Enabling data science for the majority

Parameswaran, Aditya

doi:10.14778/3352063.3352148

Cited by 8 publications

(3 citation statements)

References 58 publications

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“…Visual exploration tools for time series data. A signiicant research study on data science [47] illustrates a comprehensive set of tools e.g., Zenvisage [53] for searching line chart patterns, FastMatch [44] for obtaining the histogram visualizations interactively most similar to a user-speciied target, IncVisage [50] for trendline visualization, etc. Rahman et al incrementally improve visualizations of line chart by selecting better samples in the IncVisage tool [50].…”

Section: Related Workmentioning

confidence: 99%

Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data

Ahmed

Islam

Rajan

2021

ACM/IMS Trans. Data Sci.

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We propose PASS , a O ( n ) algorithm for data reduction that is specifically aimed at preserving the semantics of time series data visualization in the form of line chart. Visualization of large trend line data is a challenge and current sampling approaches do produce reduction but result in loss of semantics and anomalous behavior. We have evaluated PASS using 7 large and well-vetted datasets (Taxi, Temperature, DEBS challenge 2012-2014 dataset, New York Stock Exchange data, and Integrated Surface Data) and found that it has several benefits when compared to existing state-of-the-art time series data reduction techniques. First, it can preserve the semantics of the trend. Second, the visualization quality using the reduced data from PASS is very close to the original visualization. Third, the anomalous behavior is preserved and can be well observed from the visualizations created using the reduced data. We have conducted two user surveys collecting 3000+ users’ responses for visual preference as well as perceptual effectiveness and found that the users prefer PASS over other techniques for different datasets. We also compare PASS using visualization metrics where it outperforms other techniques in 5 out of the 7 datasets.

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Section: Related Workmentioning

confidence: 99%

Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data

Ahmed

Islam

Rajan

2021

ACM/IMS Trans. Data Sci.

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“…In contrast, the analytical query 𝑞 2 partitions the table in place and calculates aggregated values in a new column. Analytical SQL's increased expressiveness make it harder to program, especially for inexperienced engineers and data scientists [24]. 39], data wrangling [11,12,40].…”

Section: Introductionmentioning

confidence: 99%

Synthesizing Analytical SQL Queries from Computation Demonstration

Zhou,

Bodik,

Cheung

et al. 2022

Preprint

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Analytical SQL is widely used in modern database applications and data analysis. However, its partitioning and grouping operators are challenging for novice users. Unfortunately, programming by example, shown effective on standard SQL, are less attractive because examples for analytical queries are more laborious to solve by hand.To make demonstrations easier to create, we designed a new end-user specification, programming by computation demonstration, that allows the user to demonstrate the task using a (possibly incomplete) cell-level computation trace. This specification is exploited in a new abstraction-based synthesis algorithm to prove that a partially formed query cannot be completed to satisfy the specification, allowing us to prune the search space.We implemented our approach in a tool named Sickle and tested it on 80 real-world analytical SQL tasks. Results show that even from small demonstrations, Sickle can solve 76 tasks, in 12.8 seconds on average, while the prior approaches can solve only 60 tasks and are on average 22.5× slower. Our user study with 13 participants reveals that our specification increases user efficiency and confidence on challenging tasks.

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“…As tools explaining the decision-making process, XAI methods can be applied in human-in-the-loop machine learning systems [11,21,23] or model debugging systems [17,29,35]. However, to deploy XAI methods in practical systems, a methodological challenge appears: how to evaluate these methods such that the users can choose the most suitable one for their needs.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Evaluations on Saliency Methods: An Experimental Study

Li,

Shi,

et al. 2020

Preprint

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It has been long debated that eXplainable AI (XAI) is an important topic, but it lacks rigorous definition and fair metrics. In this paper, we briefly summarize the status quo of the metrics, along with an exhaustive experimental study based on them, including faithfulness, localization, false-positives, sensitivity check, and stability. With the experimental results, we conclude that among all the methods we compare, no single explanation method dominates others in all metrics. Nonetheless, Gradient-weighted Class Activation Mapping (Grad-CAM) and Randomly Input Sampling for Explanation (RISE) perform fairly well in most of the metrics. Utilizing a set of filtered metrics, we further present a case study to diagnose the classification bases for models. While providing a comprehensive experimental study of metrics, we also examine measuring factors that are missed in current metrics and hope this valuable work could serve as a guide for future research.

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Enabling data science for the majority

Cited by 8 publications

References 58 publications

Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data

Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data

Synthesizing Analytical SQL Queries from Computation Demonstration

Quantitative Evaluations on Saliency Methods: An Experimental Study

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