Les experiències prèvies en l'àmbit dels magatzems de dades (o data warehouse), mostren que l'esquema multidimensional del data warehouse ha de ser fruit d'un enfocament híbrid; això és, una proposta que consideri tant els requeriments d'usuari com les fonts de dades durant el procés de disseny.Com a qualsevol altre sistema, els requeriments són necessaris per garantir que el sistema desenvolupat satisfà les necessitats de l'usuari. A més, essent aquest un procés de reenginyeria, les fonts de dades s'han de tenir en compte per: (i) garantir que el magatzem de dades resultant pot ésser poblat amb dades de l'organització, i, a més, (ii) descobrir capacitats d'anàlisis no evidents o no conegudes per l'usuari.Actualment, a la literatura s'han presentat diversos mètodes per donar suport al procés de modelatge del magatzem de dades. No obstant això, les propostes basades en un anàlisi dels requeriments assumeixen que aquestos són exhaustius, i no consideren que pot haver-hi informació rellevant amagada a les fonts de dades. Contràriament, les propostes basades en un anàlisi exhaustiu de les fonts de dades maximitzen aquest enfocament, i proposen tot el coneixement multidimensional que es pot derivar des de les fonts de dades i, conseqüentment, generen massa resultats. En aquest escenari, l'automatització del disseny del magatzem de dades és essencial per evitar que tot el pes de la tasca recaigui en el dissenyador (d'aquesta forma, no hem de confiar únicament en la seva habilitat i coneixement per aplicar el mètode de disseny elegit). A més, l'automatització de la tasca allibera al dissenyador del sempre complex i costós anàlisi de les fonts de dades (que pot arribar a ser inviable per grans fonts de dades).Avui dia, els mètodes automatitzables analitzen en detall les fonts de dades i passen per alt els requeriments. En canvi, els mètodes basats en l'anàlisi dels requeriments no consideren l'automatització del procés, ja que treballen amb requeriments expressats en llenguatges d'alt nivell que un ordenador no pot manegar. Aquesta mateixa situació es dona en els mètodes híbrids actual, que proposen un enfocament seqüencial, on l'anàlisi de les dades es complementa amb l'anàlisi dels requeriments, ja que totes dues tasques pateixen els mateixos problemes que els enfocament purs.En aquesta tesi proposem dos mètodes per donar suport a la tasca de modelatge del magatzem de dades: MDBE (Multidimensional Design Based on Examples) and AMDO (Automating the Multidimensional Design from Ontologies). Totes dues consideren els requeriments i les fonts de dades per portar a terme la tasca de modelatge i a més, van ser pensades per superar les limitacions dels enfocaments actuals.1. MDBE segueix un enfocament clàssic, en el que els requeriments d'usuari són coneguts d'avantmà. Aquest mètode es beneficia del coneixement capturat a les fonts de dades, però guia el procés des dels requeriments i, conseqüentment, és capaç de treballar sobre fonts de dades semànticament pobres. És a dir, explotant el fet que amb uns requeriments de qualitat, podem superar els inconvenients de disposar de fonts de dades que no capturen apropiadament el nostre domini de treball.2. A diferència d'MDBE, AMDO assumeix un escenari on es disposa de fonts de dades semànticament riques. Per aquest motiu, dirigeix el procés de modelatge des de les fonts de dades, i empra els requeriments per donar forma i adaptar els resultats generats a les necessitats de l'usuari. En aquest context, a diferència de l'anterior, unes fonts de dades semànticament riques esmorteeixen el fet de no tenir clars els requeriments d'usuari d'avantmà.Cal notar que els nostres mètodes estableixen un marc de treball combinat que es pot emprar per decidir, donat un escenari concret, quin enfocament és més adient. Per exemple, no es pot seguir el mateix enfocament en un escenari on els requeriments són ben coneguts d'avantmà i en un escenari on aquestos encara no estan clars (un cas recorrent d'aquesta situació és quan l'usuari no té clares les capacitats d'anàlisi del seu propi sistema). De fet, disposar d'uns bons requeriments d'avantmà esmorteeix la necessitat de disposar de fonts de dades semànticament riques, mentre que a l'inversa, si disposem de fonts de dades que capturen adequadament el nostre domini de treball, els requeriments no són necessaris d'avantmà. Per aquests motius, en aquesta tesi aportem un marc de treball combinat que cobreix tots els possibles escenaris que podem trobar durant la tasca de modelatge del magatzem de dades. / Previous experiences in the data warehouse field have shown that the data warehouse multidimensional conceptual schema must be derived from a hybrid approach: i.e., by considering both the end-user requirements and the data sources, as first-class citizens. Like in any other system, requirements guarantee that the system devised meets the end-user necessities. In addition, since the data warehouse design task is a reengineering process, it must consider the underlying data sources of the organization: (i) to guarantee that the data warehouse must be populated from data available within the organization, and (ii) to allow the end-user discover unknown additional analysis capabilities.Currently, several methods for supporting the data warehouse modeling task have been provided. However, they suffer from some significant drawbacks. In short, requirement-driven approaches assume that requirements are exhaustive (and therefore, do not consider the data sources to contain alternative interesting evidences of analysis), whereas data-driven approaches (i.e., those leading the design task from a thorough analysis of the data sources) rely on discovering as much multidimensional knowledge as possible from the data sources. As a consequence, data-driven approaches generate too many results, which mislead the user. Furthermore, the design task automation is essential in this scenario, as it removes the dependency on an expert's ability to properly apply the method chosen, and the need to analyze the data sources, which is a tedious and timeconsuming task (which can be unfeasible when working with large databases). In this sense, current automatable methods follow a data-driven approach, whereas current requirement-driven approaches overlook the process automation, since they tend to work with requirements at a high level of abstraction. Indeed, this scenario is repeated regarding data-driven and requirement-driven stages within current hybrid approaches, which suffer from the same drawbacks than pure data-driven or requirement-driven approaches.In this thesis we introduce two different approaches for automating the multidimensional design of the data warehouse: MDBE (Multidimensional Design Based on Examples) and AMDO (Automating the Multidimensional Design from Ontologies). Both approaches were devised to overcome the limitations from which current approaches suffer. Importantly, our approaches consider opposite initial assumptions, but both consider the end-user requirements and the data sources as first-class citizens.1. MDBE follows a classical approach, in which the end-user requirements are well-known beforehand. This approach benefits from the knowledge captured in the data sources, but guides the design task according to requirements and consequently, it is able to work and handle semantically poorer data sources. In other words, providing high-quality end-user requirements, we can guide the process from the knowledge they contain, and overcome the fact of disposing of bad quality (from a semantical point of view) data sources.2. AMDO, as counterpart, assumes a scenario in which the data sources available are semantically richer. Thus, the approach proposed is guided by a thorough analysis of the data sources, which is properly adapted to shape the output result according to the end-user requirements. In this context, disposing of high-quality data sources, we can overcome the fact of lacking of expressive end-user requirements.Importantly, our methods establish a combined and comprehensive framework that can be used to decide, according to the inputs provided in each scenario, which is the best approach to follow. For example, we cannot follow the same approach in a scenario where the end-user requirements are clear and well-known, and in a scenario in which the end-user requirements are not evident or cannot be easily elicited (e.g., this may happen when the users are not aware of the analysis capabilities of their own sources). Interestingly, the need to dispose of requirements beforehand is smoothed by the fact of having semantically rich data sources. In lack of that, requirements gain relevance to extract the multidimensional knowledge from the sources.So that, we claim to provide two approaches whose combination turns up to be exhaustive with regard to the scenarios discussed in the literature

For being in this changed rapidly new economic era, network technology has brought significant reform for enterprise operational mode, human resource information system has developed with the advantage of information technology, and become a necessary manage instrument gradually, adopt systematic and statistical analyze mode, collocated to display with visional graphic table (such as analytic form or statistic sketch), let high-level and human resource chief be capable of scientific and specific policy assistant data. Data Warehouse is a new technology for data storage, within the data warehouse not only could compile data, even more proceed to decompose¡Bmerge and intersect in different range and layer, and then utilizing the function of On-Line Analytical Processing (OLAP) or Data Mining to obtain one step ahead of information, providing applicable message for policy maker. Therefore, in recent year, data warehouse has become the main data source of Decision Support System (DSS) gradually. This research attempts to establish a data warehouse fledgling model for human resource management, providing the basic requirement for rapidly inquire related statistic data for policy maker, and extract data from human manage information system data base, establish a related multiple dimension data model. And apply the technology of Data Warehouse and OLAP, via Internet, policy maker could depends on his requirement inquiring related statistic data elastically and rapidly, and enhance the quality and time effectiveness of decision. This research could establish the systematic benefits as below¡G ¢¹.Provide convenience to inquire data: via mouse proceed dragging action, rapidly and time effectively let user operate conveniently in data inquiring procedure. ¢º.Multi-dimensional analyze data: owing to OLAP could support multi-dimensional inquire, and makes different intersect analyze and variation comparison, could let the manager make decision reference material more explicitly. ¢».Obtain necessary information elastically: could depend on user¡¦s requirement, arbitrarily change dimension, obtain necessary information, increase user¡¦s inquiring elasticity. ¢¼.Via network medium access: establishing the base system by web-base, via network and browser could forward to inquire, enhance the system¡¦s mobility and convenience. ¢½.Function of data base examination: through OLAP statistic outcome, could reach examine database correctly and completely.

This thesis is written at the department of Business Studies at Uppsala University. The study addresses the differences in possibilities and difficulties of implementing business intelligence (BI)-systems among small IT-enterprises. BI-systems support enterprises in decision-making. To answer the aim of this thesis, theories regarding organizational factors determining a successful implementation of a BI-system were used. Theories regarding components of BI- systems, data warehouse (DW) and online analytical processing (OLAP) were also used. These components enable the decision-support provided by a BI-system. A qualitative study was performed, at four different IT-enterprises, to gather the empirical material. Interviews were performed with CEOs and additional employees at the enterprises. After the empirical material was gathered an analysis was performed to draw conclusion regarding the research topic. The study has concluded that there are differences in possibilities and difficulties of implementing BI-systems among small IT-enterprises. A difference among the enterprises is the perceived ability to finance an implementation. Another difference is in the managerial- and organizational support of an implementation, but also in the business need of using a BI- system in decision-making. There are also differences in how the enterprises use a DW. Not all enterprises benefits from the ability of a DW to manage complex and large amounts of data, neither from the advanced analysis performed by OLAP. The enterprises thus need to examine further if the use of a BI-system is beneficial and would be used successfully in their company.

Every day Internet sites collect and store lots of user data. For effective usage of that data, special tools are required. Online Analytical Processing (OLAP) tools are suggested for that purpose. Usage of these tools is related to usage of Data Warehousing tools. Therefore issues of Data Warehouse design, data transformation and transferring to data warehouses were discussed. In the present years there are new trends in business computer systems industry – WEB Data Warehouses. New characteristics of such systems were analyzed. OLAP tools usage is not possible without multidimensional data model. Main entities and operations with these entities were reviewed and mathematical definitions given. Data transformation process was proposed. This process flow shows how transformations can be used for transferring data from WEB site to multidimensional database. OLAP tools of Microsoft SQL server and Oracle database server (the most popular database management systems in Lithuania) were analyzed in experimental part of work. Data transformation and reviewing in desktop applications, WEB systems and office applications tools were compared and recommendations given.

This work investigates the problem of company not able to handle it’s data analysis using Navision Attain tools alone. A more powerful system is needed, and Microsoft SQL Server OLAP tools are selected as such. The work carried out covers data extraction, transformation and loading (ETL) for analyzing Navision Attain data using OLAP data analysis tools. Different integration architectures, as well as needed transformations and most often encountered problems are covered, together with possible solutions to them.. After completing business process analysis, and identifying user roles and required data, four different data cube were created on data from Navision Attain. Namely cubes for sales analysis, purchase analysis, item inventory tracking and customer debt analysis. In addition, an example schedules for cube updating are created, and wider usage guidelines are provided. The complete system is then optimised to provide efficient performance and low main system load. The optimisation results are compiled into generic suggestions for further analysis system development.

Thesis (M.S.)--Ohio University, August, 2002. / Title from PDF t.p. Includes bibliographical references (leaves 51-54).

Univ., Diss.--Hamburg, 2007.

Univ., Diss.--Mannheim, 2008.

The thesis deals with the design and implementation of Business Intelligence solution for evaluation of cash flow in organization. In the theoretical part, author introduces the reader with theoretical knowledge of Business Intelligence, cash flow and analysis of enterprise information system Helios Green. In the practical part is description of the design and implementation of data warehouse and ana-lytic database. In the part of implementation is explained strategic planning of cash flow, enabled using writeback to the data warehouse. Next chapter is about building financial reports. Thesis is finished by evaluating of the solution in terms of its benefits in comparison with the previous situation.

This thesis deals with different approaches in the development of an analytical model of the data warehouse with a focus on tabular mode and its associated technologies and tools from the Microsoft company. In the theoretical part the thesis introduces the principles of Business Intelligence and also the concept of the semantic model. It also states tabular model as a new approach of creating an analytical database stored in the RAM memory. The tabular model is developed on the top of the data warehouse of a fictitious company Contoso in the practical part. The emphasis is put mainly on the comparison between the tabular and multidimensional model. The work ends with deploying both models on a virtual server with a comparison of their computing power for each of the designed scenarios.