Michael Boehnlein

Bio: Michael Boehnlein is an academic researcher from University of Bamberg. The author has contributed to research in topics: Entity–relationship model & Data warehouse. The author has an hindex of 1, co-authored 1 publications receiving 113 citations.

Deriving initial data warehouse structures from the conceptual data models of the underlying operational information systems

Abstract: In recent years the construction of large scale data schemes for operational systems has been the major problem of conceptual data modeling for business needs. Multidimensional data structures used for decision support applications in data warehouses have rather different requirements to data modeling techniques. In case of operational systems the data models are created from application specific requirements. The data models in data warehouses base on the analytical requirements of the users. Furthermore, the development of data warehouse structures implicates the consideration of user-defined information requirements as well as the underlying operational source systems. In this paper we show that the conceptual data models of the underlying operational information systems can support the construction of multidimensional structures. We would like to point out that the special features of the Structured Entity Relationship Model (SERM) are not only useful for the development of big operational systems but can also help with the derivation of data warehouse structures. The SERM is an extension of the conventional Entity Relationship Model (ERM) and the conceptual basis of the data modeling technique used by the SAP Corporation. To illustrate the usefulness of this approach we explain the derivation of the warehouse structures from the conceptual data model of a flight reservation system.

Automating data warehouse conceptual schema design and evaluation.

Abstract: The popularity of data warehouses for analysis of data has grown tremendously, but much of the creation of data warehouses is done manually. We propose and illustrate algorithms for automatic conceptual schema development and evaluation. Our creation algorithm uses an enterprise schema of an operational database as a starting point for source-driven data warehouse schema design. Candidate conceptual schemas are created using the ME/R model, extended to note where additional user input can be used to further refine a schema. Our evaluation algorithm follows a user-driven requirements approach that utilizes queries to guide selection of candidate schemas most likely to meet user needs. In addition, we propose a guideline of manual steps to refine a conceptual schema to suit additional user needs, for example, the level of detail needed for date fields. The algorithms are illustrated using the TPC-H Benchmark schema and queries. Our algorithms provide a foundation for a software tool to create and evaluate data warehouse conceptual schemas.

Ontology-Based Conceptual Design of ETL Processes for Both Structured and Semi-Structured Data

Abstract: One of the main tasks in the early stages of a data warehouse project is the identification of the appropriate transformations and the specification of inter-schema mappings from the data sources to the data warehouse. In this article, we propose an ontology-based approach to facilitate the conceptual design of the back stage of a data warehouse. A graph-based representation is used as a conceptual model for the datastores, so that both structured and semi-structured data are supported and handled in a uniform way. The proposed approach is based on the use of Semantic Web technologies to semantically annotate the data sources and the data warehouse, so that mappings between them can be inferred, thereby resolving the issue of heterogeneity. Specifically, a suitable application ontology is created and used to annotate the datastores. The language used for describing the ontology is OWL-DL. Based on the provided annotations, a DL reasoner is employed to infer semantic correspondences and conflicts among the datastores, and to propose a set of conceptual operations for transforming data from the source datastores to the data warehouse.

A Survey of Multidimensional Modeling Methodologies

Abstract: Many methodologies have been presented to support the multidimensional design of the data warehouse First methodologies introduced were requirement-driven but the semantics of a data warehouse require to also consider data sources along the design process In the following years, data sources gained relevance in multidimensional modeling and gave rise to several data-driven methodologies that automate the data warehouse design process from relational sources Currently, research on multidimensional modeling is still a hot topic and we have two main research lines On the one hand, new hybrid automatic methodologies have been introduced proposing to combine data-driven and requirement-driven approaches On the other hand, new approaches focus on considering other kind of structured data sources that have gained relevance in the last years such as ontologies or XML In this article we present the most relevant methodologies introduced in the literature and a detailed comparison showing main features of each approach

Designing ETL processes using semantic web technologies

Abstract: One of the most important tasks performed in the early stages of a data warehouse project is the analysis of the structure and content of the existing data sources and their intentional mapping to a common data model. Establishing the appropriate mappings between the attributes of the data sources and the attributes of the data warehouse tables is critical in specifying the required transformations in an ETL workflow. The selected data model should besuitable for facilitating the redefinition and revision efforts, typically occurring during the early phases of a data warehouse project, and serve as the means of communication between the involved parties. In this paper, we argue that ontologies constitute a very suitable model for this purpose and show how the usage of ontologies can enable a high degree of automation regarding the construction of an ETL design.

Data Warehouse Systems: Design and Implementation

Abstract: With this textbook, Vaisman and Zimnyi deliver excellent coverage of data warehousing and business intelligence technologies ranging from the most basic principles to recent findings and applications. To this end, their work is structured into three parts. Part I describes Fundamental Concepts including multi-dimensional models; conceptual and logical data warehouse design and MDX and SQL/OLAP. Subsequently, Part II details Implementation and Deployment, which includes physical data warehouse design; data extraction, transformation, and loading (ETL) and data analytics. Lastly, Part III covers Advanced Topics such as spatial data warehouses; trajectory data warehouses; semantic technologies in data warehouses and novel technologies like Map Reduce, column-store databases and in-memory databases. As a key characteristic of the book, most of the topics are presented and illustrated using application tools. Specifically, a case study based on the well-known Northwind database illustrates how the concepts presented in the book can be implemented using Microsoft Analysis Services and Pentaho Business Analytics. All chapters are summarized using review questions and exercises to support comprehensive student learning. Supplemental material to assist instructors using this book as a course text is available at http://cs.ulb.ac.be/DWSDIbook/, including electronic versions of the figures, solutions to all exercises, and a set of slides accompanying each chapter. Overall, students, practitioners and researchers alike will find this book the most comprehensive reference work on data warehouses, with key topics described in a clear and educational style.