A data cube model for analysis of high volumes of ambient data

Ambient systems generate large volumes of data for many of their application areas with XML often the format for data exchange. As a result, large scale ambient systems such as smart cities require some form of optimization before different components can merge their data streams. In data warehousing, the cube structure is often used for optimizing the analytics process with more recent structures such as dwarf, providing new orders of magnitude in terms of optimizing data extraction. However, these systems were developed for relational data and as a result, we now present the development of an XML dwarf to manage ambient systems generating XML data

Integrating data warehouses with web data : a survey

This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query, and retrieve Web data and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semistructured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources, and the XML extensions of OnLine Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich document collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as to identify open research line

Efficient cube construction for smart city data

To deliver powerful smart city environments, there is a requirement to analyse web produced data streams in close to real time so that city planners can employ up to date predictive models in both short and long term planning. Data cubes, fused from multiple sources provide a popular input to predictive models. A key component in this infrastructure is an efficient mechanism for transforming web data (XML or JSON) into multi-dimensional cubes. In our research, we have developed a framework for efficient transformation of XML data from multiple smart city services into DWARF cubes using a NoSQL storage engine. Our evaluation shows a high level of performance when compared to other approaches and thus, provides a platform for predictive models in a smart city environment

The interrogation of the OLAP CUBE by using multidimensional analyses

The present paper proposes to speak about the interrogation of the OLAP cubes, by using the multidimensional analyses. The first part of the paper deals with the dynamics of the business environment, a fact which triggers the necessity of the informatics systems for decision assistance. The second point of the paper is the presentation of the online analytical processing (OLAP). Connected to this point is the declarative interrogation language MDX, which assures the access to the OLAP interrogation, functions, offering us the possibility of defining the calculated members of the dimensions. A few examples show us how they work. In the end, we draw the conclusions, stressing the importance and the benefits of the intelligent solutions

Pedagogical Opportunities Of Microsoft’s Adventure Works Business Case And Data Model

Database management and querying skills are a key element of a robust information systems curriculum. The data structure and content of a useful pedagogically-oriented database should be realistic and lifelike, and the database should contain data that accurately depicts the business processes, functions, and entities of a realistic organization, organized in a way that demonstrates best practices in database design. Most database textbooks include some sample databases, but these are often relatively small and sparse of data. By contrast, Microsoft’s Adventure Works (AW) database presents a robust, realistic, and comprehensive framework for many important educational objectives in an Information Systems curriculum. This paper introduces the AW business case and database, and illustrates several pedagogical uses in an undergraduate CIS program

Active Learning via a Sample Database: The Case of Microsoft\u27s Adventure Works

This paper describes the use and benefits of Microsoft’s Adventure Works (AW) database to teach advanced database skills in a hands-on, realistic environment. Database management and querying skills are a key element of a robust information systems curriculum, and active learning is an important way to develop these skills. To facilitate active learning and produce a compelling narrative, the data structure and content of a useful pedagogically-oriented database should be realistic and lifelike. It should contain data that accurately depicts the business processes, functions, and entities of a realistic organization, structured in a way that demonstrates best practices in database design. Most database textbooks include sample databases, but these are often small and sparse of data. By contrast, Microsoft’s AW database presents a robust, realistic, and comprehensive framework for many important educational objectives in an IS curriculum. This paper introduces the AW business case and database, and illustrates several pedagogical uses in an undergraduate CIS program

Multidimensional modeling and analysis of large and complex watercourse data: an OLAP-based solution

International audienceThis paper presents the application of Data Warehouse (DW) and On-Line Analytical Processing (OLAP) technologies to the field of water quality assessment. The European Water Framework Directive (DCE, 2000) underlined the necessity of having operational tools to help in the interpretation of the complex and abundant information regarding running waters and their functioning. Several studies have exemplified the interest in DWs for integrating large volumes of data and in OLAP tools for data exploration and analysis. Based on free software tools, we propose an extensible relational OLAP system for the analysis of physicochemical and hydrobiological watercourse data. This system includes: (i) two data cubes; (ii) an Extract, Transform and Load (ETL) tool for data integration; and (iii) tools for OLAP exploration. Many examples of OLAP analysis (thematic, temporal, spatiotemporal, and multiscale) are provided. We have extended an existing framework with complex aggregate functions that are used to define complex analysis indicators. Additional analysis dimensions are also introduced to allow their calculation and also for purposes of rendering information. Finally, we propose two strategies to address the problem of summarizing heterogeneous measurement units by: (i) transforming source data at the ETL tier, and (ii) introducing an additional analysis dimension at the OLAP server tier