The existing evaluation methodologies of semantic web databases are limited because they measure performance of the databases only in terms of time and do not cover resource utilization for their data manipulation ope...
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The existing evaluation methodologies of semantic web databases are limited because they measure performance of the databases only in terms of time and do not cover resource utilization for their data manipulation operations. To cope with deficiencies of the existing methodologies, we have proposed a new scalability and performance evaluation methodology to perform comparative analysis of the databases and defined metrics for their query cost estimation. The proposed methodology comprises test cases for data access methods and query optimization techniques to analyze performance of the databases. The methodology was applied to the existing semantic web databases using the Barton library dataset. We highlighted the key strengths and weaknesses of the databases and discovered their scalability behavior. The evaluation results show that the proposed methodology depicts scalability behavior and performance analysis of each database better than the existing evaluation methodologies.
While the amount of information steadily increases, the requirements on the response time to query these information become more strict. Under those conditions, conventional database systems reach their limits and can...
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While the amount of information steadily increases, the requirements on the response time to query these information become more strict. Under those conditions, conventional database systems reach their limits and cannot meet these performance requirements anymore. In recent years, systems with many processing cores are considered to satisfy these demands. Furthermore, these systems include more and more heterogeneous cores tailor-made to solve one specific task in an efficient manner. However, dedicated hardware accelerators are inflexible and cannot be adapted to the requirements of a dedicated query. Thus, the challenge is orchestrating the diversity of the functionality of all the cores to be optimized for performance/energy efficiency. In this paper, a concept is introduced on how to develop a flexible Field-Programmable Gate Arrays (FPGA)-based hardware accelerator to improve the performance of query evaluation in a semantic web database. As a first step to the hardware/software system, several joint algorithms are implemented on an FPGA and evaluated against a well-developed software solution (implemented in C). The comparison shows a significant speedup of up to 10 times. Because of the complexity of the join operator, it is promising that the overall performance of query evaluation can be further enhanced by processing whole queries on an FPGA. Copyright (c) 2015 John Wiley & Sons, Ltd.
While the amount of information steadily increases, the requirements on the response time to query these information become more strict. Under those conditions, conventional database systems reach their limits and can...
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While the amount of information steadily increases, the requirements on the response time to query these information become more strict. Under those conditions, conventional database systems reach their limits and cannot meet these performance requirements anymore. In recent years, systems with many processing cores are considered to satisfy these demands. Furthermore, these systems include more and more heterogeneous cores tailor-made to solve one specific task in an efficient manner. However, dedicated hardware accelerators are inflexible and cannot be adapted to the requirements of a dedicated query. Thus, the challenge is orchestrating the diversity of the functionality of all the cores to be optimized for performance/energy efficiency. In this paper, a concept is introduced on how to develop a flexible Field-Programmable Gate Arrays (FPGA)-based hardware accelerator to improve the performance of query evaluation in a semantic web database. As a first step to the hardware/software system, several joint algorithms are implemented on an FPGA and evaluated against a well-developed software solution (implemented in C). The comparison shows a significant speedup of up to 10 times. Because of the complexity of the join operator, it is promising that the overall performance of query evaluation can be further enhanced by processing whole queries on an FPGA. Copyright (c) 2015 John Wiley & Sons, Ltd.
In this paper, an optimized data structure for managing triples used in a semantic web database and a hardware engine for index construction are presented. We propose an FPGA-centric design, which we call Hardware-Tri...
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ISBN:
(纸本)9781728102139
In this paper, an optimized data structure for managing triples used in a semantic web database and a hardware engine for index construction are presented. We propose an FPGA-centric design, which we call Hardware-Triplestore. As part of the design, a scalable and parallel architecture for Triplestore construction is introduced. We propose a hybrid data structure consisting of three layers, one for every element of the semantic triple. The data structure is optimized for our hardware-centric design and is stored on an external DDR4-Memory. The Hardware-Triplestore is evaluated separately from the rest of the database system and achieves an insertion rate of 1.24 million triples per second, which is 17 times faster than one of the fastest software Triplestore-RDF-3X-.
The choice of a good join order plays an important role in the query performance of databases. However, determining the best join order is known to be an NP-hard problem with exponential growth with the number of join...
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The choice of a good join order plays an important role in the query performance of databases. However, determining the best join order is known to be an NP-hard problem with exponential growth with the number of joins. Because of this, nonlearning approaches to join order optimization have a longer optimization and execution time. In comparison, the models of machine learning, once trained, can construct optimized query plans very quickly. Several efforts have applied machine learning to optimize join order for SQL queries outperforming traditional approaches. In this work, we suggest a reinforcement learning technique for join optimization for SPARQL queries, ReJOOSp. SPARQL queries typically contain a much higher number of joins than SQL queries and so are more difficult to optimize. To evaluate ReJOOSp, we further develop a join order optimizer based on ReJOOSp and integrate it into the semanticweb DBMS Luposdate3000. The evaluation of ReJOOSp shows its capability to significantly enhance query performance by achieving high-quality execution plans for a substantial portion of queries across synthetic and real-world datasets.
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