Relational Cloud: The Case for a Database Service

In this paper, we make the case for â databases as a serviceâ (DaaS), with two target scenarios in mind: (i) consolidation of data management functionality for large organizations and (ii) outsourcing data management to a cloud-based service provider for small/medium organizations. We analyze the many challenges to be faced, and discuss the design of a database service we are building, called Relational Cloud. The system has been designed from scratch and combines many recent advances and novel solutions. The prototype we present exploits multiple dedicated storage engines, provides high-availability via transparent replication, supports automatic workload partitioning and live data migration, and provides serializable distributed transactions. While the system is still under active development, we are able to present promising initial results that showcase the key features of our system. The tests are based on TPC benchmarks and real-world data from epinions.com, and show our partitioning, scalability and balancing capabilities

Student Engagement in Aviation Moocs: Identifying Subgroups and Their Differences

The purpose of this study was to expand the current understanding of learner engagement in aviation-related Massive Open Online Courses (MOOCs) through cluster analysis. MOOCs, regarded for their low- or no-cost educational content, often attract thousands of students who are free to engage with the provided content to the extent of their choosing. As online training for pilots, flight attendants, mechanics, and small unmanned aerial system operators continues to expand, understanding how learners engage in optional aviation-focused, online course material may help inform course design and instruction in the aviation industry. In this study, Moore’s theory of transactional distance, which posits psychological or communicative distance can impede learning and success, was used as a descriptive framework for analysis. Archived learning analytics datasets from two 2018 iterations of the same small unmanned aerial systems MOOC were cluster-analyzed (N = 1,032 and N = 4,037). The enrolled students included individuals worldwide; some were affiliated with the host institution, but most were not. The data sets were cluster analyzed separately to categorize participants into common subpopulations based on discussion post pages viewed and posts written, video pages viewed, and quiz grades. Subgroup differences were examined in days of activity and record of completion. Pre- and postcourse survey data provided additional variables for analysis of subgroup differences in demographics (age, geographic location, education level, employment in the aviation industry) and learning goals. Analysis of engagement variables revealed three significantly different subgroups for each MOOC. Engagement patterns were similar between MOOCs for the most and least engaged groups, but differences were noted in the middle groups; MOOC 1’s middle group had a broader interest in optional content (both in discussions and videos); whereas MOOC 2’s middle group had a narrower interest in optional discussions. Mandatory items (Mandatory Discussion or Quizzes) were the best predictors in classifying subgroups for both MOOCs. Significant associations were found between subgroups and education levels, days of activity, and total quiz scores. This study addressed two known problems: a lack of information on student engagement in aviation-related MOOCs, and more broadly, a growing imperative to examine learners who utilize MOOCs but do not complete them. This study served as an important first step for course developers and instructors who aim to meet the diverse needs of the aviation-education community

Integrative bioinformatics applications for complex human disease contexts

This thesis presents new methods for the analysis of high-throughput data from modern sources in the context of complex human diseases, at the example of a bioinformatics analysis workflow. New measurement techniques improve the resolution with which cellular and molecular processes can be monitored. While RNA sequencing (RNA-seq) measures mRNA expression, single-cell RNA-seq (scRNA-seq) resolves this on a per-cell basis. Long-read sequencing is increasingly used in genomics. With imaging mass spectrometry (IMS) the protein level in tissues is measured spatially resolved. All these techniques induce specific challenges, which need to be addressed with new computational methods. Collecting knowledge with contextual annotations is important for integrative data analyses. Such knowledge is available through large literature repositories, from which information, such as miRNA-gene interactions, can be extracted using text mining methods. After aggregating this information in new databases, specific questions can be answered with traceable evidence. The combination of experimental data with these databases offers new possibilities for data integrative methods and for answering questions relevant for complex human diseases. Several data sources are made available, such as literature for text mining miRNA-gene interactions (Chapter 2), next- and third-generation sequencing data for genomics and transcriptomics (Chapters 4.1, 5), and IMS for spatially resolved proteomics (Chapter 4.4). For these data sources new methods for information extraction and pre-processing are developed. For instance, third-generation sequencing runs can be monitored and evaluated using the poreSTAT and sequ-into methods. The integrative (down-stream) analyses make use of these (heterogeneous) data sources. The cPred method (Chapter 4.2) for cell type prediction from scRNA-seq data was successfully applied in the context of the SARS-CoV-2 pandemic. The robust differential expression (DE) analysis pipeline RoDE (Chapter 6.1) contains a large set of methods for (differential) data analysis, reporting and visualization of RNA-seq data. Topics of accessibility of bioinformatics software are discussed along practical applications (Chapter 3). The developed miRNA-gene interaction database gives valuable insights into atherosclerosis-relevant processes and serves as regulatory network for the prediction of active miRNA regulators in RoDE (Chapter 6.1). The cPred predictions, RoDE results, scRNA-seq and IMS data are unified as input for the 3D-index Aorta3D (Chapter 6.2), which makes atherosclerosis related datasets browsable. Finally, the scRNA-seq analysis with subsequent cPred cell type prediction, and the robust analysis of bulk-RNA-seq datasets, led to novel insights into COVID-19. Taken all discussed methods together, the integrative analysis methods for complex human disease contexts have been improved at essential positions.Die Dissertation beschreibt Methoden zur Prozessierung von aktuellen Hochdurchsatzdaten, sowie Verfahren zu deren weiterer integrativen Analyse. Diese findet Anwendung vor allem im Kontext von komplexen menschlichen Krankheiten. Neue Messtechniken erlauben eine detailliertere Beobachtung biomedizinischer Prozesse. Mit RNA-Sequenzierung (RNA-seq) wird mRNA-Expression gemessen, mit Hilfe von moderner single-cell-RNA-seq (scRNA-seq) sogar für (sehr viele) einzelne Zellen. Long-Read-Sequenzierung wird zunehmend zur Sequenzierung ganzer Genome eingesetzt. Mittels bildgebender Massenspektrometrie (IMS) können Proteine in Geweben räumlich aufgelöst quantifiziert werden. Diese Techniken bringen spezifische Herausforderungen mit sich, die mit neuen bioinformatischen Methoden angegangen werden müssen. Für die integrative Datenanalyse ist auch die Gewinnung von geeignetem Kontextwissen wichtig. Wissenschaftliche Erkenntnisse werden in Artikeln veröffentlicht, die über große Literaturdatenbanken zugänglich sind. Mittels Textmining können daraus Informationen extrahiert werden, z.B. miRNA-Gen-Interaktionen, die in eigenen Datenbank aggregiert werden um spezifische Fragen mit nachvollziehbaren Belegen zu beantworten. In Kombination mit experimentellen Daten bieten sich so neue Möglichkeiten für integrative Methoden. Durch die Extraktion von Rohdaten und deren Vorprozessierung werden mehrere Datenquellen erschlossen, wie z.B. Literatur für Textmining von miRNA-Gen-Interaktionen (Kapitel 2), Long-Read- und RNA-seq-Daten für Genomics und Transcriptomics (Kapitel 4.2, 5) und IMS für Protein-Messungen (Kapitel 4.4). So dienen z.B. die poreSTAT und sequ-into Methoden der Vorprozessierung und Auswertung von Long-Read-Sequenzierungen. In der integrativen (down-stream) Analyse werden diese (heterogenen) Datenquellen verwendet. Für die Bestimmung von Zelltypen in scRNA-seq-Experimenten wurde die cPred-Methode (Kapitel 4.2) erfolgreich im Kontext der SARS-CoV-2-Pandemie eingesetzt. Auch die robuste Pipeline RoDE fand dort Anwendung, die viele Methoden zur (differentiellen) Datenanalyse, zum Reporting und zur Visualisierung bereitstellt (Kapitel 6.1). Themen der Benutzbarkeit von (bioinformatischer) Software werden an Hand von praktischen Anwendungen diskutiert (Kapitel 3). Die entwickelte miRNA-Gen-Interaktionsdatenbank gibt wertvolle Einblicke in Atherosklerose-relevante Prozesse und dient als regulatorisches Netzwerk für die Vorhersage von aktiven miRNA-Regulatoren in RoDE (Kapitel 6.1). Die cPred-Methode, RoDE-Ergebnisse, scRNA-seq- und IMS-Daten werden im 3D-Index Aorta3D (Kapitel 6.2) zusammengeführt, der relevante Datensätze durchsuchbar macht. Die diskutierten Methoden führen zu erheblichen Verbesserungen für die integrative Datenanalyse in komplexen menschlichen Krankheitskontexten

Adaptive algorithms for real-world transactional data mining.

The accurate identification of the right customer to target with the right product at the right time, through the right channel, to satisfy the customer’s evolving needs, is a key performance driver and enhancer for businesses. Data mining is an analytic process designed to explore usually large amounts of data (typically business or market related) in search of consistent patterns and/or systematic relationships between variables for the purpose of generating explanatory/predictive data models from the detected patterns. It provides an effective and established mechanism for accurate identification and classification of customers. Data models derived from the data mining process can aid in effectively recognizing the status and preference of customers - individually and as a group. Such data models can be incorporated into the business market segmentation, customer targeting and channelling decisions with the goal of maximizing the total customer lifetime profit. However, due to costs, privacy and/or data protection reasons, the customer data available for data mining is often restricted to verified and validated data,(in most cases,only the business owned transactional data is available). Transactional data is a valuable resource for generating such data models. Transactional data can be electronically collected and readily made available for data mining in large quantity at minimum extra cost. Transactional data is however, inherently sparse and skewed. These inherent characteristics of transactional data give rise to the poor performance of data models built using customer data based on transactional data. Data models for identifying, describing, and classifying customers, constructed using evolving transactional data thus need to effectively handle the inherent sparseness and skewness of evolving transactional data in order to be efficient and accurate. Using real-world transactional data, this thesis presents the findings and results from the investigation of data mining algorithms for analysing, describing, identifying and classifying customers with evolving needs. In particular, methods for handling the issues of scalability, uncertainty and adaptation whilst mining evolving transactional data are analysed and presented. A novel application of a new framework for integrating transactional data binning and classification techniques is presented alongside an effective prototype selection algorithm for efficient transactional data model building. A new change mining architecture for monitoring, detecting and visualizing the change in customer behaviour using transactional data is proposed and discussed as an effective means for analysing and understanding the change in customer buying behaviour over time. Finally, the challenging problem of discerning between the change in the customer profile (which may necessitate the effective change of the customer’s label) and the change in performance of the model(s) (which may necessitate changing or adapting the model(s)) is introduced and discussed by way of a novel flexible and efficient architecture for classifier model adaptation and customer profiles class relabeling

Benefits of the application of web-mining methods and techniques for the field of analytical customer relationship management of the marketing function in a knowledge management perspective

Le Web Mining (WM) reste une technologie relativement méconnue. Toutefois, si elle est utilisée adéquatement, elle s'avère être d'une grande utilité pour l'identification des profils et des comportements des clients prospects et existants, dans un contexte internet. Les avancées techniques du WM améliorent grandement le volet analytique de la Gestion de la Relation Client (GRC). Cette étude suit une approche exploratoire afin de déterminer si le WM atteint, à lui seul, tous les objectifs fondamentaux de la GRC, ou le cas échéant, devrait être utilisé de manière conjointe avec la recherche marketing traditionnelle et les méthodes classiques de la GRC analytique (GRCa) pour optimiser la GRC, et de fait le marketing, dans un contexte internet. La connaissance obtenue par le WM peut ensuite être administrée au sein de l'organisation dans un cadre de Gestion de la Connaissance (GC), afin d'optimiser les relations avec les clients nouveaux et/ou existants, améliorer leur expérience client et ultimement, leur fournir de la meilleure valeur. Dans un cadre de recherche exploratoire, des entrevues semi-structurés et en profondeur furent menées afin d'obtenir le point de vue de plusieurs experts en (web) data rnining. L'étude révéla que le WM est bien approprié pour segmenter les clients prospects et existants, pour comprendre les comportements transactionnels en ligne des clients existants et prospects, ainsi que pour déterminer le statut de loyauté (ou de défection) des clients existants. Il constitue, à ce titre, un outil d'une redoutable efficacité prédictive par le biais de la classification et de l'estimation, mais aussi descriptive par le biais de la segmentation et de l'association. En revanche, le WM est moins performant dans la compréhension des dimensions sous-jacentes, moins évidentes du comportement client. L'utilisation du WM est moins appropriée pour remplir des objectifs liés à la description de la manière dont les clients existants ou prospects développent loyauté, satisfaction, défection ou attachement envers une enseigne sur internet. Cet exercice est d'autant plus difficile que la communication multicanale dans laquelle évoluent les consommateurs a une forte influence sur les relations qu'ils développent avec une marque. Ainsi le comportement en ligne ne serait qu'une transposition ou tout du moins une extension du comportement du consommateur lorsqu'il n'est pas en ligne. Le WM est également un outil relativement incomplet pour identifier le développement de la défection vers et depuis les concurrents ainsi que le développement de la loyauté envers ces derniers. Le WM nécessite toujours d'être complété par la recherche marketing traditionnelle afin d'atteindre ces objectives plus difficiles mais essentiels de la GRCa. Finalement, les conclusions de cette recherche sont principalement dirigées à l'encontre des firmes et des gestionnaires plus que du côté des clients-internautes, car ces premiers plus que ces derniers possèdent les ressources et les processus pour mettre en œuvre les projets de recherche en WM décrits.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Web mining, Gestion de la connaissance, Gestion de la relation client, Données internet, Comportement du consommateur, Forage de données, Connaissance du consommateu

Computing the everyday: social media as data platforms

We conceive social media platforms as sociotechnical entities that variously shape user platform involvement and participation. Such shaping develops along three fundamental data operations that we subsume under the terms of encoding, aggregation, and computation. Encoding entails the engineering of user platform participation along narrow and standardized activity types (e.g., tagging, liking, sharing, following). This heavily scripted platform participation serves as the basis for the procurement of discrete and calculable data tokens that are possible to aggregate and, subsequently, compute in a variety of ways. We expose these operations by investigating a social media platform for shopping. We contribute to the current debate on social media and digital platforms by describing social media as posttransactional spaces that are predominantly concerned with charting and profiling the online predispositions, habits, and opinions of their user base. Such an orientation sets social media platforms apart from other forms of mediating online interaction. In social media, we claim, platform participation is driven toward an endless online conversation that delivers the data footprint through which a computed sociality is made the source of value creation and monetization

Mind Economy: Dynamic Graph Analysis of Communications

Social networks are growing in reach and impact but little is known about their structure, dynamics, or users’ behaviors. New techniques and approaches are needed to study and understand why these networks attract users’ persistent attention, and how the networks evolve. This thesis investigates questions that arise when modeling human behavior in social networks, and its main contributions are: • an infrastructure and methodology for understanding communication on graphs; • identification and exploration of sub-communities; • metrics for identifying effective communicators in dynamic graphs; • a new definition of dynamic, reciprocal social capital and its iterative computation • a methodology to study influence in social networks in detail, using • a class hierarchy established by social capital • simulations mixed with reality across time and capital classes • various attachment strategies, e.g. via friends-of-friends or full utility optimization • a framework for answering questions such as “are these influentials accidental” • discovery of the “middle class” of social networks, which as shown with our new metrics and simulations is the real influential in many processes Our methods have already lead to the discovery of “mind economies” within Twitter, where interactions are designed to increase ratings as well as promoting topics of interest and whole subgroups. Reciprocal social capital metrics identify the “middle class” of Twitter which does most of the “long-term” talking, carrying the bulk of the system-sustaining conversations. We show that this middle class wields the most of the actual influence we should care about — these are not “accidental influentials.” Our approach is of interest to computer scientists, social scientists, economists, marketers, recruiters, and social media builders who want to find and present new ways of exploring, browsing, analyzing, and sustaining online social networks