This master’s thesis will look into the use of data mining in the area of segmentation and the prediction of onboarding candidates of a recruitment agency. The obtained results should serve to make company processes more effective concerning the processing of orders, and should also facilitate a more personal approach to candidates. The first chapter includes imperetive theoretical bases from the studies of Business Intelligence, data warehouses, data mining and marketing. Thereafter an analysis of the current state is presented with a focus on the capture of the key processes in processing and order. The last chapter looks at the proposed solution and implementation on the platform Microsoft SQL Server 2014. To conclude there are proposals of utilizing data mining in direct marketing.

Performance management of engineering education emerges from the need to assure proper training of future engineers in order to meet the constantly evolving expectations and challenges for the engineering profession. The process of accreditation ensures that engineering graduates are adequately prepared for their professional careers and responsibilities by ensuring that they possess an expected set of mandatory graduate attributes. Engineering programs are required by accreditation bodies to have systematic performance management of their programs that informs a continuous improvement process. Unfortunately, the vast diversity of engineering disciplines, varieties of information systems, and the large number of actors involved in the process makes this task challenging and complex. We performed a systematic literature review of jurisdictions around the world who are doing accreditation and examined how universities across Canada, US and other countries, have addressed tool support for performance management of engineering education. Our initial systematic approach for tool supported performance management evolved from this, and then we refined it through an iterative process of combined action research and design science research. We developed a prototype, Graduate Attribute Information Analysis (GAIA) in collaboration with the School of Electrical Engineering and Computer Science at the University of Ottawa, to support a systematic approach for accreditation of three engineering programs. This thesis contributes to research on the problem by developing a systematic approach, a tool that supports it, a set of related data transformations, and a tool-assessment checklist. Our systematic approach for tool-supported performance management addresses system architecture, a common continuous improvement process, a common set of key performance indicators, and identifies the performance management forms and reports needed to analyze graduate attribute data. The data transformation and analysis techniques we demonstrate ensure the accurate analysis of statistical and historical trends.

Die vorliegende Arbeit beschreibt einen Architekturansatz, der im Rahmen einer Machbarkeitsstudie bei IBM entwickelt wurde. Dadurch wird der IBM DB2 Analytics Accelerator als eine Data-Warehouse-Appliance dazu in die Lage versetzt, über angepasste Schnittstellen Data-Mining-Modelle über entsprechende Algorithmen direkt auf dem Accelerator zu erstellen. Neben dieser Beschreibung wird die bisherige Verwendung des DB2 Analytics Accelerators sowie das zugehörige Umfeld von Datenbanksystemen bis zum System z Mainframe vorgestellt. Darauf aufbauend werden praxisnahe Anwendungsfälle präsentiert, die unter Anwendung von intelligenten Methoden auf gespeicherten Kundendaten statistische Modelle erstellen. Für diesen Prozess wird die Datengrundlage zuerst vorbereitet und angepasst, um sie dann in dem zentralen Data-Mining-Schritt nach neuen Zusammenhängen zu durchsuchen.

Recommendations are crucial for the success of large websites. While there are many ways to determine recommendations, the relative quality of these recommenders depends on many factors and is largely unknown. We present the architecture and implementation of AWESOME (Adaptive website recommendations), a data warehouse-based recommendation system. It allows the coordinated use of a large number of recommenders to automatically generate website recommendations. Recommendations are dynamically selected by efficient rule-based approaches utilizing continuously measured user feedback on presented recommendations. AWESOME supports a completely automatic generation and optimization of selection rules to minimize website administration overhead and quickly adapt to changing situations. We propose a classification of recommenders and use AWESOME to comparatively evaluate the relative quality of several recommenders for a sample website. Furthermore, we propose and evaluate several rule-based schemes for dynamically selecting the most promising recommendations. In particular, we investigate two-step selection approaches that first determine the most promising recommenders and then apply their recommendations for the current situation. We also evaluate one-step schemes that try to directly determine the most promising recommendations.

Learning Health Systems (LHS) sind sozio-technische Systeme, die gesundheitsbezogene Dienstleistungen erbringen und dabei mit Hilfe von Informationstechnologie neues Wissen aus Daten erzeugen, um die Gesundheitsversorgung kontinuierlich zu verbessern. Durch die zunehmende Digitalisierung des Gesundheitswesens entstehen vielerorts Daten, die zur Gewinnung von Wissen in LHS genutzt werden können. Dies setzt allerdings eine informationstechnische Infrastruktur voraus, die die Daten integriert und geeignete Algorithmen zur Wissensgewinnung bereitstellt. Der verbreitete Ansatz, solche Infrastrukturen in großen Institutionsverbünden zu entwickeln, zeigte bislang nicht den gewünschten Erfolg. Deshalb wurde in dieser Arbeit stattdessen von einer einzelnen Organisationseinheit ausgegangen, der Zentralen Notaufnahme eines Klinikums, und eine informationstechnische Infrastruktur für ein lokales Learning Health System entwickelt. Es wurden dabei Fragestellungen aus den Bereichen Datenintegration und -analyse behandelt. Zum Einen wurde gefragt, wie sich heterogene, semantisch zeitvariante, longitudinale Gesundheitsdaten flexibel auf Datenmodellebene integrieren lassen. Zum Anderen war Untersuchungsgegenstand, wie auf den so integrierten Gesundheitsdaten zwei datenanalytische Anwendungsfälle konkret realisiert werden können: Es wurde erstens untersucht, welche Untergruppen von Patienten mit häufigen Inanspruchnahmen (häufige Wiederkehrer, frequent users) sich ermitteln lassen und welches Wiederkehrrisiko mit bestimmten Diagnosen verbunden ist. Zweitens wurde untersucht, welche Aussagen über das Ankunftsverhalten und die Fallkomplexität von gebrechlichen, älteren Patienten getroffen werden können. Für die Beantwortung der Fragestellungen erfolgte die Datenextraktion und -integration nach dem Data-Warehouse-Ansatz. Es wurden Daten des Krankenhausinformationssystems des Klinikums Osnabrück mit Krankenhausqualitätsdaten, Fallklassifikationsdaten sowie Wetter-, Luftqualitäts- und Verkehrsdaten integriert. Für die Datenintegration wurde das Entity-Attribute-Value/Data Vault-Modell (EAV/DV) als ein neuer Modellierungsansatz entwickelt. Die Datenanalysen wurden mit einem Data-Mining-Verfahren zur Faktorisierung von Patientenmerkmalen sowie statistischen Methoden der Zeitreihenanalyse durchgeführt. Für Wiederkehrer ergaben sich vier distinkte Untergruppen von Patienten. Weiterhin konnte das relative Wiederkehr-Risiko für einzelne Diagnosen geschätzt werden. Zeitreihenanalytisch ergaben sich ausgeprägte Unterschiede im Ankunftsverhalten gebrechlicher, älterer Patienten im Vergleich zu allen übrigen Patienten. Eine höhere Fallkomplexität konnte bestätigt werden, war aber im Allgemeinen nicht tageszeitabhängig. Der Modellierungsansatz (EAV/DV) für longitudinale Gesundheitsdaten erleichterte die Integration heterogener sowie sich zeitlich ändernder Daten durch flexible Datenschemata innerhalb des Data Warehouses. Die datenanalytischen Modelle lassen sich laufend mit neuen Daten aus dem Krankenhausinformationssystem aktualisieren und realisieren damit die Wissensgewinnung aus Daten nach dem LHS-Ansatz. Sie können als Entscheidungsunterstützung für eine bessere personelle Ressourcenplanung und zielgruppengerechte Ansprache von ressourcenintensiven Patienten in der Notaufnahme dienen. Die vorgelegte Implementierung einer IT-Infrastruktur zeigt auf, wie die Wissensgewinnung aus Daten exemplarisch für das lokale Learning Health System der Organisationseinheit Zentrale Notaufnahme umgesetzt werden kann. Die schnelle prototypische Umsetzung und der erfolgreiche Wissensgewinn zu inhaltlichen Fragestellungen belegt, dass der gewählte bottom-up-Ansatz tragfähig ist und sinnvoll weiter ausgebaut werden kann.

This Master's Thesis deals with the integration of Business Intelligence tools into an information system. There are concepts of BI, data warehouses, the OLAP analysis introduced as well as the knowledge discovery from databases, especially the association rule mining. In the chapters focused on practical part of the thesis, the design and implementation of resultant application are depicted. There are also the applied technologies like i.e. Microsoft SQL Server 2005 described.

Data warehouse technology has been successfully integrated into the information infrastructure of major organizations as potential solution for eliminating redundancy and providing for comprehensive data integration. Realizing the importance of a data warehouse as the main data repository within an organization, this dissertation addresses different aspects related to the data warehouse architecture and performance issues. Many data warehouse architectures have been presented by industry analysts and research organizations. These architectures vary from the independent and physical business unit centric data marts to the centralised two-tier hub-and-spoke data warehouse. The operational data store is a third tier which was offered later to address the business requirements for inter-day data loading. While the industry-available architectures are all valid, I found them to be suboptimal in efficiency (cost) and effectiveness (productivity). In this dissertation, I am advocating a new architecture (The Hybrid Architecture) which encompasses the industry advocated architecture. The hybrid architecture demands the acquisition, loading and consolidation of enterprise atomic and detailed data into a single integrated enterprise data store (The Enterprise Data Warehouse) where businessunit centric Data Marts and Operational Data Stores (ODS) are built in the same instance of the Enterprise Data Warehouse. For the purpose of highlighting the role of data warehouses for different applications, we describe an effort to develop a data warehouse for a geographical information system (GIS). We further study the importance of data practices, quality and governance for financial institutions by commenting on the RBC Financial Group case. v The development and deployment of the Enterprise Data Warehouse based on the Hybrid Architecture spawned its own issues and challenges. Organic data growth and business requirements to load additional new data significantly will increase the amount of stored data. Consequently, the number of users will increase significantly. Enterprise data warehouse obesity, performance degradation and navigation difficulties are chief amongst the issues and challenges. Association rules mining and social networks have been adopted in this thesis to address the above mentioned issues and challenges. We describe an approach that uses frequent pattern mining and social network techniques to discover different communities within the data warehouse. These communities include sets of tables frequently accessed together, sets of tables retrieved together most of the time and sets of attributes that mostly appear together in the queries. We concentrate on tables in the discussion; however, the model is general enough to discover other communities. We first build a frequent pattern mining model by considering each query as a transaction and the tables as items. Then, we mine closed frequent itemsets of tables; these itemsets include tables that are mostly accessed together and hence should be treated as one unit in storage and retrieval for better overall performance. We utilize social network construction and analysis to find maximum-sized sets of related tables; this is a more robust approach as opposed to a union of overlapping itemsets. We derive the Jaccard distance between the closed itemsets and construct the social network of tables by adding links that represent distance above a given threshold. The constructed network is analyzed to discover communities of tables that are mostly accessed together. The reported test results are promising and demonstrate the applicability and effectiveness of the developed approach.

The secondary use of Dementia Care Mapping (DCM) data, if that data were held in a data warehouse, could contribute to global efforts in monitoring and improving dementia care quality. This qualitative study identifies requirements for the secondary use of DCM data within a data warehouse using a user-driven approach. The thesis critically analyses various technical methodologies and then argues the use and further demonstrates the applicability of a modified grounded theory as a user-driven methodology for a data warehouse. Interviews were conducted with 29 DCM researchers, trainers and practitioners in three phases. 19 interviews were face to face with the others on Skype and telephone with an average length of individual interview 45-60 minutes. The interview data was systematically analysed using open, axial and selective coding techniques and constant comparison methods. The study data highlighted benchmarking, mappers’ support and research as three perceived potential secondary uses of DCM data within a data warehouse. DCM researchers identified concerns regarding the quality and security of DCM data for secondary uses, which led to identifying the requirements for additional provenance, ethical and contextual data to be included in a warehouse alongside DCM data to meet requirements for secondary uses of this data for research. The study data was also used to extrapolate three main factors such as an individual mapper, the organization and an electronic data management that can influence the quality and availability of DCM data for secondary uses. The study makes further recommendations for designing a future DCM data warehouse.

Answers to the most complex biological questions are rarely determined solely from the experimental evidence. It requires subsequent analysis of many data sources that are often heterogeneous. Most biological data repositories focus on providing only one particular type of data, such as sequences, molecular interactions, protein structure, or gene expression. In many cases, it is required for researchers to visit several different databases to answer one scientific question. It is essential to develop strategies to integrate disparate biological data sources that are efficient and seamless to facilitate the discovery of novel associations and validate existing hypotheses. This thesis presents the design and development of different integration strategies of biological and clinical systems. The BioSPIDA system is a data warehousing solution that integrates many NCBI databases and other biological sources on protein sequences, protein domains, and biological pathways. It utilizes a universal parser facilitating integration without developing separate source code for each data site. This enables users to execute fine-grained queries that can filter genes by their protein interactions, gene expressions, functional annotation, and protein domain representation. Relational databases can powerfully return and generate quickly filtered results to research questions, but they are not the most suitable solution in all cases. Clinical patients and genes are typically annotated by concepts in hierarchical ontologies and performance of relational databases are weakened considerably when traversing and representing graph structures. This thesis illustrates when relational databases are most suitable as well as comparing the performance benchmarks of semantic web technologies and graph databases when comparing ontological concepts. Several approaches of analyzing integrated data will be discussed to demonstrate the advantages over dependencies on remote data centers. Intensive Care Patients are prioritized by their length of stay and their severity class is estimated by their diagnosis to help minimize wait time and preferentially treat patients by their condition. In a separate study, semantic clustering of patients is conducted by integrating a clinical database and a medical ontology to help identify multi-morbidity patterns. In the biological area, gene pathways, protein interaction networks, and functional annotation are integrated to help predict and prioritize candidate disease genes. This thesis will present the results that were able to be generated from each project through utilizing a local repository of genes, functional annotations, protein interactions, clinical patients, and medical ontologies.

本研究之方向係探究建置大型中醫藥倉儲所需之後設資料(Metadata)，並透過此一初探，瞭解與描述其中所需之分析方法，本研究設想Zachman Framework為合適之資料倉儲開發方法，因而由5W1H面向來衍生該專業領域所需之概念。此類概念可再透過一分析程序，確立後設資料。 因時間之限制，本研究採用「本草備要」、「醫方集解」為範例文件進行相關分析，以減少中醫流派林立及中文本身不準確性帶來之問題，當然，本研究在其中亦力求在整體架構上維持其他中醫藥典籍之適用性。 為達成目標，本研究首先探討了至今中草藥資料庫、資料倉儲、電子超文件領域之發展，因而本研究決定將個別之中醫典籍視為「資料專櫃」，而將分類樹、Metadata描述性資料置於目次(catalog)的概念之下，這樣的做法有利於整合其他典籍及其後設資料於大型資料倉儲中。 首先，本研究由重要中醫藥典籍導出基礎性中草藥概念與名詞，其後透過典藏面及應用面之統計分析，確認範例典籍中的Metadata。在實作方面，本研究以BNF來描述和定義Metadata，並以XML為工具完成雛型以供測試之。其中，本研究發現，基於資料倉儲觀點所擷取之後設資料的分析單位較傳統圖書典藏所得之為小。此外，本研究擷取過程中所涉及之Metadata，以功能性者為多，本研究亦採取了若干語言分析以期同時能維持典籍之文字結構。 / The objective of this research work is to acquisit and design Metadata for the construction of data warehouse of Traditional Chinese Medicine (TCM) literatures in the context of knowledge management. In order to solve the problem of preservation and utilization of TCM literatures, this work aims to designate the Metadata based on the viewpoint of knowledge engineering and data warehouse. In this work, the characteristics of the TCM regarding Metadata result in the 5W1H’s principle, while this work argues for its advantages for deriving more functional descriptions and keeping the syntax structure of the originals at the same time. To minimize the constraints of time, this work chooses “Ben Cao Bei Yao” and “Yi Fang Ji Jie” as the target to analyze. In constructing a prototype, the tacit knowledge in the example TCM literatures is converted through an analytic process explicitly into the organizational knowledge that can be easily preserved and processed by machines. Therefore, a statistical process is employed to derive and verify the Metadata in the context of the example TCM literatures. Then, the components regarding the Metadata are implemented with XML tools to develop the prototype. Last but not the least, this work presents its findings as follows: 1. The unit of analysis for deriving Metadata related to data warehouse is usually in a smaller degree of finesse in comparison to what is addressed in the area of traditional library management. 2. Through the Metadata derived in this work based on a data warehouse approach presents more functional elements, we can still maintain the linguistic structure of the example literatures with some careful linguistically analyses in the last step.