The Experts below are selected from a list of 90 Experts worldwide ranked by ideXlab platform
Yansong Zhang - One of the best experts on this subject based on the ideXlab platform.
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Fusion OLAP: Fusing the Pros of MOLAP and ROLAP Together for In-Memory OLAP
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Yansong Zhang, Yu Zhang, Shan WangAbstract:OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multidimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. This is achieved by mapping the relation tables into virtual multidimensional model and binding the multidimensional operations into a set of vector indexes to enable multidimensional computing on relation tables. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional Surrogate Key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35, 365, and 169 percent performance improvements based on the Hyper, Vectorwise, and MonetDB databases, respectively, for the Star Schema Benchmark (SSB) with scale factor 100.
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ICDE - Fusion OLAP: Fusing the Pros of MOLAP and ROLAP Together for In-memory OLAP (Extended Abstract)
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019Co-Authors: Yansong Zhang, Yu Zhang, Shan WangAbstract:OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multi-dimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional Surrogate Key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35%, 365% and 169% performance improvements based on the Hyper, Vectorwise and MonetDB databases respectively, for the Star Schema Benchmark (SSB) with scale factor 100.
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Main-memory foreign Key joins on advanced processors: design and re-evaluations for OLAP workloads
Distributed and Parallel Databases, 2018Co-Authors: Yansong Zhang, Yu Zhang, Zhou XuanAbstract:The hash join algorithm family is one of the leading techniques for equi-join performance evaluation. OLAP systems borrow this line of research to efficiently implement foreign Key joins between dimension tables and big fact tables. From data warehouse schema and workload feature perspective, the hash join algorithm can be further simplified with multidimensional mapping, and the foreign Key join algorithms can be evaluated from multiple perspectives instead of single performance perspective. In this paper, we introduce the Surrogate Key index oriented foreign Key join as schema-conscious and OLAP workload customized design foreign Key join to comprehensively evaluate how state-of-the-art join algorithms perform in OLAP workloads. Our experiments and analysis gave the following insights: (1) customized foreign Key join algorithm for OLAP workload can make join performance step forward than general-purpose hash joins; (2) each join algorithm shows strong and weak performance regions dominated by the cache locality ratio of input_size/cache_size with a fine-grained micro join benchmark; (3) the simple hardware-oblivious shared hash table join outperforms complex hardware-conscious radix partitioning hash join in most benchmark cases; (4) the customized foreign Key join algorithm with Surrogate Key index simplified the algorithm complexity for hardware accelerators and make it easy to be implemented for different hardware accelerators. Overall, we argue that improving join performance is a systematic work opposite to merely hardware-conscious algorithm optimizations, and the OLAP domain knowledge enables Surrogate Key index to be effective for foreign Key joins in data warehousing workloads for both CPU and hardware accelerators.
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APWeb - C DDTA-JOIN: one-pass OLAP algorithm for column-oriented databases
Web Technologies and Applications, 2012Co-Authors: Min Jiao, Yansong Zhang, Shan Wang, Yan Sun, Xuan ZhouAbstract:Row-store commonly uses a volcano-style "once-a-tuple" pipeline processor for processing efficiency but looses the I/O efficiency when only a small part of columns are accessed in a wide table. The academic column-store usually uses "once-a-column" style processing for I/O and cache efficiency but it has to suffer multi-pass column scan for complex query. This paper focuses on how to achieve the maximal gains from storage models for both pipeline processing efficiency and column processing efficiency. Based on the "address-value" mapping for Surrogate Key in dimension table, we can map incremental primary Keys as offset addresses, so the foreign Keys in fact table can be utilized as native join index for dimensional tuples. We use predicate vector as bitmap vector filters for dimensions to enable star-join as pipeline operator and pre-generate hash aggregators for aggregat based on the column. Using these approaches, star-join and pre-grouping can be completed in one-pass scan on dimensional attributes in fact table, and the following aggregate column scanning responses for the sparse accessing aggregation. We can gain both I/O efficiency for vector processing and CPU efficiency for pipeline aggregating. We perform the experiments for both simulated algorithm based on the column and the commercial column-store database.
Shan Wang - One of the best experts on this subject based on the ideXlab platform.
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Fusion OLAP: Fusing the Pros of MOLAP and ROLAP Together for In-Memory OLAP
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Yansong Zhang, Yu Zhang, Shan WangAbstract:OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multidimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. This is achieved by mapping the relation tables into virtual multidimensional model and binding the multidimensional operations into a set of vector indexes to enable multidimensional computing on relation tables. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional Surrogate Key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35, 365, and 169 percent performance improvements based on the Hyper, Vectorwise, and MonetDB databases, respectively, for the Star Schema Benchmark (SSB) with scale factor 100.
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ICDE - Fusion OLAP: Fusing the Pros of MOLAP and ROLAP Together for In-memory OLAP (Extended Abstract)
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019Co-Authors: Yansong Zhang, Yu Zhang, Shan WangAbstract:OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multi-dimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional Surrogate Key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35%, 365% and 169% performance improvements based on the Hyper, Vectorwise and MonetDB databases respectively, for the Star Schema Benchmark (SSB) with scale factor 100.
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APWeb - C DDTA-JOIN: one-pass OLAP algorithm for column-oriented databases
Web Technologies and Applications, 2012Co-Authors: Min Jiao, Yansong Zhang, Shan Wang, Yan Sun, Xuan ZhouAbstract:Row-store commonly uses a volcano-style "once-a-tuple" pipeline processor for processing efficiency but looses the I/O efficiency when only a small part of columns are accessed in a wide table. The academic column-store usually uses "once-a-column" style processing for I/O and cache efficiency but it has to suffer multi-pass column scan for complex query. This paper focuses on how to achieve the maximal gains from storage models for both pipeline processing efficiency and column processing efficiency. Based on the "address-value" mapping for Surrogate Key in dimension table, we can map incremental primary Keys as offset addresses, so the foreign Keys in fact table can be utilized as native join index for dimensional tuples. We use predicate vector as bitmap vector filters for dimensions to enable star-join as pipeline operator and pre-generate hash aggregators for aggregat based on the column. Using these approaches, star-join and pre-grouping can be completed in one-pass scan on dimensional attributes in fact table, and the following aggregate column scanning responses for the sparse accessing aggregation. We can gain both I/O efficiency for vector processing and CPU efficiency for pipeline aggregating. We perform the experiments for both simulated algorithm based on the column and the commercial column-store database.
Yu Zhang - One of the best experts on this subject based on the ideXlab platform.
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Fusion OLAP: Fusing the Pros of MOLAP and ROLAP Together for In-Memory OLAP
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Yansong Zhang, Yu Zhang, Shan WangAbstract:OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multidimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. This is achieved by mapping the relation tables into virtual multidimensional model and binding the multidimensional operations into a set of vector indexes to enable multidimensional computing on relation tables. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional Surrogate Key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35, 365, and 169 percent performance improvements based on the Hyper, Vectorwise, and MonetDB databases, respectively, for the Star Schema Benchmark (SSB) with scale factor 100.
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Main-memory foreign Key joins on advanced processors : design and re-evaluations for OLAP workloads
'Springer Science and Business Media LLC', 2019Co-Authors: Zhang Yansong, Yu Zhang, Zhou Xuan, Lu JiahengAbstract:The hash join algorithm family is one of the leading techniques for equi-join performance evaluation. OLAP systems borrow this line of research to efficiently implement foreign Key joins between dimension tables and big fact tables. From data warehouse schema and workload feature perspective, the hash join algorithm can be further simplified with multidimensional mapping, and the foreign Key join algorithms can be evaluated from multiple perspectives instead of single performance perspective. In this paper, we introduce the Surrogate Key index oriented foreign Key join as schema-conscious and OLAP workload customized design foreign Key join to comprehensively evaluate how state-of-the-art join algorithms perform in OLAP workloads. Our experiments and analysis gave the following insights: (1) customized foreign Key join algorithm for OLAP workload can make join performance step forward than general-purpose hash joins; (2) each join algorithm shows strong and weak performance regions dominated by the cache locality ratio of input_size/cache_size with a fine-grained micro join benchmark; (3) the simple hardware-oblivious shared hash table join outperforms complex hardware-conscious radix partitioning hash join in most benchmark cases; (4) the customized foreign Key join algorithm with Surrogate Key index simplified the algorithm complexity for hardware accelerators and make it easy to be implemented for different hardware accelerators. Overall, we argue that improving join performance is a systematic work opposite to merely hardware-conscious algorithm optimizations, and the OLAP domain knowledge enables Surrogate Key index to be effective for foreign Key joins in data warehousing workloads for both CPU and hardware accelerators.Peer reviewe
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ICDE - Fusion OLAP: Fusing the Pros of MOLAP and ROLAP Together for In-memory OLAP (Extended Abstract)
2019 IEEE 35th International Conference on Data Engineering (ICDE), 2019Co-Authors: Yansong Zhang, Yu Zhang, Shan WangAbstract:OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multi-dimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional Surrogate Key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35%, 365% and 169% performance improvements based on the Hyper, Vectorwise and MonetDB databases respectively, for the Star Schema Benchmark (SSB) with scale factor 100.
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Main-memory foreign Key joins on advanced processors: design and re-evaluations for OLAP workloads
Distributed and Parallel Databases, 2018Co-Authors: Yansong Zhang, Yu Zhang, Zhou XuanAbstract:The hash join algorithm family is one of the leading techniques for equi-join performance evaluation. OLAP systems borrow this line of research to efficiently implement foreign Key joins between dimension tables and big fact tables. From data warehouse schema and workload feature perspective, the hash join algorithm can be further simplified with multidimensional mapping, and the foreign Key join algorithms can be evaluated from multiple perspectives instead of single performance perspective. In this paper, we introduce the Surrogate Key index oriented foreign Key join as schema-conscious and OLAP workload customized design foreign Key join to comprehensively evaluate how state-of-the-art join algorithms perform in OLAP workloads. Our experiments and analysis gave the following insights: (1) customized foreign Key join algorithm for OLAP workload can make join performance step forward than general-purpose hash joins; (2) each join algorithm shows strong and weak performance regions dominated by the cache locality ratio of input_size/cache_size with a fine-grained micro join benchmark; (3) the simple hardware-oblivious shared hash table join outperforms complex hardware-conscious radix partitioning hash join in most benchmark cases; (4) the customized foreign Key join algorithm with Surrogate Key index simplified the algorithm complexity for hardware accelerators and make it easy to be implemented for different hardware accelerators. Overall, we argue that improving join performance is a systematic work opposite to merely hardware-conscious algorithm optimizations, and the OLAP domain knowledge enables Surrogate Key index to be effective for foreign Key joins in data warehousing workloads for both CPU and hardware accelerators.
Richmond Sarpong - One of the best experts on this subject based on the ideXlab platform.
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Total Synthesis of Alkaloid (±)-G. B. 13 Using a Rh(I)-Catalyzed Ketone Hydroarylation and Late-Stage Pyridine Reduction
Journal of the American Chemical Society, 2009Co-Authors: Kimberly K Larson, Richmond SarpongAbstract:Total synthesis of the Galbulimima alkaloid G. B. 13 was achieved utilizing a functionalized pyridine moiety as a piperidine Surrogate. Key to the success of the synthesis was the development of an unprecedented rhodium-catalyzed 1,2-addition of an arylboronic ester into an unactivated ketone.
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total synthesis of alkaloid g b 13 using a rh i catalyzed ketone hydroarylation and late stage pyridine reduction
Journal of the American Chemical Society, 2009Co-Authors: Kimberly K Larson, Richmond SarpongAbstract:Total synthesis of the Galbulimima alkaloid G. B. 13 was achieved utilizing a functionalized pyridine moiety as a piperidine Surrogate. Key to the success of the synthesis was the development of an unprecedented rhodium-catalyzed 1,2-addition of an arylboronic ester into an unactivated ketone.
Kimberly K Larson - One of the best experts on this subject based on the ideXlab platform.
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Total Synthesis of Alkaloid (±)-G. B. 13 Using a Rh(I)-Catalyzed Ketone Hydroarylation and Late-Stage Pyridine Reduction
Journal of the American Chemical Society, 2009Co-Authors: Kimberly K Larson, Richmond SarpongAbstract:Total synthesis of the Galbulimima alkaloid G. B. 13 was achieved utilizing a functionalized pyridine moiety as a piperidine Surrogate. Key to the success of the synthesis was the development of an unprecedented rhodium-catalyzed 1,2-addition of an arylboronic ester into an unactivated ketone.
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total synthesis of alkaloid g b 13 using a rh i catalyzed ketone hydroarylation and late stage pyridine reduction
Journal of the American Chemical Society, 2009Co-Authors: Kimberly K Larson, Richmond SarpongAbstract:Total synthesis of the Galbulimima alkaloid G. B. 13 was achieved utilizing a functionalized pyridine moiety as a piperidine Surrogate. Key to the success of the synthesis was the development of an unprecedented rhodium-catalyzed 1,2-addition of an arylboronic ester into an unactivated ketone.