Matteo Golfarelli

THE DIMENSIONAL FACT MODEL: A CONCEPTUAL MODEL FOR DATA WAREHOUSES

Data warehousing systems enable enterprise managers to acquire and integrate information from heterogeneous sources and to query very large databases efficiently. Building a data warehouse requires adopting design and implementation techniques completely different from those underlying operational information systems. Though most scientific literature on the design of data warehouses concerns their logical and physical models, an accurate conceptual design is the necessary foundations for building a DW which is well-documented and fully satisfies requirements. In this paper we formalize a graphical conceptual model for data warehouses, called Dimensional Fact model, and propose a semi-automated methodology to build it from the pre-existing (conceptual or logical) schemes describing the enterprise relational database. The representation of reality built using our conceptual model consists of a set of fact schemes whose basic elements are facts, measures, attributes, dimensions and hierarchies; other featur...

Beyond data warehousing: what's next in business intelligence?

During the last ten years the approach to business management has deeply changed, and companies have understood the importance of enforcing achievement of the goals defined by their strategy through metrics-driven management. The DW process, though supporting bottom-up extraction of information from data, fails in top-down enforcing the company strategy. A new approach to BI, called Business Performance Management (BPM), is emerging from this framework: it includes DW but it also requires a reactive component capable of monitoring the time-critical operational processes to allow tactical and operational decision-makers to tune their actions according to the company strategy. The aim of this paper is to encourage the research community to acknowledge the coming of a second era in BI, to propose a general architecture for BPM, and to lay the premises for investigating the most challenging of the related issues.

Conceptual design of data warehouses from E/R schemes

Data warehousing systems enable enterprise managers to acquire and integrate information from heterogeneous sources and to query very large databases efficiently. Building a data warehouse requires adopting design and implementation techniques completely different from those underlying information systems. We present a graphical conceptual model for data warehouses, called Dimensional Fact model, and propose a semi-automated methodology to build it from the pre-existing entity/relationship schemes describing a database. Our conceptual model consists of tree-structured fact schemes whose basic elements are facts, attributes, dimensions and hierarchies; other features which may be represented on fact schemes are the additivity of fact attributes along dimensions, the optionality of dimension attributes and the existence of non-dimension attributes. Compatible fact schemes may be overlapped in order to relate and compare data. Fact schemes may be integrated with information of the conjectured workload, expressed in terms of query patterns, to be used as the input of a design phase whose output are the logical and physical schemes of the data warehouse.

A methodological framework for data warehouse design

Though designing a data warehouse requires techniques completely different from those adopted for operational systems, no significant effort has been made so far to develop a complete and consistent design methodology for data warehouses. In this paper we outline a general methodological framework for data warehouse design, based on our Dimensional Fact Model (DFM). After analyzing the existing information system and collecting the user requirements, conceptual design is carried out semi-automatically starting from the operational database scheme. A workload is then characterized in terms of data volumes and expected queries, to be used as the input of the logical and physical design phases whose output is the final scheme for the data warehouse.

Data warehouse design from XML sources

A large amount of data needed in decision-making processes is stored in the XML data format, which is widely used for e-commerce and Internet-based information exchange. Thus, as more organizations view the web as an integral part of their communication and business, the importance of integrating XML data in data warehousing environments is becoming increasingly high. In this paper we show how the design of a data mart can be carried out starting directly from an XML source. Two main issues arise: on the one hand, since XML models semi-structured data, not all the information needed for design can be safely derived; on the other, different approaches for representing relationships in XML DTDs and Schemas are possible, each with different expressive power. After discussing these issues, we propose a semi-automatic approach for building the conceptual schema for a data mart starting from the XML sources.

Schema versioning in data warehouses: enabling cross-version querying via schema augmentation

As several mature implementations of data warehousing systems are fully operational, a crucial role in preserving their up-to-dateness is played by the ability to manage the changes that the data warehouse (DW) schema undergoes over time in response to evolving business requirements. In this paper we propose an approach to schema versioning in DWs, where the designer may decide to undertake some actions on old data aimed at increasing the flexibility in formulating cross-version queries, i.e., queries spanning multiple schema versions. First, we introduce a representation of DW schemata as graphs of simple functional dependencies, and discuss its properties. Then, after defining an algebra of schema graph modification operations aimed at creating new schema versions, we discuss how augmented schemata can be introduced to increase flexibility in cross-version querying. Next, we show how a history of versions for DW schemata is managed and discuss the relationship between the temporal horizon spanned by a query and the schema on which it can consistently be formulated.

A Survey on Temporal Data Warehousing

Data warehouses are information repositories specialized in supporting decision making. Since the decisional process typically requires an analysis of historical trends, time and its management acquire a huge importance. In this paper we consider the variety of issues, often grouped under term temporal data warehousing, implied by the need for accurately describing how information changes over time in data warehousing systems. We recognize that, with reference to a three-levels architecture, these issues can be classified into some topics, namely: handling data/schema changes in the data warehouse, handling data/schema changes in the data mart, querying temporal data, and designing temporal data warehouses. After introducing the main concepts and terminology of temporal databases, we separately survey these topics. Finally, we discuss the open research issues also in connection with their implementation on commercial tools.

Schema Versioning in Data Warehouses

As several mature implementations of data warehousing systems are fully operational, a crucial role in preserving their up-to-dateness is played by the ability to manage the changes that the data warehouse (DW) schema undergoes over time in response to evolving business requirements. In this paper we propose an approach to schema versioning in DWs, where the designer may decide to undertake some actions on old data aimed at increasing the flexibility in formulating cross-version queries, i.e., queries spanning multiple schema versions. After introducing an algebra of DW schema operations, we define a history of versions for data warehouse schemata and discuss the relationship between the temporal horizon spanned by a query and the schema on which it can consistently be formulated.

myOLAP: An Approach to Express and Evaluate OLAP Preferences

Multidimensional databases are the core of business intelligence systems. Their users express complex OLAP queries, often returning large volumes of facts, sometimes providing little or no information. Thus, expressing preferences could be highly valuable in this domain. The OLAP domain is representative of an unexplored class of preference queries, characterized by three peculiarities: preferences can be expressed on both numerical and categorical domains; they can also be expressed on the aggregation level of facts; the space on which preferences are expressed includes both elemental and aggregated facts. In this paper, we present myOLAP, an approach for expressing and evaluating OLAP preferences, devised by taking into account the three peculiarities above. We first propose a preference algebra where users are enabled to express their preferences, besides on attributes and measures, also on the aggregation level of facts, for instance, by stating that monthly data are preferred to yearly and daily data. Then, with respect to preference evaluation, we propose an algorithm called WeSt that relies on a novel graph representation where two types of domination between sets of facts may be expressed, which considerably improves efficiency. The approach is extensively tested for efficiency and effectiveness on real data, and compared against two other approaches in the literature.

Correction of dead-reckoning errors in map building for mobile robots

Map building is an important issue for all the applications in mobile robotics in which the environment is unknown and, in general, in order to have a robot exhibit a fully autonomous behavior. A major problem in map building is due to the imprecision of sensor measures. In this paper, we propose a technique, called elastic correction, for correcting the dead-reckoning errors made during the exploration of an environment by a robot capable of identifying landmarks. Knowledge being acquired is modeled by a relational graph whose vertices and arcs represent, respectively, landmarks and routes. Elastic correction is based on an analogy between the graph modeling the environment and a mechanical structure: the map is regarded as a truss where each route is an elastic bar and each landmark a node. Errors are corrected as a result of the deformations induced from the forces arising within the structure as inconsistent measures are taken. The elasticity parameters characterizing the structure are used to model the uncertainty on odometry. The paper presents results from simulations showing the effectiveness of the method for reducing the overall metric error and proving its robustness with reference to topological errors and to unpredictable sensor errors.