Christian Koncilia

Creation and management of versions in multiversion data warehouse

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.

Changes of Dimension Data in Temporal Data Warehouses

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.

Data Warehouses And Olap: Concepts, Architectures And Solutions

Covering a wide range of technical, technological, and research issues, this text provides theoretical frameworks, presents challenges and their possible solutions, and examines the latest empirical research findings in the area of data warehousing.

Automatic Detection of Structural Changes in Data Warehouses

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.

Modelling changes in ontologies

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.

On-Demand ELT Architecture for Right-Time BI: Extending the Vision

In a typical BI infrastructure, data, extracted from operational data sources, is transformed, cleansed, and loaded into a data warehouse by a periodic ETL process, typically executed on a nightly basis, i.e., a full days worth of data is processed and loaded during off-hours. However, it is desirable to have fresher data for business insights at near real-time. To this end, the authors propose to leverage a data warehouses capability to directly import raw, unprocessed records and defer the transformation and data cleaning until needed by pending reports. At that time, the databases own processing mechanisms can be deployed to process the data on-demand. Event-processing capabilities are seamlessly woven into our proposed architecture. Besides outlining an overall architecture, the authors also developed a roadmap for implementing a complete prototype using conventional database technology in the form of hierarchical materialized views.

Interval OLAP: Analyzing Interval Data

The ability to analyze data organized as sequences of events or intervals became important by nowadays applications since such data became ubiquitous. In this paper we propose a formal model and briefly discuss a prototypical implementation for processing interval data in an OLAP style. The fundamental constructs of the formal model include: events, intervals, sequences of intervals, dimensions, dimension hierarchies, a dimension members, and an iCube. The model supports: (1) defining multiple sets of intervals over sequential data, (2) defining measures computed from both, events and intervals, and (3) analyzing the measures in the context set up by dimensions.

Analysing Slices of Data Warehouses to Detect Structural Modifications

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.

Maintaining Temporal Warehouse Models

DWT is a tool for the maintenance of data warehouse structures based on the temporal data warehouse model COMET. Data warehouse systems do not provide support for maintaining changes in dimension data. DWT allows keeping track of modifications made in the dimension-structure of multidimensional cubes stored in an OLAP (On-Line Analytical Processing) system. We present the overall structure of the DWT system, which allows to upload and download warehouse models in different modeling notations in a time conscious manner, load edit scripts describing changes between versions of warehouse models and apply these edit scripts. We present the workflows for maintenance of warehouse models and discuss how maintenance can be supported with the various integrated tools of DWT.

k-Anonymity of Microdata with NULL Values

Releasing, publishing or transferring microdata is restricted by the necessity to protect the privacy of data owners. K-anonymity is one of the most widespread concepts for anonymizing microdata but it does not explicitly cover NULL values frequently found in microdata. We study the problem of NULL values (missing values, non-applicable attributes, etc.) for anonymization in detail, present a set of new definitions for k-anonymity explicitly considering NULL and analyze which definition protects from which attacks. We show that an adequate treatment of missing values in microdata can be easily achieved by an extension of generalization algorithms and show that NULL aware generalization algorithms have less information loss than standard algorithms.