Online Analytical Processing Applications Research Articles

OLAP MULTIDIMENSIONAL MODEL FOR POLLUTANT EMISSIONS IN INDUSTRIAL INSTALLATIONS

SUMMARY OLAP (On-Line Analytical Processing) performs multidimensional analysis of business data and provides the capability for complex calcul ations, trend analysis, and sophisticated data modeling. It became the fundamental foundation for Intelligent Solutions including Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting. OLAP enables end-users to perform ad-hoc analysis of dat a in multiple dimensions, thereby providing the insight and understanding they need f or better decision making. For achieving the main objective of the three years research PN2 Project “Sustainable Management System of Resources Used for Monitoring and Evaluating the Environmental Risks in Order to Prevent the Negative Effects and to Manage Crises Situations MEMDUR”, code D11-037/18.09.2007, webpage: http://memdur.ssai.valahia.ro), in the second phase of the research, an analysis of the ex isting risk management systems was fulfilled and a solution based on the OLAP systems was proposed with the view of its implementation. The OLAP system designing was made based on the solution offered by Microsoft and it proposes a multidimensional model for data organizing which allows the analysis of data through various visualizations of multidimensional cubes. The multitude of possible views over the data collection together wi th the choosing of the convenient measures can be taken into account for the risk calculus whe n is needed to offer a real image on the air pollution rate as a result of different pollutants resulted from the industrial processes (R adulescu 2005). Looking forward, a SOLAP (Spatial On-Line Analytical Processing) instrument will integrate the advantages offered by the Geographic Information System (GIS) with the OLAP applications ones. The classical data warehouse architecture (structured on three levels: data level, OLAP server level and cli ent level) can be extended to a SOLAP model where spatial data (with related attributes), measures and hierarchies have to be added. At the OLAP server level, the metadata warehouse integrates concepts like the relational model, the multidimensional model and GIS concepts. (Rivest et all. 2003).

How to implement multidimensional security into OLAP tools

Data Warehouses (DWs) manage historical information for the decision-making process and for enterprises. Online Analytical Processing Applications (OLAP) tools are the most used tools for implementing and consulting DWs and it is necessary to define security measures to avoid the accessing of unauthorised information by users by executing queries. It is vitally important to consider security requirements from the earliest stages of the development process. We have created a Model-Driven Architecture (MDA) to develop a secure DW and in this paper, we propose how to implement the security measures that are defined at upper abstraction levels using our approach to SQL Server Analysis Services (SSAS).

Approximate Query Processing in Cube Streams

Data cubes have become important components in most data warehouse systems and decision support systems. In such systems, users usually pose very complex queries to the online analytical processing (OLAP) system, and systems usually have to deal with a huge amount of data because of the large dimensionality of the sets; thus, approximating query processing has emerged as a viable solution. Specifically, the applications of cube streams handle multidimensional data sets in a continuous manner in contrast to the traditional cube approximation. Such an application collects data events for cube streams online, generates snapshots with limited resources, and keeps the approximated information in a synopsis memory for further analysis. Compared to the OLAP applications, applications of cube streams are subject to many more resource constraints on both the processing time and the memory and cannot be dealt with by existing methods due to the limited resources. In this paper, we propose the DAWA algorithm, which is a hybrid algorithm of discrete cosine transform (DCT) for data and the discrete wavelet transform (DWT), to approximate cube streams. Our algorithm combines the advantages of the high compression rate of DWT and the low memory cost of DCT. Consequently, DAWA requires much smaller working buffer and outperforms both DWT-based and DCT-based methods in execution efficiency. Also, it is shown that DAWA provides a good solution for an approximate query processing of cube streams with a small working buffer and a short execution time. The optimality of the DAWA algorithm is theoretically proved and empirically demonstrated by our experiments.

Caching in multidimensional databases

One utilisation of multidimensional databases is the field of On-line Analytical Processing (OLAP). The applications in this area are designed to make the analysis of shared multidimensional information fast [9]. On one hand, speed can be achieved by specially devised data structures and algorithms. On the other hand, the analytical process is cyclic. In other words, the user of the OLAP application runs his or her queries one after the other. The output of the last query may be there (at least partly) in one of the previous results. Therefore caching also plays an important role in the operation of these systems. However, caching itself may not be enough to ensure acceptable performance. Size does matter: The more memory is available, the more we gain by loading and keeping information in there. Oftentimes, the cache size is fixed. This limits the performance of the multidimensional database, as well, unless we compress the data in order to move a greater proportion of them into the memory. Caching combined with proper compression methods promise further performance improvements. In this paper, we investigate how caching influences the speed of OLAP systems. Different physical representations (multidimensional and table) are evaluated. For the thorough comparison, models are proposed. We draw conclusions based on these models, and the conclusions are verified with empirical data.

Models of multi-dimensional analysis for qualitative data and its application

Online analytical processing (OLAP) is one of the technologies that enable client applications to efficiently access data multi-dimensionally. This powerful tool helps users create new views of data, based on a rich array of ad hoc calculation functions. However, the complexity of queries required to support OLAP applications in the multi-dimensional model makes OLAP difficult to implement by simply using standard relational database technology in a static manner. Moreover, OLAP requires numerical data input. In contrast, qualitative data cannot be operated on using OLAP technique. This paper develops models of multi-dimensional analysis, based on traditional multi-dimensional techniques and OLAP techniques to analyze qualitative data dynamically. The models are able to discover the kernel knowledge from the current formulated knowledge. The proposed model is used to develop multi-dimensional algebra to facilitate operation in data warehouse.

Developing secure data warehouses with a UML extension

Data Warehouses (DWs), Multidimensional (MD) Databases, and On-Line Analytical Processing Applications are used as a very powerful mechanism for discovering crucial business information. Considering the extreme importance of the information managed by these kinds of applications, it is essential to specify security measures from the early stages of the DW design in the MD modeling process, and enforce them. In the past years, some proposals for representing main MD modeling properties at the conceptual level have been stated. Nevertheless, none of these proposals considers security issues as an important element in its model, so they do not allow us to specify confidentiality constraints to be enforced by the applications that will use these MD models. In this paper, we will discuss the specific confidentiality problems regarding DWs as well as present an extension of the Unified Modeling Language for specifying security constraints in the conceptual MD modeling, thereby allowing us to design secure DWs. One key advantage of our approach is that we accomplish the conceptual modeling of secure DWs independently of the target platform where the DW has to be implemented, allowing the implementation of the corresponding DWs on any secure commercial database management system. Finally, we will present a case study to show how a conceptual model designed with our approach can be directly implemented on top of Oracle 10g.

A UML profile for designing secure data warehouses

Data Warehouses (DW's), Multidimensional Databases, and On-Line Analytical Processing applications are used as a very powerful mechanism for discovering crucial business information. Considering the extreme importance of the information managed by these kinds of applications, it is essential to specify security measures from early stages of the DW design in the MD modeling process, and enforce them. In the past years, there have been some proposals for representing the main MD modeling properties at the conceptual level. Nevertheless, none of these proposals, considers security issues as an important element in their models, so they do not allow us to specify confidentiality constraints to be enforced by the applications that will regarding DW´s and their corresponding MD modeling properties and we propose an UML profile that allows us to specify main security aspects in the conceptual MD modeling. We show the benefit of our approach by applying this profile to an example. Finally, we also sketch how to implement the security aspects considered in our conceptual modeling approach in a commercial DBMS

Using Online Analytical Processing to Manage Emergency Department Operations

The emergency department (ED) is a unique setting in which to explore and evaluate the utility of information technology to improve health care operations. A potentially useful software tool in managing this complex environment is online analytical processing (OLAP). An OLAP system has the ability to provide managers, providers, and researchers with the necessary information to make decisions quickly and effectively by allowing them to examine patterns and trends in operations and patient flow. OLAP software quickly summarizes and processes data acquired from a variety of data sources, including computerized ED tracking systems. It allows the user to form a comprehensive picture of the ED from both system-wide and patient-specific perspectives and to interactively view the data using an approach that meets his or her needs. This article describes OLAP software tools and provides examples of potential OLAP applications for care improvement projects, primarily from the perspective of the ED. While OLAP is clearly a helpful tool in the ED, it is far more useful when integrated into the larger continuum of health information systems across a hospital or health care delivery system.

Applying UML and XML for Designing and Interchanging Information for Data Warehouses and OLAP Applications

Multidimensional (MD) modeling is the basis for data warehouses (DW), multidimensional databases (MDB) and on-line analytical processing (OLAP) applications. In this paper, we present how the unified modeling language (UML) can be successfully used to represent both structural and dynamic properties of these systems at the conceptual level. The structure of the system is specified by means of a UML class diagram that considers the main properties of MD modeling with minimal use of constraints and extensions of the UML. If the system to be modeled is too complex, thereby leading us to a considerable number of classes and relationships, we describe how to use the package grouping mechanism provided by the UML to simplify the final model. Furthermore, we provide a UML-compliant class notation (called cube class) to represent OLAP users’ initial requirements. We also describe how we can use the UML state and interaction diagrams to model the behavior of a data warehouse system. To facilitate the interchange of conceptual MD models, we provide a Document Type Definition (DTD) which allows us to represent the same MD modeling properties that can be considered by using our approach. From this DTD, we can directly generate valid eXtensible Markup Language (XML) documents that represent MD models at the conceptual level. We believe that our innovative approach provides a theoretical foundation for simplifying the conceptual design of MD systems and the examples included in this paper clearly illustrate the use of our approach.

OLAP and bibliographic databases

The application of online analytical processing (OLAP) technology to bibliographic databases is addressed. We show that OLAP tools can be used by librarians for periodic and ad hoc reporting, quality assurance, and data integrity checking, as well as by research policy makers for monitoring the development of science and evaluating or comparing disciplines, fields or research groups. It is argued that traditional relational database management systems, used mainly for day-to-day data storage and transactional processing, are not appropriate for performing such tasks on a regular basis. For the purpose, a fully functional OLAP solution has been implemented on Biomedicina Slovenica, a Slovenian national bibliographic database. We demonstrate the system's usefulness by extracting data for studying a selection of scientometric issues: changes in the number of published papers, citations and pure citations over time, their dependence on the number of co-operating authors and on the number of organisations the authors are affiliated to, and time-patterns of citations. Hardware, software and feasibility considerations are discussed and the phases of the process of developing bibliographic OLAP applications are outlined.

Processing OLAP queries in hierarchically clustered databases

On-Line Analytical Processing (OLAP) is a technology that encompasses applications requiring a multidimensional and hierarchical view of data. OLAP applications often require fast response time to complex grouping/aggregation queries on enormous quantities of data. Commercial relational database management systems use mainly multiple one-dimensional indexes to process OLAP queries that restrict multiple dimensions. However, in many cases, multidimensional access methods outperform one-dimensional indexing methods. We present an architecture for multidimensional databases that are clustered with respect to multiple hierarchical dimensions. It is based on the star schema and is called CSB star. We focus on processing OLAP queries over this schema using multidimensional access methods. Users can still formulate their queries over a traditional star schema, which are then rewritten by the query processor over the CSB star. We exploit the different clustering features of the CSB star to efficiently process a class of typical OLAP queries. We detect cases where the construction of an evaluation plan can be simplified, and other cases where additional processing techniques can be applied.

A Conceptual Model and Algebra for On-Line Analytical Processing in Decision Support Databases

Data warehousing and On-Line Analytical Processing (OLAP) are two of the most significant new technologies in the business data processing arena. A data warehouse, or decision support database, can be defined as a “very large” repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries required to support OLAP applications makes it difficult to implement using standard relational database technology. Moreover, currently there is no standard conceptual model for OLAP. There clearly is a need for such a model and an algebra as evidenced by the numerous SQL extensions offered by many vendors of OLAP products. In this paper we address this issue by proposing a model of a data cube and an algebra to support OLAP operations on this cube. The model we present is simple and intuitive, and the algebra provides a means to concisely express complex OLAP queries.

Designing data warehouses with OO conceptual models

The authors propose an approach that provides a theoretical foundation for the use of object-oriented databases and object-relational databases in data warehouse, multidimensional database, and online analytical processing applications. This approach introduces a set of minimal constraints and extensions to the Unified Modeling Language for representing multidimensional modeling properties for these applications. Multidimensional modeling offers two benefits. First, the model closely parallels how data analyzers think and, therefore, helps users understand data. Second, multidimensional modeling helps predict what final users want to do, thereby facilitating performance improvements. The authors are using their approach to create an automatic implementation of a multidimensional model. They plan to integrate commercial online-analytical-processing tool facilities within their GOLD model case tool as well, a task that involves data warehouse prototyping and sample data generation issues.

MOTC with Examples: An Interactive Aid for Multidimensional Hypothesis Generation

This paper reports on conceptual development in the areas of database mining, and knowledge discovery in databases (KDD). Our efforts have also led to a prototype implementation, called MOTC, for exploring hypothesis space in large and complex data sets. Our KDD conceptual development rests on two main principles. First, we use the crosstab representation for working with qualitative data. This is by now standard practice in OLAP (on-line analytical processing) applications and we reaffirm it with additional reasons. Second, and innovatively, we use Prediction Analysis as a measure of goodness for hypotheses. Prediction Analysis is an established statistical technique for analysis of associations among qualitative variables. It generalizes and subsumes a large number of other such measures of association, depending upon specific assumptions the user is willing to make. As such, it provides a very useful framework for exploring hypothesis space in a KDD context. The paper illustrates these points with an extensive discussion of MOTC.

The cube data model: a conceptual model and algebra for on-line analytical processing in data warehouses

Data warehousing and On-Line Analytical Processing (OLAP) are two of the most significant new technologies in the business data processing arena. A data warehouse can be defined as a “very large” repository of historical data pertaining to an organization. OLAP refers to the technique of performing complex analysis over the information stored in a data warehouse. The complexity of queries required to support OLAP applications makes it difficult to implement using standard relational database technology. Moreover, there is currently no standard conceptual model for OLAP. There is clearly a need for such a model and an algebra as evidenced by the numerous SQL extensions offered by many vendors of OLAP products. In this paper, we address this issue by proposing a model of a data cube and an algebra to support OLAP operations on this cube. The model we present is simple and intuitive, and the algebra provides a means to concisely express complex OLAP queries.

High Performance OLAP and Data Mining on Parallel Computers

On-Line Analytical Processing (OLAP) techniques are increasingly being used in decision support systems to provide analysis of data. Queries posed on such systems are quite complex and require different views of data. Analytical models need to capture the multidimensionality of the underlying data, a task for which multidimensional databases are well suited. Multidimensional OLAP systems store data in multidimensional arrays on which analytical operations are performed. Knowledge discovery and data mining requires complex operations on the underlying data which can be very expensive in terms of computation time. High performance parallel systems can reduce this analysis time. Precomputed aggregate calculations in a Data Cube can provide efficient query processing for OLAP applications. In this article, we present algorithms for construction of data cubes on distributed-memory parallel computers. Data is loaded from a relational database into a multidimensional array. We present two methods, sort-based and hash-based for loading the base cube and compare their performances. Data cubes are used to perform consolidation queries used in roll-up operations using dimension hierarchies. Finally, we show how data cubes are used for data mining using Attribute Focusing techniques. We present results for these on the IBM-SP2 parallel machine. Results show that our algorithms and techniques for OLAP and data mining on parallel systems are scalable to a large number of processors, providing a high performance platform for such applications.