a spatial/temporal query language for multiple data sources is needed. In this paper we describe a spatial/temporal query language, the ∑QL, which is based upon the σ-operator sequence and in practice expressible in an SQL-like syntax. The general σ-operator and temporal σ-operator are explained, and applications of the σ-query language to vertical/horizontal reasoning and hypermapped virtual world are discussed.

scholarly journals A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 1822
Author(s):  
Ana Claudia Sima ◽  
Christophe Dessimoz ◽  
Kurt Stockinger ◽  
Monique Zahn-Zabal ◽  
Tarcisio Mendes de Farias
Keyword(s):  
Query Language ◽  
Data Sources ◽  
Semantic Web Technologies ◽  
Genome Database ◽  
Steep Learning Curve ◽  
Web Technologies ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Hands On ◽  
Description Framework

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple data sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the equivalent SPARQL constructs required to benefit from this data – in particular, recursive property paths. In this article, we provide a hands-on introduction to querying evolutionary data across several data sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different data sources can be compared, through the use of federated SPARQL queries.

scholarly journals Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting

2020 ◽  
Author(s):  
Lijing Wang ◽  
Aniruddha Adiga ◽  
Srinivasan Venkatramanan ◽  
Jiangzhuo Chen ◽  
Bryan Lewis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Sources ◽  
Learning Models ◽  
Multiple Data Sources ◽  
Multiple Data

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

Omega ◽  
2021 ◽  
pp. 102479
Author(s):  
Zhongbao Zhou ◽  
Meng Gao ◽  
Helu Xiao ◽  
Rui Wang ◽  
Wenbin Liu
Keyword(s):  
Big Data ◽  
Portfolio Optimization ◽  
Data Sources ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Novel Approach

scholarly journals A dataset on affiliation of venture capitalists in China between 2000 and 2016

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jin Chen ◽  
Tianyuan Chen ◽  
Yifei Song ◽  
Bin Hao ◽  
Ling Ma
Keyword(s):  
Data Sources ◽  
Venture Capitalists ◽  
High Quality ◽  
Public Agency ◽  
Multiple Data Sources ◽  
Multiple Data ◽  
Multi Stage ◽  
The World ◽  
Prior Literature ◽  
Innovation And Entrepreneurship

AbstractPrior literature emphasizes the distinct roles of differently affiliated venture capitalists (VCs) in nurturing innovation and entrepreneurship. Although China has become the second largest VC market in the world, the unavailability of high-quality datasets on VC affiliation in China’s market hinders such research efforts. To fill up this important gap, we compiled a new panel dataset of VC affiliation in China’s market from multiple data sources. Specifically, we drew on a list of 6,553 VCs that have invested in China between 2000 and 2016 from CVSource database, collected VC’s shareholder information from public sources, and developed a multi-stage procedure to label each VC as the following types: GVC (public agency-affiliated, state-owned enterprise-affiliated), CVC (corporate VC), IVC (independent VC), BVC (bank-affiliated VC), FVC (financial/non-bank-affiliated VC), UVC (university endowment/spin-out unit), and PenVC (pension-affiliated VC). We also denoted whether a VC has foreign background. This dataset helps researchers conduct more nuanced investigations into the investment behaviors of different VCs and their distinct impacts on innovation and entrepreneurship in China’s context.

Sign in / Sign up

Export Citation Format

Share Document