Fusion Cubes: Towards Self-Service Business Intelligence

Introduction

Today’s business and social environments are complex, hyper-competitive, and highly dynamic. When decisions have to be made quickly and under uncertainty in such a context, the selection of an action plan must be based on reliable data, accurate predictions, and evaluations of the potential consequences. Business intelligence (BI) tools provide fundamental support in this direction. For instance, in medium and large companies, BI tools lean on an integrated, consistent, and certified repository of information called a data warehouse (DW), which is periodically fed with operational data. Information is stored in the DW in the form of multidimensional cubes that are interactively queried by decision makers according to the OLAP paradigm (Golfarelli & Rizzi, 2009). In this work, we call stationary the data that are owned by the decision maker and can be directly incorporated into the decisional process. Stationary data may take either operational or multidimensional form; in both cases, their quality and reliability is under the decision maker’s control. In a corporate scenario, the data stored in the company DW and information system are stationary.

However, well-informed and effective decisions often require a tight relationship to be established between stationary data and other data that fall outside the decision maker’s control (Pérez et al., 2008; Trujillo & Mate, 2011; Golfarelli, Rizzi, & Cella, 2004; Darmont et al., 2005). These valuable data may be related, for instance, to the market, to competitors, or to potential customers, and are sometimes called situational data (Löser, Hueske, & Markl, 2008):

In some cases, situational data can be retrieved (for free or for a fee) in a semi-structured form by accessing established data providers, such as DBpedia (Auer et al., 2007, cross-domain), ProductDB (productdb.org, commerce domain), Geonames (sws.geonames.org, geography), or DATA.GOV (www.data.gov, public institutions); for instance, in DBpedia the structured content extracted from Wikipedia is mapped onto a cross-domain ontology and can be queried using SPARQL. In other cases, situational data are chaotically scattered across heterogeneous and unstructured sources available on the Web (e.g., opinions expressed by users on social networks, ratings of products on portals, etc.). In general, situational data tend to be highly dynamic in contrast to stationary data, which are used to address a large set of decisional problems and impose a slow and careful management. A quick comparison of the main features of situational and stationary data is reported in Table 1.