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Data analytics tasks are often formulated as data workflows represented as directed acyclic graphs (DAGs) of operators. The recent trend of adopting machine learning (ML) techniques in workflows results in increasingly complicated DAGs with many operators and edges. Compared to the operator-at-a-time execution paradigm, pipelined execution has benefits of reducing the materialization cost of intermediate results and allowing operators to produce results early, which are critical in iterative analysis on large data volumes. Correctly scheduling a workflow DAG for pipelined execution is non-trivial due to the richer semantics of operators and the increasing complexity of DAGs. Several existing data systems adopt simple heuristics to solve the problem without considering costs such as materialization sizes. In this paper, we systematically study the problem of scheduling a workflow DAG for pipelined execution, and develop a novel cost-based optimizer called Pasta for generating a high-quality schedule. The Pasta optimizer is not only general and applicable to a wide variety of cost functions, but also capable of utilizing properties inherent in a broad class of cost functions to improve its performance significantly. We conducted a thorough evaluation of developed techniques on real-world workflows and show the efficiency and efficacy of these solutions. 

Abstract Climate communication scientists search for effective message strategies to engage the ambivalent public in support of climate advocacy. The personal experience of wildfire is expected to render climate change impacts more concretely, pointing to a potential message strategy to engage the public. This study examined Twitter discourse related to climate change during the onset of 20 wildfires in California between the years 2017 and 2021. In this mixed method study, we analyzed tweets geographically and temporally proximal to the occurrence of wildfires to discover framings and examined how frequencies in climate framings changed before and after fires. Results identified three predominant climate framings: linking wildfire to climate change, suggesting climate actions, and attributing climate change to adversities besides wildfires. Mean tweet frequencies linking wildfire to climate change and attributing adversities increased significantly after the onset of fire. While suggesting climate action tweets also increased, the increase was not statistically significant. Temporal analysis of tweet frequencies for the three themes of tweets showed that discussion increased after the onset of a fire but persisted typically no more than 2 weeks. For fires that burned for longer periods of more than a month, external events triggered climate discussions. Our findings contribute to identifying how the personal experience of wildfire shapes Twitter discussion related to climate change, and how these framings change over time during wildfire events, leading to insights into critical time points after wildfire for implementing message strategies to increase public engagement on climate change impacts and policy. 

Collaborative data analytics is becoming increasingly important due to the higher complexity of data science, more diverse skills from different disciplines, more common asynchronous schedules of team members, and the global trend of working remotely. In this demo we will show how Texera supports this emerging computing paradigm to achieve high productivity among collaborators with various backgrounds. Based on our active joint projects on the system, we use a scenario of social media analysis to show how a data science task can be conducted on a user friendly yet powerful platform by a multi-disciplinary team including domain scientists with limited coding skills and experienced machine learning experts. We will present how to do collaborative editing of a workflow and collaborative execution of the workflow in Texera. We will focus on data-centric features such as synchronization of operator schemas among the users during the construction phase, and monitoring and controlling the shared runtime during the execution phase.