Dissertations / Theses on the topic 'Warehousing Design'

Dissertation (Ph.D.)--University of Toledo, 2005.
Typescript. "Submitted as partial fulfillment of the requirements for the Doctor of Philosophy degree in Manufacturing Management and Engineering. " "A dissertation entitled"--at head of title. Title from title page of PDF document. Bibliography: p. 113-118.

Users require applications to help them obtaining knowledge from the web. However, the specific characteristics of web data make it difficult to create these applications. One possible solution to facilitate this task is to extract information from the web, transform and load it to a Web Warehouse, which provides uniform access methods for automatic processing of the data. Web Warehousing is conceptually similar to Data Warehousing approaches used to integrate relational information from databases. However, the structure of the web is very dynamic and cannot be controlled by the Warehouse designers. Web models frequently do not reflect the current state of the web. Thus, Web Warehouses must be redesigned at a late stage of development. These changes have high costs and may jeopardize entire projects. This thesis addresses the problem of modelling the web and its influence in the design of Web Warehouses. A model of a web portion was derived and based on it, a Web Warehouse prototype was designed. The prototype was validated in several real-usage scenarios. The obtained results show that web modelling is a fundamental step of the web data integration process

The use of electronic services results in the generation of vast amounts of Online Resource Usage (ORU) data. ORU data typically consists of user login, printing and executed process information. The structure of this type of data restricts the ability of decision makers to effectively and efficiently analyse ORU data. A data warehouse (DW) structure is required which satisfies an organisation’s information requirements. In order to design a DW structure a methodology is needed to provide a design template according to acknowledged practices. The aim of this research was to primarily propose a methodology specifically for the design of a DW structure to support the efficient and effective analysis of ORU data. A variety of relevant DW structure design methodologies were investigated and a number of limitations were identified. These methodologies do not provide methodological support for metadata documentation, physical design and implementation. The most comprehensive methodology identified in the investigation was modified and the Adapted Triple-Driven DW Structure Design Methodology (ATDM) was proposed. The ATDM was successfully applied to the information and communication technology services (ICTS) department of the Nelson Mandela Metropolitan University as the case study for this research. The proposed ATDM consists of different phases which include a requirements analysis phase that was adapted from the identified comprehensive methodology. A physical design and an implementation phase were included in the ATDM. The ATDM was successfully applied to the ICTS case study as a proof of concept. The application of the ATDM to ICTS resulted in the generation and documentation of semantic and technical metadata which describes the DW structure derived from the application of the ATDM at a logical and physical level respectively. The implementation phase was applied using the Microsoft SQL Server integrated tool to obtain an implemented DW structure for ICTS that is described by technical metadata at an implementation level. This research has shown that the ATDM can be successfully applied to obtain an effective and efficient DW structure for analysing ORU data. The ATDM provides guidelines to develop a DW structure for ORU data and future research includes the generalisation of the ATDM to accommodate various domains and different data types.

Nowadays warehousing became one of the most important and critical part in supply chain systems due to the fact that it consumes a considerable part of logistic cost. Designing phase of warehousing system is the most important part in warehousing since most of the strategic and tactical decisions should be taken in this phase. Majority of academic papers are primarily analysis oriented and does not give a systematic method and techniques as a basis for warehouse redesign. So there is a need to develop a structured procedure that can be applied for different type of warehouses. Therefore the purpose of this thesis is to develop a process for redesigning production warehouses, and analyzing major problems during redesign steps. The thesis is designed as a case study, and a mix of quantitative and qualitative methods were used for data collection and data analysis. The methodology focuses around the warehousing process and redesign steps as described in the literature. Results of the thesis develop a seven steps procedure for redesigning of the production warehouse, also different problems and challenges are faced during redesign steps. It was tried to choose the best redesigning method which fit with the characteristics of the warehouse, in order to cover the space reduction of the warehouse with the consideration of existing facilities and reducing of cost. In addition, the performance of the current warehouse system was evaluated based on current design of the warehouse in order to avoid repeating of same mistake in redesign process. Storage assignment policy as one of the redesign steps was discussed and a framework for storage system of the components were suggested. The findings of the thesis to some extent can be applicable to other production warehouses. Further research is suggested for more specific results and new developed redesign methods for all types of warehouses.

L'apparition du phénomène Big-Data, a conduit à l'arrivée de nouvelles besoins croissants et urgents de partage de données qui a engendré un grand nombre de requêtes que les SGBD doivent gérer. Ce problème a été aggravé par d 'autres besoins de recommandation et d 'exploration des requêtes. Vu que le traitement de données est toujours possible grâce aux solutions liées à l'optimisation de requêtes, la conception physique et l'architecture de déploiement, où ces solutions sont des résultats de problèmes combinatoires basés sur les requêtes, il est indispensable de revoir les méthodes traditionnelles pour répondre aux nouvelles besoins de passage à l'échelle. Cette thèse s'intéresse à ce problème de nombreuses requêtes et propose une approche, implémentée par un Framework appelé Big-Quereis, qui passe à l'échelle et basée sur le hypergraph, une structure de données flexible qui a une grande puissance de modélisation et permet des formulations précises de nombreux problèmes d•combinatoire informatique. Cette approche est. le fruit. de collaboration avec l'entreprise Mentor Graphies. Elle vise à capturer l'interaction de requêtes dans un plan unifié de requêtes et utiliser des algorithmes de partitionnement pour assurer le passage à l'échelle et avoir des structures d'optimisation optimales (vues matérialisées et partitionnement de données). Ce plan unifié est. utilisé dans la phase de déploiement des entrepôts de données parallèles, par le partitionnement de données en fragments et l'allocation de ces fragments dans les noeuds de calcule correspondants. Une étude expérimentale intensive a montré l'intérêt de notre approche en termes de passage à l'échelle des algorithmes et de réduction de temps de réponse de requêtes
The emergence of the phenomenon Big-Data conducts to the introduction of new increased and urgent needs to share data between users and communities, which has engender a large number of queries that DBMS must handle. This problem has been compounded by other needs of recommendation and exploration of queries. Since data processing is still possible through solutions of query optimization, physical design and deployment architectures, in which these solutions are the results of combinatorial problems based on queries, it is essential to review traditional methods to respond to new needs of scalability. This thesis focuses on the problem of numerous queries and proposes a scalable approach implemented on framework called Big-queries and based on the hypergraph, a flexible data structure, which bas a larger modeling power and may allow accurate formulation of many problems of combinatorial scientific computing. This approach is the result of collaboration with the company Mentor Graphies. It aims to capture the queries interaction in an unified query plan and to use partitioning algorithms to ensure scalability and to optimal optimization structures (materialized views and data partitioning). Also, the unified plan is used in the deploymemt phase of parallel data warehouses, by allowing data partitioning in fragments and allocating these fragments in the correspond processing nodes. Intensive experimental study sbowed the interest of our approach in terms of scaling algorithms and minimization of query response time

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

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

Data have become number one assets of today's business world. Thus, its exploitation and analysis attracted the attention of people from different fields and having different technical backgrounds. 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. However, designing and optimizing such data flows, to satisfy both users' information needs and agreed quality standards, have been known as a burdensome task, typically left to the manual efforts of a BI system designer. These tasks have become even more challenging for next generation BI systems, where data flows typically need to combine data from in-house transactional storages, and data coming from external sources, in a variety of formats (e.g., social media, governmental data, news feeds). Moreover, for making an impact to business outcomes, data flows are expected to answer unanticipated analytical needs of a broader set of business users' and deliver valuable information in near real-time (i.e., at the right time). These challenges largely indicate a need for boosting the automation of the design and optimization of data-intensive flows. This PhD thesis aims at providing automatable means for managing the lifecycle of data-intensive flows. The study primarily analyzes the remaining challenges to be solved in the field of data-intensive flows, by performing a survey of current literature, and envisioning an architecture for managing the lifecycle of data-intensive flows. Following the proposed architecture, we further focus on providing automatic techniques for covering different phases of the data-intensive flows' lifecycle. In particular, the thesis first proposes an approach (CoAl) for incremental design of data-intensive flows, by means of multi-flow consolidation. CoAl not only facilitates the maintenance of data flow designs in front of changing information needs, but also supports the multi-flow optimization of data-intensive flows, by maximizing their reuse. Next, in the data warehousing (DW) context, we propose a complementary method (ORE) for incremental design of the target DW schema, along with systematically tracing the evolution metadata, which can further facilitate the design of back-end data-intensive flows (i.e., ETL processes). The thesis then studies the problem of implementing data-intensive flows into deployable formats of different execution engines, and proposes the BabbleFlow system for translating logical data-intensive flows into executable formats, spanning single or multiple execution engines. Lastly, the thesis focuses on managing the execution of data-intensive flows on distributed data processing platforms, and to this end, proposes an algorithm (H-WorD) for supporting the scheduling of data-intensive flows by workload-driven redistribution of data in computing clusters. The overall outcome of this thesis an end-to-end platform for managing the lifecycle of data-intensive flows, called Quarry. The techniques proposed in this thesis, plugged to the Quarry platform, largely facilitate the manual efforts, and assist users of different technical skills in their analytical tasks. Finally, the results of this thesis largely contribute to the field of data-intensive flows in today's BI systems, and advocate for further attention by both academia and industry to the problems of design and optimization of data-intensive flows.
Actualment, les dades han esdevingut el principal actiu del món empresarial. En conseqüència, la seva explotació i anàlisi ha atret l'atenció de gent provinent de diferents camps i experiència tècnica. Els fluxes de dades intensius són processos centrals en els actuals sistemes d'inteligència de negoci (BI), desplegant diferents tecnologies per a proporcionar dades, provinents de diferents fonts i centrant-se en formats orientats a l'usuari. Tantmateix, el disseny i l'optimització de tals fluxes, per tal de satisfer ambdós usuaris de la informació i els estàndars de qualitat, resulta una tasca tediosa, normalment dirigida als esforços manuals del dissenyador del sistema BI. Aquestes tasques han esdevingut encara més complexes en el context dels sistemes BI de nova generació, on els fluxes de dades típicament combinen dades internes de fonts transaccionals, amb dades externes representades amb diferents formats (xarxes socials, dades governamentals, notícies). A més a més, per tal de tenir un impacte en el negoci, s'espera que els fluxes de dades responguin a necessitats analítiques no anticipades en un marge de temps proper a temps real. Aquests reptes clarament indiquen la necessitat de millora en l'automatització del disseny i optimització dels fluxes de dades intensius. L'objectiu d'aquesta tesi doctoral és el de proporcionar mitjans automàtics per tal de manegar el cicle de vida de fluxes de dades intensius. L'estudi primerament analitza els reptes pendents de resoldre en l'àrea de fluxes intensius de dades, mitjançant l'anàlisi de la literatura recent, i concebent una arquitectura per a la gestió del cicle de vida dels fluxes de dades intensius. A partir de l'arquitectura proposada, ens centrem en la proposta de tècniques automàtiques per tal de cobrir cadascuna de les fases del cicle de vida dels fluxes intensius de dades. Particularment, aquesta tesi inicialment proposa una tècnica (CoAl) per el disseny incremental dels fluxes de dades intensius, mitjançant la consolidació de multiples fluxes. CoAl no només facilita el manteniment dels flux de dades davant de noves necessitats d'informació, sinó que també permet la optimització de múltiples fluxes mitjançant la maximització de la reusabilitat. Posteriorment, en un contexte de magatzems de dades (DW), proposem un mètode complementari (ORE) per el disseny incremental d'un esquema de DW objectiu, acompanyat per la traça sistemàtica de metadades d'evolució, les quals poden facilitar el disseny dels fluxes intensius de dades (processos ETL). A continuació, la tesi estudia el problema d'implementació de fluxes de dades intensius a diferents sistemes d'execució, i proposa el sistema BabbleFlow per la traducció de fluxes de dades intensius lògics a formats executables, a un o múltiples sistemes d'execució. Finalment, la tesi es centra en la gestió dels fluxes de dades intensius en plataformes distribuïdes de processament de dades, amb aquest objectiu es proposa un algorisme (H-WorD) per donar suport a la planificació de l'execució de fluxes intensius de dades mitjançant la redistribució de dades dirigides per la carga de treball. El resultat general d'aquesta tesi és una plataforma d'inici a fi per tal de gestionar el cicle de vida dels fluxes intensius de dades, anomenada Quarry. Les tècniques propostes en aquesta tesi, incorporades a la plataforma Quarry, en gran part simplifiquen els esforços manuals i assisteixen usuaris amb diferent experiència tècnica a les seves tasques analítiques. Finalment, els resultats d'aquesta tesi contribueixen a l'àrea de fluxes intensius de dades en els sistemes de BI actuals. A més a més, reflecteixen la necessitat de més atenció per part dels mons acadèmic i industrial als problemes de disseny i optimització de fluxes de dades intensius.

Data warehousing provides organizations with strategic tools to achieve the competitive advantage that organazations are constantly seeking. The use of tools such as data mining, indexing and summaries enables management to retrieve information and perform thorough analysis, planning and forcasting to meet the changes in the market environment. in addition, The data warehouse is providing security measures that, if properly implemented and planned, are helping organizations ensure that their data quality and validity remain intact.

Decision support systems are interactive, computer-based information systems that provide data and analysis tools in order to better assist managers on different levels of organization in the process of decision making. Data warehouses (DWs) have been developed and deployed as an integral part of decision support systems.

A data warehouse is a database that allows to store high volume of historical data required for analytical purposes. This data is extracted from operational databases, transformed into a coherent whole, and loaded into a DW during the extraction-transformation-loading (ETL) process.

DW data can be dynamically manipulated using on-line analytical processing (OLAP) systems. DW and OLAP systems rely on a multidimensional model that includes measures, dimensions, and hierarchies. Measures are usually numeric additive values that are used for quantitative evaluation of different aspects about organization. Dimensions provide different analysis perspectives while hierarchies allow to analyze measures on different levels of detail.

Nevertheless, currently, designers as well as users find difficult to specify multidimensional elements required for analysis. One reason for that is the lack of conceptual models for DW and OLAP system design, which would allow to express data requirements on an abstract level without considering implementation details. Another problem is that many kinds of complex hierarchies arising in real-world situations are not addressed by current DW and OLAP systems.

In order to help designers to build conceptual models for decision-support systems and to help users in better understanding the data to be analyzed, in this thesis we propose the MultiDimER model - a conceptual model used for representing multidimensional data for DW and OLAP applications. Our model is mainly based on the existing ER constructs, for example, entity types, attributes, relationship types with their usual semantics, allowing to represent the common concepts of dimensions, hierarchies, and measures. It also includes a conceptual classification of different kinds of hierarchies existing in real-world situations and proposes graphical notations for them.

On the other hand, currently users of DW and OLAP systems demand also the inclusion of spatial data, visualization of which allows to reveal patterns that are difficult to discover otherwise. The advantage of using spatial data in the analysis process is widely recognized since it allows to reveal patterns that are difficult to discover otherwise.

However, although DWs typically include a spatial or a location dimension, this dimension is usually represented in an alphanumeric format. Furthermore, there is still a lack of a systematic study that analyze the inclusion as well as the management of hierarchies and measures that are represented using spatial data.

With the aim of satisfying the growing requirements of decision-making users, we extend the MultiDimER model by allowing to include spatial data in the different elements composing the multidimensional model. The novelty of our contribution lays in the fact that a multidimensional model is seldom used for representing spatial data. To succeed with our proposal, we applied the research achievements in the field of spatial databases to the specific features of a multidimensional model. The spatial extension of a multidimensional model raises several issues, to which we refer in this thesis, such as the influence of different topological relationships between spatial objects forming a hierarchy on the procedures required for measure aggregations, aggregations of spatial measures, the inclusion of spatial measures without the presence of spatial dimensions, among others.

Moreover, one of the important characteristics of multidimensional models is the presence of a time dimension for keeping track of changes in measures. However, this dimension cannot be used to model changes in other dimensions.

Therefore, usual multidimensional models are not symmetric in the way of representing changes for measures and dimensions. Further, there is still a lack of analysis indicating which concepts already developed for providing temporal support in conventional databases can be applied and be useful for different elements composing a multidimensional model.

In order to handle in a similar manner temporal changes to all elements of a multidimensional model, we introduce a temporal extension for the MultiDimER model. This extension is based on the research in the area of temporal databases, which have been successfully used for modeling time-varying information for several decades. We propose the inclusion of different temporal types, such as valid and transaction time, which are obtained from source systems, in addition to the DW loading time generated in DWs. We use this temporal support for a conceptual representation of time-varying dimensions, hierarchies, and measures. We also refer to specific constraints that should be imposed on time-varying hierarchies and to the problem of handling multiple time granularities between source systems and DWs.

Furthermore, the design of DWs is not an easy task. It requires to consider all phases from the requirements specification to the final implementation including the ETL process. It should also take into account that the inclusion of different data items in a DW depends on both, users' needs and data availability in source systems. However, currently, designers must rely on their experience due to the lack of a methodological framework that considers above-mentioned aspects.

In order to assist developers during the DW design process, we propose a methodology for the design of conventional, spatial, and temporal DWs. We refer to different phases, such as requirements specification, conceptual, logical, and physical modeling. We include three different methods for requirements specification depending on whether users, operational data sources, or both are the driving force in the process of requirement gathering. We show how each method leads to the creation of a conceptual multidimensional model. We also present logical and physical design phases that refer to DW structures and the ETL process.

To ensure the correctness of the proposed conceptual models, i.e. with conventional data, with the spatial data, and with time-varying data, we formally define them providing their syntax and semantics. With the aim of assessing the usability of our conceptual model including representation of different kinds of hierarchies as well as spatial and temporal support, we present real-world examples. Pursuing the goal that the proposed conceptual solutions can be implemented, we include their logical representations using relational and object-relational databases.

Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

Data warehousing is the foundation of DSS (decision support system), of which the goal is to enable decision makers to make better business decisions based on analysis of historic data related to the business operation. Data warehousing has become a major business trend, both for product and service sectors and for application to daily business in all industries. In this thesis, we compare and discuss the two data models and two methods of OLAP (MOLAP and ROLAP). The two data models and two OLAPs are not mutually exclusive in any project. An example of the hybrid design is presented in a case study. In this project, we are able to apply two data models at different stages of the data warehouse. By combining ER model, Star Schema and aggregate table in one architecture, we are able to maximize on both storage capacity and query performance. In addition to the selection of data models, the choice between MOLAP and ROLAP is discussed. When the objectives of the client are clearly defined, the functional requirements of the data warehouse are determined, and the amount of data is relatively small, MOLAP would likely be a better choice because of its superior performance and intuitive querying mechanism. ROLAP becomes a more suitable candidate for OLAP when the amount of data is exceedingly large, and the client does not want to leave out anything. Again, it is possible, even desirable, for both OLAP methods to be applied in one project to compliment each other.

碩士
長庚大學
企業管理研究所
92
After the completion of the ERP in an enterprise, the transaction processing of companies has almost been done. But, as the storage of the information is expanded, there is a huge profit of decision which must be heavily considered. It would be necessary to enforce competitive ability. A company must think about how to make a good strategy in order to have a huge profit and discipline of itself own. In order to supplement shortness on the strategy, this thesis wants to provide a considerable analyses of processes, i,e., these are required, immediate, correct and useful. This study tries to use a lot of information based on the development for the ERP. Both technologies of DW and OLAP are applied to integrate the disorderly and complicate information. A considerate information would be provided to the managers making the correct decision and lead the companies to make a good policy. In order to reach the objective, this study will discuss the feasibility of the OLAP for the strategy and setup some experiments. The results of these experiments can show that the ERP can provide some special contributins to enterprises. In addition, supply chain management(SCM) and customer relationship management(CRM) are two advanced important studies and can be one direction for future researches.

Data warehouse, as the foundation of decision support system, is critical for the managers to make decisions. It is different with operational database. Data warehouse reads data from multiple operational databases instead of getting the data from the end user transaction input. In a warehousing environment, the data lineage problem is that of tracing warehouse data items back to the original source items from which they were derived. Enabling lineage tracing in a data warehouse environment has several benefits and applications, including in-depth data analysis and data mining, authorization management, efficient warehouse recovery, etc. In this report, we firstly introduce the basic concept and architecture of data warehouse, as well as the development tools and methods about data warehouse. Secondly, we discuss the lineage tracing problems and challenges in the data warehousing system, and then use an example to present the algorithms and procedure of lineage tracing. As well, we will present our design and implementation of a prototype system called LTI, to demonstrate the lineage tracing procedures using an inventory system as a data warehouse system. We also developed various graphical user interfaces required to facilitate interacting with the system in order to update the source databases in the LTI system. Finally, we will show the experimentation of using our LTI system through tracing inventory and sales order data in the data warehouse system

Doctoral Thesis (Information Systems and Technologies)
Current Information Technology advancements have led organizations to pursue high business value and competitive advantages through the collection, storage, processing, and analysis of vast amounts of heterogonous data, generated at ever-growing rates. Since a Data Warehouse (DW) is one of the most remarkable and fundamental enterprise data assets, nowadays, a current research trend is the concept of Big Data Warehouse (BDW), characterizing real-time, scalable, and high-performance systems with flexible storage based on commodity hardware, which can overcome the limitations of traditional DWs to assure mixed and complex Big Data analytics workloads. The state-of-the-art in Big Data Warehousing (BDWing) reflects the young age of the concept, as well as the ambiguity and lack of integrated approaches for designing and implementing these systems. Fulfilling this gap is of major relevance, reason why this work proposes an approach composed of several models and methods for the design and implementation of BDWs, focusing on the logical components, data flows, technological infrastructure, data modeling, and data Collection, Preparation, and Enrichment (CPE). To demonstrate the usefulness, effectiveness, and efficiency of the proposed approach, this work considers four demonstration cases: 1) the application of the proposed data modeling method in several potential real-world applications, including retail, manufacturing, finance, software development, sensor-based systems, and worldwide news and events; 2) the application of the CPE method to process batch and streaming data arriving at the BDW from several source systems; 3) a custom-made extension of the Star Schema Benchmark (SSB), named the SSB+, in which several workloads were developed to benchmark a BDW implemented using the proposed approach, comparing its performance against a traditional dimensional DW; 4) a real-world instantiation based on the development of a BDWing system in the context of smart cities. The results of this research work reveal that the approach can be applied and generalized to support several application contexts, providing adequate and flexible data models that can reduce the implementation time between data collection and data analysis. Moreover, the proposed approach frequently presents faster query execution times and more efficient resource usage than a traditional dimensional modeling approach. Consequently, the proposed approach is able to provide general models and methods that can be used to design and implement BDWs, advancing the state-of-the-art based on a systematic approach rather than an ad hoc and use case driven one, which is seen as a valuable contribution to the technical and scientific community related to this research topic.
Os avanços atuais das Tecnologias da Informação têm levado as organizações a procurar um elevado valor do negócio e vantagens competitivas através da recolha, armazenamento, processamento, e análise de vastas quantidades de dados heterogéneos, gerados a velocidades cada vez maiores. Dado que um DW é um artefacto de dados fundamental nas organizações, uma linha de investigação atual é o conceito de BDW, caracterizando sistemas em tempo-real, escaláveis, de elevado desempenho, com armazenamento flexível, e baseados em commodity hardware, sendo capazes de ultrapassar as limitações dos DWs tradicionais de forma a assegurar uma variedade de tarefas complexas de Big Data analytics. O estado da arte em BDWing reflete o facto de ser um conceito emergente, bem como a ambiguidade e falta de abordagens integradas para a conceção e implementação destes sistemas. Preencher esta lacuna é significativamente relevante, razão pela qual este trabalho propõe uma abordagem composta por modelos e métodos para conceber e implementar BDWs, focando-se nos componentes lógicos, fluxos de dados, infraestrutura tecnológica, modelação de dados, e na recolha, preparação, e enriquecimento dos dados. Para demonstrar a utilidade, eficácia, e eficiência da solução proposta, este trabalho considera quatro casos de demonstração: 1) a aplicação do método proposto para a modelação de dados em várias potenciais aplicações do mundo-real, incluindo retalho, produção, finanças, desenvolvimento de software, sistemas baseados em sensores, e notícias e eventos a nível mundial; 2) a aplicação do método para recolher, preparar e enriquecer dados (batch e streaming ) provenientes de vários sistemas-fonte; 3) uma extensão do SSB desenvolvida à medida (SSB+), na qual várias workloads foram executadas de modo a avaliar o desempenho de um BDW implementado usando a abordagem proposta, comparando-o com um DW dimensional tradicional; 4) uma instância do mundo-real baseada no desenvolvimento de um sistema de BDWing no contexto de smart cities. Os resultados deste trabalho revelam que a abordagem pode ser aplicada e generalizada para suportar vários contextos de aplicação, disponibilizando modelos de dados adequados e flexíveis que conseguem reduzir o tempo de implementação entre a recolha de dados e a análise de dados. Além disso, a abordagem apresenta frequentemente tempos mais rápidos na execução de queries e um uso de recursos mais eficiente do que uma abordagem dimensional tradicional. Consequentemente, a abordagem proposta pode ser usada para a conceção e implementação de BDWs seguindo uma abordagem sistémica, em vez de uma abordagem ad hoc e use case driven, o que é visto como um contributo valioso para a comunidade técnico-científica relacionada com este tópico.
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043; FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013; the SusCity project, MITPTB/ CS/0026/2013; and CCG - Centro de Computação Gráfica, providing me the adequate conditions to conclude this doctoral thesis while working as a Big Data Engineer. Moreover, some figures in this document use icons made by Freepik from www.flaticon.com.

碩士
元智大學
工業工程與管理學系
90
This thesis presents a successful demonstration of application of data warehousing technique to perform various functions required in Taguchi experimental design. The demonstrated Taguchi data, which were simulated from the semiconductor manufacturing process described in a published academic paper, were warehoused in the structure of the proposed data cubes where OLAP (on-line analysis processing) is capable of performing the following functions: (a) backup of critical experiment parameters, such as orthogonal tables, signal-to-noise ratio, and the combination of design parameters, (b) prediction of complete product design quality with few variables, and (c) estimation of design parameters that cannot be measured. The proposed Taguchi data warehouse is a necessary and important preparation work for the fulfillment of both data mining and knowledge discovery functions.

碩士
大同大學
資訊工程學系(所)
93
This research attempts to design and construct a data warehouse fledgling model for a garment company based on the ERP (Enterprise Resource Planning) system to provide basic requirement for rapidly inquiring related statistic data for decision makers. By applying the technology of Data Warehouse and OLAP, and extracting data from ERP system’s database, establishing a related multi-dimensional data model, decision makers can inquire related statistic data elastically and rapidly depending on their requirements and hence enhancing the quality and time effectiveness of decision making. With the policy of implementing the most important data/information first consideration, we use bottom-up approach to implement the data warehousing system. We gathered the users’ requirements to define the needed metrics and the architecture of the data warehouse: data acquisition from the ERP system on IBM AS/400 DB2, data storage and information delivery on a SQL Server platform.

碩士
元智大學
工業工程研究所
89
One of sophisticate tasks in optimization theory is the university timetabling. There are two jobs that must be done for the establishment of a practical university timetable, data management of courses and knowledge design of scheduling requirements. Either of them fails, the university timetable must be infeasible. To avoid an inconsistent system design, this thesis proposes a star architecture of a data warehousing within MS SQL system to maintain the course data and uses the MS VB language to design knowledge of timetabling constraints. The constraints will be satisfied for the final university timetable, in other words, the rules of constraints satisfaction within AI expert systems are designed to reschedule and resolve conflict problems existed in the course of university timetabling. The proposed constraints satisfaction technique overcomes the difficulty of the formulation problem for real cases solved by traditional OR techniques.

With Moore’s law grinding to a halt, accelerators are one of the ways that new silicon can improve performance, and they are already a key component in modern datacenters. Accelerators are integrated circuits that implement parts of an application with the objective of higher energy efficiency compared to execution on a standard general purpose CPU. Many accelerators can target any particular workload, generally with a wide range of performance, and costs such as area or power. Exploring these design choices, called Design Space Exploration (DSE), is a crucial step in trying to find the most efficient accelerator design, the one that produces the largest reduction of the total cost of ownership. This work aims to improve this design space exploration phase for accelerators and to avoid pitfalls in the process. This dissertation supports the thesis that early design choices – including the level of specialization – are critical for accelerator development and therefore require benchmarks reflective of production workloads. We present three studies that support this thesis. First, we show how to benchmark datacenter applications by creating a benchmark for large video sharing infrastructures. Then, we present two studies focused on accelerators for analytical query processing. The first is an analysis on the impact of Network on Chip specialization while the second analyses the impact of the level of specialization. The first part of this dissertation introduces vbench: a video transcoding benchmark tailored to the growing video-as-a-service market. Video transcoding is not accurately represented in current computer architecture benchmarks such as SPEC or PARSEC. Despite posing a big computational burden for cloud video providers, such as YouTube and Facebook, it is not included in cloud benchmarks such as CloudSuite. Using vbench, we found that the microarchitectural profile of video transcoding is highly dependent on the input video, that SIMD extensions provide limited benefits, and that commercial hardware transcoders impose tradeoffs that are not ideal for cloud video providers. Our benchmark should spur architectural innovations for this critical workload. This work shows how to benchmark a real world warehouse scale application and the possible pitfalls in case of a mischaracterization. When considering accelerators for the different, but no less important, application of analytical query processing, design space exploration plays a critical role. We analyzed the Q100, a class of accelerators for this application domain, using TPC-H as the reference benchmark. We found that the hardware computational blocks have to be tailored to the requirements of the application, but also the Network on Chip (NoC) can be specialized. We developed an algorithm capable of producing more effective Q100 designs by tailoring the NoC to the communication requirements of the system. Our algorithm is capable of producing designs that are Pareto optimal compared to standard NoC topologies. This shows how NoC specialization is highly effective for accelerators and it should be an integral part of design space exploration for large accelerators’ designs. The third part of this dissertation analyzes the impact of the level of specialization, e.g. using an ASIC or Coarse Grain Reconfigurable Architecture (CGRA) implementation, on an accelerator performance. We developed a CGRA architecture capable of executing SQL query plans. We compare this architecture against Q100, an ASIC that targets the same class of workloads. Despite being less specialized, this programmable architecture shows comparable performance to the Q100 given an area and power budget. Resource usage explains this counterintuitive result, since a well programmed, homogeneous array of resources is able to more effectively harness silicon for the workload at hand. This suggests that a balanced accelerator research portfolio must include alternative programmable architectures – and their software stacks.

碩士
國立陽明大學
公共衛生研究所
88
It has been at least five years since National Heath Insurance established in 1995. The object of the insured contains national-wide people; therefore, it brings precious medical treatment data to the entire countrymen. These medical treatment data appear to be considerably valuable for the manager of the hospital, the staffs engaged in the medical treatment work, the researchers in public health area, the National Health Insurance itself, and other staffs of Insurance research. Considering the present Health Insurance data warehouse are all limited to the supply of data, not the production of information, and the storage of Health Insurance data is large and not easily to be analyzed. They become the hindrance of research, so by utilizing the 1997 academic research Health Insurance data formal version provided by National Health Research Institute, we imitate the database of National Health Research Institute and apply the data warehouse technique to redesign a data warehouse suitable for a on-line analyze system and practically apply it to the Internet. We hope it can help shorten the hours of scholar making use of the Health Insurance data and lower the difficulty of the users using this system. In this research, the Business Dimensional Lifecycle methodology brought up by Kimball, Reeves, Ross, and Thornthwaite (1998) is the framework of this project. We retransform the original Health Insurance data and concentrate it to the new database designed for the function of answer and query, design three-tier architecture network, and inject into the design tools of on-line analyze system. It can help the users directly utilize the Internet browser to do on-line analyze.

A data warehouse is a single data store, where data from multiple data sources is integrated for online business analytical processing (OLAP) of an entire organisation. The rationale being single and integrated is to ensure a consistent view of the organisational business performance independent from different angels of business perspectives. Due to its wide coverage of subjects, data warehouse design is a highly complex, lengthy and error-prone process. Furthermore, the business analytical tasks change over time, which results in changes in the requirements for the OLAP systems. Thus, data warehouse and OLAP systems are rather dynamic and the design process is continuous. In this thesis, we propose a method that is integrated, formal and application-tailored to overcome the complexity problem, deal with the system dynamics, improve the quality of the system and the chance of success. Our method comprises three important parts: the general ASMs method with types, the application tailored design framework for data warehouse and OLAP, and the schema integration method with a set of provably correct refinement rules. By using the ASM method, we are able to model both data and operations in a uniform conceptual framework, which enables us to design an integrated approach for data warehouse and OLAP design. The freedom given by the ASM method allows us to model the system at an abstract level that is easy to understand for both users and designers. More specifically, the language allows us to use the terms from the user domain not biased by the terms used in computer systems. The pseudo-code like transition rules, which gives the simplest form of operational semantics in ASMs, give the closeness to programming languages for designers to understand. Furthermore, these rules are rooted in mathematics to assist in improving the quality of the system design. By extending the ASMs with types, the modelling language is tailored for data warehouse with the terms that are well developed for data-intensive applications, which makes it easy to model the schema evolution as refinements in the dynamic data warehouse design. By providing the application-tailored design framework, we break down the design complexity by business processes (also called subjects in data warehousing) and design concerns. By designing the data warehouse by subjects, our method resembles Kimball's "bottom-up" approach. However, with the schema integration method, our method resolves the stovepipe issue of the approach. By building up a data warehouse iteratively in an integrated framework, our method not only results in an integrated data warehouse, but also resolves the issues of complexity and delayed ROI (Return On Investment) in Inmon's "top-down" approach. By dealing with the user change requests in the same way as new subjects, and modelling data and operations explicitly in a three-tier architecture, namely the data sources, the data warehouse and the OLAP (online Analytical Processing), our method facilitates dynamic design with system integrity. By introducing a notion of refinement specific to schema evolution, namely schema refinement, for capturing the notion of schema dominance in schema integration, we are able to build a set of correctness-proven refinement rules. By providing the set of refinement rules, we simplify the designers's work in correctness design verification. Nevertheless, we do not aim for a complete set due to the fact that there are many different ways for schema integration, and neither a prescribed way of integration to allow designer favored design. Furthermore, given its °exibility in the process, our method can be extended for new emerging design issues easily.

Private Branch eXchange (PBX) is a tool indispensable in the business world. The telephone exchanges allow employees to perform internal connections between telephones, or make calls to the external network also known as Public Switched Telephone Network (PSTN). With increasing Internet usage, there is interest in understanding what services are offered. Enterprise Courier is a commercial Internet Protocol Private Branch eXchange (IP PBX) based on open source Asterisk web-based PBX software for Linux, which supports multiple protocols and services, like Interactive Voice Response (IVR). Cisco Unified Communications Manager (CUCM) or CallManager, is a software based call-processing system (IP PBX) developed by Cisco Systems. CUCM tracks all active Voice over IP (VoIP) network components; including phones, gateways, conference bridges, among others. IVR is part of the Academic Services costumer contact and ticketing of University of Beira Interior (UBI). IVR monitoring and analysis are essential for effective operation and resource management, in particular, multidimensional analysis for long-term data is necessary for comprehensive understanding of the trend, the quality of customer service and costumer experience. In this paper, we propose a new IVR analysis model for large volumes of IVR data accumulated over a long period of time. The IVRCube proposed is an analysis model using online analytical processing (OLAP) on a multidimensional data cube that provides an easy and fast way to construct a multidimensional IVR analysis system for comprehensive and detailed evaluation of long-term data. The feasibility and applicability are validated, as the proposed IVRCube analysis model is implemented and applied to Academic Services costumer contact and ticketing IVR data.
A Private Branch eXchange (PBX) é uma ferramenta indispensável no mundo dos negócios. As centrais telefónicas permitem que os funcionários realizem chamadas internas entre telefones, ou façam chamadas para a rede externa, também conhecida como Public Switched Telephone Network (PSTN). Com o aumento sistemático da utilização da Internet, há um interesse acrescido em entender quais os serviços que são oferecidos nas redes baseadas em Internet Protocol (IP). Um destes serviços é o Voice over IP (VoIP). O Enterprise Courier é um software IP PBX comercial para VoIP baseado na aplicação de código aberto Asterisk, que opera sobre Linux. O IP PBX Enterprise Courier suporta vários protocolos e serviços, por exemplo o Interactive Voice Response (IVR). O Cisco Unified Communications Manager (CUCM) também chamado de CallManager, é um sistema de processamento de chamadas IP, ou IP PBX, desenvolvido pela Cisco Systems. O CUCM permite fazer a gestão e operação de todos os componentes ativos de voz, incluindo telefones, gateways, equipamentos de conferência entre outros. Estes sistemas coexistem na rede de gestão de comunicações de voz da Universidade da Beira Interior (UBI), sendo que o sistema automatizado utilizado para o encaminhamento de chamadas dos Serviços Académicos na UBI utiliza a tecnologia IVR. Este serviço da UBI é uma das formas que os clientes da Universidade (alunos e não alunos) têm para obter informações e resolver questões de forma rápida e simples usando o telefone. Por ser um importante ponto de interface entre a universidade e a comunidade, a monitorização e análise de desempenho do IVR são essenciais para o funcionamento eficaz e gestão de recursos humanos atribuídos a este serviço, o que torna a tarefa de extrair os dados do sistema de VoIP e apresentá-los de forma a poder extrair deles informação útil à gestão, o centro deste trabalho de investigação. Para a análise dos dados, foi usada uma técnica de análise multidimensional de dados a longo prazo, necessária para uma compreensão abrangente da evolução e qualidade de serviço prestada ao cliente tendo como objetivo a melhor experiência possível por parte do cliente. Neste trabalho, propomos um novo modelo de análise de IVR para grandes volumes de dados acumulados ao longo de um extenso período de tempo. O IVRCube é um modelo de análise utilizando online analytical processing (OLAP) num cubo de dados multidimensional que fornece uma forma fácil e rápida de construir um sistema de análise multidimensional para avaliação exaustiva e pormenorizada dos dados ao longo do tempo. A viabilidade e aplicabilidade deste modelo são validadas, uma vez que o modelo de análise IVRCube proposto é implementado e aplicado ao serviço de contacto telefónico (IVR) dos Serviços Académicos da UBI.

This dissertation focuses on the re-use of business process reference models, available in a business process model warehouse, to enable the definition of more comprehensive business requirements. It proposes a business process model warehouse framework to promote the re-use of multiple business process reference models and the flexible visualisation of business process models. The critical success factor for such a framework is that it should contribute to minimise to some extent the causes of inadequate business requirements. The proposed framework is based on an analogy with a data warehouse framework, consisting of the following components: usage of multiple business process reference models as source models, the conceptual design of a process to extract, load and transform multiple business process reference models into a repository, a description of repository functionality for managing enterprise architecture artefacts, and motivation of flexible visualisation of business process models to ensure more comprehensive business requirements.
Computer Science (School of Computing)
M.Sc. (Information Systems)