Metabolomics as a field has been used to track changes and perturbations in the human body by investigating metabolite profiles indicating the change of metabolite levels over time and in response to different challenges. In this thesis work, the main focus was on applying multiplatform-metabolomics to study the human metabolome following exposure to perturbations, such as diet (in the form of a challenge meal) and exhaust emissions (air pollution exposure in a controlled setting). The cutting-edge analytical platforms used for this purpose were nuclear magnetic resonance (NMR), as well as gas chromatography (GC) and liquid chromatography (LC) coupled to mass spectrometry (MS). Each platform offered unique characterization features, allowing detection and identification of a specific range of metabolites. The use of multiplatform-metabolomics was found to enhance the metabolome coverage and to provide complementary findings that enabled a better understanding of the biochemical processes reflected by the metabolite profiles. Using non-targeted analysis, a wide range of unknown metabolites in plasma were identified during the postprandial stage after a well-defined challenge meal (in Paper I). In addition, a considerable number of metabolites were detected and identified in lung lavage fluid after biodiesel exhaust exposure compared to filtered air exposure (in Paper II). In parallel, using targeted analysis, both lung lavage and plasma fatty acid metabolites were detected and quantified in response to filtered air and biodiesel exhaust exposure (in Paper III and IV). Data processing of raw data followed by data analysis, using both univariate and multivariate methods, enabled changes occurring in metabolites levels to be screened and investigated. For the initial pilot postprandial study, the aim was to investigate the plasma metabolome response after a well-defined meal during the postprandial stage for two types of diet. It was found that independent of the background diet type, levels of metabolites returned to their baseline levels after three hours. This finding was taken into consideration for the biodiesel exhaust exposures studies, designed to limit the impact of dietary effects. Both targeted and non-targeted approaches resulted in important findings. For instance, different metabolite profiles were detected in bronchial wash (BW) compared to bronchoalveolar lavage (BAL) fluid with mainly NMR and LC-MS. Furthermore, biodiesel exhaust exposure resulted in different metabolite profiles as observed by GC-MS, especially in BAL. In addition, fatty acid metabolites in BW, BAL, and plasma were shown to be responsive to biodiesel exhaust exposure, as measured by a targeted LC-MS/MS protocol. In summary, the new analytical methods developed to investigate the responsiveness of the human plasma and lung lavage metabolome proved to be useful in an analytical perspective, and provided important biological findings. However, further studies are needed to validate these results. / Metabolomik har använts för att spåra förändringar och störningar i kroppens funktioner genom undersökning av metabolit-profiler. I detta avhandlingasarbete har huvudfokus varit på tillämpning av flera olika analytiska plattformar för metabolomikstudier av det mänskliga metabolomet efter exponering för olika kost och avgasutsläpp från biodieselbränsle. De sofistikerade analytiska plattformarna som användes för detta ändamål var kärnmagnetisk resonans (NMR), samt gaskromatografi (GC) och vätskekromatografi (LC) kopplat till masspektrometri (MS). Varje plattform erbjöd unika karakteriseringsmöjligheter med detektion och identifiering av specifika grupper av metaboliter. Användningen av multipattformmetabolomik förbättrade täckningen av metabolomet och genererade kompletterande resultat som möjliggjorde en bättre förståelse av de biokemiska processer som reflekteras av metabolitprofilerna. Med hjälp av breda analyser har ett stort antal okända metaboliter i plasma identifierats under den postprandial fasen efter en väldefinerad måltid (i Paper I). Dessutom har ett stort antal metaboliter påvisats och identifierats i lungsköljvätska efter exponering av biodieselavgaser jämfört med kontollexponering med filtrerad luft (i Paper II). Parallellt med dessa breda analyser har också riktade analyser genomförts av både lungsköljvätska och plasma. Därigenom har bioaktiva lipider detekterats och kvantifieras efter avgasexponering och resultaten har jämförts med filtrerad luft som kontrollexponering (Paper III och IV). Processning av rådata följt av dataanalys, med både univariata och multivariata metoder möjliggjorde screening och fördjupad undersökning av förändringen i metabolitnivåer. I den första pilotstudien av postprandiala nivåer var syftet att undersöka responsen i plasmametabolomet efter en väldefinierad måltid under den postprandiala fasen vid två olika typer av kost. Resultaten visade att oberoende av kosten, så återvände metabolitnivåerna till sina baslinjenivåer tre timmar efter måltiden. Detta togs i beaktande vid exponeringsstudierna för biodieselavgaser, som designades så att dietens inverkan minimerades. Både breda och riktade analyser resulterade i viktiga resultat. Exempelvis så detekterades olika metabolitprofiler i bronkiell sköljvätska (BW) jämfört med bronkoalveolär sköljvätska (BAL), speciellt med NMR och LC-MS. Dessutom resulterade avgasexponering i förändrade metabolitprofiler, observerade med GC-MS, särskilt i BAL. Dessutom uppvisade fettsyrametaboliter i BW, BAL och plasma förändrade halter efter avgasexponering, uppmätt genom en riktad LC-MS/MS-analys. Sammanfattningsvis så visade sig de nya metoderna som utvecklats för att undersöka  förändringar i metabolithalterna i plasma och lungsköljvätska fungera väl ur ett analytiskt perspektiv och resulterade i viktiga biologiska fynd. Fördjupade studier behövs dock för att validera resultaten.

The diploma thesis deals with migration of SAP Business Warehouse system to the new in-memory technology SAP HANA. Theoretical basis defines basic terms, usage in-memory technology in Business Intelligence and migration options. In the practical part, the best option of migration and best practices for the company was designed.

Using high resolution optical spectra from Mount John University Observatory, Mount Stromlo Observatory and the Anglo-Australian Observatory, new, high accuracy radial velocity curves have been obtained for the two bright southern Cepheids l carinae (HR 3884) and beta doradus (HR 1922). An indepth investigation into period variations, cycle-to-cycle and long-term variations in the velocity curves and the reliability of the combination of velocity data from different observatories is carried out. Evidence for shock waves in the atmosphere of l car and resonance in beta dor is discussed. A grid of static model atmospheres incorporating plane-parallel geometry is compared with the observational spectra of both Cepheids, using line depth ratios, to determine the variation in effective temperature, surface gravity and microturbulence with phase. This information is used to determine the phase dependence of the surface-brightness for both Cepheids. The surface brightness variation with phase was found to follow an almost linear relationship. The distance to and radius of the Cepheids are determined using both a near-infrared version of the Barnes-Evans method and the Fourier Baade-Wesselink (BW) method. The derived radii and distances agree within the limits of the errors for both methods. The Fourier BW method was found to be very sensitive to phase shifts between the photometric and spectroscopic data and the derived distance highly dependent on the assumed reddening. An investigation into line profile variations in l car and beta dor has revealed the magnitude of these phenomena increase as the pulsational period of the Cepheid increases. It is estimated that line level variations introduce an additional uncertainty into derived radii of approximately 4 per cent for beta dor and 10 per cent in lcar. The uncertainty introduced into derived distances and radii by line profile asymmetries was estimated to be of the order of 6 per cent in beta dor and 10 per cent in l car. A comparative analysis is made of the hydrogen line radial velocity curves of l car and beta dor. A trend in the properties of these radial velocity curves with period has been revealed. In longer period Cepheids, the Halpha line seems to be forming in a region that does not partake in the pulsation as a whole, probably in a chromospheric shell. A quantitative analysis of the asymmetries in these lines reveal large redward asymmetries near maximum infall velocity. The magnitude of these asymmetries and the period for which they are present are larger in l Car than in beta dor. The blueward asymmetries in the Halpha line in l Car are comparable in magnitude to the redward asymmetries while the other lines exhibit only small blueward asymmetries. A qualitative analysis of these line profiles with phase reveal no conclusive evidence for line doubling in these Cepheids. Evidence of emission is found in the Halpha and H Beta lines of beta dor and l car. The strength and duration of the emission is found to be greater in the longer period Cepheid. Although it is likely that this emission is shock-related, theoretical work is needed to determine the exact origin of the emission. A non-LTE radiative hydrodynamic model for l Car has been created. This atmosphere will be used in further work to calculate synthetic spectral line profiles which will aid the interpretation of our observational results.

The main purpose of this thesis was to study, whether drinkable yoghurt enriched with probiotic bacteria could have any effect on constipation and body weight (BW) among older people with dementia. Further, it concerns poor nutritional status among older people with physical and cognitive impairments and its relationship with factors commonly occur in older people living in residential care facilities. It also discusses how body composition changes with ageing and the associations between changes in muscle mass and functional balance after a high-intensity weight-bearing exercise program (the HIFE program) and the ingestion of an additional milk-based protein-enriched energy supplement. A six-month feasibility study that included a probiotic drink was performed among 15 old people who were living in special units for people with dementia and who all had constipation. The effects of the probiotic drink on stool habits, and BW were studied. The outcome measures were followed daily for bowel movements and at three and six months for BW. The staff found the study easy to carry out and that the drink was well accepted by the participants. No convincing beneficial effects on stool habits were observed. In addition, a mean BW loss of 0.65 kg/month was registered. A poor nutritional intake, low physical activity level, and an over-night fast of almost 15 hours, 4 hours longer than recommended were also observed. As a part of the FOPANU Study (Frail Older People-Activity and Nutrition Study), a randomized controlled trial was carried out in Umeå - the associations between nutritional status and factors common among old people with physical and cognitive impairments living in residential care facilities was studied. Assessments were made of nutritional status using the Mini Nutritional Assessment (MNA) scale, fat-free mass (FFM) and fat mass (FM) using both bioelectrical impedance spectroscopy (BIS) and skinfold thickness measurements. The effects of a high- intensity functional exercise program with an additional protein-enriched milk drink on ability to build muscle mass were evaluated. Analyses were made to investigate whether nutritional status, assessed using the MNA scale, was associated with medical conditions, drugs, activities of daily living (Barthel ADL index), cognitive impairment (Mini Mental State Examination (MMSE)), and depressive symptoms (Geriatric Depression Scale (GDS)) at baseline. The associations were assessed with multiple linear regression analyses with additional interaction analyses. An independent association was found between poor nutritional status and having had a urinary tract infection (UTI) during the preceding year and being dependent in feeding for both women and men, and having lower MMSE scores for women. A large proportion of the participants, were at risk of malnutrition or were already malnourished. Women, but not men, had significantly lower Fat-Free Mass Index (FFMI) and Fat Mass Index (FMI) with age. Bioelectrical impedance spectroscopy results correlated with skinfold thickness measurements, but on different levels on value for FM%. Despite the high-intensity exercise had long-term effects (at six months, three months after the exercise) on functional balance, walking ability and leg strength. No effects on muscle mass and no additional effects from the protein-enriched drink could be observed after the three months of high intensity exercise. A negative, long-term effect on the amount of muscle mass and BW was revealed at six months (three months after the intervention had ended). The effects from the exercise did not differ for participants who were malnourished. No statistical interactions were observed between sex, depression, dementia disorder, and nutritional status, and the level of functional balance capacity on the outcome at three or six months. In summary, the majority of the included older people with dementia had a low dietary intake, low physical activity level, and lost BW despite receiving a probiotic drink supplement every day for six months. The supplementation had no detectable effect on constipation. Among the participants in the FOPANU Study, UTI during the preceding year was independently associated with poor nutritional status. Being dependent in feeding was associated with poor nutritional status as were lower MMSE scores for women but not for men. Despite the high-intensity exercise program had long-term effects on the fysical function was no effect on the amount of muscle mass at three months observed. The FFM and FM expressed as indexes of body height were inversely related to age for women, but not for men. A high-intensity exercise program did not have any effect on the amount of muscle mass. The ingestion of a protein-enriched drink immediately after exercise produced no additional effect on the outcome and the results did not differ for participants who were malnourished. The negative long-term effect on amount of muscle mass, and BW, indicate that it is necessary to compensate for increased energy demands during a high-intensity exercise program. High age, female sex, depression, mild to moderate dementia syndrome, malnutrition, and severe physical impairment do not seem to have a negative impact on the effect of a high-intensity functional weight-bearing exercise program. Consequently, people with these characteristics in residential care facilities should not be excluded from training and rehabilitation including nutrition. More research is needed in large randomized controlled trials to further explore the association between energy balance and malnutrition among frail old people, with a special focus on UTI and constipation, but also to study how physical exercise affects older people’s nutritional status. / Embargo

Using high resolution optical spectra from Mount John University Observatory, Mount Stromlo Observatory and the Anglo-Australian Observatory, new, high accuracy radial velocity curves have been obtained for the two bright southern Cepheids l carinae (HR 3884) and beta doradus (HR 1922). An indepth investigation into period variations, cycle-to-cycle and long-term variations in the velocity curves and the reliability of the combination of velocity data from different observatories is carried out. Evidence for shock waves in the atmosphere of l car and resonance in beta dor is discussed. A grid of static model atmospheres incorporating plane-parallel geometry is compared with the observational spectra of both Cepheids, using line depth ratios, to determine the variation in effective temperature, surface gravity and microturbulence with phase. This information is used to determine the phase dependence of the surface-brightness for both Cepheids. The surface brightness variation with phase was found to follow an almost linear relationship. The distance to and radius of the Cepheids are determined using both a near-infrared version of the Barnes-Evans method and the Fourier Baade-Wesselink (BW) method. The derived radii and distances agree within the limits of the errors for both methods. The Fourier BW method was found to be very sensitive to phase shifts between the photometric and spectroscopic data and the derived distance highly dependent on the assumed reddening. An investigation into line profile variations in l car and beta dor has revealed the magnitude of these phenomena increase as the pulsational period of the Cepheid increases. It is estimated that line level variations introduce an additional uncertainty into derived radii of approximately 4 per cent for beta dor and 10 per cent in lcar. The uncertainty introduced into derived distances and radii by line profile asymmetries was estimated to be of the order of 6 per cent in beta dor and 10 per cent in l car. A comparative analysis is made of the hydrogen line radial velocity curves of l car and beta dor. A trend in the properties of these radial velocity curves with period has been revealed. In longer period Cepheids, the Halpha line seems to be forming in a region that does not partake in the pulsation as a whole, probably in a chromospheric shell. A quantitative analysis of the asymmetries in these lines reveal large redward asymmetries near maximum infall velocity. The magnitude of these asymmetries and the period for which they are present are larger in l Car than in beta dor. The blueward asymmetries in the Halpha line in l Car are comparable in magnitude to the redward asymmetries while the other lines exhibit only small blueward asymmetries. A qualitative analysis of these line profiles with phase reveal no conclusive evidence for line doubling in these Cepheids. Evidence of emission is found in the Halpha and H Beta lines of beta dor and l car. The strength and duration of the emission is found to be greater in the longer period Cepheid. Although it is likely that this emission is shock-related, theoretical work is needed to determine the exact origin of the emission. A non-LTE radiative hydrodynamic model for l Car has been created. This atmosphere will be used in further work to calculate synthetic spectral line profiles which will aid the interpretation of our observational results.

Master’s thesis proposes a solution of data and communication model in the ŽOS Trnava, a.s. and GOŠA FŠV. Thesis proposes model for customer, which generates reports for individual management areas.

In this thesis we study a particular realization of the Domain wall/Quantum Field Theory correspondence, a modification of the Anti de-Sitter/Conformal Field Theory correspondence that is used to study deformations of a Conformal Field Theory. In theQuantum gravity side of the duality we analyze a N = 1 gauged supergravity theory in 7 dimensions which presents two different Anti de-Sitter vacua, one of which preserves the full supersymmetry and one that breaks half of the supercharges. We will find a scalar 1/2-BPS solitonic solution describing a domain wall in an asymptotically Anti de-Sitter spacetime which interpolates between the supersymmetric AdS vacuum and a divergent AdS space situated at infinity, and we will calculate its tension and the effective mass of the scalar field when evaluated at the AdS vacuum. The dual theory of our gauged  supergravity is the 6-dimensional N = (1,0) Superconformal Field Theory, and the scalar 1/2-BPS field is dual to two relevant operators that induce a relevant deformation of the SCFT which can be identified with a renormalization group-flow. Here we will first compute the scaling dimension and the one-point functions of these operators in the SCFT, as well as indicating how to compute the two-point and three-point functions, and then we will study the c-function along the renormalization group-flow they induce and the beta function that characterizes this flow in order to derive some properties of the resulting Quantum Field Theory.

Die folgende Arbeit befasst sich mit der Alltagstauglichkeit moderner Massendatenverarbeitung, insbesondere mit dem Problem der Assoziationsregelanalyse. Vorhandene Datenmengen wachsen stark an, aber deren Auswertung ist für ungeübte Anwender schwierig. Daher verzichten Unternehmen auf Informationen, welche prinzipiell vorhanden sind. Assoziationsregeln zeigen in diesen Daten Abhängigkeiten zwischen den Elementen eines Datenbestandes, beispielsweise zwischen verkauften Produkten. Diese Regeln können mit Interessantheitsmaßen versehen werden, welche dem Anwender das Erkennen wichtiger Zusammenhänge ermöglichen. Es werden Ansätze gezeigt, dem Nutzer die Auswertung der Daten zu erleichtern. Das betrifft sowohl die robuste Arbeitsweise der Verfahren als auch die einfache Auswertung der Regeln. Die vorgestellten Algorithmen passen sich dabei an die zu verarbeitenden Daten an, was sie von anderen Verfahren unterscheidet. Assoziationsregelsuchen benötigen die Extraktion häufiger Kombinationen (EHK). Hierfür werden Möglichkeiten gezeigt, Lösungsansätze auf die Eigenschaften moderne System anzupassen. Als Ansatz werden Verfahren zur Berechnung der häufigsten $N$ Kombinationen erläutert, welche anders als bekannte Ansätze leicht konfigurierbar sind. Moderne Systeme rechnen zudem oft verteilt. Diese Rechnerverbünde können große Datenmengen parallel verarbeiten, benötigen jedoch die Vereinigung lokaler Ergebnisse. Für verteilte Top-N-EHK auf realistischen Partitionierungen werden hierfür Ansätze mit verschiedenen Eigenschaften präsentiert. Aus den häufigen Kombinationen werden Assoziationsregeln gebildet, deren Aufbereitung ebenfalls einfach durchführbar sein soll. In der Literatur wurden viele Maße vorgestellt. Je nach den Anforderungen entsprechen sie je einer subjektiven Bewertung, allerdings nicht zwingend der des Anwenders. Hierfür wird untersucht, wie mehrere Interessantheitsmaßen zu einem globalen Maß vereinigt werden können. Dies findet Regeln, welche mehrfach wichtig erschienen. Der Nutzer kann mit den Vorschlägen sein Suchziel eingrenzen. Ein zweiter Ansatz gruppiert Regeln. Dies erfolgt über die Häufigkeiten der Regelelemente, welche die Grundlage von Interessantheitsmaßen bilden. Die Regeln einer solchen Gruppe sind daher bezüglich vieler Interessantheitsmaßen ähnlich und können gemeinsam ausgewertet werden. Dies reduziert den manuellen Aufwand des Nutzers. Diese Arbeit zeigt Möglichkeiten, Assoziationsregelsuchen auf einen breiten Benutzerkreis zu erweitern und neue Anwender zu erreichen. Die Assoziationsregelsuche wird dabei derart vereinfacht, dass sie statt als Spezialanwendung als leicht nutzbares Werkzeug zur Datenanalyse verwendet werden kann. / The importance of data mining is widely acknowledged today. Mining for association rules and frequent patterns is a central activity in data mining. Three main strategies are available for such mining: APRIORI , FP-tree-based approaches like FP-GROWTH, and algorithms based on vertical data structures and depth-first mining strategies like ECLAT and CHARM. Unfortunately, most of these algorithms are only moderately suitable for many “real-world” scenarios because their usability and the special characteristics of the data are two aspects of practical association rule mining that require further work. All mining strategies for frequent patterns use a parameter called minimum support to define a minimum occurrence frequency for searched patterns. This parameter cuts down the number of patterns searched to improve the relevance of the results. In complex business scenarios, it can be difficult and expensive to define a suitable value for the minimum support because it depends strongly on the particular datasets. Users are often unable to set this parameter for unknown datasets, and unsuitable minimum-support values can extract millions of frequent patterns and generate enormous runtimes. For this reason, it is not feasible to permit ad-hoc data mining by unskilled users. Such users do not have the knowledge and time to define suitable parameters by trial-and-error procedures. Discussions with users of SAP software have revealed great interest in the results of association-rule mining techniques, but most of these users are unable or unwilling to set very technical parameters. Given such user constraints, several studies have addressed the problem of replacing the minimum-support parameter with more intuitive top-n strategies. We have developed an adaptive mining algorithm to give untrained SAP users a tool to analyze their data easily without the need for elaborate data preparation and parameter determination. Previously implemented approaches of distributed frequent-pattern mining were expensive and time-consuming tasks for specialists. In contrast, we propose a method to accelerate and simplify the mining process by using top-n strategies and relaxing some requirements on the results, such as completeness. Unlike such data approximation techniques as sampling, our algorithm always returns exact frequency counts. The only drawback is that the result set may fail to include some of the patterns up to a specific frequency threshold. Another aspect of real-world datasets is the fact that they are often partitioned for shared-nothing architectures, following business-specific parameters like location, fiscal year, or branch office. Users may also want to conduct mining operations spanning data from different partners, even if the local data from the respective partners cannot be integrated at a single location for data security reasons or due to their large volume. Almost every data mining solution is constrained by the need to hide complexity. As far as possible, the solution should offer a simple user interface that hides technical aspects like data distribution and data preparation. Given that BW Accelerator users have such simplicity and distribution requirements, we have developed an adaptive mining algorithm to give unskilled users a tool to analyze their data easily, without the need for complex data preparation or consolidation. For example, Business Intelligence scenarios often partition large data volumes by fiscal year to enable efficient optimizations for the data used in actual workloads. For most mining queries, more than one data partition is of interest, and therefore, distribution handling that leaves the data unaffected is necessary. The algorithms presented in this paper have been developed to work with data stored in SAP BW. A salient feature of SAP BW Accelerator is that it is implemented as a distributed landscape that sits on top of a large number of shared-nothing blade servers. Its main task is to execute OLAP queries that require fast aggregation of many millions of rows of data. Therefore, the distribution of data over the dedicated storage is optimized for such workloads. Data mining scenarios use the same data from storage, but reporting takes precedence over data mining, and hence, the data cannot be redistributed without massive costs. Distribution by special data semantics or user-defined selections can produce many partitions and very different partition sizes. The handling of such real-world distributions for frequent-pattern mining is an important task, but it conflicts with the requirement of balanced partition.

Die folgende Arbeit befasst sich mit der Alltagstauglichkeit moderner Massendatenverarbeitung, insbesondere mit dem Problem der Assoziationsregelanalyse. Vorhandene Datenmengen wachsen stark an, aber deren Auswertung ist für ungeübte Anwender schwierig. Daher verzichten Unternehmen auf Informationen, welche prinzipiell vorhanden sind. Assoziationsregeln zeigen in diesen Daten Abhängigkeiten zwischen den Elementen eines Datenbestandes, beispielsweise zwischen verkauften Produkten. Diese Regeln können mit Interessantheitsmaßen versehen werden, welche dem Anwender das Erkennen wichtiger Zusammenhänge ermöglichen. Es werden Ansätze gezeigt, dem Nutzer die Auswertung der Daten zu erleichtern. Das betrifft sowohl die robuste Arbeitsweise der Verfahren als auch die einfache Auswertung der Regeln. Die vorgestellten Algorithmen passen sich dabei an die zu verarbeitenden Daten an, was sie von anderen Verfahren unterscheidet. Assoziationsregelsuchen benötigen die Extraktion häufiger Kombinationen (EHK). Hierfür werden Möglichkeiten gezeigt, Lösungsansätze auf die Eigenschaften moderne System anzupassen. Als Ansatz werden Verfahren zur Berechnung der häufigsten $N$ Kombinationen erläutert, welche anders als bekannte Ansätze leicht konfigurierbar sind. Moderne Systeme rechnen zudem oft verteilt. Diese Rechnerverbünde können große Datenmengen parallel verarbeiten, benötigen jedoch die Vereinigung lokaler Ergebnisse. Für verteilte Top-N-EHK auf realistischen Partitionierungen werden hierfür Ansätze mit verschiedenen Eigenschaften präsentiert. Aus den häufigen Kombinationen werden Assoziationsregeln gebildet, deren Aufbereitung ebenfalls einfach durchführbar sein soll. In der Literatur wurden viele Maße vorgestellt. Je nach den Anforderungen entsprechen sie je einer subjektiven Bewertung, allerdings nicht zwingend der des Anwenders. Hierfür wird untersucht, wie mehrere Interessantheitsmaßen zu einem globalen Maß vereinigt werden können. Dies findet Regeln, welche mehrfach wichtig erschienen. Der Nutzer kann mit den Vorschlägen sein Suchziel eingrenzen. Ein zweiter Ansatz gruppiert Regeln. Dies erfolgt über die Häufigkeiten der Regelelemente, welche die Grundlage von Interessantheitsmaßen bilden. Die Regeln einer solchen Gruppe sind daher bezüglich vieler Interessantheitsmaßen ähnlich und können gemeinsam ausgewertet werden. Dies reduziert den manuellen Aufwand des Nutzers. Diese Arbeit zeigt Möglichkeiten, Assoziationsregelsuchen auf einen breiten Benutzerkreis zu erweitern und neue Anwender zu erreichen. Die Assoziationsregelsuche wird dabei derart vereinfacht, dass sie statt als Spezialanwendung als leicht nutzbares Werkzeug zur Datenanalyse verwendet werden kann. / The importance of data mining is widely acknowledged today. Mining for association rules and frequent patterns is a central activity in data mining. Three main strategies are available for such mining: APRIORI , FP-tree-based approaches like FP-GROWTH, and algorithms based on vertical data structures and depth-first mining strategies like ECLAT and CHARM. Unfortunately, most of these algorithms are only moderately suitable for many “real-world” scenarios because their usability and the special characteristics of the data are two aspects of practical association rule mining that require further work. All mining strategies for frequent patterns use a parameter called minimum support to define a minimum occurrence frequency for searched patterns. This parameter cuts down the number of patterns searched to improve the relevance of the results. In complex business scenarios, it can be difficult and expensive to define a suitable value for the minimum support because it depends strongly on the particular datasets. Users are often unable to set this parameter for unknown datasets, and unsuitable minimum-support values can extract millions of frequent patterns and generate enormous runtimes. For this reason, it is not feasible to permit ad-hoc data mining by unskilled users. Such users do not have the knowledge and time to define suitable parameters by trial-and-error procedures. Discussions with users of SAP software have revealed great interest in the results of association-rule mining techniques, but most of these users are unable or unwilling to set very technical parameters. Given such user constraints, several studies have addressed the problem of replacing the minimum-support parameter with more intuitive top-n strategies. We have developed an adaptive mining algorithm to give untrained SAP users a tool to analyze their data easily without the need for elaborate data preparation and parameter determination. Previously implemented approaches of distributed frequent-pattern mining were expensive and time-consuming tasks for specialists. In contrast, we propose a method to accelerate and simplify the mining process by using top-n strategies and relaxing some requirements on the results, such as completeness. Unlike such data approximation techniques as sampling, our algorithm always returns exact frequency counts. The only drawback is that the result set may fail to include some of the patterns up to a specific frequency threshold. Another aspect of real-world datasets is the fact that they are often partitioned for shared-nothing architectures, following business-specific parameters like location, fiscal year, or branch office. Users may also want to conduct mining operations spanning data from different partners, even if the local data from the respective partners cannot be integrated at a single location for data security reasons or due to their large volume. Almost every data mining solution is constrained by the need to hide complexity. As far as possible, the solution should offer a simple user interface that hides technical aspects like data distribution and data preparation. Given that BW Accelerator users have such simplicity and distribution requirements, we have developed an adaptive mining algorithm to give unskilled users a tool to analyze their data easily, without the need for complex data preparation or consolidation. For example, Business Intelligence scenarios often partition large data volumes by fiscal year to enable efficient optimizations for the data used in actual workloads. For most mining queries, more than one data partition is of interest, and therefore, distribution handling that leaves the data unaffected is necessary. The algorithms presented in this paper have been developed to work with data stored in SAP BW. A salient feature of SAP BW Accelerator is that it is implemented as a distributed landscape that sits on top of a large number of shared-nothing blade servers. Its main task is to execute OLAP queries that require fast aggregation of many millions of rows of data. Therefore, the distribution of data over the dedicated storage is optimized for such workloads. Data mining scenarios use the same data from storage, but reporting takes precedence over data mining, and hence, the data cannot be redistributed without massive costs. Distribution by special data semantics or user-defined selections can produce many partitions and very different partition sizes. The handling of such real-world distributions for frequent-pattern mining is an important task, but it conflicts with the requirement of balanced partition.

In this study, the evolution of the numerical water wave models with the theoretical background and the governing equations are briefly discussed and a numerical model MIKE21 BW which can be applied to wave problems in nearshore zone is presented. The numerical model is based on the numerical solution of the Boussinesq type equations formulated on time domain. Nonlinearity and frequency dispersion is included in the model. In order to make comparison between the results of nonlinear shallow water equations with Boussinesq terms, MIKE21 BW and NAMIDANCE are applied to the problem of wave propagation in the long distances and runup on simple and composite slopes. The numerical experiments are applied to Dat&ccedil / a Marina and the results are compared to the results of the physical experiments on wave disturbance in Dat&ccedil / a Marina. In these comparisons the reflection characteristics of different coastal boundaries in the harbor area are tested and the internal parameters in the model are calibrated accordingly. The numerical model MIKE21 BW is applied to skenderun harbor as a case study. The input wave parameters are selected from the wave climate study for Iskenderun Harbor. The model is set up and the agitation inside the harbor is computed according to four different incoming wave scenarios. The disturbance maps inside the harbor for different incoming wave scenarios are obtained. The critical regions v of the harbor according to disturbance under different wave conditions are presented and discussed.