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A Deep Learning-based Approach for Banana Leaf Diseases Classification- A Particle Swarm-based Approach for Semantic Similarity Computation
- A Review on Visualization Recommendation Strategies
- Accelerating relational database operations using both CPU and GPU co-processor
- An Ontology-based Scientific Data Integration Workflow
- CNN based Chart Image Classification
- Context-Based Web Service Discovery Framework with QoS Considerations
- Essential Annotation Schema for Ecology (EASE)—A framework supporting the efficient data annotation and faceted navigation in ecology
- Exploratorien zur funktionellen Biodiversitätsforschung – ein DFG Schwerpunktprogramm
- Honey bee versus Apis mellifera: A Semantic Search for Biological Data
- Integrated Semantic Search on Structured and Unstructured Data in the ADOnIS System
- Integrative Data Management for Reproducibility of Microscopy Experiments
- Ontology Visualization: An Overview
- QUIS: in-situ heterogeneous data source querying
- REPRODUCE-ME: Ontology-based Data Access for Reproducibility of Microscopy Experiments
- ScholarLens: extracting competences from research publications for the automatic generation of semantic user profiles
- Towards Personalized Support for Ontology Selection
- Towards Using Semantic Data Mining for Predicting Community Assembly
- Visualization Taxonomy based on the Specification of User’s Goal and Data Dimensions
- What do Biodiversity Scholars Search for? Identifying High-Level Entities for Biological Metadata
- Why the mapping process in ontology integration deserves attention
Accelerating relational database operations using both CPU and GPU co-processor
| Title: | Accelerating relational database operations using both CPU and GPU co-processor |
|---|---|
| Authors: | Esraa Shehab, Alsayed Algergawy, and Amany Sarhan |
| Source: | Computers and Electrical Engineering, 57 (2017) 69–80 |
| Place: | Science Direct |
| Date: | 2017-01-01 |
| Type: | Journal Paper |
| URL: | http://www.sciencedirect.com/science/article/pii/S0045790616310631 |