The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of resources.The challenge of query optimization for relational database systems is...The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of resources.The challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes rise.Increases in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance systems.For performance enhancement,compression and query optimization are the two most factors.Compression reduces the volume of data,whereas query optimization minimizes execution time.Compressing the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more diminutive.This paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a query.The factors compression and query optimization improve the performance of the databases.This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.展开更多
In this paper, we approach the design of ID caching technology(IDCT) for graph databases, with the purpose of accelerating the queries on graph database data and avoiding redundant graph database query operations whic...In this paper, we approach the design of ID caching technology(IDCT) for graph databases, with the purpose of accelerating the queries on graph database data and avoiding redundant graph database query operations which will consume great computer resources. Traditional graph database caching technology(GDCT)needs a large memory to store data and has the problems of serious data consistency and low cache utilization. To address these issues, in the paper we propose a new technology which focuses on ID allocation mechanism and high-speed queries of ID on graph databases. Specifically, ID of the query result is cached in memory and data consistency is achieved through the real-time synchronization and cache memory adaptation. In addition, we set up complex queries and simple queries to satisfy all query requirements and design a mechanism of cache replacement based on query action time, query times, and memory capacity, thus improving the performance furthermore.Extensive experiments show the superiority of our techniques compared with the traditional query approach of graph databases.展开更多
The idea of positional inverted index is exploited for indexing of graph database. The main idea is the use of hashing tables in order to prune a considerable portion of graph database that cannot contain the answer s...The idea of positional inverted index is exploited for indexing of graph database. The main idea is the use of hashing tables in order to prune a considerable portion of graph database that cannot contain the answer set. These tables are implemented using column-based techniques and are used to store graphs of database, frequent sub-graphs and the neighborhood of nodes. In order to exact checking of remaining graphs, the vertex invariant is used for isomorphism test which can be parallel implemented. The results of evaluation indicate that proposed method outperforms existing methods.展开更多
Graphs are widely used for modeling complicated data such as social networks,chemical compounds,protein interactions and semantic web.To effiectively understand and utilize any collection of graphs,a graph database th...Graphs are widely used for modeling complicated data such as social networks,chemical compounds,protein interactions and semantic web.To effiectively understand and utilize any collection of graphs,a graph database that efficiently supports elementary querying mechanisms is crucially required.For example,Subgraph and Supergraph queries are important types of graph queries which have many applications in practice.A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems.Relational database management systems（RDBMSs） have repeatedly been shown to be able to efficiently host different types of data such as complex objects and XML data.RDBMSs derive much of their performance from sophisticated optimizer components which make use of physical properties that are specific to the relational model such as sortedness,proper join ordering and powerful indexing mechanisms.In this article,we study the problem of indexing and querying graph databases using the relational infrastructure.We present a purely relational framework for processing graph queries.This framework relies on building a layer of graph features knowledge which capture metadata and summary features of the underlying graph database.We describe different querying mechanisms which make use of the layer of graph features knowledge to achieve scalable performance for processing graph queries.Finally,we conduct an extensive set of experiments on real and synthetic datasets to demonstrate the efficiency and the scalability of our techniques.展开更多
文摘The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of resources.The challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes rise.Increases in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance systems.For performance enhancement,compression and query optimization are the two most factors.Compression reduces the volume of data,whereas query optimization minimizes execution time.Compressing the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more diminutive.This paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a query.The factors compression and query optimization improve the performance of the databases.This research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.
基金supported by the Research Fund of National Key Laboratory of Computer Architecture under Grant No.CARCH201501the Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing under Grant No.2016A09
文摘In this paper, we approach the design of ID caching technology(IDCT) for graph databases, with the purpose of accelerating the queries on graph database data and avoiding redundant graph database query operations which will consume great computer resources. Traditional graph database caching technology(GDCT)needs a large memory to store data and has the problems of serious data consistency and low cache utilization. To address these issues, in the paper we propose a new technology which focuses on ID allocation mechanism and high-speed queries of ID on graph databases. Specifically, ID of the query result is cached in memory and data consistency is achieved through the real-time synchronization and cache memory adaptation. In addition, we set up complex queries and simple queries to satisfy all query requirements and design a mechanism of cache replacement based on query action time, query times, and memory capacity, thus improving the performance furthermore.Extensive experiments show the superiority of our techniques compared with the traditional query approach of graph databases.
文摘The idea of positional inverted index is exploited for indexing of graph database. The main idea is the use of hashing tables in order to prune a considerable portion of graph database that cannot contain the answer set. These tables are implemented using column-based techniques and are used to store graphs of database, frequent sub-graphs and the neighborhood of nodes. In order to exact checking of remaining graphs, the vertex invariant is used for isomorphism test which can be parallel implemented. The results of evaluation indicate that proposed method outperforms existing methods.
文摘Graphs are widely used for modeling complicated data such as social networks,chemical compounds,protein interactions and semantic web.To effiectively understand and utilize any collection of graphs,a graph database that efficiently supports elementary querying mechanisms is crucially required.For example,Subgraph and Supergraph queries are important types of graph queries which have many applications in practice.A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems.Relational database management systems（RDBMSs） have repeatedly been shown to be able to efficiently host different types of data such as complex objects and XML data.RDBMSs derive much of their performance from sophisticated optimizer components which make use of physical properties that are specific to the relational model such as sortedness,proper join ordering and powerful indexing mechanisms.In this article,we study the problem of indexing and querying graph databases using the relational infrastructure.We present a purely relational framework for processing graph queries.This framework relies on building a layer of graph features knowledge which capture metadata and summary features of the underlying graph database.We describe different querying mechanisms which make use of the layer of graph features knowledge to achieve scalable performance for processing graph queries.Finally,we conduct an extensive set of experiments on real and synthetic datasets to demonstrate the efficiency and the scalability of our techniques.
基金国家自然科学基金 Grant Nos.6117303161272178+3 种基金国家自然科学基金海外及港澳学者合作基金 Grant No.61129002高等学校博士学科点专项科研基金 Grant No.20110042110028中央高校基本科研业务费专项资金 Grant Nos.N120504001N110404015~~
