Riccardo Torlone

A Logical Approach to Multidimensional Databases

In this paper we present MD, a logical model for OLAP systems, and show how it can be used in the design of multidimensional databases. Unlike other models for multidimensional databases, MD is independent of any specific implementation (relational or proprietary multidimensional) and as such it provides a clear separation between practical and conceptual aspects. In this framework, we present a design methodology, to obtain an MD scheme from an operational database. We then show how an MD database can be implemented, describing translations into relational tables and into multidimensional arrays.

Querying Multidimensional Databases

Multidimensional databases are large collections of data, often historical, used for sophisticated analysis oriented to decision making. This activity is supported by an emerging category of software technology, called On-Line Analytical Processing (OLAP). In spite of a lot of commercial tools already available, a fundamental study for OLAP systems is still lacking. In this paper we introduce a model and a query language to establish a theoretical basis for multi-dimensional data. The model is based on the notions of dimension and f-table. Dimensions are linguistic categories corresponding to different ways of looking at the information. F-tables are the constructs used to represent factual data, and are the logical counterpart of multi-dimensional arrays, the way in which current analytical tools store data. The query language is a calculus for f-tables, and as such it offers a high-level support to multi-dimensional data analysis. Scalar and aggregate functions can be embedded in calculus expressions in a natural way. We discuss on conceptual problems related with the design of multidimensional query languages, and compare our model and language with other approaches.

Management of Multiple Models in an Extensible Database Design Tool

We describe the development of a tool, called MDM, for the management of multiple models and the translation of database schemes. This tool can be at the basis of an integrated CASE environment, supporting the analysis and design of information systems, that allows different representations for the same data schemes. We first present a graph-theoretic framework that allows us to formally investigate desirable properties of schema translations. The formalism is based on a classification of the constructs used in the known data model into a limited set of types. Then, on the basis of formal results, we develop general methodologies for deriving“good” translations between schemes and, more in general, between models. Finally, we define the architecture and the functionalities of a first prototype that implements the various features of the approach.

Model-independent schema translation

We discuss a proposal for the implementation of the model management operator ModelGen, which translates schemas from one model to another, for example from object-oriented to SQL or from SQL to XML schema descriptions. The operator can be used to generate database wrappers (e.g., object-oriented or XML to relational), default user interfaces (e.g., relational to forms), or default database schemas from other representations. The approach translates schemas from a model to another, within a predefined, but large and extensible, set of models: given a source schema S expressed in a source model, and a target model TM, it generates a schema S′ expressed in TM that is “equivalent” to S. A wide family of models is handled by using a metamodel in which models can be succinctly and precisely described. The approach expresses the translation as Datalog rules and exposes the source and target of the translation in a generic relational dictionary. This makes the translation transparent, easy to customize and model-independent. The proposal includes automatic generation of translations as composition of basic steps.

Conceptual multidimensional models

A variety of multidimensional data models have recently been proposed by both academic and industry communities. but consensus on formalism or even a common terminology has not yet emerged. In this chapter, we first discuss the requirements that an ideal conceptual multidimensional model should fulfill. These requirements are suggested by general information system modeling principles and the specific characteristics of OLAP applications. Building on these requirements, we then present a general conceptual multidimensional data model and show how it can be used to describe the basic aspects of a business application in a way that is easy to understand and independent of the criteria for actual data organization in the various systems. Starting from the characteristics of the model proposed, we summarize the general features that a multidimensional conceptual model should support. We then survey various multidimensional models proposed and relate their characteristics to these general features. Finally, we discuss the main points raised in the chapter and some problems that remain to be solved in this context.

From a procedural to a visual query language for OLAP

We address the issue of designing effective query languages for OLAP databases. The basis of our investigation is MD, a new data model for multidimensional databases that, unlike other multidimensional models, is independent of any specific implementation and as such provides a clear separation between practical and conceptual aspects. In this framework, we present and compare two query languages, based on different paradigms, for OLAP databases. The first language is algebraic and provides an effective way to manipulate multidimensional data in a procedural fashion. Although this language is clean and powerful, it is clearly not suited for final users. We therefore propose a high-level graphical language that allows the user to specify analytical queries in a natural and intuitive way. It turns out that the two languages have the same expressive power.

Two approaches to the integration of heterogeneous data warehouses

Abstract In this paper we address the problem of integrating independent and possibly heterogeneous data warehouses, a problem that has received little attention so far, but that arises very often in practice. We start by tackling the basic issue of matching heterogeneous dimensions and provide a number of general properties that a dimension matching should fulfill. We then propose two different approaches to the problem of integration that try to enforce matchings satisfying these properties. The first approach refers to a scenario of loosely coupled integration, in which we just need to identify the common information between data sources and perform join operations over the original sources. The goal of the second approach is the derivation of a materialized view built by merging the sources, and refers to a scenario of tightly coupled integration in which queries are performed against the view. We also illustrate architecture and functionality of a practical system that we have developed to demonstrate the effectiveness of our integration strategies.

The relational model is dead, SQL is dead, and I don't feel so good myself

We report the opinions expressed by well-known database researchers on the future of the relational model and SQL during a panel at the International Workshop on Non-Conventional Data Access (NoCoDa 2012), held in Florence, Italy in October 2012 in conjunction with the 31st International Conference on Conceptual Modeling. The panelists include: Paolo Atzeni (Università Roma Tre, Italy), Umeshwar Dayal (HP Labs, USA), Christian S. Jensen (Aarhus University, Denmark), and Sudha Ram (University of Arizona, USA). Quotations from movies are used as a playful though effective way to convey the dramatic changes that database technology and research are currently undergoing.

A metamodel approach for the management of multiple models and the translation of schemes

Abstract A metamodel approach is proposed as a framework for the definition of different data models and the management of translations of schemes from one model to another. This notion is useful in an environment for the support of the design and development of information systems, since different data models can be used and schemes referring to different models need to be exchanged. The approach is based on the observation that the constructs used in the various models can be classified into a limited set of basic types, such as lexical type, abstract type, aggregation, function. It follows that the translations of schemes can be specified on the basis of translations of the involved types of constructs: this is effectively performed by means of a procedural language and a number of predefined modules that express the standard translations between the basic constructs.

Converting relational to graph databases

Graph Database Management Systems provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In this framework, the conversion of the persistent layer of an application from a relational to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a relational to a graph database by exploiting the schema and the constraints of the source. The approach supports the translation of conjunctive SQL queries over the source into graph traversal operations over the target. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.