POTENTIAL is a virtual database machine based on general computing platforms, especially parallel computing platforms. It provides a complete solution to high-performance database systems by a 'virtual processor ...POTENTIAL is a virtual database machine based on general computing platforms, especially parallel computing platforms. It provides a complete solution to high-performance database systems by a 'virtual processor + virtual data bus + virtual memory' architecture. Virtual processors manage all CPU resources in the system, on which various operations are running. Virtual data bus is responsible for the management of data transmission between associated operations, which forms the hinges of the entire system. Virtual memory provides efficient data storage and buffering mechanisms that conform to data reference behaviors in database systems. The architecture of POTENTIAL is very clear and has many good features, including high efficiency, high scalability, high extensibility, high portability, etc.展开更多
We developed a parallel object relational DBMS named PORLES. It uses BSP model as its parallel computing model, and monoid calculus as its basis of data model. In this paper, we introduce its data model, parallel que...We developed a parallel object relational DBMS named PORLES. It uses BSP model as its parallel computing model, and monoid calculus as its basis of data model. In this paper, we introduce its data model, parallel query optimization, transaction processing system and parallel access method in detail.展开更多
A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data items. Based on t...A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data items. Based on the improved Hilbert curve, the algorithm can be designed to achieve almost-uniform spatial data partitioning among multiple disks in parallel spatial databases. Thus, the phenomenon of data imbalance can be significantly avoided and search and query efficiency can be enhanced.展开更多
Since Multimode data is composed of many modes and their complex relationships,it cannot be retrieved or mined effectively by utilizing traditional analysis and processing techniques for single mode data.To address th...Since Multimode data is composed of many modes and their complex relationships,it cannot be retrieved or mined effectively by utilizing traditional analysis and processing techniques for single mode data.To address the challenges,we design and implement a graph-based storage and parallel loading system aimed at multimode medical image data.The system is a framework designed to flexibly store and rapidly load these multimode data.Specifically,the system utilizes the Mode Network to model the modes and their relationships in multimode medical image data and the graph database to store the data with a parallel loading technique.展开更多
提出一种基于Yarn云平台的基因启发式多序列比对算法。建立核酸替换等价矩阵作为基因启发式数学模型,构建Yarn云平台逻辑架构,通过对基因数据预处理、基因数据存储、基因序列比对、基因数据管理、基因数据分析等步骤,对数据分类保存,划...提出一种基于Yarn云平台的基因启发式多序列比对算法。建立核酸替换等价矩阵作为基因启发式数学模型,构建Yarn云平台逻辑架构,通过对基因数据预处理、基因数据存储、基因序列比对、基因数据管理、基因数据分析等步骤,对数据分类保存,划分错误率较高的长序列,得到多个较短的基因片段。对不同片段实施定位,将其中的变长种子生成,进行骨架构建和孔隙填补,可以实现基因启发式多序列比对。结果表明,设计的算法在不同数据集下处理时间缩短,多序列比对SP(Sum of Pairs)的分值较高,实验验证了该多序列比对方法具有很好的应用价值。展开更多
Database system is the infrastructure of the modern information system. The R＆D in the database system and its technologies is one of the important research topics in the field. The database R＆D in China took off la...Database system is the infrastructure of the modern information system. The R＆D in the database system and its technologies is one of the important research topics in the field. The database R＆D in China took off later but it moves along by giant steps. This report presents the achievements Renmin University of China （RUC） has made in the past 25 years and at the same time addresses some of the research projects we, RUC, are currently working on. The National Natural Science Foundation of China supports and initiates most of our research projects and these successfully conducted projects have produced fruitful results.展开更多
The huge amount of information stored in databases owned by corporations (e.g., retail, financial, telecom) has spurred a tremendous interest in the area of knowledge discovery and data mining. Clustering, in data mi...The huge amount of information stored in databases owned by corporations (e.g., retail, financial, telecom) has spurred a tremendous interest in the area of knowledge discovery and data mining. Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and other business applications. Although researchers have been working on clustering algorithms for decades, and a lot of algorithms for clustering have been developed, there is still no efficient algorithm for clustering very large databases and high dimensional data. As an outstanding representative of clustering algorithms, DBSCAN algorithm shows good performance in spatial data clustering. However, for large spatial databases, DBSCAN requires large volume of memory support and could incur substantial I/O costs because it operates directly on the entire database. In this paper, several approaches are proposed to scale DBSCAN algorithm to large spatial databases. To begin with, a fast DBSCAN algorithm is developed, which considerably speeds up the original DBSCAN algorithm. Then a sampling based DBSCAN algorithm, a partitioning-based DBSCAN algorithm, and a parallel DBSCAN algorithm are introduced consecutively. Following that, based on the above-proposed algorithms, a synthetic algorithm is also given. Finally, some experimental results are given to demonstrate the effectiveness and efficiency of these algorithms.展开更多
First, a model of static data flow computer and a model of data flow graph are pro-posed; then a model of system is presented to calculate practical parallelism degree withoverhead of instruction execution on data flo...First, a model of static data flow computer and a model of data flow graph are pro-posed; then a model of system is presented to calculate practical parallelism degree withoverhead of instruction execution on data flow computers as its parameter. From the compu-tation, the maximum practical parallelism degree of a program running on a static dataflow computer is determined with MP/OH (MP is the mean parallelism degree of a program,OH is the overhead of instruction execution on the computer). Therefore the overhead hasgreat influence on the performance of a data flow computer.展开更多
基金This work is supported by the National .'863' High-Tech Programme under grant! No.863-306-02-04-1the National Natural Scienc
文摘POTENTIAL is a virtual database machine based on general computing platforms, especially parallel computing platforms. It provides a complete solution to high-performance database systems by a 'virtual processor + virtual data bus + virtual memory' architecture. Virtual processors manage all CPU resources in the system, on which various operations are running. Virtual data bus is responsible for the management of data transmission between associated operations, which forms the hinges of the entire system. Virtual memory provides efficient data storage and buffering mechanisms that conform to data reference behaviors in database systems. The architecture of POTENTIAL is very clear and has many good features, including high efficiency, high scalability, high extensibility, high portability, etc.
文摘We developed a parallel object relational DBMS named PORLES. It uses BSP model as its parallel computing model, and monoid calculus as its basis of data model. In this paper, we introduce its data model, parallel query optimization, transaction processing system and parallel access method in detail.
基金Funded by the National 863 Program of China （No. 2005AA113150）, and the National Natural Science Foundation of China （No.40701158）.
文摘A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data items. Based on the improved Hilbert curve, the algorithm can be designed to achieve almost-uniform spatial data partitioning among multiple disks in parallel spatial databases. Thus, the phenomenon of data imbalance can be significantly avoided and search and query efficiency can be enhanced.
文摘Since Multimode data is composed of many modes and their complex relationships,it cannot be retrieved or mined effectively by utilizing traditional analysis and processing techniques for single mode data.To address the challenges,we design and implement a graph-based storage and parallel loading system aimed at multimode medical image data.The system is a framework designed to flexibly store and rapidly load these multimode data.Specifically,the system utilizes the Mode Network to model the modes and their relationships in multimode medical image data and the graph database to store the data with a parallel loading technique.
文摘提出一种基于Yarn云平台的基因启发式多序列比对算法。建立核酸替换等价矩阵作为基因启发式数学模型,构建Yarn云平台逻辑架构,通过对基因数据预处理、基因数据存储、基因序列比对、基因数据管理、基因数据分析等步骤,对数据分类保存,划分错误率较高的长序列,得到多个较短的基因片段。对不同片段实施定位,将其中的变长种子生成,进行骨架构建和孔隙填补,可以实现基因启发式多序列比对。结果表明,设计的算法在不同数据集下处理时间缩短,多序列比对SP(Sum of Pairs)的分值较高,实验验证了该多序列比对方法具有很好的应用价值。
基金Supported by the National Natural Science Foundation of China. Acknowledgements The National Science Foundation of China supported these works. Thanks to NSFC and all the members of the research groups in Renmin University of China.
文摘Database system is the infrastructure of the modern information system. The R＆D in the database system and its technologies is one of the important research topics in the field. The database R＆D in China took off later but it moves along by giant steps. This report presents the achievements Renmin University of China （RUC） has made in the past 25 years and at the same time addresses some of the research projects we, RUC, are currently working on. The National Natural Science Foundation of China supports and initiates most of our research projects and these successfully conducted projects have produced fruitful results.
基金This work was supported by the National Natural Science Foundation of China! (No.69743001) the National Doctoral Subject Fou
文摘The huge amount of information stored in databases owned by corporations (e.g., retail, financial, telecom) has spurred a tremendous interest in the area of knowledge discovery and data mining. Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and other business applications. Although researchers have been working on clustering algorithms for decades, and a lot of algorithms for clustering have been developed, there is still no efficient algorithm for clustering very large databases and high dimensional data. As an outstanding representative of clustering algorithms, DBSCAN algorithm shows good performance in spatial data clustering. However, for large spatial databases, DBSCAN requires large volume of memory support and could incur substantial I/O costs because it operates directly on the entire database. In this paper, several approaches are proposed to scale DBSCAN algorithm to large spatial databases. To begin with, a fast DBSCAN algorithm is developed, which considerably speeds up the original DBSCAN algorithm. Then a sampling based DBSCAN algorithm, a partitioning-based DBSCAN algorithm, and a parallel DBSCAN algorithm are introduced consecutively. Following that, based on the above-proposed algorithms, a synthetic algorithm is also given. Finally, some experimental results are given to demonstrate the effectiveness and efficiency of these algorithms.
文摘First, a model of static data flow computer and a model of data flow graph are pro-posed; then a model of system is presented to calculate practical parallelism degree withoverhead of instruction execution on data flow computers as its parameter. From the compu-tation, the maximum practical parallelism degree of a program running on a static dataflow computer is determined with MP/OH (MP is the mean parallelism degree of a program,OH is the overhead of instruction execution on the computer). Therefore the overhead hasgreat influence on the performance of a data flow computer.
