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1. Semantic-aware Workflow Construction and Analysis for Distributed Data Analytics Systems

   https://doi.org/10.1145/3307681.3325404  

   Pi, Aidi; Chen, Wei; Wang, Shaoqi; Zhou, Xiaobo (June 2019, HPDC '19: Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing, ACM)

   Logging is a universal approach to recording important events in system workflows of distributed systems. Current log analysis tools ignore the semantic knowledge that is key to workflow construction and analysis. In addition, they focus on infrastructure-level distributed systems. Because of fundamental differences in log features, they are ineffective in distributed data analytics systems. This paper proposes IntelLog, a semantic-aware non-intrusive workflow reconstruction tool for distributed data analytics systems. It is capable of building hierarchical relationships between components and events from logs generated by the targeted systems with little or even no domain knowledge. Leveraging natural language processing, IntelLog automatically extracts and formats semantic information in each log message, including system events, identifiers, locality information, and metrics values. It builds a graph to represent the hierarchical relationship of components in the targeted system via nomenclature conventions. We implement IntelLog for Hadoop MapReduce, Spark and Tez. Evaluation results show that IntelLog provides a fine-grained view of the system workflows with semantics. It outperforms existing tools in automatically detecting anomalies caused by real-world problems, misconfigurations and system bugs. Users can query the formatted semantic knowledge to understand and further troubleshoot the systems. 

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2. It Can Understand the Logs, Literally

   https://doi.org/10.1109/IPDPSW.2019.00084  

   Pi, Aidi; Chen, Wei; Zeller, Will; Zhou, Xiaobo (July 2019, 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW))

   Workflow reconstruction through logs is crucial for troubleshooting targeted distributed systems. It is also challenging to extract enough information from logs and keep a concise view, which makes manual log analysis hard to practice. However, currently popular tools rely on identifier-based log parsing, leaving a large amount of workflow information unexploited. In this paper, we propose a log extraction approach NLog, which utilizes a natural language processing based approach to obtain the key information from log messages and identify the same object in logs generated by different statements without any domain knowledge. We propose to use keyed message, a new log storage structure to store the parsed logs. We implement NLog and apply it to distributed data analytics frameworks Spark and MapReduce. Evaluation results show that NLog can accurately identify the objects in log messages even without explicit identifiers. By using keyed messages, users can have a concise as well as flexible view of the workflows. 

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3. Log Discovery for Troubleshooting Open Distributed Systems with TLQ

   https://doi.org/10.1145/3311790.3396633  

   Kremer-Herman, Nathaniel; Thain, Douglas (July 2020, Practice and Experience of Advanced Research Computing (PEARC))

   null (Ed.)

   Troubleshooting a distributed system can be incredibly difficult. It is rarely feasible to expect a user to know the fine-grained interactions between their system and the environment configuration of each machine used in the system. Because of this, work can grind to a halt when a seemingly trivial detail changes. To address this, there is a plethora of state-of-the-art log analysis tools, debuggers, and visualization suites. However, a user may be executing in an open distributed system where the placement of their components are not known before runtime. This makes the process of tracking debug logs almost as difficult as troubleshooting the failures these logs have recorded because the location of those logs is usually not transparent to the user (and by association the troubleshooting tools they are using). We present TLQ, a framework designed from first principles for log discovery to enable troubleshooting of open distributed systems. TLQ consists of a querying client and a set of servers which track relevant debug logs spread across an open distributed system. Through a series of examples, we demonstrate how TLQ enables users to discover the locations of their system’s debug logs and in turn use well-defined troubleshooting tools upon those logs in a distributed fashion. Both of these tasks were previously impractical to ask of an open distributed system without significant a priori knowledge. We also concretely verify TLQ’s effectiveness by way of a production system: a biodiversity scientific workflow. We note the potential storage and performance overheads of TLQ compared to a centralized, closed system approach. 

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4. Assessing the landscape of toolkits, frameworks, and authoring tools for urban visual analytics systems

   https://doi.org/10.1016/j.cag.2024.104013  

   Ferreira, Leonardo; Moreira, Gustavo; Hosseini, Maryam; Lage, Marcos; Ferreira, Nivan; Miranda, Fabio (October 2024, Computers & Graphics)

   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 

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5. Curio: A Dataflow-Based Framework for Collaborative Urban Visual Analytics

   https://doi.org/10.1109/TVCG.2024.3456353  

   Moreira, Gustavo; Hosseini, Maryam; Veiga, Carolina; Alexandre, Lucas; Colaninno, Nicola; de_Oliveira, Daniel; Ferreira, Nivan; Lage, Marcos; Miranda, Fabio (January 2025, IEEE Transactions on Visualization and Computer Graphics)

   Over the past decade, several urban visual analytics systems and tools have been proposed to tackle a host of challenges faced by cities, in areas as diverse as transportation, weather, and real estate. Many of these tools have been designed through collaborations with urban experts, aiming to distill intricate urban analysis workflows into interactive visualizations and interfaces. However, the design, implementation, and practical use of these tools still rely on siloed approaches, resulting in bespoke systems that are difficult to reproduce and extend. At the design level, these tools undervalue rich data workflows from urban experts, typically treating them only as data providers and evaluators. At the implementation level, they lack interoperability with other technical frameworks. At the practical use level, they tend to be narrowly focused on specific fields, inadvertently creating barriers to cross-domain collaboration. To address these gaps, we present Curio, a framework for collaborative urban visual analytics. Curio uses a dataflow model with multiple abstraction levels (code, grammar, GUI elements) to facilitate collaboration across the design and implementation of visual analytics components. The framework allows experts to intertwine data preprocessing, management, and visualization stages while tracking the provenance of code and visualizations. In collaboration with urban experts, we evaluate Curio through a diverse set of usage scenarios targeting urban accessibility, urban microclimate, and sunlight access. These scenarios use different types of data and domain methodologies to illustrate Curio’s flexibility in tackling pressing societal challenges. Curio is available at urbantk.org/curio. 

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