Chabok: a Map-Reduce based method to solve data warehouse problems

Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent d...
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Autor*in:	Barkhordari, Mohammadhossein [verfasserIn] Niamanesh, Mahdi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Big data MapReduce Data warehouse Data locality

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: Journal of Big Data - Berlin : SpringerOpen, 2014, 5(2018), 1 vom: 26. Okt.
Übergeordnetes Werk:	volume:5 ; year:2018 ; number:1 ; day:26 ; month:10

Links:	Volltext

DOI / URN:	10.1186/s40537-018-0144-5

Katalog-ID:	SPR036631507

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520			\|a Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent data warehouse model is star schema, which consists of a fact table and some number of dimension tables. It is necessary to join the facts and dimensions for query executions on the data warehouse. In shared nothing architecture, all of the required information is not placed on a single node so it is necessary to retrieve information from other nodes, which causes network congestion and low speeds of query execution. To avoid this problem and achieve maximum parallelism, dimensions can be replicated over nodes if they are not too large. However, if there are dimensions with data volumes greater than the capacity of a node or dimensions where the data volume summation exceeds node capacity, the query execution is confronted with serious problems. In big data problems, the amount of data is immense, and thus replicating immense data cannot be considered an appropriate method. In this paper, we propose a method called Chabok, which uses two-phased Map-Reduce to solve the data warehouse problem. In this method, aggregation is performed completely on Mappers, and intermediate results are sent to the Reducer. Chabok does not need data replication for join omission. The proposed method was implemented on Hadoop, and TPC-DS queries were executed for benchmarking. The query execution time on Chabok surpassed prominent big data products for data warehousing.
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allfields	10.1186/s40537-018-0144-5 doi (DE-627)SPR036631507 (SPR)s40537-018-0144-5-e DE-627 ger DE-627 rakwb eng Barkhordari, Mohammadhossein verfasserin aut Chabok: a Map-Reduce based method to solve data warehouse problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent data warehouse model is star schema, which consists of a fact table and some number of dimension tables. It is necessary to join the facts and dimensions for query executions on the data warehouse. In shared nothing architecture, all of the required information is not placed on a single node so it is necessary to retrieve information from other nodes, which causes network congestion and low speeds of query execution. To avoid this problem and achieve maximum parallelism, dimensions can be replicated over nodes if they are not too large. However, if there are dimensions with data volumes greater than the capacity of a node or dimensions where the data volume summation exceeds node capacity, the query execution is confronted with serious problems. In big data problems, the amount of data is immense, and thus replicating immense data cannot be considered an appropriate method. In this paper, we propose a method called Chabok, which uses two-phased Map-Reduce to solve the data warehouse problem. In this method, aggregation is performed completely on Mappers, and intermediate results are sent to the Reducer. Chabok does not need data replication for join omission. The proposed method was implemented on Hadoop, and TPC-DS queries were executed for benchmarking. The query execution time on Chabok surpassed prominent big data products for data warehousing. Big data (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Data warehouse (dpeaa)DE-He213 Data locality (dpeaa)DE-He213 Niamanesh, Mahdi aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 5(2018), 1 vom: 26. Okt. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:5 year:2018 number:1 day:26 month:10 https://dx.doi.org/10.1186/s40537-018-0144-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 26 10
spelling	10.1186/s40537-018-0144-5 doi (DE-627)SPR036631507 (SPR)s40537-018-0144-5-e DE-627 ger DE-627 rakwb eng Barkhordari, Mohammadhossein verfasserin aut Chabok: a Map-Reduce based method to solve data warehouse problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent data warehouse model is star schema, which consists of a fact table and some number of dimension tables. It is necessary to join the facts and dimensions for query executions on the data warehouse. In shared nothing architecture, all of the required information is not placed on a single node so it is necessary to retrieve information from other nodes, which causes network congestion and low speeds of query execution. To avoid this problem and achieve maximum parallelism, dimensions can be replicated over nodes if they are not too large. However, if there are dimensions with data volumes greater than the capacity of a node or dimensions where the data volume summation exceeds node capacity, the query execution is confronted with serious problems. In big data problems, the amount of data is immense, and thus replicating immense data cannot be considered an appropriate method. In this paper, we propose a method called Chabok, which uses two-phased Map-Reduce to solve the data warehouse problem. In this method, aggregation is performed completely on Mappers, and intermediate results are sent to the Reducer. Chabok does not need data replication for join omission. The proposed method was implemented on Hadoop, and TPC-DS queries were executed for benchmarking. The query execution time on Chabok surpassed prominent big data products for data warehousing. Big data (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Data warehouse (dpeaa)DE-He213 Data locality (dpeaa)DE-He213 Niamanesh, Mahdi aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 5(2018), 1 vom: 26. Okt. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:5 year:2018 number:1 day:26 month:10 https://dx.doi.org/10.1186/s40537-018-0144-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 26 10
allfields_unstemmed	10.1186/s40537-018-0144-5 doi (DE-627)SPR036631507 (SPR)s40537-018-0144-5-e DE-627 ger DE-627 rakwb eng Barkhordari, Mohammadhossein verfasserin aut Chabok: a Map-Reduce based method to solve data warehouse problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent data warehouse model is star schema, which consists of a fact table and some number of dimension tables. It is necessary to join the facts and dimensions for query executions on the data warehouse. In shared nothing architecture, all of the required information is not placed on a single node so it is necessary to retrieve information from other nodes, which causes network congestion and low speeds of query execution. To avoid this problem and achieve maximum parallelism, dimensions can be replicated over nodes if they are not too large. However, if there are dimensions with data volumes greater than the capacity of a node or dimensions where the data volume summation exceeds node capacity, the query execution is confronted with serious problems. In big data problems, the amount of data is immense, and thus replicating immense data cannot be considered an appropriate method. In this paper, we propose a method called Chabok, which uses two-phased Map-Reduce to solve the data warehouse problem. In this method, aggregation is performed completely on Mappers, and intermediate results are sent to the Reducer. Chabok does not need data replication for join omission. The proposed method was implemented on Hadoop, and TPC-DS queries were executed for benchmarking. The query execution time on Chabok surpassed prominent big data products for data warehousing. Big data (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Data warehouse (dpeaa)DE-He213 Data locality (dpeaa)DE-He213 Niamanesh, Mahdi aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 5(2018), 1 vom: 26. Okt. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:5 year:2018 number:1 day:26 month:10 https://dx.doi.org/10.1186/s40537-018-0144-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 26 10
allfieldsGer	10.1186/s40537-018-0144-5 doi (DE-627)SPR036631507 (SPR)s40537-018-0144-5-e DE-627 ger DE-627 rakwb eng Barkhordari, Mohammadhossein verfasserin aut Chabok: a Map-Reduce based method to solve data warehouse problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent data warehouse model is star schema, which consists of a fact table and some number of dimension tables. It is necessary to join the facts and dimensions for query executions on the data warehouse. In shared nothing architecture, all of the required information is not placed on a single node so it is necessary to retrieve information from other nodes, which causes network congestion and low speeds of query execution. To avoid this problem and achieve maximum parallelism, dimensions can be replicated over nodes if they are not too large. However, if there are dimensions with data volumes greater than the capacity of a node or dimensions where the data volume summation exceeds node capacity, the query execution is confronted with serious problems. In big data problems, the amount of data is immense, and thus replicating immense data cannot be considered an appropriate method. In this paper, we propose a method called Chabok, which uses two-phased Map-Reduce to solve the data warehouse problem. In this method, aggregation is performed completely on Mappers, and intermediate results are sent to the Reducer. Chabok does not need data replication for join omission. The proposed method was implemented on Hadoop, and TPC-DS queries were executed for benchmarking. The query execution time on Chabok surpassed prominent big data products for data warehousing. Big data (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Data warehouse (dpeaa)DE-He213 Data locality (dpeaa)DE-He213 Niamanesh, Mahdi aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 5(2018), 1 vom: 26. Okt. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:5 year:2018 number:1 day:26 month:10 https://dx.doi.org/10.1186/s40537-018-0144-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 26 10
allfieldsSound	10.1186/s40537-018-0144-5 doi (DE-627)SPR036631507 (SPR)s40537-018-0144-5-e DE-627 ger DE-627 rakwb eng Barkhordari, Mohammadhossein verfasserin aut Chabok: a Map-Reduce based method to solve data warehouse problems 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent data warehouse model is star schema, which consists of a fact table and some number of dimension tables. It is necessary to join the facts and dimensions for query executions on the data warehouse. In shared nothing architecture, all of the required information is not placed on a single node so it is necessary to retrieve information from other nodes, which causes network congestion and low speeds of query execution. To avoid this problem and achieve maximum parallelism, dimensions can be replicated over nodes if they are not too large. However, if there are dimensions with data volumes greater than the capacity of a node or dimensions where the data volume summation exceeds node capacity, the query execution is confronted with serious problems. In big data problems, the amount of data is immense, and thus replicating immense data cannot be considered an appropriate method. In this paper, we propose a method called Chabok, which uses two-phased Map-Reduce to solve the data warehouse problem. In this method, aggregation is performed completely on Mappers, and intermediate results are sent to the Reducer. Chabok does not need data replication for join omission. The proposed method was implemented on Hadoop, and TPC-DS queries were executed for benchmarking. The query execution time on Chabok surpassed prominent big data products for data warehousing. Big data (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Data warehouse (dpeaa)DE-He213 Data locality (dpeaa)DE-He213 Niamanesh, Mahdi aut Enthalten in Journal of Big Data Berlin : SpringerOpen, 2014 5(2018), 1 vom: 26. Okt. (DE-627)79213219X (DE-600)2780218-8 2196-1115 nnns volume:5 year:2018 number:1 day:26 month:10 https://dx.doi.org/10.1186/s40537-018-0144-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2018 1 26 10
language	English
source	Enthalten in Journal of Big Data 5(2018), 1 vom: 26. Okt. volume:5 year:2018 number:1 day:26 month:10
sourceStr	Enthalten in Journal of Big Data 5(2018), 1 vom: 26. Okt. volume:5 year:2018 number:1 day:26 month:10
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institution	findex.gbv.de
topic_facet	Big data MapReduce Data warehouse Data locality
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authorswithroles_txt_mv	Barkhordari, Mohammadhossein @@aut@@ Niamanesh, Mahdi @@aut@@
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author	Barkhordari, Mohammadhossein
spellingShingle	Barkhordari, Mohammadhossein misc Big data misc MapReduce misc Data warehouse misc Data locality Chabok: a Map-Reduce based method to solve data warehouse problems
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topic_title	Chabok: a Map-Reduce based method to solve data warehouse problems Big data (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 Data warehouse (dpeaa)DE-He213 Data locality (dpeaa)DE-He213
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title	Chabok: a Map-Reduce based method to solve data warehouse problems
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title_full	Chabok: a Map-Reduce based method to solve data warehouse problems
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title_sort	chabok: a map-reduce based method to solve data warehouse problems
title_auth	Chabok: a Map-Reduce based method to solve data warehouse problems
abstract	Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent data warehouse model is star schema, which consists of a fact table and some number of dimension tables. It is necessary to join the facts and dimensions for query executions on the data warehouse. In shared nothing architecture, all of the required information is not placed on a single node so it is necessary to retrieve information from other nodes, which causes network congestion and low speeds of query execution. To avoid this problem and achieve maximum parallelism, dimensions can be replicated over nodes if they are not too large. However, if there are dimensions with data volumes greater than the capacity of a node or dimensions where the data volume summation exceeds node capacity, the query execution is confronted with serious problems. In big data problems, the amount of data is immense, and thus replicating immense data cannot be considered an appropriate method. In this paper, we propose a method called Chabok, which uses two-phased Map-Reduce to solve the data warehouse problem. In this method, aggregation is performed completely on Mappers, and intermediate results are sent to the Reducer. Chabok does not need data replication for join omission. The proposed method was implemented on Hadoop, and TPC-DS queries were executed for benchmarking. The query execution time on Chabok surpassed prominent big data products for data warehousing. © The Author(s) 2018
abstractGer	Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent data warehouse model is star schema, which consists of a fact table and some number of dimension tables. It is necessary to join the facts and dimensions for query executions on the data warehouse. In shared nothing architecture, all of the required information is not placed on a single node so it is necessary to retrieve information from other nodes, which causes network congestion and low speeds of query execution. To avoid this problem and achieve maximum parallelism, dimensions can be replicated over nodes if they are not too large. However, if there are dimensions with data volumes greater than the capacity of a node or dimensions where the data volume summation exceeds node capacity, the query execution is confronted with serious problems. In big data problems, the amount of data is immense, and thus replicating immense data cannot be considered an appropriate method. In this paper, we propose a method called Chabok, which uses two-phased Map-Reduce to solve the data warehouse problem. In this method, aggregation is performed completely on Mappers, and intermediate results are sent to the Reducer. Chabok does not need data replication for join omission. The proposed method was implemented on Hadoop, and TPC-DS queries were executed for benchmarking. The query execution time on Chabok surpassed prominent big data products for data warehousing. © The Author(s) 2018
abstract_unstemmed	Abstract Currently, immense quantities of data cannot be managed by traditional database management systems. Instead, they must be managed by big data solutions using shared nothing architectures. Data warehouse systems are systems that address very large amounts of information. The most prominent data warehouse model is star schema, which consists of a fact table and some number of dimension tables. It is necessary to join the facts and dimensions for query executions on the data warehouse. In shared nothing architecture, all of the required information is not placed on a single node so it is necessary to retrieve information from other nodes, which causes network congestion and low speeds of query execution. To avoid this problem and achieve maximum parallelism, dimensions can be replicated over nodes if they are not too large. However, if there are dimensions with data volumes greater than the capacity of a node or dimensions where the data volume summation exceeds node capacity, the query execution is confronted with serious problems. In big data problems, the amount of data is immense, and thus replicating immense data cannot be considered an appropriate method. In this paper, we propose a method called Chabok, which uses two-phased Map-Reduce to solve the data warehouse problem. In this method, aggregation is performed completely on Mappers, and intermediate results are sent to the Reducer. Chabok does not need data replication for join omission. The proposed method was implemented on Hadoop, and TPC-DS queries were executed for benchmarking. The query execution time on Chabok surpassed prominent big data products for data warehousing. © The Author(s) 2018
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title_short	Chabok: a Map-Reduce based method to solve data warehouse problems
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