462 research outputs found

    DuckPGQ: Efficient property graph queries in an analytical RDBMS

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    In the past decade, property graph databases have emerged as a growing niche in data management. Many native graph systems and query languages have been created, but the functionality and performance still leave much room for improvement. The upcoming SQL:2023 will introduce the Property Graph Queries (SQL/PGQ) sub-language, giving relational systems the opportunity to standard- ize graph queries, and provide mature graph query functionality. We argue that (i) competent graph data systems must build on all technology that makes up a state-of-the-art relational system, (ii) the graph use case requires the addition to that of a many- source/destination path-finding algorithm and compact graph rep- resentation, and (iii) incites research in practical worst-case-optimal joins and factorized query processing techniques. We outline our design of DuckPGQ that follows this recipe, by adding efficient SQL/PGQ support to the popular open-source “embeddable analytics” relational database system DuckDB, also originally developed at CWI. Our design aims at minimizing techni- cal debt using an approach that relies on efficient vectorized UDFs. We benchmark DuckPGQ showing encouraging performance and scalability on large graph data sets, but also reinforcing the need for future research under (iii)

    Visual Programming Paradigm for Organizations in Multi-Agent Systems

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    Over the past few years, due to a fast digitalization process, business activities witnessed the adoption of new technologies, such as Multi-Agent Systems, to increase the autonomy of their activities. However, the complexity of these technologies often hinders the capability of domain experts, who do not possess coding skills, to exploit them directly. To take advantage of these individuals' expertise in their field, the idea of a user-friendly and accessible Integrated Development Environment arose. Indeed, efforts have already been made to develop a block-based visual programming language for software agents. Although the latter project represents a huge step forward, it does not provide a solution for addressing complex, real-world use cases where interactions and coordination among single entities are crucial. To address this problem, Multi-Agent Oriented Programming introduces organization as a first-class abstraction for designing and implementing Multi-Agent Systems. Therefore, this thesis aims to provide a solution allowing users to impose an organization on top of the agents easily. Since ease of use and intuitiveness remain the key points for this project, users will be able to define organizations through visual language and an intuitive development environment

    Hybrid human-AI driven open personalized education

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    Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer. In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer). All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result

    Cognition-Based Evaluation of Visualisation Frameworks for Exploring Structured Cultural Heritage Data

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    It is often claimed that Information Visualisation (InfoVis) tools improve the audience’s engagement with the display of cultural heritage (CH) collections, open up CH content to new audiences and support teaching and learning through interactive experiences. But there is a lack of studies systematically evaluating these claims, particularly from the perspective of modern educational theory. As far as the author is aware no experimental investigation has been undertaken until now, that attempts to measure deeper levels of user engagement and learning with InfoVis tools. The investigation of this thesis complements InfoVis research by initiating a human-centric approach since little previous research has attempted to incorporate and integrate human cognition as one of the fundamental components of InfoVis. In this thesis, using Bloom’s taxonomy of learning objectives as well as individual learning characteristics (i.e. cognitive preferences), I have evaluated the visitor experience of an art collection both with and without InfoVis tools (between subjects design). Results indicate that whilst InfoVis tools have some positive effect on the lower levels of learning, they are less effective for higher levels. In addition, this thesis shows that InfoVis tools seem to be more effective when they match specific cognitive preferences. These results have implications for both the designers of tools and for CH venues in terms of expectation of effectiveness and exhibition design; the proposed cognitive based evaluation framework and the results of this investigation could provide a valuable baseline for assessing the effectiveness of visitors’ interaction with the artifacts of online and physical exhibitions where InfoVis tools such as Timelines and Maps along with storytelling techniques are being used

    Geographic information extraction from texts

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    A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

    Volitional Cybersecurity

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    This dissertation introduces the “Volitional Cybersecurity” (VCS) theory as a systematic way to think about adoption and manage long-term adherence to cybersecurity approaches. The validation of VCS has been performed in small- and medium-sized enterprises or businesses (SMEs/SMBs) context. The focus on volitional activities promotes theoretical viewpoints. Also, it aids in demystifying the aspects of cybersecurity behaviour in heterogeneous contexts that have neither been systematically elaborated in prior studies nor embedded in cybersecurity solutions. Abundant literature demonstrates a lack of adoption of manifold cybersecurity remediations. It is still not adequately clear how to select and compose cybersecurity approaches into solutions for meeting the needs of many diverse cybersecurity-adopting organisations. Moreover, the studied theories in this context mainly originated from disciplines other than information systems and cybersecurity. The constructs were developed based on data, for instance, in psychology or criminology, that seem not to fit properly for the cybersecurity context. Consequently, discovering new methods and theories that can be of help in active and volitional forms of cybersecurity behaviour in diverse contexts may be conducive to a better quality of cybersecurity engagement. This leads to the main research question of this dissertation: How can we support volitional forms of behaviour with a self-paced tool to increase the quality of cybersecurity engagement? The main contribution of this dissertation is the VCS theory. VCS is a cybersecurity-focused theory structured around the core concept of volitional cybersecurity behaviour. It suggests that a context can be classified based on the cybersecurity competence of target groups and their distinct requirements. This classification diminishes the complexity of the context and is predictive of improvement needs for each class. Further, the theory explicates that supporting three factors: A) personalisation, B) cybersecurity competence, and C) connectedness to cybersecurity expertise affect the adoption of cybersecurity measures and better quality of cybersecurity engagement across all classes of the context. Therefore, approaches that ignore the personalisation of cybersecurity solutions, the cybersecurity competence of target groups, and the connectedness of recipients to cybersecurity expertise may lead to poorer acceptance of the value or utility of solutions. Subsequently, it can cause a lack of motivation for adopting cybersecurity solutions and adherence to best practices. VCS generates various implications. It has implications for cybersecurity research in heterogeneous contexts to transcend the common cybersecurity compliance approaches. Building on VCS, researchers could develop interventions looking for volitional cybersecurity behaviour change. Also, it provides knowledge that can be useful in the design of self-paced cybersecurity tools. VCS explains why the new self-paced cybersecurity tool needs specific features. The findings of this dissertation have been subsequently applied to the follow-up project design. Further, it has implications for practitioners and service providers to reach out to the potential end-users of their solutions

    Comparative process mining:analyzing variability in process data

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