International Journal of Data Warehousing and Mining, X(X), X-X, XXX-XXX 2012 1 Cube Algebra: A Generic User-Centric Model and Query Language for OLAP Cubes Cristina Ciferri, Ricardo Ciferri Universidade de São Paulo em São Carlos, Brazil Leticia Gómez Instituto Tecnológico de Buenos Aires, Argentina Markus Schneider University of Florida, USA Alejandro Vaisman, Esteban Zimányi Université Libre de Bruxelles, Belgium ABSTRACT The lack of an appropriate conceptual model for data warehouses and OLAP systems has led to the tendency to deploy logical models (for example, star, snowflake, and constellation schemas) for them as conceptual models. ER model extensions, UML extensions, special graphical user interfaces, and dashboards have been proposed as conceptual approaches. However, they introduce their own problems, are somehow complex and difficult to understand, and are not always user-friendly. They also require a high learning curve, and most of them address only structural design, not considering associated operations. Therefore, they are not really an improvement and, in the end, only represent a reflection of the logical model. The essential drawback of offering this system-centric view as a user concept is that knowledge workers are confronted with the full and overwhelming complexity of these systems as well as complicated and user-unfriendly query languages such as SQL OLAP and MDX. In this article, we propose a user-centric conceptual model for data warehouses and OLAP systems, called the Cube Algebra. It takes the cube metaphor literally and provides the knowledge worker with high- level cube objects and related concepts. A novel query language leverages well known high- level operations such as roll-up, drill-down, slice, and drill-across. As a result, the logical and physical levels are hidden from the unskilled end user. Keywords : data warehouses, OLAP, cube, conceptual model, user-centric model, query language over the information stored in the data INTRODUCTION warehouse is commonly called Online Analytical Processing (OLAP). A review of Nowadays, data warehouses are at the the evolution of data warehouse technology forefront of information technology reveals that research and development has applications as a way for organizations to mainly focused on system aspects such as effectively use and analyze information for the construction of data warehouses, business planning and decision making. materialization, indexing, and the Data warehouses are large repositories of implementation of OLAP functionality. analytical and subject-oriented data This system-centric view has led to well- integrated from several heterogeneous established and commercialized sources over a large period of time. The technologies such as relational OLAP technique of performing complex analysis (ROLAP), multidimensional OLAP Copyright © 2012, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. International Journal of Data Warehousing and Mining, X(X), X-X, XXX-XXX 2012 2 (MOLAP), and hybrid OLAP (HOLAP) at MOLAP, HOLAP) at the logical level. the logical and the physical levels. Third, it should be able to cooperate with However, the unskilled user such as the any of these logical models and manager in a consulting company or the technologies. Fourth, it should enable the analyst in a financial institution is user to generically and abstractly represent confronted with the problem that the and query hierarchical multidimensional handling of data warehouses and OLAP data. Fifth, it should have an associated systems requires expert knowledge due to query language based exclusively on the complicated data warehouse structures and conceptual level, thus providing high-level the complexity of OLAP systems and query query operations for the user. The goal of languages. Two main reasons are this article is to propose and formally responsible for this problem. First, due to describe a conceptual and user-centric data the lack of a generic, user-friendly, and warehouse model and query language that comprehensible conceptual data model, data satisfies these design criteria. Surprisingly, warehouse design is usually performed at the conceptual view this model adopts is not the logical level and leads to the exposure new; on the contrary, it is well known. of the logical design schemas that are However, the way and resoluteness in difficult to understand by the unskilled user. which we offer this concept is novel. Our In a ROLAP environment, for example, the proposed conceptual model leverages the user is faced with the logical design of cube view of data warehouses but takes the relational tables in terms of star, snowflake, cube metaphor literally . This means that the or fact constellation schemas. The proposal user’s conceptual world is solely the cube to alleviate the problem by providing that the user can create, manipulate, update, extensions to the Entity-Relationship Model and query. The cube is used as the user and the Unified Modeling Language, or by concept that completely abstracts from any offering specific graphical user interfaces or logical and physical implementation details. dashboards for data warehouse design is not Technically, this implies that cubes can be really convincing since ultimately they regarded as an abstract data type that represent a reflection and visualization of provides cubes as the only kind of values relational technology concepts and, in (objects), offers high-level operations on addition, reveal their own problems. cubes or between cubes such as slice , dice , Second, available OLAP query and analysis drill-down , roll-up , and drill-across as the languages such as MDX and SQL OLAP only available access methods, and hides operate at the logical level and require the any data representation and algorithmic user’s deep understanding of the data details from the user, who can concentrate warehouse structure in order to be able to on her main interest, namely to analyze formulate queries. These languages are large volumes of data. Another quite complex, overwhelm the unskilled characterization is to say that we define a user, and are therefore inappropriate as end- universal algebra with cubes as the only user languages. sort and a collection of unary and binary We conclude that a generic , conceptual , operations on cubes. We therefore name our and user-centric data warehouse model that approach Cube Algebra . We will show that focuses on user requirements is missing and this algebra develops its full power and needed. Such a model should fulfill several expressiveness if it is used as a high-level design criteria. First, it should be located query language. above the logical level. Second, it should The paper is organized as follows. Next abstract from and be independent of the section discusses related work and models and technologies (ROLAP, compares available data warehouse models Copyright © 2012, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. International Journal of Data Warehousing and Mining, X(X), X-X, XXX-XXX 2012 3 with our Cube Algebra. Then, we describe reference algebra in (Romero and Abelló an application scenario that we use (2007)), these proposals do not abstract throughout the paper to illustrate important from the logical level and thus, do not aspects of the Cube Algebra. In the same provide high-level query operations for the section, we provide a three-level user. Taking the aforementioned discussion architecture of a data warehouse and OLAP into account, we next comment on related system that includes our Cube Algebra. We work, and present an analysis against our further specify the formal data model proposal. supporting the Cube Algebra. The section We first classify existing models into concludes with a sketch of a data definition three classes: (a) conceptual models based language to specify the structure of a cube. on extensions to the Entity-Relationship Then, we define high-level OLAP cube (ER) Model (Chen, 1976); (b) conceptual operations such as slice and drill-across , models based on extensions to the Unified and illustrate their use in a number of Modeling Language (UML); (c) models queries that refer to our application based on a view of data as a cube. scenario. Finally, the last section draws We start with a discussion on models in some conclusions and sketches future work. class (a) ( ER-based models). Rizzi (2007) proposed the Dimensional RELATED WORK Fact Model (DFM), which uses the typical DW concepts of facts, dimensions, Several data warehouse (DW) models measures, hierarchies, descriptive and have been proposed in the literature (see for cross-dimension attributes; the model also example the survey in (Marcel, 1999)). supports shared, incomplete, recursive, and Most of these models address the logical dynamic hierarchies, and notions such as level (e.g., Li & Wang, 1996; Cabibbo & additivity. To represent these concepts, Torlone, 1997; Cabibbo & Torlone, 1998, DFM relies on a graphical notation that Lehner, 1998). Therefore, they are facilitates the understanding of the dependent of specific technologies, for conceptual schema, and that is an example ROLAP, MOLAP, and HOLAP, abstraction of the star schema, in which which lead to complex and non-user there is a central fact entity and a graph per friendly query languages. In this section, we dimension to represent the attribute limit ourselves to conceptual