The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Ken Higuchi - One of the best experts on this subject based on the ideXlab platform.
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A range key query scheme for Multidimensional databases
2008 International Conference on Electrical and Computer Engineering, 2008Co-Authors: K.m.a. Hasan, Tatsuo Tsuji, Ken HiguchiAbstract:In this paper, a new implementation scheme of range key query for Multidimensional databases is proposed and evaluated. The scheme implements a Multidimensional database by employing an extendible Multidimensional Array. By using Multidimensional Arrays, fast random addressing functions for element access can be invoked by knowing a tuple of subscripts of an Array element. However these kinds of Multidimensional Arrays suffer from some problems. In our scheme, these problems are solved by an efficient scheme of record encoding based on the notion of extendible Array. The scheme shows good retrieval performance for range key query compared with conventional implementation of RDMS.
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a parallel implementation scheme of relational tables based on Multidimensional extendible Array
International Journal of Data Warehousing and Mining, 2006Co-Authors: K. Azharul M. Hasan, Tatsuo Tsuji, Ken HiguchiAbstract:In this article, an efficient parallel implementation scheme of relational tables is proposed and evaluated. The scheme implements a relational table by employing an extendible Multidimensional Array. Data allocation is a key performance factor for parallel database systems. This holds especially for data warehousing environments in which huge amounts of data have to be dealt with. In our scheme, an efficient data allocation technique is used, based on the notion of extendible Array. The dynamic load balancing is conducted when load on each processor is not uniformly distributed in order to maximize processor utilization.
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an extendible Multidimensional Array system for molap
ACM Symposium on Applied Computing, 2006Co-Authors: Tatsuo Tsuji, Akihiro Hara, Ken HiguchiAbstract:In MOLAP systems, Multidimensional Arrays are employed to store fact tables dumped from the frontend relational database. On these fact tables, various kinds of statistical computations such as aggregate operations can be performed efficiently by utilizing the fast random accessing capability of Arrays. This capability depends on that the size of an employed Multidimensional Array is fixed in every dimension, so a simple addressing function can be used to access Array elements. But, if a new column value emerges after constructing the fact table, the existing fixed size Multidimensional Array cannot involve the value. In this paper, we provide an extendible Multidimensional Array system for MOLAP. Such an Array can extend its size dynamically along an arbitrary dimension without any relocation of existing data. This property enables incremental aggregate operations without relocating any data dumped at the latest time. Some problems in making this system work as a basis for MOLAP are stated, and their countermeasures are proposed.
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DASFAA - Data compression for incremental data cube maintenance
Database Systems for Advanced Applications, 1Co-Authors: Tatsuo Tsuji, Dong Jin, Ken HiguchiAbstract:We have proposed an incremental maintenance scheme of data cubes employing extendible Multidimensional Array model. Such an Array enables incremental cube maintenance without relocating any data dumped at an earlier time, while computing the data cube efficiently by utilizing the fast random accessing capability of Arrays. But in practice, most Multidimensional Arrays for data cube are large but sparse. In this paper, we describe a data compression scheme for our proposed cube maintenance method, and demonstrate the physical refreshing algorithm working on the data structure thus compressed.
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DASFAA - An efficient implementation for MOLAP basic data structure and its evaluation
Advances in Databases: Concepts Systems and Applications, 1Co-Authors: K. M. Azharul Hasan, Tatsuo Tsuji, Ken HiguchiAbstract:In this paper we describe an efficient implementation scheme for MOLAP internal basic data structure based on extendible Multidimensional Arrays. In general, MOLAP implementation scheme employs Multidimensional Array as their basic data structure. But most of the cases the implemented Arrays are very sparse when employed to store front end relational tables in OLTP systems. Moreover conventional Multidimensional Arrays cannot be extended when new column values needs to be added. In this paper, to solve these problems, the concept of extendible Array is used. The effectiveness of extendible Array for MOLAP implementation is shown by means of both theoretical analysis and experimental results.
K. M. Azharul Hasan - One of the best experts on this subject based on the ideXlab platform.
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Efficient Query Processing for Multidimensional Data Cubes
Cyber Security and Computer Science, 2020Co-Authors: Rejwana Tasnim Rimi, K. M. Azharul HasanAbstract:Data cubes come up with a suitable paradigm for storing, accessing, processing and analysis Multidimensional data. Conventional Multidimensional Arrays (CMA) are the basic data structure to process such Multidimensional data. But the performance of the MDAs degrades when the number of dimension increases. In this paper, we propose a new approach for computing Multidimensional data cube using conversion of dimensions of the Multidimensional Array. We design efficient algorithms for Multidimensional On Line Analytical Processing (MOLAP) operations using the Converted two dimensional Array (C2A). We represent the MOLAP Array as a Converted two dimensional Array where n-dimension is converted into two dimension. Then we apply the operations of data cube namely slice and dice on both CMA and C2A. We calculate the time for slice and dice operations for CMA and C2A. The proposed model requires less time for index computation when number of dimension is high. The cache miss rate is also lower for C2A based implementation. Therefore, our proposed algorithm shows superior performance than the traditional scheme.
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Efficient Multidimensional range key query by dimension transformation
2020 IEEE Region 10 Symposium (TENSYMP), 2020Co-Authors: Rejwana Tasnim Rimi, K. M. Azharul HasanAbstract:We propose an efficient range key query scheme for Multidimensional sparse data using higher dimensional Array. We construct a Converted two dimensional Array (C2A) where multidimension is transformed into two dimension that facilitates to design simple and efficient algorithms. The technique improves the cache hit rate and increases the data locality. We demonstrate the range key query in main memory on both Traditional Multidimensional Array (TMA) and Converted two dimensional Array (C2A). The index computation cost becomes inevitable when the number of dimension increases in a Multidimensional Array. But for C2A the index computation cost is less because it needs two loops only. Therefore, the cache hit rate in C2A is higher and increases data locality. We developed algorithms for range key query and our experimental results show that the C2A based algorithms has improved performance than the TMA based algorithms. We applied algorithms for different levels and the average improvement was about 25%. The technique is explained with sufficient figures and examples.
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Representing Higher Dimensional Arrays into Generalized Two-Dimensional Array: G2A
Advances in Parallel and Distributed Computing and Ubiquitous Services, 2016Co-Authors: K. M. Azharul Hasan, Abu Hanif ShaikhAbstract:Two dimensional Array operations are prominent for Array applications because of their simplicity and good performance. But in practical applications, the Array dimension is large and hence efficient design of Multidimensional Array operation is an important research issue. In this paper, we propose a two dimensional representation of Multidimensional Array. The scheme converts an n dimensional Array into a two dimensional Array. We design efficient algorithms for matrix-matrix addition/subtraction and multiplication using our scheme. The experimental results show that the proposed scheme outperforms the Traditional Multidimensional Array (TMA) based algorithms.
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EaChOff: Chunk offset compression scheme for high dimensional data based on Extendible Multidimensional Array
2015 2nd International Conference on Electrical Information and Communication Technologies (EICT), 2015Co-Authors: K. M. Azharul Hasan, M. R. IslamAbstract:The Traditional Multidimensional Array (MA) is not extendible during run time and also it creates high degree of sparsity for picturing the actual data. Therefore it is important to find good storage scheme to compress the high dimensional data so that the maintenance becomes easier. To solve these sparsity and dynamic extension problem, we introduce a new methodology EaChOff (Extendible Multidimensional Array based Chunk Offset Compression). The EaChOff linearizes each subArray of the EMA independently and break it into chunks for compressing. We evaluated our proposed schemes by comparing compression ratio and range of usability for Chunk-Offset Compression on MA. The analysis shows that the proposed scheme is efficient if dynamic behavior of the schema is concerned for practical applications.
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Efficient index computation for Array based structured data
2015 2nd International Conference on Electrical Information and Communication Technologies (EICT), 2015Co-Authors: Abu Hanif Shaikh, Mehnuma Tabassum Omar, K. M. Azharul HasanAbstract:Traditional computing techniques widely vary over large scale computing. Some techniques are useful for structured data while some other techniques are useful for non-structured data. To store and operation on the structured data, Multidimensional Array is widely used as basic data structure. But the performance of Multidimensional Array is very poor when the number of dimension is very high i.e. 32-dimension. Data scientist suggests linearization of dimensions for higher dimensional data. But linearized data are highly compute intensive for retrieving into original data format as well as operations are very expensive on that data. It suffers from the higher index computational cost and lower data locality. In this paper, we propose an index computation algorithm based on generalized two-dimensional structure. We showed that the accessing cost of Array elements is higher for traditional Multidimensional Array over generalized two-dimensional representation. Though the most sequential access of main memory is itself very simple for any compiler yet experimental results showed a better performance. Both theoretical analysis and experiment are done to show its' effectiveness of the proposed algorithm.
Peter Baumann - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical Storage Support and Management for Large-Scale Multidimensional Array Database Management Systems
2010Co-Authors: Bernd Reiner, Gabriele Höfling, Karl Hahn, Peter BaumannAbstract:Large-scale scientific experiments or simulation programs often generate large amounts of Multidimensional data. Data volume may reach hundreds of terabytes (up to petabytes). In the present and the near future, the only practicable way for storing such large volumes of Multidimensional data are tertiary storage systems. But commercial (Multidimensional) database systems are optimized for performance with primary and secondary memory access. So tertiary storage memory is only in an insufficient way supported for storing or retrieval of Multidimensional Array data. To combine the advantages of both techniques, storing large amounts of data on tertiary storage media and optimizing data access for retrieval with Multidimensional database management systems is the intention of this paper. We introduce concepts for efficient hierarchical storage support and management for large-scale Multidimensional Array database management systems and their integration into the commercial Array database management system RasDaMan.
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performance evaluation of Multidimensional Array storage techniques in databases
International Database Engineering and Applications Symposium, 1999Co-Authors: Norbert Widmann, Peter BaumannAbstract:Storing Multidimensional data in databases is an important topic both in the industrial and scientific database communities. Arrays are offered as a Multidimensional data structure by most programming languages. Conventional database systems, however, do not support Arrays of arbitrary dimensionality and base type. RasDaMan is a DBMS integrating Arrays as a first class data type offering both a declarative query language and a specialised storage structure for Arrays. The work presented evaluates the performance of queries on Multidimensional Array data stored in RasDaMan versus storage in a conventional RDBMS. In the relational system, the data is both mapped to relations and stored directly as binary data in BLOBs. The queries executed were modelled after queries common in scientific applications and decision support.
Karl Hahn - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical Storage Support and Management for Large-Scale Multidimensional Array Database Management Systems
2010Co-Authors: Bernd Reiner, Gabriele Höfling, Karl Hahn, Peter BaumannAbstract:Large-scale scientific experiments or simulation programs often generate large amounts of Multidimensional data. Data volume may reach hundreds of terabytes (up to petabytes). In the present and the near future, the only practicable way for storing such large volumes of Multidimensional data are tertiary storage systems. But commercial (Multidimensional) database systems are optimized for performance with primary and secondary memory access. So tertiary storage memory is only in an insufficient way supported for storing or retrieval of Multidimensional Array data. To combine the advantages of both techniques, storing large amounts of data on tertiary storage media and optimizing data access for retrieval with Multidimensional database management systems is the intention of this paper. We introduce concepts for efficient hierarchical storage support and management for large-scale Multidimensional Array database management systems and their integration into the commercial Array database management system RasDaMan.
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HEAVEN: A hierarchical storage and archive environment for Multidimensional Array database management systems
Lecture Notes in Computer Science, 2004Co-Authors: Bernd Reiner, Karl HahnAbstract:The intention of this paper is to present HEAVEN, a solution of intelligent management of large-scale datasets held on tertiary storage systems. We introduce the common state of the art technique storage and retrieval of large spatio-temporal Array data in the High Performance Computing (HPC) area An identified major bottleneck today is fast and efficient access to and evaluation of high performance computing results. We address the necessity of developing techniques for efficient retrieval of requested subsets of large datasets from mass storage devices. Furthermore, we show the benefit of managing large spatio-temporal data sets, e.g. generated by simulations of climate models, with Database Management Systems (DMBS). This means DBMS need a smart connection to tertiary storage systems with optimized access strategies. HEAVEN is based on the Multidimensional Array DBMS RasDaMan.
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EDBT - HEAVEN: A Hierarchical Storage and Archive Environment for Multidimensional Array Database Management Systems
Advances in Database Technology - EDBT 2004, 2004Co-Authors: Bernd Reiner, Karl HahnAbstract:The intention of this paper is to present HEAVEN, a solution of intelligent management of large-scale datasets held on tertiary storage systems. We introduce the common state of the art technique storage and retrieval of large spatio-temporal Array data in the High Performance Computing (HPC) area. An identified major bottleneck today is fast and efficient access to and evaluation of high performance computing results. We address the necessity of developing techniques for efficient retrieval of requested subsets of large datasets from mass storage devices. Furthermore, we show the benefit of managing large spatio-temporal data sets, e.g. generated by simulations of climate models, with Database Management Systems (DMBS). This means DBMS need a smart connection to tertiary storage systems with optimized access strategies. HEAVEN is based on the Multidimensional Array DBMS RasDaMan.
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tertiary storage support for large scale Multidimensional Array database management systems
2002Co-Authors: Bernd Reiner, Karl HahnAbstract:Many large-scale scientific domains often generate huge amounts (hundreds of terabytes) of Multidimensional data. The only practicable way for storing such large volumes of Multidimensional data is a tertiary storage system. Unfortunately in commercial Multidimensional Database Management Systems (DBMS) the access is optimized for performance with primary and secondary memory. Tertiary storage memory is not or only in an insufficient way supported for storing or retrieval of Multidimensional Array data. The intention of this paper is, to combine the advantage of both techniques, storing large amounts of data on tertiary storage media and realizing efficient data access for retrieval with the commercial Multidimensional Array DBMS RasDaMan.
Bernd Reiner - One of the best experts on this subject based on the ideXlab platform.
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Hierarchical Storage Support and Management for Large-Scale Multidimensional Array Database Management Systems
2010Co-Authors: Bernd Reiner, Gabriele Höfling, Karl Hahn, Peter BaumannAbstract:Large-scale scientific experiments or simulation programs often generate large amounts of Multidimensional data. Data volume may reach hundreds of terabytes (up to petabytes). In the present and the near future, the only practicable way for storing such large volumes of Multidimensional data are tertiary storage systems. But commercial (Multidimensional) database systems are optimized for performance with primary and secondary memory access. So tertiary storage memory is only in an insufficient way supported for storing or retrieval of Multidimensional Array data. To combine the advantages of both techniques, storing large amounts of data on tertiary storage media and optimizing data access for retrieval with Multidimensional database management systems is the intention of this paper. We introduce concepts for efficient hierarchical storage support and management for large-scale Multidimensional Array database management systems and their integration into the commercial Array database management system RasDaMan.
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HEAVEN: A hierarchical storage and archive environment for Multidimensional Array database management systems
Lecture Notes in Computer Science, 2004Co-Authors: Bernd Reiner, Karl HahnAbstract:The intention of this paper is to present HEAVEN, a solution of intelligent management of large-scale datasets held on tertiary storage systems. We introduce the common state of the art technique storage and retrieval of large spatio-temporal Array data in the High Performance Computing (HPC) area An identified major bottleneck today is fast and efficient access to and evaluation of high performance computing results. We address the necessity of developing techniques for efficient retrieval of requested subsets of large datasets from mass storage devices. Furthermore, we show the benefit of managing large spatio-temporal data sets, e.g. generated by simulations of climate models, with Database Management Systems (DMBS). This means DBMS need a smart connection to tertiary storage systems with optimized access strategies. HEAVEN is based on the Multidimensional Array DBMS RasDaMan.
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EDBT - HEAVEN: A Hierarchical Storage and Archive Environment for Multidimensional Array Database Management Systems
Advances in Database Technology - EDBT 2004, 2004Co-Authors: Bernd Reiner, Karl HahnAbstract:The intention of this paper is to present HEAVEN, a solution of intelligent management of large-scale datasets held on tertiary storage systems. We introduce the common state of the art technique storage and retrieval of large spatio-temporal Array data in the High Performance Computing (HPC) area. An identified major bottleneck today is fast and efficient access to and evaluation of high performance computing results. We address the necessity of developing techniques for efficient retrieval of requested subsets of large datasets from mass storage devices. Furthermore, we show the benefit of managing large spatio-temporal data sets, e.g. generated by simulations of climate models, with Database Management Systems (DMBS). This means DBMS need a smart connection to tertiary storage systems with optimized access strategies. HEAVEN is based on the Multidimensional Array DBMS RasDaMan.
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tertiary storage support for large scale Multidimensional Array database management systems
2002Co-Authors: Bernd Reiner, Karl HahnAbstract:Many large-scale scientific domains often generate huge amounts (hundreds of terabytes) of Multidimensional data. The only practicable way for storing such large volumes of Multidimensional data is a tertiary storage system. Unfortunately in commercial Multidimensional Database Management Systems (DBMS) the access is optimized for performance with primary and secondary memory. Tertiary storage memory is not or only in an insufficient way supported for storing or retrieval of Multidimensional Array data. The intention of this paper is, to combine the advantage of both techniques, storing large amounts of data on tertiary storage media and realizing efficient data access for retrieval with the commercial Multidimensional Array DBMS RasDaMan.
