Finding your way through multidimensional data models

Blaschka, Markus; Sapia, Carsten; Höfling, Gabriele; Dinter, Barbara

doi:10.1109/dexa.1998.707403

Cited by 46 publications

(45 citation statements)

References 9 publications

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“…The design of a Data Warehouse (DW) has been tackled mainly from the conceptual and logical point of view through multidimensional (MD) data models [3,1], but to the best of our knowledge, there is not any standard method or model that allows us to model all aspects of a DW. Moreover, as most of the research efforts in designing and modeling DWs have been focused on the development of MD data models, the interest on the physical design of DWs has been very poor.…”

Section: Introductionmentioning

confidence: 99%

Physical modeling of data warehouses using UML

Luján-Mora

Trujillo

2004

Proceedings of the 7th ACM International Workshop on Data Warehousing and OLAP

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During the few last years, several approaches have been proposed to model different aspects of a Data Warehouse (DW), such as the conceptual model of the DW, the design of the ETL (Extraction, Transformation, Loading) processes, the derivation of the DW models from the enterprise data models, etc. At the end, a DW has to be deployed to a database environment and that takes many decisions of a physical nature. However, few efforts have been dedicated to the modeling of the physical design (i.e. the physical structures that will host data together with their corresponding implementations) of a DW from the early stages of a DW project. From our previously presented DW engineering process, in this paper we present our proposal for the modeling of the physical design of DWs by using the component diagrams and deployment diagrams of the Unified Modeling Language (UML). Our approach allows the designer to anticipate important physical design decisions that may reduce the overall development time of a DW such as replicating dimension tables, vertical and horizontal partitioning of a fact table, the use of particular servers for certain ETL processes and so on. Moreover, our approach allows the designer to cover all main design phases of DWs, from the conceptual modeling phase until the final implementation, as we show with an example in this paper.

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Section: Introductionmentioning

confidence: 99%

Physical modeling of data warehouses using UML

Luján-Mora

Trujillo

2004

Proceedings of the 7th ACM International Workshop on Data Warehousing and OLAP

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“…A complete survey of the properties of several earlier logical design models can be found in the works of Blaschka et.al. [18], Vassiliadis et.al. [19] and Pedersen et.al.…”

Section: Related Workmentioning

confidence: 99%

“…Blaschka et.al. [18] provide a list of requirements for multidimensional modeling for OLAP applications. In [20], Pedersen et.al.…”

Section: Requirements For User-centric Spatial Olapmentioning

confidence: 99%

On the Requirements for User-Centric Spatial Data Warehousing and SOLAP

Viswanathan

Schneider

2011

Database Systems for Adanced Applications

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Abstract. Data warehouses and OLAP systems help to analyze complex multidimensional data and provide decision support. With the availability of large amounts of spatial data in recent years, several new models have been proposed to enable the integration of spatial data in data warehouses and to help analyze such data. This is often achieved by a combination of GIS and spatial analysis tools with OLAP and database systems, with the primary goal of supporting spatial analysis dimensions, spatial measures and spatial aggregation operations. However, this poses several new challenges related to spatial data modeling in a multidimensional context, such as the need for new spatial aggregation operations and ensuring consistent and valid results. In this paper, we review the existing modeling strategies for spatial data warehouses and SOLAP in all three levels: conceptual, logical and implementation. While studying these models, we gather the most essential requirements for handling spatial data in data warehouses and use insights from spatial databases to provide a "meta-framework" for modeling spatial data warehouses. This strategy keeps the user as the focal point and achieves a clear abstraction of the data for all stakeholders in the system. Our goal is to make analysis more user-friendly and pave the way for a clear conceptual model that defines new multidimensional abstract data types (ADTs) and operations to support spatial data in data warehouses.

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“…For this reason multidimensional data models [BSH+98], multidimensional query languages (e.g., MDX [MS98b] or the OLAP Council approach [OLA98]) and even multidimensional DBMS (MDBMS) have been developed by the research community and implemented as commercial products. Typical OLAP operations are drill-down, roll-up and slice-and-dice [Kim96] and usually multiple dimensions are restricted at the same time.…”

Section: Data Warehousing Applicationsmentioning

confidence: 99%

MISTRAL: Processing Relational Queries using a Multidimensional Access Technique

Markl

2000

Ausgezeichnete Informatikdissertationen 1999

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PrefaceClassical one dimensional B-trees have been the standard access method of all commercial database systems for many years. This dissertation is a very promising effort to introduce universal B-trees (UB-trees), the multidimensional variant of Btrees, as a basic access method into the core of fundamental DB-technology. The universal relevance of UB-trees is a consequence of the fact, that every relation can be considered as a set of points in multidimensional space. UB-trees organize this space for efficient processing of the data, resulting for many types of queries in orders of magnitude improvement over classical methods.The thesis lays the theoretical foundations of UB-tress, predicts their performance by analytical models and validates these models by experiments using large real world databases and real life applications and queries from the field of datawarehousing. The need to sort data and intermediate results frequently is an annoying drawback of today' s query processing methods. In combination with the Tetris algorithm UBtrees allow to avoid sorting in most cases, leading to dramatic improvements of response time, storage requirement and overall query processing time.Adding a new access method requires to consider all aspects of database systems:-architecture of subsystems -query optimization -query processing -multiuser operation and synchronization -bulk loading -storage requirement -parallelism, etc.Since UB-trees rely on classical B-trees for their implementation, all of these issues can be solved in a satisfactory way and can be dealt with elegantly.The performance experiments reported in this thesis were carried out with an implementation of UB-trees as a middleware layer on top of SQL. Additional performance improvements can be gained by integrating the UB-tree technology in the kernel of database systems.This thesis is a cornerstone of the MISTRAL project. MISTRAL has the goal to introduce UB-trees as a new access method into database systems with the fascinating vision, to extend fundamental database technology in an essential way. MISTRAL is financially supported by SAP, Teijin, NEC, Hitachi, the European Commission, Project MDA, TAS, Gfk and Microsoft. Abstract:A multidimensional access method offering significant performance increases by intelligently partitioning the query space is applied to relational database management systems (RDBMS). We introduce a formal model for multidimensional partitioned relations and discuss several typical query patterns. The model identifies the significance of multidimensional range queries and sort operations. The discussion of current access methods gives rise to the need for a multidimensional partitioning of relations. A detailed analysis of space partitioning focussing especially on Z-ordering illustrates the principle benefits of multidimensional indexes. After describing the UB-Tree and its standard algorithms for insertion, deletion, point queries, and range queries, we introduce the spiral algorithm for nearest neighbor queries with UB-Tree...

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Finding your way through multidimensional data models

Cited by 46 publications

References 9 publications

Physical modeling of data warehouses using UML

Physical modeling of data warehouses using UML

On the Requirements for User-Centric Spatial Data Warehousing and SOLAP

MISTRAL: Processing Relational Queries using a Multidimensional Access Technique

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