Det här arbetet har som huvudsyfte att belysa och ta fram vilka motiv det finns till att implementera datalager. Datalager är en teknik för att ta fram beslutsstödssystem där slutanvändarna själva tar fram sin information. Ett beslutsstödssystem har till uppgift att ge beslutsfattare (slutanvändare) information om en hel eller delar av en organisation. I introduktionen förklaras närmare vad ett beslutsstödssystem och datalager är för någonting samt ges exempel på i vilka sammanhang datalager används. En förklaring ges också till den historiska utvecklingen mot datalager För att få fram motiven har intervjuer genomförts hos fyra stycken företag som implementerat datalager. Hos de här företagen har projektledare som ansvarat för implementationerna intervjuats samt hos ett företag har fyra stycken slutanvändare intervjuats. Resultatet från intervjuerna visar att de vanligaste motiven finns betraktat ur en slutanvändares perspektiv. Motiven är bland annat att tillgängliggöra information och slutanvändarna kan anpassa sin information i förhållande till sitt verksamhetsområde.

Syftet med denna rapport är att undersöka om datalager, en sorts databas som fungerar som beslutsstödssystem, kan användas av mindre företag och organisationer inom en snar framtid. Datalager har tidigare varit en fråga främst för stora företag. Mindre företag nämns sällan i datalagersammanhang. Datalagerteknologin är ny vilket gör att den fortfarande utvecklas i hög takt. Svaret söks genom en litteraturstudie samt en empirisk studie. I den senare tillfrågas ett antal datalagerutvecklare. Litteraturstudien fokuserar på fyra synvinklar: en storleksmässig synvinkel, en nyttomässig synvinkel, en ekonomisk synvinkel och framtida möjligheter. Ur dessa synvinklar studeras mindre företags möjligheter att använda datalager. Den empiriska studien fokuserar på spridningen av datalager i Sverige och mindre företags möjlighet att använda datalager. Resultatet visar att det redan idag finns möjligheter för mindre företag att använda datalager. Även om möjligheterna finns är det en komplex process att implementera ett datalager och arbetet med datalagret upphör inte efter implementationen.

Current database management systems use Graphic Processing Units (GPUs) as dedicated accelerators to process each individual query, which results in underutilization of GPU. When a single query data warehousing workload was run on an open source GPU query engine, the utilization of main GPU resources was found to be less than 25%. The low utilization then leads to low system throughput. To resolve this problem, this paper suggests a way to transfer all of the desired data into the global memory of GPU and keep it until all queries are executed as one batch. The PCIe transfer time from CPU to GPU is minimized, which results in better performance in less time of overall query processing. The execution time was improved by up to 40% when running multiple queries, compared to dedicated processing.

This thesis focuses on developing methodics for performance comparison of data warehouse systems. Firstly, the thesis defines data warehouses in various development stages of organization BI and describes knowledge about data warehouses. Methodics developed in the thesis describes architecture of standardized data warehouse, data flow between areas of data warehouse and processes in data warehouse. Understanding of these concepts is crucial for assurance of methodics applicability. Methodics offer logical progression of steps, which start and include testing of data warehouse systems. The contribution of the thesis is in having guide, what needs to be done when organization must do while testing various systems for data warehouses. Also it describes how this testing should be done on middle level detail, which is the absolute minimum level of abstraction that can be done due to wide applicability of methodics. Methodics offers solution to the problem of performance comparison when organization need to answer question - Which data warehouse system should we use in our organization? However, methodics expects already some knowledge about data warehouse content.

Master thesis deals with a problem of data integration between Big Data platform and enterprise data warehouse. Main goal of this thesis is to create a complex transfer system to move data from a data warehouse to this platform using a suitable tool for this task. This system should also store and manage all metadata information about previous transfers. Theoretical part focuses on describing concepts of Big Data, brief introduction into their history and presents factors which led to need for this new approach. Next chapters describe main principles and attributes of these technologies and discuss benefits of their implementation within an enterprise. Thesis also describes technologies known as Business Intelligence, their typical use cases and their relation to Big Data. Minor chapter presents main components of Hadoop system and most popular related applications. Practical part of this work consists of implementation of a system to execute and manage transfers from traditional relation database, in this case representing a data warehouse, to cluster of a few computers running a Hadoop system. This part also includes a summary of most used applications to move data into Hadoop and a design of database metadata schema, which is used to manage these transfers and to store transfer metadata.

The Kimball methodology, often referred to as dimensional modelling, is well established in data warehousing and business intelligence as a highly successful means for turning data into information. Yet weaknesses exist in the Kimball approach that make it difficult to rapidly extend or interrelate dimensional models in complex business areas such as Health Care. This Thesis looks at the development of a methodology that will provide for the rapid extension and interrelation of Kimball dimensional models. This is achieved through the use of techniques similar to those employed in the semantic web. These techniques allow for rapid analysis and insight into highly variable data which previously was difficult to achieve. / Graduate

This thesis analyzes the situation in the area of Business Intelligence at O2 Czech Republic. It focuses on identifying key issues and finding solutions to the overall improvement of the service within the company. It can be a guide for managers on how to proceed in the analysis of the BI environment, finding opportunities for improving organization setup and technical aspects, especially in the data warehouse solution. Here one can also find recommendations on how to proceed with the definition of development methodologies and how to effectively set the tools and processes associated with the development of the data warehouse. This approach results in an efficient, clearly documented and sustainable development concept of the data warehouse in a large company.

In this diploma thesis I analyze data architecture and data marts in a bank area. Firstly I analyze different theoretical approaches to build the data warehouse. Consequently I specialize in operational and analytical data marts -- I analyze their business value, differences and location at the warehouse. In the third chapter I analyze reasons that have led to implement Asset Liability Management data mart. In the following chapter I analyze a life-cycle of this data mart not only from the technical point of view, but also from the project management perspective. The main benefit from this thesis is in the analysis of these aspects of ALM life cycle.

The thesis deals with banking data warehouse and the factors which influence their architecture. The goal of the thesis is to analyze the influence by fulfilling the three partial goals -- defining banks' requirements on data warehouses, analyzing a data warehouse in the real banking environment and making a comparison of the Czech banks' data warehouses. First, the thesis briefly introduces elements of business intelligence (BI) and data warehousing. Second, after deducing reasons for BI implementation in commercial banks, the requirements on data warehouse are synthesized and their impacts on the data warehouse architecture are analyzed. Finally, conclusions of the thesis are applied on creation of the survey which enables comparison of the Czech banks' data warehouses. The thesis applies the theory of relationship between BI maturity, requirements on BI and data warehouse architecture and creates a tool for evaluating and comparing the data warehouses of banks with respect to bank's BI maturity and predisposition for a particular architecture. The tool can serve as a basis for a general tool able to compare data warehouses in other branches of economy and can help to better understand relations between organizations' need for information, BI solutions used and underlying data warehouses.

This diploma thesis deals with an analysis of SAP Business Intelligence platform (SAP NetWeaver BI 7.0) and compares its chosen tools with Microsoft SQL Server BI. The main aim of the thesis is to evaluate SAP BI system in a comprehensive way whereas extra attention is paid to certain areas, such as a data warehouse. One of the goals is the comparison of approaches and methods of the creation of the data warehouse with MS SQL Server BI solution. The thesis consists of theoretical insight into Business Intelligence, which is focused on the architecture of data warehouse, and OLAP technologies. There is an evaluation of the role of SAP in the BI market followed by the introduction of SAP NetWeaver, an application and integration platform. Included is also an analysis of the architecture of data warehouse, methods of storing data, ETL process and other chosen functions and tools (including presentation tools) in next sections. A sample application in the final part provides the reader with better understanding of different ETL approaches, which are analyzed into detail and evaluated. There is the comparison of OLAP technologies in the end and the comparison of basic reporting tools, as well.