A natural language-based approach for a semi-automatic data mart design and ETL generation

Bargui, Fahmi; Ben-Abdallah, Hanêne; Feki, Jamel

doi:10.1080/12460125.2016.1158066

Cited by 5 publications

(10 citation statements)

References 30 publications

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“…(4) Consequently, we can't fully adopt the recommendation models adopted in the recommendation approaches proposed in the CQA community. (5) In this context, we propose in this paper an approach for the recommendation of decisional NL Why-Questions, based on the parameters content (DW) and requirement (the Why-Question emitted by the decision maker). To perform the recommendations, we rely on a grammar that captures the DW's multidimensional aspect and formlises the Why-Question model proposed in [16].…”

Section: By Analysing the Table 1 We Elucidate What Followsmentioning

confidence: 99%

“…In this section, we present our proposed grammar that aims at formalizing the content of a Why-Question [16] which principally refers to the DW's multidimensional concepts. To build this grammar, we adopt the linguistic patterns proposed by "BARGUI and al" in [4,5]. More details regarding the proposed grammar are presented in the section 3.2 but we start first by reminding about the Why-Question's model proposed in [16] in the following section.…”

Section: By Analysing the Table 1 We Elucidate What Followsmentioning

confidence: 99%

“…Indeed, our grammar captures all the restrictions defined in the Why-Question's model [16]. To build this grammar, we adopt first the linguistic patterns proposed by "BARGUI and al" in [4,5] and then we extend them with respect to our Why-Question's model. This grammar can be reusable and extensible according to what is defined in the Why-Question's model.…”

Section: Introductionmentioning

confidence: 99%

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Natural language why-question in Business Intelligence applications: model and recommendation approach

et al. 2022

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Business Intelligence is the key technology for users to effectively extract valuable information from oceans of data for decision-making. Data warehouses and on-line analytical processing systems have therefore been developed to contribute effectively to the decision-making process. To extract information that is useful to decision-making, decision-makers express their needs in natural language. Such requirements may be formulated in natural language interfaces in free syntax, avoiding unfamiliar language (SQL, MDX). Natural Language queries can stand for WH-questions ("What, Who, Where, Why, etc.") or a set of Keywords. In this paper, we emphasize on the "Why-Question". This type of question provides answers that help in the diagnosis analysis of the data Warehouse. To deal with a Why-Question, we propose a model that mainly captures the components that reflect the multidimensional aspect of the Data Warehouse. When a decision-maker formulates his Why-Question in natural language, he uses his own terms. This decisional need can be not precise because the decision-maker is not always aware of the Data Warehouse's lexicon as well as the Why-Question's model. Consequently, the decision-maker must reformulate his initial question. Otherwise, the Why-Question's answering process will not be triggered. This situation is not obvious for a decision-maker, especially when the reformulation of the question becomes iterative. To handle these issues, we lean towards a Why-Question's recommendation approach based on both the Data Warehouse's content and the decision maker's requirement. This proposal aims to recommend to the decision-maker a set of natural language Why-Questions instead of rephrasing his initial question. To guide the recommendation process, we rely on a grammar that formalizes the Why-Question's model. To validate our approach, a tool called "WQ-Recommender" is developed. An experimental study is presented to evaluate the relevance of the proposal.

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Section: By Analysing the Table 1 We Elucidate What Followsmentioning

confidence: 99%

Section: By Analysing the Table 1 We Elucidate What Followsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Natural language why-question in Business Intelligence applications: model and recommendation approach

et al. 2022

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“…The DW construction process is complex, tedious and time-consuming [1]; these difficulties do not came from nonsense. Indeed, each step in this construction process consists of sub-steps, which can be carried out using various methods and/or techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, the DW offers efficient capabilities for supporting decision-makers. Despite the valuable efforts and attempts from researchers devoted to developing DW approaches, several DW projects failed [1] [2]. In fact, researchers agree that any successful attempt to develop a DW should consider two features namely i) the construction of the DW multidimensional schema by using a hybrid approach relying on user requirements and data sources; and ii) the use of semantic resource to overcome the heterogeneity issues complicating the DW construction process [3] [6][9] [12].…”

Section: Introductionmentioning

confidence: 99%

A Semantic Resource Based Approach for Star Schemas Matching

Elamin¹,

Alzaidi²,

Feki³

2018

IJDMS

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The hybrid approach is widely used in constructing data warehouse (DW) schemas. It relies on a complex process for matching two sets of multidimensional star schemas: schemas built from business requirements (BR-Star schemas) and schemas constructed on the organization data source (DS-Star schemas). Using a semantic resource during this matching helps solving heterogeneity problems. This paper suggests a semiautomatic approach for the construction of approved star schemas by matching DS-Star schemas with BR-Stars, and by using WordNet as a semantic resource for solving heterogeneity issues. This approach consists of three steps: i) Match DS-Stars with BR-Stars; ii) Involve the DW designer for approval of the matching process; and then iii) Generate approved star schemas. We have defined two Boolean functions and four semantic metrics for the matching process. We have developed a software prototype for testing our approach.

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