A Hybrid Approach for Data Mart Schema Design from NL-OLAP Requirements

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

doi:10.1007/978-3-642-12550-8_26

Cited by 3 publications

(5 citation statements)

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“…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%

“…For the definition of our grammar, we have been inspired by the linguistic patterns proposed by "BARGUI and al" in [4,6]. These patterns have been defined to handle the specifications of the dimensions expressed in a NL analytical query in order to generate semi-automatically the conceptual schema of a data mart in BI context.…”

Section: Description Of the Proposed Grammarmentioning

confidence: 99%

“…In [4,6], BARGUI shows that the linguistic patterns that specify "dimensions" are often expressed in a nominal form known as a nominal groups (NG), which can be defined as: (determinant + noun), (preposition + determinant+ noun), (preposition + determinant + noun + qualifying adjective), etc. such as: customer, product, country, postal code of a customer, etc.…”

Section: Description Of the Proposed Grammarmentioning

confidence: 99%

“…2. The measures are, in general, numerical attributes used in the calculation of the key performance indicators [4]. They are, in general, expressed in nominal forms with terms that indicate quantifications such as: quantity of milk, accident's number, internet sales amount, etc.…”

Section: Description Of the Proposed Grammarmentioning

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: Description Of the Proposed Grammarmentioning

confidence: 99%

Section: Description Of the Proposed Grammarmentioning

confidence: 99%

Section: Description Of the Proposed Grammarmentioning

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 design of valid multidimensional star schemas assisted by repair solutions

Salem

Ben-Abdallah

2015

Vietnam J Comput Sci

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A Prototyped NL-Based Approach for the Design of Multidimensional Data Warehouse

Alzahrani

Alqarni

Feki

2020

IJCIT

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Organizations are more and more interested in the Data Warehouse (DW) technology and data analytics to base their decision-making processes on scientific arguments instead of intuition. Despite the efforts invested, the DW design issue remains a great challenging research domain. The design quality of the DW depends on several aspects, as the requirement gathering. In this context, we propose a Natural Language (NL) based design approach, which is twofold, first, it facilitates the involvement of the decision-makers in the DW design process; indeed, NL can encourage the decision-makers to express their requirements as English-like sentences conform to NL-templates. Secondly, our approach aims to generate semi-automatically a DW schema from a set of requirements gathered as analytical queries compliant to the NL-templates. This design approach relies on (i) two easy-to-use NL-templates to specifying the analysis components, and (ii) a set of five heuristic rules for extracting the multidimensional concepts from the requirements. We demonstrate the feasibility of our approach by developing the prototype Natural Language Decisional Requirements to DW Schema (NLDR2DWS).

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A Hybrid Approach for Data Mart Schema Design from NL-OLAP Requirements

Cited by 3 publications

References 4 publications

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

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

The design of valid multidimensional star schemas assisted by repair solutions

A Prototyped NL-Based Approach for the Design of Multidimensional Data Warehouse

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