Lutz Schlesinger

Management of multidimensional aggregates for efficient online analytical processing

Proper management of multidimensional aggregates is a fundamental prerequisite for efficient OLAP. The experimental OLAP server CUBESTAR whose concepts are described, was designed exactly for that purpose. All logical query processing is based solely on a specific algebra for multidimensional data. However, a relational database system is used for the physical storage of the data. Therefore, in popular terms, CUBESTAR can be classified as a ROLAP system. In comparison to commercially available systems, CUBESTAR is superior in two aspects. First, the implemented multidimensional data model allows more adequate modeling of hierarchical dimensions, because properties which apply only to certain dimensional elements can be modeled context-sensitively. This fact is reflected by an extended star schema on the relational side. Second, CUBESTAR supports multidimensional query optimization by caching multidimensional aggregates. Since summary tables are not created in advance but as needed, hot spots can be adequately represented. The dynamic and partition-oriented caching method allows cost reductions of up to 60% with space requirements of less than 10% of the size of the fact table.

A Decathlon in Multidimensional Modeling: Open Issues and Some Solutions

The concept of multidimensional modeling has proven extremely successful in the area of Online Analytical Processing (OLAP) as one of many applications running on top of a data warehouse installation. Although many different modeling techniques expressed in extended multidimensional data models were proposed in the recent past, we feel that many hot issues are not properly reflected. In this paper we address ten common problems reaching from defects within dimensional structures over multidimensional structures to new analytical requirements and more.

Supporting the ETL-process by Web Service technologies

Extracting data from heterogeneous data sources and transferring data into the data warehouse system is one of the most cost intensive tasks in setting up and operating a data warehouse. Special tools may be used to connect different sources and target systems. In this paper, we propose an architecture which enables the flexible integration of data sources into any target database system. The approach is based on the idea of splitting the classical wrapping module into a source specific and a target specific part and establishing the communication between these components based on Web Service technology. We describe the general architecture, the use of Web Service technology to describe and dynamically integrate participating data sources and the deployment within a specific database system.

Efficiently synchronizing multidimensional schema data

Most existing concepts in data warehousing provide a central data¿base system storing gathered raw data and redundantly computed materialized views. While in current system architectures client tools are sending queries to a central data warehouse system and are only used to graphically present the result, the steady rise in power of personal computers and the expansion of network bandwidth makes it possible to store replicated parts of the data warehouse at the client thus saving network bandwidth and utilizing local com¿puting power. Within such a scenario a - potentially mobile - client does not need to be connected to a central server while performing local analyses. Although this scenario seems attractive, several pro¿blems arise by introducing such an architecture: For example schema data could be changed or new fact data could be available. This paper is focusing on the first problem and presents ideas on how changed schema data can be detected and efficiently synchro¿nized between client and server exploiting the special needs and requirements of data warehousing.

Query optimization by using derivability in a data warehouse environment

Materialized summary tables and cached query results are frequently used for the optimization of aggregate queries in a data warehouse. Query rewriting techniques are incorporated into database systems to use those materialized views and thus avoid the access of the possibly huge raw data. A rewriting is only possible if the query is derivable from these views. Several approaches can be found in the literature to check the derivability and find query rewritings. The specific application scenario of a data warehouse with its multidimensional perspective allows the consideration of much more semantic information, e.g. structural dependencies within the dimension hierarchies and different characteristics of measures. The motivation of this article is to use this information to present conditions for derivability in a large number of relevant cases which go beyond previous approaches.

Processing reporting function views in a data warehouse environment

Reporting functions reflect a novel technique to formulate sequence-oriented queries in SQL. They extend the classical way of grouping and applying aggregation functions by additionally providing a column-based ordering, partitioning, and windowing mechanism. The application area of reporting functions ranges from simple ranking queries (TOP(n)-analyses) over cumulative (Year-To-Date-analyses) to sliding window queries. We discuss the problem of deriving reporting function queries from materialized reporting function views, which is one of the most important issues in efficiently processing queries in a data warehouse environment. Two different derivation algorithms, including their relational mappings are introduced and compared in a test scenario.

Using Semantics for Query Derivability in Data Warehouse Applications

Materialized summary tables and cached query results are frequently used for the optimization of aggregate queries in a data warehouse. Query rewriting techniques are incorporated into database systems to use those materialized views and thus avoid accessing the possibly huge raw data. A rewriting is only possible if the query is derivable from these views. Several approaches can be found in the literature to check the derivability and find query rewritings. However, most algorithms either find rewritings only in very restricted cases or in complex cases which rarely occur in data warehouse environments. The specific application scenario of a data warehouse with its multidimensional perspective allows the consideration of much more semantic information, e.g. structural dependencies within the dimension hierarchies and different characteristics of measures. The motivation of this article is to use this information to present simple conditions for derivability in a large number of relevant cases which go beyond previous approaches.

SCINTRA: A Model for Quantifying Inconsistencies in Grid-Organized Sensor Database Systems

Sensor data sets are usually collected in a centralized sensor database system or replicated cached in a distributed system to speed up query evaluation. However, a high data refresh rate disallows the usage of traditional replicated approaches with its strong consistency property. Instead we propose a combination of grid computing technology with sensor database systems. Each node holds cached data of other grid members. Since cached information may become stale fast, the access to outdated data may sometimes be acceptable if the user has knowledge about the degree of inconsistency if unsynchronized data are combined. The contribution of this paper is the presentation and discussion of a model for describing inconsistencies in grid organized sensor database systems.

Adaptive Präaggregation in multidimensionalen Datenbanksystemen

Das Konzept des ‘Online Analytical Processing’ umfast die interaktive Analyse multidimensionaler Daten. Die dabei zu verarbeitenden Datenmengen sind in der Regel sehr gros. Deshalb mussen geeignete Optimierungsverfahren fur die Gewahrleistung der Interaktivitat eingesetzt werden. Das durch die multidimensionalen Datenstrukturen gefuhrte Benutzerverhalten im Anwendungsfeld OLAP bildet die Grundlage fur das in diesem Beitrag vorgestellte redundanzbasierte Optimierungsverfahren. Die Grundidee dieses adaptiven Verfahrens besteht darin, bereits berechnete Ergebnisse und Teilergebnisse in einem reservierten Speicherbereich fester Grose zu puffem, um neue Anfrageergebnisse daraus abzuleiten. Notwendig fur einen solchen multidimensionalen Aggregatpuffer ist zum einen ein Algorithmus, um konstruktiv die Ableitbarkeit eines Aggregates aus einer Menge anderer Aggregate zu bestimmen. Zum anderen wird eine geeignete Verdrangungsstrategie benotigt, welche die Informationen uber das multidimensionale Schema und uber das Zugriffsverhalten moglichst gut ausnutzt. Der Beitrag erlautert die verwendeten Techniken und zeigt das Potential des Ansatzes anhand von Simulationsstudien auf.

Nutzung von Datenbankdiensten in Data-Warehouse-Anwendungen (Connecting Data Warehouse Applications with Database Services)

Zusammenfassung Zentral für eine effiziente Analyse der in Data-Warehouse-Systemen gespeicherten Daten ist das Zusammenspiel zwischen Anwendung und Datenbanksystem. Der vorliegende Artikel klassifiziert und diskutiert unterschiedliche Wege, Data-Warehouse-Anwendungen mit dem Datenbanksystem zu koppeln, um komplexe OLAP-Szenarien zur Berechnung dem Datenbankdienst zu überlassen. Dabei werden vier unterschiedliche Kategorien, die Spracherweiterung (SQL), die anwendungsspezifische Sprachneuentwicklung (MDX), die Nutzung spezifischer Objektmodelle (JOLAP) und schließlich der Rückgriff auf XML-basierte WebServices (XCube) im einzelnen diskutiert und vergleichend gegenübergestellt.