E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley Computer Publishing, New York, 1997.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....times significantly. Furthermore, the performance of the optimized federation queries is comparable to the performance achieved with physical integration of the data, showing that our federated OLAP approach is indeed a feasible alternative to physical integration. 1 Introduction OLAP systems [17] enable powerful decision support based on multidimensional analysis of large amounts of detail data. OLAP data are often organized in multidimensional cubes containing measured values that are characterized by a number of hierarchical dimensions. However, dynamic data, such as stock quotes or ....

....is sometimes vital. Also, OLAP systems lack the necessary flexibility when faced with unanticipated or rapidly changing data requirements. These problems are due to the fact that physically integrating data can be a complex and time consuming process, requiring the cube to be rebuilt [17]. Thus, logical, rather than physical, integration is desirable, i.e. a federated database system [15] is called for. The increasing use of Extended Markup Language (XML) 19] e.g. in B2B applications, suggests that the required external data will often be available in XML format. Also, most ....

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E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley, 1997.

....star schema, tables are arranged in the following ways. A central fact table is connected to a set of dimension tables, one per dimension. The name star comes from the usual diagrammatic depiction of this schema with the fact table in the center and each dimension table shown surrounding it [23]. The BEAGLE data repository has four fact tables. They model the system structure and relations between program entities at various abstract levels. The four levels of abstraction are: entity, file, high, and architecture. Each level of architecture fact is stored in its own table for all the ....

E. Thomsen. OLAP solutions: Building Multidimensional Information Systems. John Wiley and Sons, New York, U.S.A., 1997.

....modeling [1] Dimensional Modeling Dimensional Modeling (DM) is a logical design technique that attempts to present data in a standard, intuitive framework allowing for high performance access. Essentially, this is a relational database modeling technique, but with some important restrictions [3]: Every dimensional model is composed of one table with a multi part key, called the fact table. This table typically contains one or more numeric facts or measures. The model also includes a set of smaller dimension tables, each having a single part key. These tables contain descriptive ....

Thomsen, E. et al., OLAP Solutions: Building Multidimensional Information Systems, John Wiley & Sons 1997, pp. 575-592.

....of Polaris before discussing our extensions to Polaris in the rest of the paper. 3.1 Relational Databases vs. Data Cubes Relational databases organize data into relations, where each row in a relation corresponds to a basic entity or fact and each column represents a property of that entity [24]. For example, a relation may represent transactions in a bank, where each row corresponds to a single transaction, and each transaction has multiple properties, such as the transaction amount, the account balance, the bank branch, and the customer. We refer to a row in a relation as a tuple or ....

E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley Computer Publishing, New York, 1997.

....to backend databases and other transactional subsystems. Typical transaction processing applications, such as those for banking, transportation, and manufacturing, perform simple data entry and retrieval. In contrast, typical non transactional applications, such as those for analytical processing [8] and scientific computing, are more compute intensive. 2.1 Multi tier Cluster Architectures Enterprise computing systems are organized into logical tiers, each of which may contain multiple servers or other processes, as illustrated in Figure 1. The client tier contains personal devices such as ....

E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems, Second Edition. Wiley. 2002.

....Snowflake Schema [10] Therefore in many cases, ROLAP products are not suitable for handling dimensions with multihierarchical and unbalanced structures. Furthermore, existing relational query languages (e.g. SQL) are not sufficiently powerful or flexible enough to support true OLAP capabilities [19]. 3] clearly demonstrated the mismatch between multidimensional operations and SQL. Although MOLAP (Multidimensional OLAP) easily supports dimensions with multiple and unbalanced hierarchical structures and MOLAP queries are very powerful and flexible in terms of OLAP processing [14] there are ....

Thomsen, E.: OLAP solutions: Building Multidimensional Information Systems. John Wiley& Sons, Inc., 1997.

....and slice and dice, which performs selection and projection on a cube. The multidimensional approach offers a number of advantages over traditional types of DBMSs, including automatic application of the pre specified aggregation functions (automatic aggregation) Rafanelli et al. 1990, Thomsen, 1997] visual querying [Thomsen, 1997, Thomsen, 1999] and good query performance due to the use of preaggregation [Gupta et al. 1995, Pedersen et al. 1999b] Additionally, the dimensional approach is most often a natural fit for data analysis problems. To be able to capture the complex data found ....

....performs selection and projection on a cube. The multidimensional approach offers a number of advantages over traditional types of DBMSs, including automatic application of the pre specified aggregation functions (automatic aggregation) Rafanelli et al. 1990, Thomsen, 1997] visual querying [Thomsen, 1997, Thomsen, 1999] and good query performance due to the use of preaggregation [Gupta et al. 1995, Pedersen et al. 1999b] Additionally, the dimensional approach is most often a natural fit for data analysis problems. To be able to capture the complex data found in many real world applications, ....

E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. John Wiley & Sons, 1997.

....In a cube, dimensions are first class concepts with associated domains, meaning that the addition of new dimension values is easily handled. Although the term cube implies 3 dimensions, a cube can have any number of dimensions. It turns out that most real world cubes have 4 12 dimensions [3, 9]. Although there is no theoretical limit to the number of dimensions, current tools often experience performance problems when the number of dimensions is more than 10 15. To better illustrate the high number of dimensions, the term hypercube is often used instead of cube. Depending on the ....

....aimed at the mass market, leading to the current situation where multidimensional systems are becoming commodity products, shipped at no extra cost together with leading relational database systems. A more in depth coverage of the history of multidimensional databases is available elsewhere [9]. 2000 2001 Aalborg Copenhagen Milk Bread 123 127 57 45 56 67 211 Figure 1: Sales Cube via aggregation of the measure values in the projected out dimensions, resulting in higher level measure values for the desired view of data. For example, if we want to view just sales by City and ....

[Article contains additional citation context not shown here]

E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley, 1997.

....not exposed to the user. Consequently, the user must have explicit knowledge of the hierarchy and manually incorporate this knowledge when using the visualization. The database community describes hierarchical multi dimensional structures through the use of star and snowflake schemas [22][59]. Both types of schemas have a root fact table, consisting of the basic data that the user wants to explore. Each field in the root fact table is either a dimension or a measure: dimensions are independent fields, either nominal or quantitative, whereas measures are dependent quantitative fields. ....

Thomsen, E. OLAP Solutions: Building Multidimensional Information Systems. Wiley Computer Publishing, New York, 1997.

....we believe this paper to be the first to consider the integrated querying of data from independent summary and object databases without prior physical integration, with the objective of giving OLAP users enhanced, aggregation safe query capabilities. Surveys of OLAP data models and languages [26, 33, 34] indicate 1 Although the paper s contributions are applicable to almost all current OLAP systems, we use the term SDB instead of OLAP DB to emphasize the focus on aggregate queries over summary data. 3 that this issue has not been addressed previously. To our knowledge, the paper is also the ....

E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley, 1997.

....drawn from a physical integration of transactional databases, is organized in multidimensional data models, categorizing data as either measurable facts (measures) or hierarchically organized dimensions characterizing the facts. Features like automatic aggregation [28] and visual querying [33] supported by OLAP tools ease the process of decision support compared to traditional database management systems. Integration of distributed data sources is becoming increasingly important as more business relevant data appear on the web, e.g. on B2B marketplaces, and enterprises cooperate more ....

Thomsen, E. OLAP Solutions: Building Multidimensional Information Systems, Wiley, 1997.

....of sequences have to be performed using specialized complex data mining techniques, which are tailored for one specific analysis; a quite inflexible solution. Also, the complex data mining techniques cannot be efficiently supported by performance enhancing OLAP techniques such as pre aggregation [14]. Ralph Kimball s latest book [9] is the current industrial standard for clickstream modeling. Here, a DW contains only click and session facts, which means that questions about sequences are hard to answer compared to our approach. Work has been done on combining clickstream data with other ....

....standard techniques enables the use of pre aggregation, providing much faster data analysis than with other approaches, e.g. data mining. The proposed design can achieve fast query performance using pre aggregation with any OLAP system, whether it is relational OLAP [15] or multidimensional OLAP [14]. The techniques proposed for avoiding an explosion in the number of subsessions, and the practical evaluation of their effectiveness, we also believe to be novel. The remainder of the paper is structured as follows: Section 2 offers an overview of clickstream data and its uses. Section 3 ....

E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley, 1997. 15

....we believe this paper to be the first to consider the integrated querying of data from independent summary and object databases without prior physical integration, with the objective of giving OLAP users enhanced, aggregation safe query capabilities. Surveys of OLAP data models and languages [18, 23, 24] indicate that this issue has not been addressed previously. To our knowledge, the paper is also the first to demonstrate a multiparadigm (or multi model ) federation [1, 9] where one of the data models is a summary data model. The remainder of the paper is structured as follows. Section 2 ....

E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley, 1997.

....we believe this paper to be the first to consider the integrated querying of data from independent summary and object databases without prior physical integration, with the objective of giving OLAP users enhanced, aggregation safe query capabilities. Surveys of OLAP data models and languages [95, 125, 128] indicate that this issue has not been addressed previously. To our knowledge, the paper is also the first to demonstrate a multi paradigm (or multi model ) federation [6, 50, 51] where one of the data models is a dedicated summary data model. Finally, the paper is the first to investigate how ....

E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley, 1997.

....Snowflake Schema [10] Therefore in many cases, ROLAP products are not suitable for handling dimensions with multihierarchical and unbalanced structures. Furthermore, existing relational query languages (e.g. SQL) are not sufficiently powerful or flexible enough to support true OLAP capabilities [19]. 3] clearly demonstrated the mismatch between multidimensional operations and SQL. Although MOLAP (Multidimensional OLAP) easily supports dimensions with multiple and unbalanced hierarchical structures and MOLAP queries are very powerful and flexible in terms of OLAP processing [14] there are ....

Thomsen, E.: OLAP solutions: Building Multidimensional Information Systems. John Wiley& Sons, Inc., 1997.

.... prototype phase (Ritter, 1994) Once information from different sources are integrated into a warehouse , it is possible to use On Line Analytical Processing (OLAP) systems as tools to explore and package the information further so it can be delivered in the form(s) needed (Dhar and Stein, 1997; Thomsen, 1997). The goal of data warehousing is to provide a decision support infrastructure for OLAP servers. The goal of OLAP servers is to provide views of the data that allow varying perspectives along many dimensions. Entry points can be provided based on the characteristics of the data themselves (Dhar ....

Thomsen, E., 1997. OLAP Solutions: building multidimensional information systems. Wiley Computer Publishing, New York, NY.

....it can be turned into competitive weapons. Unfortunately, the fast growing, tremendous amounts of data collected has far exceeded the human ability for comprehension and examination. They clearly overwhelm the traditional manual methods of data analysis such as spreadsheets and ad hoc queries [Tho97] Thus, a lot of important decisions are made based on the CHAPTER 1. INTRODUCTION 2 decision makers intuition, experience or expertise rather than on the information rich data stored in databases. As a result, large databases become data tombs and this phenomenon has been described as data ....

....the cube contain multiple levels of abstraction. The main goals for OLAP are fast access, fast calculations, user friendly interfaces, and flexible viewing, given the challenges of lots of data with many levels of detail and multiple factors in a decentralized multi user environment. Tho97] OLAP should support ad hoc analytical queries, some of which may require computations performed on the fly. A typical ad hoc analysis example is querying how overall product profitability was in California last quarter. Seeing that profitability was lower than expected, the manager might ....

E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. John Wiley & Sons, 1997.

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E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley Computer Publishing, New York, 1997.

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E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley Computer Publishing, New York, 1997.

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E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley Computer Publishing, New York, 1997.

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E. Thomsen. OLAP Solutions: Building Multidimensional Information Systems. Wiley, 1997.

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Thomsen, E.: OLAP solutions: Building Multidimensional Information Systems. John Wiley& Sons, Inc., 1997.

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Thomsen, E.: OLAP solutions: Building Multidimensional Information Systems. John Wiley& Sons, Inc., 1997.

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