Designing and building visual analytics (VA) systems is a complex, iterative process that requires the seamless integration of data processing, analytics capabilities, and visualization techniques. While prior research has extensively examined the social and collaborative aspects of VA system authoring, the practical challenges of developing these systems remain underexplored. As a result, despite the growing number of VA systems, there are only a few structured knowledge bases to guide their design and development. To tackle this gap, we propose VA-Blueprint, a methodology and knowledge base that systematically reviews and categorizes the fundamental building blocks of urban VA systems, a domain particularly rich and representative due to its intricate data and unique problem sets. Applying this methodology to an initial set of 20 systems, we identify and organize their core components into a multi-level structure, forming an initial knowledge base with a structured blueprint for VA system development. To scale this effort, we leverage a large language model to automate the extraction of these components for other 81 papers (completing a corpus of 101 papers), assessing its effectiveness in scaling knowledge base construction. We evaluate our method through interviews with experts and a quantitative analysis of annotation metrics. Our contributions provide a deeper understanding of VA systems' composition and establish a practical foundation to support more structured, reproducible, and efficient system development. VA-Blueprint is available at urbantk.org/va-blueprint.

With the growing availability of urban data and the increasing complexity of societal challenges, visual analytics has become essential for deriving insights into pressing real-world problems. However, analyzing such data is inherently complex and iterative, requiring expertise across multiple domains. The need to manage diverse datasets, distill intricate workflows, and integrate various analytical methods presents a high barrier to entry, especially for researchers and urban experts who lack proficiency in data management, machine learning, and visualization. Advancements in large language models offer a promising solution to lower the barriers to the construction of analytics systems by enabling users to specify intent rather than define precise computational operations. However, this shift from explicit operations to intent-based interaction introduces challenges in ensuring alignment throughout the design and development process. Without proper mechanisms, gaps can emerge between user intent, system behavior, and analytical outcomes. To address these challenges, we propose Urbanite, a framework for human-AI collaboration in urban visual analytics. Urbanite leverages a dataflow-based model that allows users to specify intent at multiple scopes, enabling interactive alignment across the specification, process, and evaluation stages of urban analytics. Based on findings from a survey to uncover challenges, Urbanite incorporates features to facilitate explainability, multi-resolution definition of tasks across dataflows, nodes, and parameters, while supporting the provenance of interactions. We demonstrate Urbanite's effectiveness through usage scenarios created in collaboration with urban experts. Urbanite is available at urbantk.org/urbanite.

Abstract The availability of suitable datasets and data generators is crucial for developing intelligent systems, especially in helpdesk services. However, the lack of publicly accessible data generators focused on helpdesk operations, where incidents are often closed without detailing the treatment procedures, poses challenges to implementing intelligent systems such as recommender systems. To address this issue, a dataset generator can be employed to simulate helpdesk incidents. This paper introduces SNOOKER (dataSet geNeratOr fOr helpdesK sERvices), a customizable dataset generator designed to create and treat helpdesk tickets, including domain-specific incidents (e.g., cybersecurity) by orchestrating simulated actions and multiple IT teams. SNOOKER’s output is compared against a real anonymized dataset from S21Sec Cyber Solutions by Thales. The datasets are evaluated using Kolmogorov-Smirnov, Kullback-Leibler Divergence, and Hellinger distance tests, with results indicating similar distributions. For example, the first metric returned a low K-S value and a p-value exceeding 5%, while the second and third measures presented 0.003 and 0.03, respectively. Furthermore, experiments with different team configurations revealed that ticket scheduling highly depends on each team’s operators’ numbers and work shifts, increasing with unbalanced shifts and fewer operators.

Over the past decade, there has been a significant increase in the development of visual analytics systems dedicated to addressing urban issues. These systems distill intricate urban analysis workflows into intuitive, interactive visual representations and interfaces, enabling users to explore, understand, and derive insights from large and complex data, including street-level imagery, street networks, and building geometries. Developing urban visual analytics systems, however, is a challenging endeavor that requires considerable programming expertise and interaction between various multidisciplinary stakeholders. This situation often leads to monolithic and isolated prototypes that are hard to reproduce, combine, or extend. Concurrently, there has been an increase in the availability of general and urban-specific toolkits, frameworks, and authoring tools that are open source and abstract away the need to implement low-level visual analytics functionalities. This paper provides a hierarchical taxonomy of urban visual analytics systems to contextualize how they are usually designed, implemented, and evaluated. We develop this taxonomy across three distinct levels (i.e., dimensions, categories, and tags), juxtaposing visualization with analytics, data, and system dimensions. We then assess the extent to which current open-source toolkits, frameworks, and authoring tools can effectively support the development of components tailored to urban visual analytics, identifying their strengths and limitations in addressing the unique challenges posed by urban data. In doing so, we offer a roadmap that can guide the effective employment of existing resources and chart a pathway for developing and refining future systems.

Abstract With the growth of CyberTerrorism, enterprises worldwide have been struggling to stop intruders from obtaining private data. Despite the efforts made by Cybersecurity experts, the shortage of skillful security teams and the usage of intelligent attacks have slowed down the enhancement of defense mechanisms. Furthermore, the pandemic in 2020 forced organizations to work in remote environments with poor security, leading to increased cyberattacks. One possible solution for these problems is the implementation of Recommender Systems to assist Cybersecurity human operators. Our goal is to survey the application of Recommender Systems in Cybersecurity architectures. These decision-support tools deal with information overload through filtering and prioritization methods, allowing businesses to increase revenue, achieve better user satisfaction, and make faster and more efficient decisions in various domains (e-commerce, healthcare, finance, and other fields). Several reports demonstrate the potential of using these recommendation structures to enhance the detection and prevention of cyberattacks and aid Cybersecurity experts in treating client incidents. This survey discusses several studies where Recommender Systems are implemented in Cybersecurity with encouraging results. One promising direction explored by the community is using Recommender Systems as attack predictors and navigation assistance tools. As contributions, we show the recent efforts in this area and summarize them in a table. Furthermore, we provide an in-depth analysis of potential research lines. For example, the inclusion of Recommender Systems in security information event management systems and security orchestration, automation, and response applications could decrease their complexity and information overload.