Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems

Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in Production Rule Representation (PRR) language. Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a data warehouse project.Aggregation; Conceptual Multidimensional Model; Data Warehouse; On-line Analytical Processing (OLAP); Production Rule; UML

IS THERE STILL A NEED FOR MULTIDIMENSIONAL DATA MODELS?

Organizational and technical changes challenge standards of data warehouse design and initiate a redesign of contemporary Business Intelligence and Analytics environments. As a result, the use of multidimensional models for performance oriented reasons is not necessarily taken for granted. Simple data models or operational structures emerge as a basis for complex analyses. The paper therefore conducts a laboratory experiment to examine from a non-technical perspective the influnce of different data modeling types on the representational information quality of end users. A comparison is made between the multidimensional model and the transactional model respectively the flat file model. The experiment involves 78 participants and aims to compare perceived and observed representational information quality aspects of ad hoc analyses regarding the data modeling type. The results indicate a higher observed quality for multidimensional modeled data, while different types of data models do not influnce the end user perception of the representational information quality

Latent space models for multidimensional network data

Network data are any relational data recorded among a group of individuals, the nodes. When multiple relations are recorded among the same set of nodes, a more complex object arises, which we refer to as “multidimensional network”, or “multiplex”, where different relations corresponding to different networks. In the past, statistical analysis of networks has mainly focused on single-relation network data, referring to a single relation of interest. Only in recent years statistical models specifically tailored for multiplex data begun to be developed. In this context, only a few works have been introduced in the literature with the aim at extending the latent space modeling framework to multiplex data. Such framework postulates that nodes may be characterized by latent positions in a p-dimensional Euclidean space and that the presence/absence of an edge between any two nodes depends on such positions. When considering multidimensional network data, latent space models can help capture the associations between the nodes and summarize the observed structure in the different networks composing a multiplex. This dissertation discusses some latent space models for multidimensional network data, to account for different features that observed multiplex data may present. A first proposal allows to jointly represent the different networks into a single latent space, so that average similarities between the nodes may be captured as proximities in such space. A second work introduces a class of latent space models with node-specific effects, in order to deal with different degrees of heterogeneity within and between networks in multiplex data, corresponding to different types of node-specific behaviours. A third work addresses the issue of clustering of the nodes in the latent space, a frequently observed feature in many real world network and multidimensional network data. Here, clusters of nodes in the latent space correspond to communities of nodes in the multiplex. The proposed models are illustrated both via simulation studies and real world applications, to study their perfomances and abilities

Design of a Multidimensional Model Using Object Oriented Features in UML

A data warehouse is a single repository of data which includes data generated from various operational systems. Conceptual modeling is an important concept in the successful design of a data warehouse. The Unified Modeling Language (UML) has become a standard for object modeling during analysis and design steps of software system development. The paper proposes an object oriented approach to model the process of data warehouse design. The hierarchies of each data element can be explicitly defined, thus highlighting the data granularity. We propose a UML multidimensional model using various data sources based on UML schemas. We present a conceptual-level integration framework on diverse UML data sources on which OLAP operations can be performed. Our integration framework takes into account the benefits of UML (its concepts, relationships and extended features) which is more close to the real world and can model even the complex problems easily and accurately. Two steps are involved in our integration framework. The first one is to convert UML schemas into UML class diagrams. The second is to build a multidimensional model from the UML class diagrams. The white-paper focuses on the transformations used in the second step. We describe how to represent a multidimensional model using a UML star or snowflake diagram with the help of a case study. To the best of our knowledge, we are the first people to represent a UML snowflake diagram that integrates heterogeneous UML data sources

A study of multidimensional modeling approaches for data warehouse

Data warehouse system is used to support the process of organizational decision making. Hence, the system must extract and integrate information from heterogeneous data sources in order to uncover relevant knowledge suitable for decision making process. However, the development of data warehouse is a difficult and complex process especially in its conceptual design (multidimensional modeling). Thus, there have been various approaches proposed to overcome the difficulty. This study surveys and compares the approaches of multidimensional modeling and highlights the issues, trend and solution proposed to date. The contribution is on the state of the art of the multidimensional modeling design

TopoGraph: an end-to-end framework to build and analyze graph cubes

Graphs are a fundamental structure that provides an intuitive abstraction for modeling and analyzing complex and highly interconnected data. Given the potential complexity of such data, some approaches proposed extending decision-support systems with multidimensional analysis capabilities over graphs. In this paper, we introduce TopoGraph, an end-to-end framwork for building and analyzing graph cubes. TopoGraph extends the existing graph cube models by defining new types of dimensions and measures and organizing them within a multidimensional space that guarantees multidimensional integrity constraints. This results in defining three new types of graph cubes: property graph cubes, topological graph cubes, and graph-structured cubes. Afterwards, we define the algebraic OLAP operations for such novel cubes. We implement and experimentally validate TopoGraph with different types of real-world datasets.Peer ReviewedPostprint (author's final draft