Maryvonne Miquel

A multidimensional and multiversion structure for OLAP applications

When changes occur on data organization, conventional multidimensional structures are not adapted because dimensions are supposed to be static. In many cases, especially when time covered by the data warehouse is large, dimensions of the hypercube must be redesigned in order to integrate evolutions. We propose an approach allowing to track history but also to compare data, mapped into static structures. We define a conceptual model building a Mutiversion Fact Table from the Temporal Multidimensional Schema and we introduce the notion of temporal modes of representation corresponding to different ways to analyze data and their evolution.

Handling evolutions in multidimensional structures

Building multidimensional systems requires gathering data from heterogeneous sources throughout time. Then, data is integrated in multidimensional structures organized around several axes of analysis, or dimensions. But these analysis structures are likely to vary over time and the existing multidimensional models do not (or only partially) take these evolutions into account. Hence, a dilemma appears for the designer of data warehouses: either keeping trace of evolutions therefore limiting the capability of comparison for analysts, or mapping all data in a given version of the structure that entails alteration (or even loss) of data. We propose a novel approach that offers another alternative, allowing to track history but also to compare data, mapped into static structures. We define a conceptual model and provide possible logical adaptations to implement it on current commercial OLAP systems. At last, we present the global architecture that we used for our prototype.

Towards a spatial multidimensional model

Data warehouses and OLAP systems help to interactively analyze huge volume of data. This data, extracted from transactional databases, frequently contains spatial information which is useful for decision-making process. Integration of spatial data in multidimensional models leads to the concept of SOLAP (Spatial OLAP). Using a spatial measure as a geographical object, i.e. with geometric and descriptive attributes, raises problems regarding the aggregation operation in its semantic and implementation aspects. This paper shows the requirements for a multidimensional spatial data model and presents a multidimensional data model which is able to support complex objects as measures, inter-dependent attributes for measures and aggregation functions, use of ad-hoc aggregation functions and n to n relations between fact and dimension, in order to handle geographical data, according to its particular nature in an OLAP context.

When Spatial Analysis Meets OLAP: Multidimensional Model and Operators

Introducing spatial data into multidimensional models leads to the concept of Spatial OLAP SOLAP. Existing SOLAP models do not completely integrate the semantic component of geographic information alphanumeric attributes and relationships or the flexibility of spatial analysis into multidimensional analysis. In this paper, the authors propose the GeoCube model and its associated operators to overcome these limitations. GeoCube enriches the SOLAP concepts of spatial measure and spatial dimension and take into account the semantic component of geographic information. The authors define geographic measures and dimensions as geographic and/or complex objects belonging to hierarchy schemas. GeoCubes algebra extends SOLAP operators with five new operators, i.e., Classify, Specialize, Permute, OLAP-Buffer and OLAP-Overlay. In addition to classical drill-and-slice OLAP operators, GeoCube provides two operators for navigating the hierarchy of the measures, and two spatial analysis operators that dynamically modify the structure of the geographic hypercube. Finally, to exploit the symmetrical representation of dimensions and measures, GeoCube provides an operator capable of permuting dimension and measure. In this paper, GeoCube is presented using environmental data on the pollution of the Venetian Lagoon.

Multidimensional structures dedicated to continuous spatiotemporal phenomena

Multidimensional structures or hypercubes are commonly used in OLAP to store and organize data to optimize query response time. The multidimensional approach is based on the concept of facts analyzed with respect to various dimensions. Dimensions are seen as axes of analysis forming a vector space in which each cell is located by a set of coordinates. In conventional multidimensional structures, dimensions have discrete values and are organized in different levels of hierarchies. However, when analysing natural phenomena like meteorology or pollution the discrete structures are not adequate. We will introduce mechanisms, based on interpolation, to spatial and temporal dimensions which will give the user the impression of navigating in a continuous hypercube. In this paper we go over the research issues associated with continuous multidimensional structures, we give some of their potentials and we propose a multidimensional model and some operations used for an OLAP of field-based data.

GeoCube, a multidimensional model and navigation operators handling complex measures: application in spatial OLAP

Data warehouses and OLAP systems help to interactively analyze huge volume of data. Frequently this data contains spatial information which is useful for decision-making process. Spatial OLAP (SOLAP) refers to the integration of spatial data in multidimensional applications at physical, logical and conceptual level. Using spatial measure as a geographical object, i.e. taking in account its geometric and descriptive attributes, raises problems regarding the aggregation operation and the cube navigation in their semantic and implementation aspects. This paper defines an extended multidimensional data model which is able to support complex objects as measures, in order to handle geographical data according with its particular nature in an OLAP context. The model allows the multidimensional navigation process. OLAP operators are described which include this new concept of measure. A prototype of a SOLAP tool that handles geographical object as measures is presented.

GeWOlap: a web based spatial OLAP proposal

Data warehouses and OLAP systems help to interactively analyze huge volumes of data Spatial OLAP refers to the integration of spatial data in multidimensional applications at the physical, logical and conceptual level In order to include spatial information as a result of the decision-making process, we propose to define spatial measures as geographical objects in the multidimensional data model This raises problems regarding aggregation operations and cube navigation in both semantic and implementation aspects This paper presents a GeWOlap, a web based, integrated and extensible GIS-OLAP prototype, able to support geographical measures Our approach is illustrated by its application in a project for the CORILA consortium (Consortium for Coordination of Research Activities concerning the Venice Lagoon System).

Multimedia data warehouses: a multiversion model and a medical application

In field such as Cardiology, data used for clinical studies is not only alphanumeric, but can also be composed of images or signals. Multimedia data warehouse then must be studied in order to provide an efficient environment for the analysis of this data. The analysis environment must include appropriate processing methods in order to compute or extract the knowledge embedded into raw data. Traditional multidimensional models have a static structure which members of dimensions are computed in a unique way. However, multimedia data is often characterized by descriptors that can be obtained by various computation modes. We define these computation modes as “functional versions” of the descriptors. We propose a Functional Multiversion Multidimensional Model by integrating the concept of “version of dimension.” This concept defines dimensions with members computed according to various functional versions. This new approach integrates different computation modes of these members into the proposed model, in order to allow the user to select the best representation of data. In this paper, a conceptual model is formally defined and a prototype for this study is presented. A multimedia data warehouse in the medical field has been implemented on a therapeutic study on acute myocardial infarction

A Grid Services-Oriented Architecture for Efficient Operation of Distributed Data Warehouses on Globus

Data warehouses store large volumes of data according to a multidimensional model that provides a fast access for online analysis. The constant growth in quantity and complexity of data stored in data warehouses has led to a variety of data warehouse applications on distributed systems. The main benefits of these architectures are parallelized query execution and higher storage capacities. Computing grids in particular are built to combine a large number of heterogeneous distributed resources. Their lack of centralized control however conflicts with the centralized structure of classical data warehouses. Autonomous data management on grid nodes requires efficient communication during query evaluation. The architecture we present supports a global data localization method with the help of a specialized catalog service. Our work is based on a model for unique identification and efficient local indexing of the warehouse data. Local indexes integrate computable aggregates for maximum utilization of locally materialized data in order to facilitate cost-optimized query execution. The grid services implementing these functionalities are deployed on the GGM projects test environment.

Handling multiple points of view in a multimedia data warehouse

Data warehouses are dedicated to collecting heterogeneous and distributed data in order to perform decision analysis. Based on multidimensional model, OLAP commercial environments such as they are currently designed in traditional applications are used to provide means for the analysis of facts that are depicted by numeric data (e.g., sales depicted by amount or quantity sold). However, in numerous fields, like in medical or bioinformatics, multimedia data are used as valuable information in the decisional process. One of the problems when integrating multimedia data as facts in a multidimensional model is to deal with dimensions built on descriptors that can be obtained by various computation modes on raw multimedia data. Taking into account these computation modes makes possible the characterization of the data by various points of view depending on the users profile, his best-practices, his level of expertise, and so on. We propose a new multidimensional model that integrates functional dimension versions allowing the descriptors of the multidimensional data to be computed by different functions. With this approach, the user is able to obtain and choose multiple points of view on the data he analyses. This model is used to develop an OLAP application for navigation into a hypercube integrating various functional dimension versions for the calculus of descriptors in a medical use case.