A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

E‐ARK Dissemination Information Package (DIP) Final Specification

The primary aim of this report is to present the final version of the E-ARK Dissemination Information Package (DIP) formats. The secondary aim is to describe the access scenarios in which these DIP formats will be rendered for use

Business Intelligence within Large Companies - Challenges and Maturity

Purpose: The purpose of the thesis is to create a Business Intelligence maturity model based on eight examined operational BI propositions. Furthermore it is to explore the recognition level and business impact of these propositions. Methodology: The work process consisted of an initial explorative phase where a theoretical framework was created based on literature research. Founding on that the maturity model was developed, and then further tested in a survey. The survey also examined the recognition level of the propositions. To explore the business impact of the propositions three in depth interviews were held. Conclusion: The main conclusions in the project are that the eight examined propositions are common challenges among large Scandinavian companies. The developed maturity model covers these challenges and can be used by organizations to see where they are at in their maturity curve and what the next step in the maturity process is. The higher the BI maturity is, the less recognized the propositions are. The recognition level of propositions and the BI maturity impact the business success, and the BI maturity among large Scandinavian companies is somewhat moderate. Other conclusions are that a well functioning BI environment is essential for any large organization since it enables the achieving of strategic goals such as efficient and effective processes. However, efficientBI-­processes and a functioning IT landscape can be difficult to establish, especially for large companies. There are extensive amounts of data to process, the implementation costs are high, and the needed change management resources are often overlooked

Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

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 efficient 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 specific case study on a pharmaceutical distribution companyData warehouse, database, data model.