1,892 research outputs found
An Alternative Relational OLAP Modeling Approach
Schema design is one of the fundamentals in database theory and practice as well. In this paper, we discuss the problem of locally valid dimensional attributes in a classification hierarchy of a typical OLAP scenario. In a first step, we show that the traditional star and snowflake schema approach is not feasible in this very natural case of a hierarchy. Therefore, we sketch two alternative modeling approaches resulting in practical solutions and a seamless extension of the traditional star and snowflake schema approach: In a pure relational approach, we replace each dimension table of a star / snowflake schema by a set of views directly reflecting the classification hierarchy. The second approach takes advantage of the object-relational extensions. Using object-relational techniques in the context for the relational representation of a multidimensional OLAP scenario is a novel approach and promises a clean and smooth schema design
XWeB: the XML Warehouse Benchmark
With the emergence of XML as a standard for representing business data, new
decision support applications are being developed. These XML data warehouses
aim at supporting On-Line Analytical Processing (OLAP) operations that
manipulate irregular XML data. To ensure feasibility of these new tools,
important performance issues must be addressed. Performance is customarily
assessed with the help of benchmarks. However, decision support benchmarks do
not currently support XML features. In this paper, we introduce the XML
Warehouse Benchmark (XWeB), which aims at filling this gap. XWeB derives from
the relational decision support benchmark TPC-H. It is mainly composed of a
test data warehouse that is based on a unified reference model for XML
warehouses and that features XML-specific structures, and its associate XQuery
decision support workload. XWeB's usage is illustrated by experiments on
several XML database management systems
Benchmarking Summarizability Processing in XML Warehouses with Complex Hierarchies
Business Intelligence plays an important role in decision making. Based on
data warehouses and Online Analytical Processing, a business intelligence tool
can be used to analyze complex data. Still, summarizability issues in data
warehouses cause ineffective analyses that may become critical problems to
businesses. To settle this issue, many researchers have studied and proposed
various solutions, both in relational and XML data warehouses. However, they
find difficulty in evaluating the performance of their proposals since the
available benchmarks lack complex hierarchies. In order to contribute to
summarizability analysis, this paper proposes an extension to the XML warehouse
benchmark (XWeB) with complex hierarchies. The benchmark enables us to generate
XML data warehouses with scalable complex hierarchies as well as
summarizability processing. We experimentally demonstrated that complex
hierarchies can definitely be included into a benchmark dataset, and that our
benchmark is able to compare two alternative approaches dealing with
summarizability issues.Comment: 15th International Workshop on Data Warehousing and OLAP (DOLAP
2012), Maui : United States (2012
NOSQL design for analytical workloads: Variability matters
Big Data has recently gained popularity and has strongly questioned relational databases as universal storage systems, especially in the presence of analytical workloads. As result, co-relational alternatives, commonly known as NOSQL (Not Only SQL) databases, are extensively used for Big Data. As the primary focus of NOSQL is on performance, NOSQL databases are directly designed at the physical level, and consequently the resulting schema is tailored to the dataset and access patterns of the problem in hand. However, we believe that NOSQL design can also benefit from traditional design approaches. In this paper we present a method to design databases for analytical workloads. Starting from the conceptual model and adopting the classical 3-phase design used for relational databases, we propose a novel design method considering the new features brought by NOSQL and encompassing relational and co-relational design altogether.Peer ReviewedPostprint (author's final draft
Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems
Les entrepĂŽts de donnĂ©es reposent sur la modĂ©lisation multidimensionnelle. A l'aide d'outils OLAP, les dĂ©cideurs analysent les donnĂ©es Ă diffĂ©rents niveaux d'agrĂ©gation. Il est donc nĂ©cessaire de reprĂ©senter les connaissances d'agrĂ©gation dans les modĂšles conceptuels multidimensionnels, puis de les traduire dans les modĂšles logiques et physiques. Cependant, les modĂšles conceptuels multidimensionnels actuels reprĂ©sentent imparfaitement les connaissances d'agrĂ©gation, qui (1) ont une structure et une dynamique complexes et (2) sont fortement contextuelles. Afin de prendre en compte les caractĂ©ristiques de ces connaissances, nous proposons de les reprĂ©senter avec des objets (diagrammes de classes UML) et des rĂšgles en langage PRR (Production Rule Representation). Les connaissances d'agrĂ©gation statiques sont reprĂ©sentĂ©es dans les digrammes de classes, tandis que les rĂšgles reprĂ©sentent la dynamique (c'est-Ă -dire comment l'agrĂ©gation peut ĂȘtre effectuĂ©e en fonction du contexte). Nous prĂ©sentons les diagrammes de classes, ainsi qu'une typologie et des exemples de rĂšgles associĂ©es.AgrĂ©gation ; EntrepĂŽt de donnĂ©es ; ModĂšle conceptuel multidimensionnel ; OLAP ; RĂšgle de production ; UML
Combining Objects with Rules to Represent Aggregation Knowledge in Data Warehouse and OLAP Systems
Data warehouses are based on multidimensional modeling. Using On-Line Analytical Processing (OLAP) tools, decision makers navigate through and analyze multidimensional data. Typically, users need to analyze data at different aggregation levels (using roll-up and drill-down functions). Therefore, aggregation knowledge should be adequately represented in conceptual multidimensional models, and mapped in subsequent logical and physical models. However, current conceptual multidimensional models poorly represent aggregation knowledge, which (1) has a complex structure and dynamics and (2) is highly contextual. In order to account for the characteristics of this knowledge, we propose to represent it with objects (UML class diagrams) and rules in Production Rule Representation (PRR) language. Static aggregation knowledge is represented in the class diagrams, while rules represent the dynamics (i.e. how aggregation may be performed depending on context). We present the class diagrams, and a typology and examples of associated rules. We argue that this representation of aggregation knowledge allows an early modeling of user requirements in a data warehouse project.Aggregation; Conceptual Multidimensional Model; Data Warehouse; On-line Analytical Processing (OLAP); Production Rule; UML
Data Warehouse Design and Management: Theory and Practice
The need to store data and information permanently, for their reuse in later stages, is a very relevant problem in the modern world and now affects a large number of people and economic agents. The storage and subsequent use of data can indeed be a valuable source for decision making or to increase commercial activity. The next step to data storage is the efïŹcient and effective use of information, particularly through the Business Intelligence, at whose base is just the implementation of a Data Warehouse. In the present paper we will analyze Data Warehouses with their theoretical models, and illustrate a practical implementation in a speciïŹc case study on a pharmaceutical distribution companyData warehouse, database, data model.
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