Evolving NoSQL Databases Without Downtime

NoSQL databases like Redis, Cassandra, and MongoDB are increasingly popular because they are flexible, lightweight, and easy to work with. Applications that use these databases will evolve over time, sometimes necessitating (or preferring) a change to the format or organization of the data. The problem we address in this paper is: How can we support the evolution of high-availability applications and their NoSQL data online, without excessive delays or interruptions, even in the presence of backward-incompatible data format changes? We present KVolve, an extension to the popular Redis NoSQL database, as a solution to this problem. KVolve permits a developer to submit an upgrade specification that defines how to transform existing data to the newest version. This transformation is applied lazily as applications interact with the database, thus avoiding long pause times. We demonstrate that KVolve is expressive enough to support substantial practical updates, including format changes to RedisFS, a Redis-backed file system, while imposing essentially no overhead in general use and minimal pause times during updates.Comment: Update to writing/structur

MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

Latent deep sequential learning of behavioural sequences

The growing use of asynchronous online education (MOOCs and e-courses) in recent years has resulted in increased economic and scientific productivity, which has worsened during the coronavirus epidemic. The widespread usage of OLEs has increased enrolment, including previously excluded students, resulting in a far higher dropout rate than in conventional classrooms. Dropouts are a significant problem, especially considering the rising proliferation of online courses, from individual MOOCs to whole academic programmes due to the pandemic. Increased efficiency in dropout prevention techniques is vital for institutions, students, and faculty members and must be prioritised. In response to the resurgence of interest in the student dropout prediction (SDP) issue, there has been a significant rise in contributions to the literature on this topic. An in-depth review of the current state of the art literature on SDP is provided, with a special emphasis on Machine Learning prediction approaches; however, this is not the only focus of the thesis. We propose a complete hierarchical categorisation of the current literature that correlates to the process of design decisions in the SDP, and we demonstrate how it may be implemented. In order to enable comparative analysis, we develop a formal notation for universally defining the multiple dropout models examined by scholars in the area, including online degrees and their attributes. We look at several other important factors that have received less attention in the literature, such as evaluation metrics, acquired data, and privacy concerns. We emphasise deep sequential machine learning approaches and are considered to be one of the most successful solutions available in this field of study. Most importantly, we present a novel technique - namely GRU-AE - for tackling the SDP problem using hidden spatial information and time-related data from student trajectories. Our method is capable of dealing with data imbalances and time-series sparsity challenges. The proposed technique outperforms current methods in various situations, including the complex scenario of full-length courses (such as online degrees). This situation was thought to be less common before the outbreak, but it is now deemed important. Finally, we extend our findings to different contexts with a similar characterisation (temporal sequences of behavioural labels). Specifically, we show that our technique can be used in real-world circumstances where the unbalanced nature of the data can be mitigated by using class balancement technique (i.e. ADASYN), e.g., survival prediction in critical care telehealth systems where balancement technique alleviates the problem of inter-activity reliance and sparsity, resulting in an overall improvement in performance

A benchmark for online non-blocking schema transformations

This paper presents a benchmark for measuring the blocking behavior of schema transformations in relational database systems. As a basis for our benchmark, we have developed criteria for the functionality and performance of schema transformation mechanisms based on the characteristics of state of the art approaches. To address limitations of existing approaches, we assert that schema transformations must be composable while satisfying the ACID guarantees like regular database transactions. Additionally, we have identified important classes of basic and complex relational schema transformations that a schema transformation mechanism should be able to perform. Based on these transformations and our criteria, we have developed a benchmark that extends the standard TPC-C benchmark with schema transformations, which can be used to analyze the blocking behavior of schema transformations in database systems. The goal of the benchmark is not only to evaluate existing solutions for non-blocking schema transformations, but also to challenge the database community to find solutions that allow more complex transactional schema transformations