What-if OLAP Queries with Changing Dimensions (original) (raw)

Analysing Slices of Data Warehouses to Detect Structural Modifications

2004

Data Warehouses provide sophisticated tools for analyzing complex data online, in particular by aggregating data along dimensions spanned by master data. Changes to these master data is a frequent threat to the correctness of OLAP results, in particular for multi- period data analysis, trend calculations, etc. As dimension data might change in underlying data sources without notifying the data warehouse we are exploring the application of data mining techniques for detecting such changes and contribute to avoiding incorrect results of OLAP queries.

Changes of Dimension Data in Temporal Data Warehouses

2001

Time is one of the dimensions we frequently find in data warehouses allowing comparisons of data in different periods. In current multi-dimensional data warehouse technology changes of dimension data cannot be represented adequately since all dimensions are (implicitly) considered as orthogonal. We propose an extension of the multi-dimensional data model employed in data warehouses allowing to cope correctly with changes in dimension data: a temporal multi-dimensional data model allows the registration of temporal versions of dimension data. Mappings are provided to transfer data between different temporal versions of the instances of dimensions and enable the system to correctly answer queries spanning multiple periods and thus different versions of dimension data.

Automatic Detection of Structural Changes in Data Warehouses

2003

Data Warehouses provide sophisticated tools for analyzing complex data online, in particular by aggregating data along dimensions spanned by master data. Changes to these master data is a frequent threat to the correctness of OLAP results, in particular for multi- period data analysis, trend calculations, etc. As dimension data might change in underlying data sources without notifying the data warehouse, we are exploring the application of data mining techniques for detecting such changes and contribute to avoiding incorrect results of OLAP queries.

An OLAP-Based Approach to Modeling and Querying Granular Temporal Trends

Lecture Notes in Computer Science, 2014

Data warehouses contain valuable information for decisionmaking purposes, they can be queried and visualised with Online Analytical Processing (OLAP) tools. They contain time-related information and thus representing and reasoning on temporal data is important both to guarantee the efficacy and the quality of decision-making processes, and to detect any emergency situation as soon as possible. Several proposals deal with temporal data models and query languages for data warehouses, allowing one to use different time granularities both when storing and when querying data. In this paper we focus on two aspects pertaining to temporal data in data warehouses, namely, temporal patterns and temporal granularities. We first motivate the need for discovering granular trends in an OLAP context. Then, we propose a model for analyzing granular temporal trends in time series by taking advantage of the hierarchical structure of the time dimension.

Representing Knowledge about Changes in Data Warehouse Structures

2002

Already Ovid claimed that everything is in flux and we see continuing metamorphoses. Knowledge about changes is essential for Knowledge management in particular for the correct interpretation of data stemming from different periods. Data Warehouses are increasingly deployed in public administrations to provide analytical data for decision making, for monitoring or for revisions. Changes in transaction data are recorded in data warehouses and sophisticated tools allow to analyze these data along time and other dimensions. But changes in master data and in structures cannot be represented in current data warehouse systems impeding their use in dynamic areas and leading to erroneous query results. For an example: if the definition of "unemployment rate" changes, then the figures cannot be compared to those of previous years. Trend calculations on basis of the available data is irrelevant or severely misleading.

DIMENSION HIERARCHIES UPDATES IN DATA WAREHOUSES - A User-driven Approach

Proceedings of the Ninth International Conference on Enterprise Information Systems, 2007

We designed a data warehouse for the French bank LCL meeting users' needs regarding marketing operations decision. However, the nature of the work of users implies that their requirements are often changing. In this paper, we propose an original and global approach to achieve a user-driven model evolution that provides answers to personalized analysis needs. We developed a prototype called WEDrik (data Warehouse Evolution Driven by Knowledge) within the Oracle 10g DBMS and applied our approach on banking data of LCL.

Supporting dimension updates in an OLAP server

Information Systems, 2004

Commercial OLAP systems usually treat OLAP dimensions as static entities. In practice, dimension updates are often needed to adapt the warehouse to changing requirements. In earlier work, we defined a taxonomy for these dimension updates and a minimal set of operators to perform them. In this paper we present TSOLAP, an OLAP server supporting fully dynamic dimensions. TSOLAP conforms to the OLE DB for OLAP norm, so it can be used by any client application based on this norm, and can use as backend any conformant relational server. We incorporate dimension update support to MDX, Microsoft's language for OLAP, and introduce TSShow, a visualization tool for dimensions and data cubes. Finally, we present the results of a real-life case study in the application of TSOLAP to a medium-sized medical center.

What-If Analysis for Data Warehouse Evolution

2007

In this paper, we deal with the problem of performing what-if analysis for changes that occur in the schema/structure of the data warehouse sources. We abstract software modules, queries, reports and views as (sequences of) queries in SQL enriched with functions. Queries and relations are uniformly modeled as a graph that is annotated with policies for the management of evolution events. Given a change at an element of the graph, our method detects the parts of the graph that are affected by this change and indicates the way they are tuned to respond to it.

A multidimensional and multiversion structure for OLAP applications

Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP - DOLAP '02, 2002

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 the Temporal Multidimensional Schema and we introduce the notion of temporal modes of representation corresponding to different ways to analyze data and their evolution.

Season queries on a temporal multidimensional model for OLAP

2010

Dimensions are usually considered static in a data warehouse. However, because of changing requirements, dimension data and dimension structure can evolve. In this paper we focus on a type of dimension data change called reclassification, i.e., when a member of a level changes its parent in a higher level of a dimension. This kind of change gives rise to the notion of season, i.e., an interval during which two members of a dimension are associated with each other. In this paper we extend a formal temporal multidimensional model with the notion of season and propose query language constructs to enable season queries. A case study about soccer illustrates the application of the proposed extensions, exemplified with several season queries.