Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

Information requirements analysis for data warehouse systems differs significantly from requirements analysis for conventional information systems. Existing data warehouse specific approaches are reviewed. A comprehensive methodology that supports the entire process of determining information requirements of data warehouse users, matching information requirements with actual information supply, evaluating and homogenizing resulting information requirements, establishing priorities for unsatisfied information requirements, and formally specifying the results as a basis for subsequent phases of the data warehouse development (sub)project is proposed. Its components as well as its overall design are based partially on literature review, but mainly on findings from a four year collaboration project with several large companies, mostly from the service sector. While an application of the entire methodology is still outstanding, some components have been successfully applied in actual data warehouse development projects of the participating companies.

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A method for demand-driven information requirements analysis in data warehousing projects

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.

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Automating data warehouse conceptual schema design and evaluation.

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.

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

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

https://www.essi.upc.edu/~aabello/publications/09.IJDWM.pdf

A Survey of Multidimensional Modeling Methodologies

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.

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

In recent years data warehouse projects have become popular to most companies. Unfortunately many of these projects come to grief due to missing engineering strategies and modeling standards. The common method for developing multidimensional data structures is deriving relevant datasets from underlying operational data sources. But in fact developing large data schemata for decision support applications requires more knowledge about the underlying business domain. It is obvious that for efficient decision making an approach is required which additionally focuses on goals and strategies of the company. This information can not be extracted by analyzing the operational data sources. We realized that commonly applied business process models can be used to handle this problem. In this paper we present a completely new approach to warehouse development — the derivation of data warehouse structures from business process models.

Business Process Oriented Development of Data Warehouse Structures

Der vorliegende Beitrag untersucht die Beziehung zwischen (a) den Leistungsprozessen der Organisations-und der Fuhrungsstruktur einer verteilten Organisation und (b) der Architektur eines zugehorigen Data-Warehouse-Systems. Dabei wird deutlich dass zentrale Data-Warehouse-Systeme zur Unterstutzung verteilter Organisationen nur bedingt geeignet sind. Anhand der Konzeption eines Data-Warehouse-Systems fur das Hochschulwesen wird exemplarisch ein fur verteilte Organisationen geeignetes Architekturkonzept eines verteilten Data-Warehouse-Systems entwickelt.

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Architekturkonzept eines verteilten Data-Warehouse-Systems für das Hochschulwesen

XML — Extensible Markup Language

In this paper, we presented a new approach of a visual multidimensional querying tool. Instead of introducing a new multidimensional query language or algebra, we focused on the support of the end users to transform easily their requirements into the syntax of a multidimensional query. To disburden the end user from memorization of the knowledge about the underlying multidimensional data structures, our tool bases on a graphical representation of the multidimensional schema in a Semantic Data Warehouse Model (SDWM). Starting from this graphical representation, the tool assists the user in specifYing multidimensional queries easily considering the corresponding integrity constraints. This lead to an improved interpretability of the query specification process

Achim Ulbrich-vom Ende

Papers

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

Business Process Oriented Development of Data Warehouse Structures

Architekturkonzept eines verteilten Data-Warehouse-Systems für das Hochschulwesen

XML — Extensible Markup Language

Visual Specification of Multidimensional Queries Based on a Semantic Data Model