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Showing papers on "Online analytical processing published in 1998"


Proceedings ArticleDOI
01 Jun 1998
TL;DR: This paper presents a technique based upon a multiresolution wavelet decomposition for building histograms on the underlying data distributions, with applications to databases, statistics, and simulation.
Abstract: Query optimization is an integral part of relational database management systems. One important task in query optimization is selectivity estimation, that is, given a query P, we need to estimate the fraction of records in the database that satisfy P. Many commercial database systems maintain histograms to approximate the frequency distribution of values in the attributes of relations.In this paper, we present a technique based upon a multiresolution wavelet decomposition for building histograms on the underlying data distributions, with applications to databases, statistics, and simulation. Histograms built on the cumulative data distributions give very good approximations with limited space usage. We give fast algorithms for constructing histograms and using them in an on-line fashion for selectivity estimation. Our histograms also provide quick approximate answers to OLAP queries when the exact answers are not required. Our method captures the joint distribution of multiple attributes effectively, even when the attributes are correlated. Experiments confirm that our histograms offer substantial improvements in accuracy over random sampling and other previous approaches.

464 citations


Journal Article
TL;DR: In this paper, the authors propose a discovery-driven exploration paradigm that mines the data for such exceptions and summarizes the exceptions at appropriate levels in advance, and then uses these exceptions to lead the analyst to interesting regions of the cube during navigation.
Abstract: Analysts predominantly use OLAP data cubes to identify regions of anomalies that may represent problem areas or new opportunities. The current OLAP systems support hypothesis-driven exploration of data cubes through operations such as drill-down, roll-up, and selection. Using these operations, an analyst navigates unaided through a huge search space looking at large number of values to spot exceptions. We propose a new discovery-driven exploration paradigm that mines the data for such exceptions and summarizes the exceptions at appropriate levels in advance. It then uses these exceptions to lead the analyst to interesting regions of the cube during navigation. We present the statistical foundation underlying our approach. We then discuss the computational issue of finding exceptions in data and making the process efficient on large multidimensional data bases.

322 citations


Book ChapterDOI
23 Mar 1998
TL;DR: This paper presents MD, a logical model for OLAP systems, and shows how it can be used in the design of multidimensional databases, and presents a design methodology, to obtain an MD scheme from an operational database.
Abstract: In this paper we present MD, a logical model for OLAP systems, and show how it can be used in the design of multidimensional databases. Unlike other models for multidimensional databases, MD is independent of any specific implementation (relational or proprietary multidimensional) and as such it provides a clear separation between practical and conceptual aspects. In this framework, we present a design methodology, to obtain an MD scheme from an operational database. We then show how an MD database can be implemented, describing translations into relational tables and into multidimensional arrays.

313 citations


Proceedings Article
24 Aug 1998
TL;DR: This dissertation describes techniques for speeding up Online Analytical Processing or OLAP queries, and presents an empirical study of PBS that demonstrates that PBS picks a surprisingly good set of aggregates even when the conditions do not hold, and proposes algorithms that perform significantly better than previously proposed algorithms for multicube workloads.
Abstract: This dissertation describes techniques for speeding up Online Analytical Processing or OLAP queries. OLAP systems allow users to quickly obtain the answers to complex business queries. Quickly answering these queries which aggregate large amounts of data, calls for various specialized techniques. One technique used by OLAP systems to speed up multidimensional data analysis is to precompute aggregates on some subsets of dimensions and their corresponding hierarchies. We first address the problem of efficiently estimating aggregate sizes. Precomputation of aggregate data improves query response time. However, the decision of what and how much to precompute is a difficult one. It is further complicated by the fact that precomputation in the presence of hierarchies can result in an unintuitively large increase in the amount of storage required by the database. Hence, it is interesting and useful to estimate the storage blowup that will result from a proposed set of precomputations without actually computing them. We propose three strategies to solve this problem, and investigate the accuracy of these algorithms in estimating the blowup for different data distributions and database schemas. Another intriguing problem that we are faced with is which aggregates to precompute. The more that is precomputed, the faster queries can be answered; however, it is often difficult to determine which are the best aggregates to be precomputed given a fixed amount of space. We study the structure of the precomputation problem and show that under certain broad conditions on the multidimensional data, a simple and fast algorithm, PBS achieves good performance bounds. We present an empirical study of PBS that demonstrates that PBS picks a surprisingly good set of aggregates even when the conditions do not hold. Queries in real world applications frequently require aggregations over multiple cubes (in a star schema, this corresponds to there being multiple fact tables). Unfortunately, most research into aggregate selection has assumed that queries are over a single cube. We analyze aggregate selection in the context of multicube queries, and propose algorithms that perform significantly better than previously proposed algorithms for multicube workloads, without any deterioration in performance for single cube query workloads.

302 citations


01 Jan 1998
TL;DR: This paper discusses the coexistence of so-called transaction databases with decision support systems and the argument that the physical design required for acceptable performance of each is incompatible and that therefore, data should be stored redundantly in multiple enterprise databases.
Abstract: Overview Recently, there has been a great deal of discussion in the trade press and elsewhere regarding the coexistence of so-called transaction databases with decision support systems. These discussions usually revolve around the argument that the physical design required for acceptable performance of each is incompatible and that therefore, data should be stored redundantly in multiple enterprise databases: one for transaction processing, and the other for decision support type activities. Also, these same arguments usually confuse physical schema with logical and conceptual schema.

296 citations


Proceedings ArticleDOI
01 Jul 1998
TL;DR: This work proposes a model for multidimensional databases, based on the notion of the base cube, which is used for the calculation of the results of cube operations, and provides a mapping of the multid dimensional model to the relational model and to multiddimensional arrays.
Abstract: Online analytical processing (OLAP) is a trend in database technology, which has attracted the interest of a lot of research work. OLAP is based on the multidimensional view of data, supported either by multidimensional databases (MOLAP) or relational engines (ROLAP). We propose a model for multidimensional databases. Dimensions, dimension hierarchies and cubes are formally introduced. We also introduce cube operations (changing of levels in the dimension hierarchy, function application, navigation etc.). The approach is based on the notion of the base cube, which is used for the calculation of the results of cube operations. We focus our approach on the support of a series of operations on cubes (i.e., the preservation of the results of previous operations and the applicability of aggregate functions in a series of operations). Furthermore, we provide a mapping of the multidimensional model to the relational model and to multidimensional arrays.

220 citations


Book ChapterDOI
01 Jan 1998
TL;DR: OLAP mining is a mechanism which integrates on-line analytical processing with data mining so that mining can be performed in different portions of databases or data warehouses and at different levels of abstraction at user’s finger tips.
Abstract: OLAP mining is a mechanism which integrates on-line analytical processing (OLAP) with data mining so that mining can be performed in different portions of databases or data warehouses and at different levels of abstraction at user’s finger tips. With rapid developments of data warehouse and OLAP technologies in database industry, it is promising to develop OLAP mining mechanisms.

188 citations


Book ChapterDOI
23 Mar 1998
TL;DR: This work extends the classical multidimensional model by grouping functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and to maintain on schema design level.
Abstract: In the recent past, different multidimensional data models were introduced to model OLAP (‘Online Analytical Processing’) scenarios. Design problems arise, when the modeled OLAP scenarios become very large and the dimensionality increases, which greatly decreases the support for an efficient ad-hoc data analysis process. Therefore, we extend the classical multidimensional model by grouping functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and to maintain on schema design level. During the multidimensional data analysis phase, this technique yields in nested data cubes reflecting an intuitive two-step navigation process: classification-oriented ‘drill-down’/ ‘roll-up’ and description-oriented‘split’/ ‘merge’ operators on data cubes. Thus, the proposed Nested Multidimensional Data Model provides great modeling flexibility during the schema design phase and application-oriented restrictiveness during the data analysis phase.

177 citations


Journal ArticleDOI
01 Mar 1998
TL;DR: In this article, a data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses, including characterization, comparison, association, classification, prediction, and clustering.
Abstract: Great efforts have been paid in the Intelligent Database Systems Research Lab for the research and development of efficient data mining methods and construction of on-line analytical data mining systems.Our work has been focused on the integration of data mining and OLAP technologies and the development of scalable, integrated, and multiple data mining functions. A data mining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of data mining functions, including characterization, comparison, association, classification, prediction, and clustering. It also builds up a user-friendly, interactive data mining environment and a set of knowledge visualization tools. In-depth research has been performed on the efficiency and scalability of data mining methods. Moreover, the research has been extended to spatial data mining, multimedia data mining, text mining, and Web mining with several new data mining system prototypes constructed or under construction, including GeoMiner, MultiMediaMiner, and WebLogMiner.This article summarizes our research and development activities in the last several years and shares our experiences and lessons with the readers.

177 citations


Book ChapterDOI
15 Apr 1998
TL;DR: A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed, and several strategies proposed, including approximation and partial materialization of the spatial objects resulted from spatial OLAP operations are proposed.
Abstract: On-line analytical processing (OLAP) has gained its popularity in database industry. With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and on-line analytical processing of spatial data. In this paper, we study methods for spatial OLAP, by integration of nonspatial on-line analytical processing (OLAP) methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies proposed, including approximation and partial materialization of the spatial objects resulted from spatial OLAP operations. Some techniques for selective materialization of the spatial computation results are worked out, and the performance study has demonstrated the effectiveness of these techniques.

177 citations


Journal Article
TL;DR: In this article, the authors propose a nested multidimensional data model for OLAP scenarios, which groups functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and maintain on schema design level.
Abstract: In the recent past, different multidimensional data models were introduced to model OLAP ('Online Analytical Processing') scenarios. Design problems arise, when the modeled OLAP scenarios become very large and the dimensionality increases, which greatly decreases the support for an efficient ad-hoc data analysis process. Therefore, we extend the classical multidimensional model by grouping functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and to maintain on schema design level. During the multidimensional data analysis phase, this technique yields in nested data cubes reflecting an intuitive two-step navigation process: classification-oriented 'drill-down'/ 'roll-up' and description-oriented 'split'/ 'merge' operators on data cubes. Thus, the proposed NESTED MULTIDIMENSIONAL DATA MODEL provides great modeling flexibility during the schema design phase and application-oriented restrictiveness during the data analysis phase.

Proceedings ArticleDOI
01 Jul 1998
TL;DR: This paper proposes two multidimensional normal forms that ensure the validity of analytical computations on the multiddimensional database, but also favor an efficient physical database design.
Abstract: In the area of online analytical processing (OLAP), the concept of multidimensional databases is receiving much popularity. Thus, a couple of different multidimensional data models were proposed from the research as well as from the commercial product side, each emphasizing different perspectives. However, very little work has been done investigating guidelines for good schema design within such a multidimensional data model. Based on a logical reconstruction of multidimensional schema design, this paper proposes two multidimensional normal forms. These normal forms define modeling constraints for summary attributes describing the cells within a multidimensional data cube and constraints to model complex dimensional structures appropriately. Multidimensional schemas compliant to these normal forms do not only ensure the validity of analytical computations on the multidimensional database, but also favor an efficient physical database design.

Journal ArticleDOI
01 Mar 1998
TL;DR: In this article, the authors summarize the versatility of relational views and their potential in a data warehouse, a redundant collection of data replicated from several possibly distributed and loosely coupled source databases, organized to answer OLAP queries, using both as a specification technique and as an execution plan for the derivation of the warehouse data.
Abstract: A data warehouse is a redundant collection of data replicated from several possibly distributed and loosely coupled source databases, organized to answer OLAP queries. Relational views are used both as a specification technique and as an execution plan for the derivation of the warehouse data. In this position paper, we summarize the versatility of relational views and their potential.

Patent
22 Jun 1998
TL;DR: In this paper, a methodology for automatically deriving and steadily improving a process model executed by a workflow management system (WFMS) is presented. But this method is restricted to a single process model.
Abstract: The present invention relates to the area of workflow management systems (WFMS). More particularly the invention is related to a methodology of automatically deriving and steadily improving a process model executed by the WFMS. The current invention dramatically simplifies and automates the process of model a business model of a business process. The invention allows to start just with set of unrelated activities and discover the real world relations between them at a later point in time; data mining and OLAP technologies are exploited for this discovery. The current invention thus proposes a posteriori methodology. For that purpose the precise underlying process model is derived at a later point in time based on audit data collected by the WFMS during the early deployment of a process model.

Patent
21 Dec 1998
TL;DR: In this paper, a rules based decision management system using online analytical processing (OLAP) technology for dynamic assessment of strategy results is presented, where the results are aggregated over time, typically in accordance with values of a discrete dimension and ranges of a continuous dimension, to prepare for the application of OLAP technology.
Abstract: A rules based decision management system using online analytical processing (OLAP) technology for dynamic assessment of strategy results. Generally, a rules based decision management system applies strategies which produce results. The results are aggregated over time, typically in accordance with values of a discrete dimension and ranges of a continuous dimension, to prepare for the application of OLAP technology. Date stamping can be used so that, when aggregating the results, different values and different ranges can be valid for different periods of time. OLAP technology is then applied to the aggregated results, to evaluate the applied strategies.

Proceedings ArticleDOI
26 Aug 1998
TL;DR: This work lists requirements that a formal model and a corresponding query language must fulfill to be suitable for OLAP and discusses four approaches that come closest to these requirements, thus providing a systematic overview.
Abstract: Multidimensional database technology is becoming more and more important in conjunction with data warehouses and OLAP analysis. What is still lacking is a commonly accepted formal foundation. Such a model can serve as a basis for future research and standardization. Recently a multitude of interesting proposals on this topic have been published. OLAP applications have some special requirements that do not apply to other areas of multidimensional analysis (e.g. GIS, PACS). We list requirements that a formal model and a corresponding query language must fulfill to be suitable for OLAP. We compare four approaches that come closest to our requirements. After a brief description we discuss their suitability as a formal foundation for OLAP, thus providing a systematic overview. Finally, we propose directions for further research.

Journal Article
Surajit Chaudhuri1
TL;DR: It is argued that (1) the authors need to focus on generic scalability requirements (rather than on features tuned to specific algorithms) wherever possible and (2) data mining systems that are not just scalable, but “SQL-aware” are needed.
Abstract: The promise of decision support systems is to exploit enterprise data for competitive advantage. The process of deciding what data to collect and how to clean such data raises nontrivial issues. However, even after a data warehouse has been set up, it is often difficult to analyze and assimilate data in a warehouse. OLAP takes an important first step at the problem by allowing us to view data multidimensionally as a giant spreadsheet with sophisticated visual tools to browse and query the data (See [3] for a survey). Data Mining promises a giant leap over OLAP where instead of a power OLAP user navigating data, the mining tools will automatically discover interesting patterns. Such functionality will be very useful in enterprise databases that are characterized by a large schema as well as large number of rows. Data Mining involves data analysis techniques that have been used by statisticians and machine learning community for quite some time now (generically referred to data analysts in this paper). This raises the question as to what role, if any, database systems research may contribute to area of data mining. In this article, I will try to present my biased view on this issue and argue that (1) we need to focus on generic scalability requirements (rather than on features tuned to specific algorithms) wherever possible and (2) we need to try to build data mining systems that are not just scalable, but “SQL-aware”.

Patent
02 Dec 1998
TL;DR: In this article, a software visualization tool consistent with the present invention integrates OLAP functionality with focus+context based techniques for navigation through and inspection of large multidimensional datasets, which is used to increase the clarity and information content provided to the user.
Abstract: A software visualization tool consistent with the present invention integrates OLAP functionality with focus+context based techniques for navigation through and inspection of large multidimensional datasets. Focus+context based navigation techniques are used to increase the clarity and information content provided to the user. The visualization tool supports a number of operations of the data set, including: select-slice, aggregation, promote/demote, repeat-variables, and sort.

Proceedings ArticleDOI
01 Jun 1998
TL;DR: The initial performance results suggest that the exploitation of common subtask evaluation and global optimization can yield substantial performance improvements when relational database systems are used as data sources for multidimensional analysis.
Abstract: Database researchers have made significant progress on several research issues related to multidimensional data analysis, including the development of fast cubing algorithms, efficient schemes for creating and maintaining precomputed group-bys, and the design of efficient storage structures for multidimensional data. However, to date there has been little or no work on multidimensional query optimization. Recently, Microsoft has proposed “OLE DB for OLAP” as a standard multidimensional interface for databases. OLE DB for OLAP defines Multi-Dimensional Expressions (MDX), which have the interesting and challenging feature of allowing clients to ask several related dimensional queries in a single MDX expression. In this paper, we present three algorithms to optimize multiple related dimensional queries. Two of the algorithms focus on how to generate a global plan from several related local plans. The third algorithm focuses on generating a good global plan without first generating local plans. We also present three new query evaluation primitives that allow related query plans to share portions of their evaluation. Our initial performance results suggest that the exploitation of common subtask evaluation and global optimization can yield substantial performance improvements when relational database systems are used as data sources for multidimensional analysis.

Proceedings ArticleDOI
01 Jun 1998
TL;DR: This paper proposes the use of Cubetrees, a collection of packed and compressed R-trees, as an alternative storage and index organization for ROLAP views and provides an efficient algorithm for mapping an arbitrary set of OLAP views to aCollection ofCubetrees that achieve excellent performance.
Abstract: The Relational On-Line Analytical Processing (ROLAP) is emerging as the dominant approach in data warehousing with decision support applications. In order to enhance query performance, the ROLAP approach relies on selecting and materializing in summary tables appropriate subsets of aggregate views which are then engaged in speeding up OLAP queries. However, a straight forward relational storage implementation of materialized ROLAP views is immensely wasteful on storage and incredibly inadequate on query performance and incremental update speed. In this paper we propose the use of Cubetrees, a collection of packed and compressed R-trees, as an alternative storage and index organization for ROLAP views and provide an efficient algorithm for mapping an arbitrary set of OLAP views to a collection of Cubetrees that achieve excellent performance. Compared to a conventional (relational) storage organization of materialized OLAP views, Cubetrees offer at least a 2-1 storage reduction, a 10-1 better OLAP query performance, and a 100-1 faster updates. We compare the two alternative approaches with data generated from the TPC-D benchmark and stored in the Informix Universal Server (IUS). The straight forward implementation materializes the ROLAP views using IUS tables and conventional B-tree indexing. The Cubetree implementation materializes the same ROLAP views using a Cubetree Datablade developed for IUS. The experiments demonstrate that the Cubetree storage organization is superior in storage, query performance and update speed.

Proceedings ArticleDOI
26 Aug 1998
TL;DR: A metadata driven approach implemented as part of the WWW-EIS-DWH project is presented in detail, and the prototype focuses on the technical realisation and is intended not to be open for use in different security policies.
Abstract: Gives an overview of security relevant aspects of existing OLAP/Data Warehouse solutions, an area which has seen rather little interest from product developers and is only beginning to be discussed in the research community. Following this description of the current situation, a metadata driven approach implemented as part of the WWW-EIS-DWH project is presented in detail. The prototype focuses on the technical realisation and is intended not to be open for use in different security policies.

Proceedings ArticleDOI
26 Aug 1998
TL;DR: This work proposes an efficient partitioning strategy based on the relational representation of a data warehouse (i.e., star schema), incorporates a particular indexing strategy, DataIndexes, to further improve query processing times and parallel resource utilization, and proposes a preliminary parallel star-join strategy.
Abstract: In recent years the database community has experienced a tremendous increase in the availability of new technologies to support efficient storage and retrieval of large volumes of data, namely data warehousing and On-Line Analytical Processing (OLAP) products. Efficient query processing is critical in such an environment, yet achieving quick response times with OLAP queries is still largely an open issue. We propose a solution approach to this problem by applying parallel processing techniques to a warehouse environment. We suggest an efficient partitioning strategy based on the relational representation of a data warehouse (i.e., star schema). Furthermore, we incorporate a particular indexing strategy, DataIndexes, to further improve query processing times and parallel resource utilization, and propose a preliminary parallel star-join strategy.

Book
29 Jun 1998
TL;DR: "Each chapter is a practice run for the way the authors all ought to design their data marts and hence their data warehouses."-Ralph Kimball, from the Foreword
Abstract: "Each chapter is... a practice run for the way we all ought to design our data marts and hence our data warehouses."-Ralph Kimball, from the Foreword. Let Do in fact table to optimize, this example data and cost effective operational systems. With odi name for more information they offer separately a hybrid design. Business intelligence queries access and relationships should have a fixed periodic. The primary key column in the table. Olap cubes based on the same levels data quickly as user applications. For the maintenance procedure is recommended on investment.

Proceedings ArticleDOI
01 Nov 1998
TL;DR: This work takes the concepts and basic ideas of the classical multidimensional model (dimensions and facts) to propose a revolutionary approach based on the Object Oriented (OO) Paradigm to MDB conceptual modeling and presents cube classes as the basic structure to allow a subsequent analysis of the data stored in the system.
Abstract: In the recent past, there has been an increasing interest in multidimensional databases (MDB) and On-line Analytical Processing (OLAP) scenarios. Several multidimensional models have been proposed in the last days. However, very few works have been focused on the area of multidimensional database conceptual modeling. Moreover, they are either conceptual extensions to the classical multidimensional model or translations from classical database conceptual models (such as the EntityRelationship model). Nevertheless, we take the concepts and basic ideas of the classical multidimensional model (dimensions and facts) to propose a revolutionary approach based on the Object Oriented (OO) Paradigm to MDB conceptual modeling. Then, the basic elements of our Object Oriented Multidimensional Model (OOMD) such as dimension classes and fact classes are introduced. We then present cube classes as the basic structure to allow a subsequent analysis of the data stored in the system. We fairly believe that the utilization of the OO Paradigm will provide us a general conceptual model to MDB conceptual modeling in a more flexible, natural and simple way than the models proposed until now.

Proceedings Article
27 Aug 1998
TL;DR: This paper advocates the use of decision table classifiers that are easy for line-of-business users to understand and describes a visualization mechanism that is implemented in MineSet.
Abstract: Business users and analysts commonly use spreadsheets and 2D plots to analyze and understand their data. On-line Analytical Processing (OLAP) provides these users with added flexibility in pivoting data around different attributes and drilling up and down the multi-dimensional cube of aggregations. Machine learning researchers, however, have concentrated on hypothesis spaces that are foreign to most users: hyper-planes (Perceptrons), neural networks, Bayesian networks, decision trees, nearest neighbors, etc. In this paper we advocate the use of decision table classifiers that are easy for line-of-business users to understand. We describe several variants of algorithms for learning decision tables, compare their performance, and describe a visualization mechanism that we have implemented in MineSet. The performance of decision tables is comparable to other known algorithms, such as C4.5/C5.0, yet the resulting classifiers use fewer attributes and are more comprehensible.


Proceedings ArticleDOI
01 Jul 1998
TL;DR: This work presents and compares two query languages for OLAP databases and proposes a high-level graphical language that allows the user to specify analytical queries in a natural and intuitive way and turns out that the two languages have the same expressive power.
Abstract: We address the issue of designing effective query languages for OLAP databases. The basis of our investigation is MD, a new data model for multidimensional databases that, unlike other multidimensional models, is independent of any specific implementation and as such provides a clear separation between practical and conceptual aspects. In this framework, we present and compare two query languages, based on different paradigms, for OLAP databases. The first language is algebraic and provides an effective way to manipulate multidimensional data in a procedural fashion. Although this language is clean and powerful, it is clearly not suited for final users. We therefore propose a high-level graphical language that allows the user to specify analytical queries in a natural and intuitive way. It turns out that the two languages have the same expressive power.

Proceedings ArticleDOI
26 Aug 1998
TL;DR: This paper examines a possible combination of both materialization and index structures for OLAP applications, and describes how this mechanism works in detail and presents results of performance evaluation.
Abstract: OLAP applications make use of fast indexes and materialization of data. Most research treats just one topic. Either the materialized values or the design of index structures are considered. The paper examines a possible combination of both techniques. The R* tree is taken as an example of a multidimensional index structure. Aggregated data is stored in the inner nodes of the index structure in addition to the references to the successor nodes. We describe how this mechanism works in detail and present results of performance evaluation.

Proceedings ArticleDOI
01 Nov 1998
TL;DR: This paper presents another physical implementation using an object-oriented database or persistent objects— Object Oriented On-line Analytic Processing (O3LAP)— as a possible alternative, compares the O3L AP model with the current models, suggests possible extensions to the current OLAP models, and defines the elements involved in the mapping of a logical model to the physical one.
Abstract: Although data warehouses are viewed as organized, summarized repositories of time-oriented data conceptually, the physical implementation determines the speed, efficiency, scalability, and extensibility of this view. Two major physical implementations exist today: data warehouses built upon relational database management systems (ROLAP) and warehouses built upon proprietary multi-dimensional databases (MOLAP). Both ROLAP and MOLAP have their own advantages and disadvantages due to their physical implementation. This paper presents another physical implementation using an object-oriented database or persistent objects— Object Oriented On-line Analytic Processing (O3LAP)— as a possible alternative, compares the O3LAP model with the current models, suggests possible extensions to the current OLAP models, defines the elements involved in the mapping of a logical model to the physical one, illustrates queries based on the O3LAP model, and discusses areas for future research.

Book ChapterDOI
19 Nov 1998
TL;DR: In this paper, the authors discuss recent developments in data warehouse modelling, view maintenance, and parallel query processing, and a number of technical issues for exploratory research are presented and possible solutions are discussed.
Abstract: In the recent years, the database community has witnessed the emergence of a new technology, namely data warehousing. A data warehouse is a global repository that stores pre-processed queries on data which resides in multiple, possibly heterogeneous, operational or legacy sources. The information stored in the data warehouse can be easily and efficiently accessed for making effective decisions. The On-Line Analytical Processing (OLAP) tools access data from the data warehouse for complex data analysis, such as multidimensional data analysis, and decision support activities. Current research has lead to new developments in all aspects of data warehousing, however, there are still a number of problems that need to be solved for making data warehousing effective. In this paper, we discuss recent developments in data warehouse modelling, view maintenance, and parallel query processing. A number of technical issues for exploratory research are presented and possible solutions are discussed.