Francesco Di Tria

Hybrid methodology for data warehouse conceptual design by UML schemas

Context: Data warehouse conceptual design is based on the metaphor of the cube, which can be derived from either requirement-driven or data-driven methodologies. Each methodology has its own advantages. The first allows designers to obtain a conceptual schema very close to the user needs but it may be not supported by the effective data availability. On the contrary, the second ensures a perfect traceability and consistence with the data sources-in fact, it guarantees the presence of data to be used in analytical processing-but does not preserve from missing business user needs. To face this issue, the necessity emerged in the last years to define hybrid methodologies for conceptual design. Objective: The objective of the paper is to use a hybrid methodology based on different multidimensional models in order to gather all advantages of each of them. Method: The proposed methodology integrates the requirement-driven strategy with the data-driven one, in that order, possibly performing alterations of functional dependencies on UML multidimensional schemas reconciled with data sources. Results: As case study, we illustrate how our methodology can be applied to the university environment. Furthermore, we evaluate quantitatively the benefits of this methodology by comparing it with some popular and conventional methodologies. Conclusion: In conclusion, we highlight how the hybrid methodology improves the conceptual schema quality. Finally, we outline our present work devoted to introduce automatic design techniques in the methodology on the basis of the logical programming.

Logic programming for data warehouse conceptual schema validation

The current lack of a standard methodology for data warehouse design has led to have many possible lifecycles. In some of them, the validation of the data warehouse conceptual schema is a specific process that precedes the translation of such a schema into a logical one. This activity must ensure that the data warehouse to be implemented effectively allows all the analytical queries to be executed correctly. To accomplish this, the validation process takes the preliminary workload into account, that is, a set of queries defined from user requirements to obtain the typical information the users are interested in. The methodologies that perform such a validation process define some guidelines that must be manually executed by an expert. In this paper, we introduce a logic program to automate this activity, by checking a set of predefined issues with an inferential engine.

GrHyMM: a graph-oriented hybrid multidimensional model

The main methodologies for the data warehouse design are based on two approaches which are opposite and alternative each other. The one, based on the data-driven approach, aims to produce a conceptual schema mainly through a reengineering process of the data sources, while minimizing the involvement of end users. The other is based on the requirement-driven approach and aims to produce a conceptual schema only on the basis of requirements expressed by end users. As each of these approaches has valuable advantages, it is emerged the necessity to adopt a hybrid methodology which combines the best features of the two approaches. We introduce a conceptual model that is based on a graph-oriented representation of the data sources. The core of the proposed hybrid methodology is constituted by an automatic process of reengineering of data sources that produces the conceptual schema using a set of requirement-derived constraints.

Dimensional fact model extension via predicate calculus

The Dimensional Fact Model is a conceptual model that allows to design the multidimensional schema of a data warehouse. Its methodology is based on the (re)modelling of the schema of a relational database; such a schema is represented by a tree, and the (re)modelling consists of the traditional operations on graphs (as prune, graft, adding child, change parent, …). The tree is generated automatically by an algorithm, starting from the logical schema of a relational database. Nowadays, this algorithm is not able to manage many-to-many relationships, and then, it stops the tree creation when it meets this kind of relationships. In this work, we propose a novel methodology, able to overcome this algorithmic limit, via the declarative approach of the logical programming.

Design process for Big Data Warehouses

Big Data Warehouses differ substantially from traditional data warehouses in that their schema should be based on novel logical models that allow more flexibility than that the relational model does. Furthermore, their design methodology also requires new principles, such as automation and agile techniques, in order to gain both a fast realization and reaction to changes in business requirements. In the paper, we discuss the new features of big data warehouses and present an innovative design methodology based on the key-value model.

Automated generation of itineraries in recommender systems for tourism

Current recommender systems can support tourists in choosing travel products (accommodation, activities, means of transport, etc.), in planning long trips, and in profitably spending time in a specific geographical area such as a region (or a city). In the last case, the system should be able to construct itineraries suited to the tourists interests. In this paper, a method for generating tourist itineraries in knowledge-based recommender systems is proposed. The method is based on a theoretical model that defines space-time relations among items of intangible cultural heritage (called events) and on transitive closure computation (of the relations), that is able to construct chains of events. The proposed method has been implemented in the T-Path recommender system, that suggests itineraries of cultural events occurring in the Apulia region.

Ontological Approach to Data Warehouse Source Integration

In the early stages of data warehouse design, the integration of several source databases must be addressed. Data-oriented and hybrid methodologies need to consider a global schema coming from the integration of source databases, in order to start the conceptual design. Since each database relies on its own conceptual schema, in the integration process a reconciliation phase is necessary, in order to solve syntactical and/or semantic inconsistencies among concepts. In this paper, we present an ontology-based approach to perform the integration of different conceptual schemas automatically.

Research Data Mart in an Academic System

Data warehousing is an activity that is getting more and more attention in several contexts. Also Universities are adopting data warehousing solutions for business intelligence purpose. In these contexts, there are specific aspects to be considered, such as the Didactics and the Research evaluation. Indeed, these are the main factors affecting the importance and the quality level of every University. In this paper, we present the architecture of a Business Intelligence system in an academic organization and we illustrate the design of a data mart devoted to the evaluation of the Research activities.

Metrics for approximate query engine evaluation

The performance evaluation of the transaction processing in Database Management Systems used for Decision Support Systems is the aim of the current TPC-H standard. Decision Support Systems also include Approximate Query Answering Systems. However, the TPC-H does not define a methodology to evaluate these systems, nor does it provide useful metrics. In this paper, we address the problem related to the extension of TPC-H, in order to adjust it for the performance evaluation of the engines used in approximate query processing to provide fast and approximate responses to analytical queries.

Cost-benefit analysis of data warehouse design methodologies

Abstract Methodologies for data warehouse design are increasing more and more in last years, and each of them proposes a different point of view. Among all the methodologies present in literature, the promising ones are the hybrid methodologies—because they represent the only way to ensure a multidimensional schema to be both consistent with data sources and adherent to user business goals—and those able to support the designer by providing some kind of automation. However, the results obtainable by the methodologies can differ substantially in terms of schema quality and required efforts. In this paper, we provide metrics for evaluating the quality of multidimensional schemata in reference to the effort spent in the design process and the automation degree of the methodology. As a case study, we apply our evaluation to the major emerging hybrid methodologies for data warehouse schema design.