Document-oriented data warehouses : complex hierarchies and summarizability

There is an increasing interest in implementing data warehouses with NoSQL document-oriented systems. In the ideal case, data can be analysed on different dimensions. These dimensions follow strict hierarchies that we can use to roll-up and drill-down on analysis axes. In this paper, we deal with non-strict and non-covering hierarchies, common issues in data warehousing a.k.a. summarizability issues. We show how to model these hierarchies in document-oriented systems and we propose an algorithm that can deal with summarizability issues. The new approach is tested and compared to existing approaches

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

Les entrepôts de données reposent sur la modélisation multidimensionnelle. A l'aide d'outils OLAP, les décideurs analysent les données à différents niveaux d'agrégation. Il est donc nécessaire de représenter les connaissances d'agrégation dans les modèles conceptuels multidimensionnels, puis de les traduire dans les modèles logiques et physiques. Cependant, les modèles conceptuels multidimensionnels actuels représentent imparfaitement les connaissances d'agrégation, qui (1) ont une structure et une dynamique complexes et (2) sont fortement contextuelles. Afin de prendre en compte les caractéristiques de ces connaissances, nous proposons de les représenter avec des objets (diagrammes de classes UML) et des règles en langage PRR (Production Rule Representation). Les connaissances d'agrégation statiques sont représentées dans les digrammes de classes, tandis que les règles représentent la dynamique (c'est-à-dire comment l'agrégation peut être effectuée en fonction du contexte). Nous présentons les diagrammes de classes, ainsi qu'une typologie et des exemples de règles associées.Agrégation ; Entrepôt de données ; Modèle conceptuel multidimensionnel ; OLAP ; Règle de production ; UML

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

A conceptual spatio-temporal multidimensional model

Today, thanks to global positioning systems technologies and mobile devices equipped with tracking sensors, and a lot of data about moving objects can be collected, e.g., spatio-temporal data related to the movement followed by objects. On the other hand, data warehouses, usually modeled using a multidimensional view of data, are specialized databases to support the decision-making process. Unfortunately, conventional data warehouses are mainly oriented to manage alphanumeric data. In this article, we incorporate temporal elements to a conceptual spatial multidimensional model resulting in a spatio-temporal multidimensional model. We illustrate our proposal with a case study related to animal migration

Context-aware OLAP for textual data warehouses

Decision Support Systems (DSS) that leverage business intelligence are based on numerical data and On-line Analytical Processing (OLAP) is often used to implement it. However, business decisions are increasingly dependent on textual data as well. Existing research work on textual data warehouses has the limitation of capturing contextual relationships when comparing only strongly related documents. This paper proposes an Information System (IS) based context-aware model that uses word embedding in conjunction with agglomerative hierarchical clustering algorithms to dynamically categorize documents in order to form the concept hierarchy. The results of the experimental evaluation provide evidence of the effectiveness of integrating textual data into a data warehouse and improving decision making through various OLAP operations