Multidimensional Model Of Data Warehouse Research Articles

Comprehensive complexity metric for data warehouse multidimensional model understandability

Data warehouse quality can be determined during the initial phases of data warehouse development by quantifying the structural complexity of multidimensional models using metrics. The structural complexity of a multidimensional model is guided by its elements, types, and relationships among those elements. So far, most of the researchers have dealt with metrics based on various elements (facts, dimensions, dimensional hierarchies, and hierarchy levels) existing in these models. However, not much consideration is given to different types of dimensions based on hierarchy types and different relationships among those elements. Therefore, this work proposes a comprehensive complexity metric for measuring multidimensional model complexity by taking into account various elements, their types and the relationships among the elements at various levels of granularity in these models. The theoretical validation of the proposed metric using the property-based framework given by Briand et al. characterises it as a complexity measure. Furthermore, the empirical study, employing statistical techniques (correlation and multinomial regression), on 26 multidimensional models and 20 subjects proved that the authors’ proposed metric is strongly correlated with multidimensional model understandability. Hence, this metric can be considered as a good predictor for data warehouse multidimensional model understandability.

Quality metrics emphasizing dimension hierarchy sharing in multidimensional models for data warehouse: a theoretical and empirical evaluation

The quality of data warehouse (DW) depends largely on the multidimensional (MD) model quality. Few researchers have proposed metrics to evaluate the MD model quality based on facts, dimensions, hierarchy relationships, foreign keys, multiple hierarchies etc. However, some dimension hierarchy aspects like sharing of hierarchy levels within a dimension, sharing of hierarchy levels among various dimensions, relationship between dimension levels etc. have not been given due consideration. The aforementioned aspects may contribute to structural complexity of MD models, which in turn can affect its quality. Our previous study defined a set of quality metrics emphasizing dimension hierarchy sharing in MD models for DW. In this paper, we attempt to thoroughly validate a subset (six) of these metrics, theoretically as well as empirically. A careful study of the mathematical properties of defined metrics is done using Briand framework, which reveals that out of six metrics, two of them correspond to coupling measure, one metric is a cohesion measure and rest three are size/complexity measures. Further, an empirical validation consisting of Spearman’s Rho correlation analysis and linear regression analysis is conducted on 20 MD schemas and 28 subjects to determine the relationship between the metrics and understandability of MD models. The experimental study shows that four of our metrics are good indicators of understandability of MD models and can help in predicting the structural complexity of MD schemas for DW.

Empirical analysis of metrics for object oriented multidimensional model of data warehouse using unsupervised machine learning techniques

Data Warehouse provides the foundation for businesses to take informed decisions for day to day operations and making future strategy. Since the role is so pivotal to the growth and success of the business, its quality is very critical. Conceptual models of data warehouses give us a great insight into the quality of the developed system during the early stages of the design process. Researchers have proposed a number of metrics to evaluate the quality of these object oriented multidimensional models. Further, for these metrics to be used in practice, empirical evaluation is crucial. There are a number of propositions in literature that work towards empirical validation of metrics. But most of them are either restricted to statistical techniques or supervised machine learning techniques. In order to empirically validate the metrics, we need to get user responses for a number of schemas and take down observations to quantify model quality aspects like understandability, efficiency etc. This can result in personal biases, errors and random outliers which impacts the evaluation model. In this paper, we have made a first attempt to assess the relationship between the object oriented multidimensional data warehouse structural metrics and understandability of its models by using unsupervised machine learning techniques with the aid of a data warehouse quality expert. The results indicate that the proposed metrics have a strong relationship with understandability and inturn quality of the data warehouse conceptual models and the unsupervised techniques are able to identify this relationship with high degree of accuracy.

Validation of Object Oriented Metrics for Evaluating Understandability of Data Warehouse Models

Datawarehouse has a key role in formulating strategic decisions thus it is very essential to maintain its quality. Metrics have been generally used to direct designers to develop quality data models. Numerous researchers have proposed metrics for multidimensional models for datawarehouse. These metrics are required to be empirically validated to prove their practical utility. Empirical validation of the object oriented metrics for multidimensional models for data warehouses at a conceptual level is presented in the paper. Quality attribute understandability is assessed through various combinations of metrics. Univariate and Multiple linear regression analysis have been used in this paper for computing the multidimensional models quality. The results show that these metrics may be considered as key indicators for quality of multidimensional data models.

Theoretical and empirical validation of comprehensive complexity metric for multidimensional models for data warehouse

Structural complexity metrics have been widely used to assess quality of an artefact. Researchers in past have defined complexity metrics to assess the quality of multidimensional models for data warehouse. These metrics have been defined considering various elements like facts, dimensions, dimension hierarchies etc., but have not taken into account the relationships among these elements of the models. In our previous work, a comprehensive complexity metric for multidimensional models for data warehouse has been proposed which not only considered complexity due to the elements but also structural complexity due to relationships among these elements. However, the proposal lacks theoretical and empirical validation of the metric. Hence, practical utility of the metric could not be established. This paper validates the proposed metric theoretically as well as empirically. The theoretical validation using Briand’s framework shows that the proposed metric satisfies most of the properties required for a complexity measure. Empirical validation is carried out to observe the relationship between the complexity metric and understandability-a sub-characteristic of maintainability of multidimensional models. The results show that the metric has significant positive correlation with understandability of multidimensional models. Predictive model based on Ordinal Regression proposed in this work indicates that the proposed complexity metric may act as objective indicator for understandability as accuracy of the model is 86.3 % which is quite high.

Validating dimension hierarchy metrics for the understandability of multidimensional models for data warehouse

Structural properties including hierarchies have been recognised as important factors influencing quality of a software product. Metrics based on structural properties (structural complexity metrics) have been popularly used to assess the quality attributes like understandability, maintainability, fault-proneness etc. of a software artefact. Although few researchers have considered metrics based on dimension hierarchies to assess the quality of multidimensional models for data warehouse, there are certain aspects of dimension hierarchies like those related to multiple hierarchies, shared dimension hierarchies among various dimensions etc. which have not been considered in the earlier works. In the authors' previous work, they identified the metrics based on these aspects which may contribute towards the structural complexity and in turn the quality of multidimensional models for data warehouse. However, the work lacks theoretical and empirical validation of the proposed metrics and any metric proposal is acceptable in practice, if it is theoretically and empirically valid. In this study, the authors provide thorough validation of the metrics considered in their previous work. The metrics have been validated theoretically on the basis of Briand's framework - a property-based framework and empirically on the basis of controlled experiment using statistical techniques like correlation and linear regression. The results of these validations indicate that these metrics are either size or length measure and hence, contribute significantly towards structural complexity of multidimensional models and have considerable impact on understandability of these models.

Predicting quality of data warehouse using fuzzy logic

Due to strategic importance of data warehouse (DW) as decision support systems, it has become crucial to guarantee that these repositories should provide quality information to the decision makers. Quality of data warehouse multidimensional model has significant effect on data warehouse quality and in turn on the information quality. Few authors have suggested metrics to assess the quality of data warehouse multidimensional models. Empirical validation using statistical techniques like correlation analysis, univariate and multivariate regression techniques, etc., indicated that these metrics are significantly related to the quality of multidimensional models for data warehouse. But these techniques are not able to model non-linear relationship between the metrics and quality of multidimensional model. In this paper, model based on fuzzy logic approach is proposed to approximate non-linear relationship between the metrics and the quality of multidimensional models. In order to empirically evaluate the effectiveness of the proposed approach, validation is done on the published data and results indicate that the proposed model is able to predict the output with significant accuracy.

Assessment of quality of data warehouse multidimensional model

Data warehouses are large repositories designed to enable the knowledge workers to take better and faster decisions. Due to its significance in strategic decisions, there is a need to assure data warehouse quality. One of the factors affecting the data warehouse quality is multidimensional model quality. Although there are some useful guidelines for designing good multidimensional data models, but objective indicators, i.e., metrics are needed to help designers to develop quality multidimensional models. Few researchers have proposed quality metrics for multidimensional models for data warehouse. These metrics need to be theoretically as well as empirically validated in order to prove their practical utility. In this paper, empirical validation using controlled experiment is carried out. We not only evaluate the effect of individual metric but also evaluate the effect of various combinations of metrics on data warehouse model quality specifically understandability, in order to best explain the variance of dependent variable due to independent variables. The results show that these metrics may be used as objective indicators for understandability. Finally, accuracy of our model in predicting the multidimensional model quality is also evaluated.