Creation and management of versions in multiversion data warehouse

ABSTRACT A data warehouse (DW) provides an information for analytical processing, decision making, and data mining tools. On the one hand, the structure and content of a data warehouse reflects a real world, i.e. data stored in a DW come from real production systems. On the other hand, a DW and its tools may be used for predicting trends and simulating a virtual business scenarios. This activity is often called the what-if analysis. Traditional DW systems have static structure of their schemas and relationships between data, and therefore they are not able to support any dynamics in their structure and content. For these purposes, multiversion data warehouses seem to be very promising. In this paper we present a concept and an ongoing implementation of a multiversion data warehouse that is capable of handling changes in the structure of its schema as well as simulating alternative business scenarios

Incorporating ICD-9 and ICD-10 Data in a Warehouse

The shift from version 9 to version 10 of the ICD-code ("International Statistical Classification of Diseases and Related Health Problems ") causes enormous problems for the exploitation of medical data warehouses, since conventional data warehouses do not support the change of the structure of dimensions, i.e. the content and relationships of master data like the diagnostic codes, or other key values. This shortcoming results in a reduction of possible analysis, and unfortunately is the cause of many wrong statistics and analysis results. In this paper we analyze the problem and show how to superimpose conventional multidimensional data warehouses with temporal master data to allow queries spanning multiple periods to return correct answers.

Representing Knowledge about Changes in Data Warehouse Structures

Abstract. 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. We propose a temporal data warehouse architecture named COMET to represent structural changes and permit correct analysis of data over periods with changing structures. 1

C.: Modelling changes in ontologies

Abstract. Ontologies are shared conceptualizations of certain domains. Especially in legal and regulatory ontologies modifications like the passing of a new law, decisions by high courts, new insights by scholars, etc. have to be considered. Otherwise, we would not be able to identify which knowledge (which ontology) was valid at an arbitrary timepoint in the past. And without this knowledge we would for instance not be able to identify why a user came to a specific decision. In this paper we will show how a simple ontology description formalism, namely a directed graph, has to be extended to represent changing knowledge. Furthermore, we will present the operations that are necessary to manipulate such an ontology. Finally, we will discuss different implementation approaches.

A Tree Comparison Approach to Detect Changes in Data Warehouse Structures

We present a technique for discovering and representing changes between versions of data warehouse structures. We select a tree comparison algorithm, adapt it for the particularities of multidimensional data structures and extend it with a module for detection of node renamings. The result of these algorithms are so called edit scripts consisting of transformation operations which, when executed in sequence, transform the earlier version to the later, and thus show the relationships between the elements of different versions of data warehouse structures. This procedure helps data warehouse administrators to register changes. We describe a prototypical implementation of the concept which imports multidimensional structures from Hyperion Essbase data warehouses, compares these versions and generates a list of differences