Data warehouses were envisioned to facilitate analytical reporting and data visualization by providing a model for the flow of data from operational databases to decision support environments. Decision support environments provide a multidimensional conceptual view of the underlying data warehouse, which is usually stored in relational DBMSs. Typically, there is an impedance mismatch between this conceptual view — shared also by all decision support applications accessing the data warehouse — and the physical model of the data stored in relational DBMSs. This thesis presents a mapping compilation algorithm in the context of the Conceptual Integration Model (CIM) [67] framework. In the CIM framework, the relationships between the conceptual model and the physical model are specified by a set of attribute-to-attribute correspondences. The algorithm compiles these correspondences into a set of mappings that associate each construct in the conceptual model with a query on the physical model. Moreover, the homogeneity and summarizability of data in conceptual models is the key to accurate query answering, a necessity in decision making environments. A data-driven approach to refactor relational models into summarizable schemas and instances is proposed as the solution of this issue. We outline the algorithms and challenges in bridging multidimensional conceptual models and the physical model of data warehouses and discuss experimental results.

This study concerns how organizations manage various problematic areaswhen implementeing Business Intelligence and experiences from BusinessIntelligence project failures on management level. In order to highlightthese questions, we have carried out interviews with four organizationswhich have great experience in working with Business Intelligence on adaily basis.The result of the study shows that all organizations deal with difficultiesregarding cost-benefit issues and justification concerning BusinessIntelligence investments. The result also showed that the organizationsfaced difficulties with integration concerning time and cost. Documentingand securing support systems presented different attitudes between theorganizations, primary in the question about sub complements to BusinessIntelligence and in the way that they were documented. In the discourse oflegal issues and privacy the majority of the organizations chose not to storepersonal information relative to legal security demands. Ethical issues werenot seen as a problem due to the organization’s restrictiveness aboutcustomer information. The result finally presents advice on how to avoid aBusiness Intelligence project failure.

Data warehouses were envisioned to facilitate analytical reporting and data visualization by providing a model for the flow of data from operational databases to decision support environments. Decision support environments provide a multidimensional conceptual view of the underlying data warehouse, which is usually stored in relational DBMSs. Typically, there is an impedance mismatch between this conceptual view — shared also by all decision support applications accessing the data warehouse — and the physical model of the data stored in relational DBMSs. This thesis presents a mapping compilation algorithm in the context of the Conceptual Integration Model (CIM) [67] framework. In the CIM framework, the relationships between the conceptual model and the physical model are specified by a set of attribute-to-attribute correspondences. The algorithm compiles these correspondences into a set of mappings that associate each construct in the conceptual model with a query on the physical model. Moreover, the homogeneity and summarizability of data in conceptual models is the key to accurate query answering, a necessity in decision making environments. A data-driven approach to refactor relational models into summarizable schemas and instances is proposed as the solution of this issue. We outline the algorithms and challenges in bridging multidimensional conceptual models and the physical model of data warehouses and discuss experimental results.

Detta examensarbete är gjort för Kårhuset Trappan AB genom att använda ramverket Geovisual Analytics Visualization (GAV), som är utvecklat av National Center Visual Analytics (NCVA). Examensarbetet producerade en applikation för att följa företagets inköp och försäljning av artiklar. Applikationen är en webbplats i flash, och skrevs i Actionscript med Adobe Flash Builder 4. För att göra applikationen användarvänlig genomfördes en förstudie, och kriterier för den visuella designen sammanställdes. / This thesis work is done for Kårhuset Trappan AB using the framework Geovisual Analytics Visualization (GAV), which is constructed by the National Center Visual Analytics (NCVA). This thesis work produced an application to monitor the company’s purchases and sales of different sale items. The application is a Flash website, and was written in Actionscript with Adobe Flash Builder 4. To make the application user friendly, a literature study was done. This resulted in criteria’s for the visual design.

Today budgeting occurs in all types of organizations, from authorities and municipalities, to private companies and non-profit associations. Depending on whether the organization is large or small it can look very different. In large organizations the budget can be such a comprehensive document that it is difficult to keep track of it. Furthermore, in large organizations, the budget work starts very early. Thus, an effective budget process could reduce resources, time and ultimately costs. This master’s thesis report describes a budget application built with the Business Intelligence software QlikView. With the application a budgeter can load desired budget data and through a QlikView Extension Object edit the loaded data and finally follow up the work of different budgets. The Extension Object has been implemented using JavaScript and HTML to create a GUI. The edited data is sent to a back-end interface built with one web server and one database server. To evaluate the usability of the Extension Object’s GUI and determine how the budget application works and to get feedback on the Extension Object and its functionality, a user study was performed. The result of the user study shows that the application simplifies budget processes and has great potential to help budgeters and controllers to increase their effectiveness.

Denna rapport behandlar vilka egenskaper som är viktiga att ta hänsyn till vid val av rapportverktyg inom området Business Intelligence. Begreppet BI är relativt omfattande och syftar till färdigheter, teknologier, applikationer och metoder av systematisk och vetenskaplig art som en organisation använder för att bättre förstå sin verksamhet, sin omgivning och omvärld. Rapportverktyg utgör således en mindre del i en större kedja av processer för att stödja beslutstagande.Landstinget Dalarna har anlitat Sogeti, som har varit vår uppdragsgivare för detta examensarbete, för att implementera BI i sin verksamhet och vår studie har sitt ursprung i att Landstinget Dalarna idag har ett stort behov av olika typer av rapporter i många olika delar av organisationen. Rapportbehovet har visat sig vara omfattande och för att lätta på arbetsbördan för de systemutvecklare som skapar rapporter har funderingar framkommit att det skulle kunna vara en bra lösning att låta användarna inom Landstinget Dalarna själva skapa en del av sina egna rapporter. Målet med arbetet är att ge de systemutvecklare som arbetar i projektet riktlinjer kring vilka egenskaper olika rapportverktyg innehar för att de enklare skall kunna avgöra vilket som är lämpligast att använda. De verktyg som i denna studie jämförs med varandra är Report Builder 3.0, PowerPivot samt Dashboard Designer 2010, samtliga från Microsoft.För att göra denna jämförelse mellan olika rapportverktyg krävs bra underlag för att kunna förstå vilka egenskaper som är relevanta att fokusera på samt om några egenskaper väger tyngre än andra.Efter att ha utfört intervjuer med systemutvecklare som arbetar med BI har vi kunnat skapa oss en tydligare bild av detta område. Egenskaperna har sammanställts för att användas i vår jämförelse mellan de olika rapportverktygen. Att dessa egenskaper är av vikt bekräftas till viss del av den teori som finns på området. De egenskaper som främst visar sig vara viktiga i valet är vilken befintlig plattform som används, verktygets möjlighet att skapa interaktiva rapporter samt vilken typ av användare verktyget riktar sig till. Även andra egenskaper visar sig vara viktiga att ta hänsyn till, men då främst beroende på vilka krav som ställs. Resultatet av den praktiska jämförelsen mellan de olika rapportverktygen visar att verktygen till viss del överlappar varandra i funktionalitet samtidigt som de är anpassade för olika typer av användare och plattformar. De utgör allihop delar i Microsofts BI-pussel som på olika sätt skall bidra till att alltid kunna täcka upp de krav som kan finnas beroende på behov och förutsättningar. Samtidigt visar det sig att jämförda rapportverktyg besitter vissa generella egenskaper som gör att verktygen i stora drag klarar, om än på olika sätt, att skapa snarlika rapporter.

Sectra has a customer database with approximately 1600 customers across the world. In this system there exists not only medical information but alsoinformation about the environment which the system runs in, usage  pattern and much more. This report is about storing data received from log les into a suitable database. Sectra wants to be able to analyze this information so that they can make strategic decisions and get a better understanding of their customers' needs. The tested databases are MongoDB, Cassandra, and MySQL. The results shows that MySQL  is not suitable for storing large amount of data with the current conguration. On the other hand, both MongoDB and Cassandra performed well with the growing amount of data.

As computing matures, it is becoming increasingly obvious that a change is necessary for the manner in which web services interact with users. Server-centric models are inconvenient for users. A new paradigm, Smart Interactions, provides a web service architecture which is centered around the user's needs, rather than the simplistic server view currently being used. The system responds to the individual user and is able to adapt to changes to better serve the user. The Smart Internet system helps the user accomplish their tasks efficiently and intuitively. An important aspect of Smart Interactions is that of cognitive support, which provides enhanced information and guidance to the system or user linked to the current task. This thesis examines predictive analytics and its application to cognitive support in Smart Interactions, and presents and evaluates a framework for using predictive analytic support within the Smart Internet model. / Thesis (Master, Computing) -- Queen's University, 2011-09-29 18:11:02.374

Data warehouses were envisioned to facilitate analytical reporting and data visualization by providing a model for the flow of data from operational databases to decision support environments. Decision support environments provide a multidimensional conceptual view of the underlying data warehouse, which is usually stored in relational DBMSs. Typically, there is an impedance mismatch between this conceptual view — shared also by all decision support applications accessing the data warehouse — and the physical model of the data stored in relational DBMSs. This thesis presents a mapping compilation algorithm in the context of the Conceptual Integration Model (CIM) [67] framework. In the CIM framework, the relationships between the conceptual model and the physical model are specified by a set of attribute-to-attribute correspondences. The algorithm compiles these correspondences into a set of mappings that associate each construct in the conceptual model with a query on the physical model. Moreover, the homogeneity and summarizability of data in conceptual models is the key to accurate query answering, a necessity in decision making environments. A data-driven approach to refactor relational models into summarizable schemas and instances is proposed as the solution of this issue. We outline the algorithms and challenges in bridging multidimensional conceptual models and the physical model of data warehouses and discuss experimental results.

Am Lehrstuhl Wirtschaftsinformatik II der TU Chemnitz arbeitet die Nachwuchsforschergruppe Computer-Aided Warehouse Engineering (CAWE), die seit August 2010 besteht, an einem vollständig modellgetriebenen Vorgehen zur Unterstützung des Lebenszyklus von Business-Intelligence-Systemen (BI-Systemen). Neben der Durchführung von Grundlagenforschung hat die Nachwuchsforschergruppe die Erstellung eines Software-Prototyps zum Ziel. Eine wichtige Funktionalität ist die automatische Erzeugung von Systemdokumentationen für verschiedene BI-Systeme mit dem Schwerpunkt auf den Architekturkomponenten. Im Rahmen des Forschungsprojektes führte die CAWE Nachwuchsforschergruppe unter der Leitung von Prof. Dr. Peter Gluchowski in 2011 eine bundesweite Umfrage bei mittelständischen bis großen Unternehmen zu folgendem Thema durch: „Softwaregestütztes Lebenszyklusmanagement und aktuelles Dokumentationsgeschehen für Business Intelligence-Systeme“.