Automatic Transformation of Data Warehouse Schema to NoSQL Data Base: Comparative Study

Yangui, Rania; Nabli, Ahlem; Gargouri, Faïez

doi:10.1016/j.procs.2016.08.138

Cited by 48 publications

(23 citation statements)

References 14 publications

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“…Regarding data modelling, an evaluation of different data modelling and organization strategies for Hive-based DWs is described [9], showing the benefits of implementing a BDW based on a fully denormalized table, when compared with a dimensional structure (star schema). Moreover, [4,5,35] analyzed the implementation of BDWs based in NoSQL databases. While [4] studied the implementation of a DW based on a documentoriented NoSQL database and [5] explored implementations of DWs on top of columnoriented NoSQL databases, [35] proposed a transformation process for moving from a dimensional DW into a column-oriented and document-oriented NoSQL data model.…”

Section: Related Workmentioning

confidence: 99%

Evaluating partitioning and bucketing strategies for Hive-based Big Data Warehousing systems

Costa

Santos

2019

J Big Data

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Section: Related Workmentioning

confidence: 99%

Evaluating partitioning and bucketing strategies for Hive-based Big Data Warehousing systems

Costa

Santos

2019

J Big Data

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“…As these operations are being carried out thousands of times, the execution time of these actions needs to be as short as possible [45]. For this reason, MongoDB (v4.0.8, open source) [46] is a very efficient database technology oriented to store documents and perform text mining operations. Appendix A includes some computational execution times of the system.…”

Section: Stage 2: Creation Of An Input Datasetmentioning

confidence: 99%

Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

et al. 2020

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Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.

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“…Also, research such as Dehdouh et al, 23 Yangui et al 24 and Boussahoua et al 25 presented approaches for modeling and implementing data warehouses in NoSQL databases. Dehdouh et al 23 proposed three approaches which allow mapping a multidimensional conceptual data model into a logical modeling adapted to the column‐oriented NoSQL data warehouses.…”

Section: Related Workmentioning

confidence: 99%

“…Each approach leads to a different logical model. Yangui et al 24 proposed new rules for transforming a multidimensional conceptual model into two NoSQL ones: column‐oriented and document‐oriented models. The proposed method by Boussahoua et al 25 is based on an attribute‐grouping strategy to define the column family schema.…”

Section: Related Workmentioning

confidence: 99%

CONST: Continuous online NoSQL schema tuning

2020

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Self‐tuning is a feature of autonomic databases that includes the problem of automatic schema design. Automating the schema design process is critical to provide an optimized schema that increases the performance of an application's workload in NoSQL databases. There has been important work on automated design tuning for NoSQL databases. Existing solutions take an offline approach to the schema design problem and leave several important decisions to database administrators. Although offline approaches recommend good and efficient schemas, their analysis is far too heavy‐weight to be run continuously. This paper develops a novel self‐tuning control loop, called CONST, for online monitoring, analysis, and schema tuning a managed NoSQL database system that automatically modify the current schema design reacting to changes in the application's workload. CONST provides components that fulfill IBM's MAPE‐K loop phases to support self‐tuning. We describe an implementation of the proposed CONST loop on top of the Cassandra wide column store and evaluate its performance experimentally. Our results show the low overhead of CONST and demonstrate its ability to tune a schema design in response to changes in the application's workload.

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Automatic Transformation of Data Warehouse Schema to NoSQL Data Base: Comparative Study

Cited by 48 publications

References 14 publications

Evaluating partitioning and bucketing strategies for Hive-based Big Data Warehousing systems

Evaluating partitioning and bucketing strategies for Hive-based Big Data Warehousing systems

Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

CONST: Continuous online NoSQL schema tuning

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