The Experts below are selected from a list of 9525 Experts worldwide ranked by ideXlab platform

Margo Seltzer - One of the best experts on this subject based on the ideXlab platform.

  • provbuild improving data scientist efficiency with provenance an extended abstract
    International Conference on Software Engineering, 2020
    Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo Seltzer
    Abstract:

    Data scientists frequently analyze data by writing scripts. We conducted a Contextual Inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative debugging process in script-based workflow pipelines. ProvBuild is a tool that leverages language-level provenance [2] to streamline the debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that debugging the simplified script lowers a programmer’s cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script’s first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first debugging tool to leverage language-level provenance to reduce cognitive load and execution time.

  • provbuild improving data scientist efficiency with provenance
    International Conference on Software Engineering, 2020
    Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo Seltzer
    Abstract:

    Data scientists frequently analyze data by writing scripts. We conducted a Contextual Inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative debugging process in script-based workflow pipelines. ProvBuild is a tool that leverages language-level provenance [2] to streamline the debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first debugging tool to leverage language-level provenance to reduce cognitive load and execution time.

  • improving data scientist efficiency with provenance
    International Conference on Software Engineering, 2020
    Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo Seltzer
    Abstract:

    Data scientists frequently analyze data by writing scripts. We conducted a Contextual Inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a tool that leverages language-level provenance to streamline the debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code.

Jiwon Joung - One of the best experts on this subject based on the ideXlab platform.

  • provbuild improving data scientist efficiency with provenance an extended abstract
    International Conference on Software Engineering, 2020
    Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo Seltzer
    Abstract:

    Data scientists frequently analyze data by writing scripts. We conducted a Contextual Inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative debugging process in script-based workflow pipelines. ProvBuild is a tool that leverages language-level provenance [2] to streamline the debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that debugging the simplified script lowers a programmer’s cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script’s first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first debugging tool to leverage language-level provenance to reduce cognitive load and execution time.

  • provbuild improving data scientist efficiency with provenance
    International Conference on Software Engineering, 2020
    Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo Seltzer
    Abstract:

    Data scientists frequently analyze data by writing scripts. We conducted a Contextual Inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative debugging process in script-based workflow pipelines. ProvBuild is a tool that leverages language-level provenance [2] to streamline the debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first debugging tool to leverage language-level provenance to reduce cognitive load and execution time.

  • improving data scientist efficiency with provenance
    International Conference on Software Engineering, 2020
    Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo Seltzer
    Abstract:

    Data scientists frequently analyze data by writing scripts. We conducted a Contextual Inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a tool that leverages language-level provenance to streamline the debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code.

David W Thompson - One of the best experts on this subject based on the ideXlab platform.

  • identifying and categorising patient safety hazards in cardiovascular operating rooms using an interdisciplinary approach a multisite study
    BMJ Quality & Safety, 2012
    Co-Authors: Ayse P Gurses, Jill A Marsteller, Lisa H Lubomski, Elizabeth A Martinez, Laura Bauer, George R Kim, Peter J Pronovost, David W Thompson
    Abstract:

    Background Cardiac surgery is a complex, high-risk procedure with potential vulnerabilities for patient safety. The evidence base describing safety hazards in the cardiovascular operating room is underdeveloped but is essential to guide future safety improvement efforts. Objective To identify and categorise hazards (anything that has the potential to cause a preventable adverse patient safety event) in the cardiovascular operating room. Methods An interdisciplinary team of researchers used prospective methods, including direct observations, Contextual Inquiry and photographs to collect hazard data pertaining to the cardiac surgery perioperative period, which started immediately before the patient was transferred to the operating room and ended immediately after patient handoff to the post-anaesthesia/intensive care unit. Data were collected between February and September 2008 in five hospitals. An interdisciplinary approach that included a human factors and systems engineering framework was used to guide the study. Results Twenty cardiac surgeries including the corresponding handoff processes from operating room to post-anaesthesia/intensive care unit were observed. A total of 58 categories of hazards related to care providers (eg, practice variations), tasks (eg, high workload), tools and technologies (eg, poor usability), physical environment (eg, cluttered workspace), organisation (eg, hierarchical culture) and processes (eg, non-compliance with guidelines) were identified. Discussion Hazards in cardiac surgery services are ubiquitous, indicating numerous opportunities to improve safety. Future efforts should focus on creating a stronger culture of safety in the cardiovascular operating room, increasing compliance with evidence-based infection control practices, improving communication and teamwork, and developing a partnership among all stakeholders to improve the design of tools and technologies.

Ayse P Gurses - One of the best experts on this subject based on the ideXlab platform.

  • roles and role ambiguity in patient and caregiver performed outpatient parenteral antimicrobial therapy
    The Joint Commission Journal on Quality and Patient Safety, 2019
    Co-Authors: Sara C Keller, Sara E Cosgrove, Alicia I Arbaje, Rachel Huaien Chang, Amanda Krosche, Deborah H Williams, Ayse P Gurses
    Abstract:

    Background Complicated medical therapies traditionally performed in acute care hospitals are increasingly moving to the home, requiring patients and informal caregivers to perform complicated medical tasks. For example, in outpatient parenteral antimicrobial therapy (OPAT), patients and caregivers perform antimicrobial infusions and venous catheter care. The objective of this study was to characterize patient understanding of patient, caregiver, and health care worker roles in OPAT and barriers to fulfilling these roles, with the goal of understanding how to best support patients and their caregivers. Methods A qualitative study using 40 semistructured telephone interviews and 20 Contextual inquiries of patients and caregivers performing OPAT tasks was performed. Eligible participants were discharged from two academic medical centers on OPAT. Interview transcripts and notes from Contextual Inquiry were coded based on a human factors engineering model. Results Four main roles are described: communicator, advocate, learner-trainer, and lay health care worker doing “high-skilled tasks.” Patients and caregivers experienced role ambiguity about OPAT task performance at the time of hospital discharge. Patients noted that their health care workers experienced role ambiguity as well, particularly regarding who was managing their care. Patients and caregivers used role transitions to achieve workload management, in which patients and caregivers transitioned OPAT tasks or non-OPAT tasks from one person to another. Conclusion Clear delineation of roles in complicated home-based medical therapies and training of all who may perform these tasks could improve the safety and quality of home-based care.

  • identifying challenges associated with the care transition workflow from hospital to skilled home health care perspectives of home health care agency providers
    Home Health Care Services Quarterly, 2015
    Co-Authors: Mahiyar F Nasarwanji, Ayse P Gurses, Bruce Leff, Nicole E Werner, Kimberly Carl, Dawn Hohl, Alicia I Arbaje
    Abstract:

    Older adults discharged from the hospital to skilled home health care (SHHC) are at high risk for experiencing suboptimal transitions. Using the human factors approach of shadowing and Contextual Inquiry, we studied the workflow for transitioning older adults from the hospital to SHHC. We created a representative diagram of the hospital to SHHC transition workflow, we examined potential workflow variations, we categorized workflow challenges, and we identified artifacts developed to manage variations and challenges. We identified three overarching challenges to optimal care transitions-information access, coordination, and communication/teamwork. Future investigations could test whether redesigning the transition from hospital to SHHC, based on our findings, improves workflow and care quality.

  • identifying and categorising patient safety hazards in cardiovascular operating rooms using an interdisciplinary approach a multisite study
    BMJ Quality & Safety, 2012
    Co-Authors: Ayse P Gurses, Jill A Marsteller, Lisa H Lubomski, Elizabeth A Martinez, Laura Bauer, George R Kim, Peter J Pronovost, David W Thompson
    Abstract:

    Background Cardiac surgery is a complex, high-risk procedure with potential vulnerabilities for patient safety. The evidence base describing safety hazards in the cardiovascular operating room is underdeveloped but is essential to guide future safety improvement efforts. Objective To identify and categorise hazards (anything that has the potential to cause a preventable adverse patient safety event) in the cardiovascular operating room. Methods An interdisciplinary team of researchers used prospective methods, including direct observations, Contextual Inquiry and photographs to collect hazard data pertaining to the cardiac surgery perioperative period, which started immediately before the patient was transferred to the operating room and ended immediately after patient handoff to the post-anaesthesia/intensive care unit. Data were collected between February and September 2008 in five hospitals. An interdisciplinary approach that included a human factors and systems engineering framework was used to guide the study. Results Twenty cardiac surgeries including the corresponding handoff processes from operating room to post-anaesthesia/intensive care unit were observed. A total of 58 categories of hazards related to care providers (eg, practice variations), tasks (eg, high workload), tools and technologies (eg, poor usability), physical environment (eg, cluttered workspace), organisation (eg, hierarchical culture) and processes (eg, non-compliance with guidelines) were identified. Discussion Hazards in cardiac surgery services are ubiquitous, indicating numerous opportunities to improve safety. Future efforts should focus on creating a stronger culture of safety in the cardiovascular operating room, increasing compliance with evidence-based infection control practices, improving communication and teamwork, and developing a partnership among all stakeholders to improve the design of tools and technologies.

  • using human factors engineering to improve patient safety in the cardiovascular operating room
    Work-a Journal of Prevention Assessment & Rehabilitation, 2012
    Co-Authors: Ayse P Gurses, Jill A Marsteller, Lisa H Lubomski, Christine A Goeschel, Elizabeth A Martinez, Laura Bauer, George R Kim, Priyadarshini R Pennathur
    Abstract:

    Despite significant medical advances, cardiac surgery remains a high risk procedure. Sub-optimal work system design characteristics can contribute to the risks associated with cardiac surgery. However, hazards due to work system characteristics have not been identified in the cardiovascular operating room (CVOR) in sufficient detail to guide improvement efforts. The purpose of this study was to identify and categorize hazards (anything that has the potential to cause a preventable adverse patient safety event) in the CVOR. An interdisciplinary research team used prospective hazard identification methods including direct observations, Contextual Inquiry, and photographing to collect data in 5 hospitals for a total 22 cardiac surgeries. We performed thematic analysis of the qualitative data guided by a work system model. 60 categories of hazards such as practice variations, high workload, non-compliance with evidence-based guidelines, not including clinicians' in medical device purchasing decisions were found. Results indicated that hazards are common in cardiac surgery and should be eliminated or mitigated to improve patient safety. To improve patient safety in the CVOR, efforts should focus on creating a culture of safety, increasing compliance with evidence based infection control practices, improving communication and teamwork, and designing better tools and technologies through partnership among all stakeholders.

Maia Jacobs - One of the best experts on this subject based on the ideXlab platform.

  • provbuild improving data scientist efficiency with provenance an extended abstract
    International Conference on Software Engineering, 2020
    Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo Seltzer
    Abstract:

    Data scientists frequently analyze data by writing scripts. We conducted a Contextual Inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative debugging process in script-based workflow pipelines. ProvBuild is a tool that leverages language-level provenance [2] to streamline the debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that debugging the simplified script lowers a programmer’s cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script’s first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first debugging tool to leverage language-level provenance to reduce cognitive load and execution time.

  • provbuild improving data scientist efficiency with provenance
    International Conference on Software Engineering, 2020
    Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo Seltzer
    Abstract:

    Data scientists frequently analyze data by writing scripts. We conducted a Contextual Inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a data analysis environment that uses change impact analysis [1] to improve the iterative debugging process in script-based workflow pipelines. ProvBuild is a tool that leverages language-level provenance [2] to streamline the debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code. To the best of our knowledge, ProvBuild is a novel application of change impact analysis and it is the first debugging tool to leverage language-level provenance to reduce cognitive load and execution time.

  • improving data scientist efficiency with provenance
    International Conference on Software Engineering, 2020
    Co-Authors: Jiwon Joung, Maia Jacobs, Krzysztof Z Gajos, Margo Seltzer
    Abstract:

    Data scientists frequently analyze data by writing scripts. We conducted a Contextual Inquiry with interdisciplinary researchers, which revealed that parameter tuning is a highly iterative process and that debugging is time-consuming. As analysis scripts evolve and become more complex, analysts have difficulty conceptualizing their workflow. In particular, after editing a script, it becomes difficult to determine precisely which code blocks depend on the edit. Consequently, scientists frequently re-run entire scripts instead of re-running only the necessary parts. We present ProvBuild, a tool that leverages language-level provenance to streamline the debugging process by reducing programmer cognitive load and decreasing subsequent runtimes, leading to an overall reduction in elapsed debugging time. ProvBuild uses provenance to track dependencies in a script. When an analyst debugs a script, ProvBuild generates a simplified script that contains only the information necessary to debug a particular problem. We demonstrate that debugging the simplified script lowers a programmer's cognitive load and permits faster re-execution when testing changes. The combination of reduced cognitive load and shorter runtime reduces the time necessary to debug a script. We quantitatively and qualitatively show that even though ProvBuild introduces overhead during a script's first execution, it is a more efficient way for users to debug and tune complex workflows. ProvBuild demonstrates a novel use of language-level provenance, in which it is used to proactively improve programmer productively rather than merely providing a way to retroactively gain insight into a body of code.