Federated Query Processing over Linked Data

2014 ◽  
pp. 369-387 ◽  
Author(s):  
Peter Haase ◽  
Katja Hose ◽  
Ralf Schenkel ◽  
Michael Schmidt ◽  
Andreas Schwarte
Keyword(s):  
Query Processing ◽  
Linked Data ◽  
Federated Query Processing

scholarly journals Federated query processing on linked data: a qualitative survey and open challenges

2015 ◽  
Vol 30 (5) ◽  
pp. 545-563 ◽  
Author(s):  
Damla Oguz ◽  
Belgin Ergenc ◽  
Shaoyi Yin ◽  
Oguz Dikenelli ◽  
Abdelkader Hameurlain
Keyword(s):  
Query Processing ◽  
Linked Data ◽  
Optimization Methods ◽  
Qualitative Comparison ◽  
Source Selection ◽  
Qualitative Survey ◽  
Web Of Data ◽  
Clear Insight ◽  
Federated Query Processing ◽  
The Web

AbstractA large number of data providers publish and connect their structured data on the Web as linked data. Thus, the Web of data becomes a global data space. In this paper, we initially give an overview of query processing approaches used in this interlinked and distributed environment, and then focus on federated query processing on linked data. We provide a detailed and clear insight on data source selection, join methods and query optimization methods of existing query federation engines. Furthermore, we present a qualitative comparison of these engines and give a complementary comparison of the measured metrics of each engine with the idea of pointing out the major strengths of each one. Finally, we discuss the major challenges of federated query processing on linked data.

Improving parallelism of federated query processing

2008 ◽  
Vol 64 (3) ◽  
pp. 511-533 ◽  
Author(s):  
Haifeng Jiang ◽  
Dengfeng Gao ◽  
Wen-Syan Li
Keyword(s):  
Query Processing ◽  
Federated Query Processing

scholarly journals P2P-Based Query Processing over Linked Data

2014 ◽  
pp. 339-368
Author(s):  
Marcel Karnstedt ◽  
Kai-Uwe Sattler ◽  
Manfred Hauswirth
Keyword(s):  
Query Processing ◽  
Linked Data

Security Issues for Cloud Computing

2010 ◽  
Vol 4 (2) ◽  
pp. 36-48 ◽  
Author(s):  
Kevin Hamlen ◽  
Murat Kantarcioglu ◽  
Latifur Khan ◽  
Bhavani Thuraisingham
Keyword(s):  
Cloud Computing ◽  
Query Processing ◽  
Third Party ◽  
Map Reduce ◽  
Major Aspect ◽  
Security Issues ◽  
Data Layer ◽  
Secure Cloud Computing ◽  
Federated Query Processing

In this paper, the authors discuss security issues for cloud computing and present a layered framework for secure clouds and then focus on two of the layers, i.e., the storage layer and the data layer. In particular, the authors discuss a scheme for secure third party publications of documents in a cloud. Next, the paper will converse secure federated query processing with map Reduce and Hadoop, and discuss the use of secure co-processors for cloud computing. Finally, the authors discuss XACML implementation for Hadoop and discuss their beliefs that building trusted applications from untrusted components will be a major aspect of secure cloud computing.

scholarly journals Linking Health Records for Federated Query Processing

2016 ◽  
Vol 2016 (3) ◽  
pp. 4-23 ◽  
Author(s):  
Rinku Dewri ◽  
Toan Ong ◽  
Ramakrishna Thurimella
Keyword(s):  
Query Processing ◽  
Data Entry ◽  
Health Record ◽  
Health Records ◽  
Geographically Dispersed ◽  
Medical Institutions ◽  
Combining Data ◽  
Epidemiology Studies ◽  
Secure Set ◽  
Federated Query Processing

Abstract A federated query portal in an electronic health record infrastructure enables large epidemiology studies by combining data from geographically dispersed medical institutions. However, an individual’s health record has been found to be distributed across multiple carrier databases in local settings. Privacy regulations may prohibit a data source from revealing clear text identifiers, thereby making it non-trivial for a query aggregator to determine which records correspond to the same underlying individual. In this paper, we explore this problem of privately detecting and tracking the health records of an individual in a distributed infrastructure. We begin with a secure set intersection protocol based on commutative encryption, and show how to make it practical on comparison spaces as large as 1010 pairs. Using bigram matching, precomputed tables, and data parallelism, we successfully reduced the execution time to a matter of minutes, while retaining a high degree of accuracy even in records with data entry errors. We also propose techniques to prevent the inference of identifier information when knowledge of underlying data distributions is known to an adversary. Finally, we discuss how records can be tracked utilizing the detection results during query processing.

scholarly journals BioFed: federated query processing over life sciences linked open data

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Ali Hasnain ◽  
Qaiser Mehmood ◽  
Syeda Sana e Zainab ◽  
Muhammad Saleem ◽  
Claude Warren ◽  
...  
Keyword(s):  
Query Processing ◽  
Life Sciences ◽  
Open Data ◽  
Linked Open Data ◽  
Federated Query Processing
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