The case for interactive data exploration accelerators (IDEAs)

Crotty, Andrew; Galakatos, Alex; Zgraggen, Emanuel; Binnig, Carsten; Kraska, Tim

doi:10.1145/2939502.2939513

Cited by 34 publications

(41 citation statements)

References 13 publications

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“…In practice, this is hardly ever the case: data may be non-uniformly distributed across the chunks, the computation may perform random moves to escape local optima, or the user may interact with the computation, readjusting it and thus introducing discontinuities in the process. Governing concepts like data consistency (intermediate visualizations should become increasingly representative of the complete dataset) and visual consistency (intermediate visualizations should become increasingly representative of the completed visualization) are difficult to enforce under such circumstances [40,41]. This makes it particularly hard to judge the reliability of preliminary findings, which can turn out to be mere artifacts of the progression and not of the data.…”

Section: Handling Fluctuating or Even Diverging Progressionsmentioning

confidence: 99%

A Review and Characterization of Progressive Visual Analytics

et al. 2018

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Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA's principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.Keywords: visual analytics; progressive visualization; incremental visualization; online algorithms MotivationWith data growing in size and complexity, and analysis methods getting more sophisticated and computationally intensive, the idea of Progressive Visual Analytics (PVA) [1,2] becomes increasingly appealing. A PVA approach can either subdivide the data to process each data chunk individually, or it can subdivide the analytic process into computational steps that iteratively refine analytic results [3]. By doing so, PVA yields partial results of increasing completeness or approximative results of increasing correctness, respectively. This is useful in a wide range of visual analytics scenarios:• to realize responsive client-server visualizations using incremental data transmissions Because of its versatility, the progressive approach to data analysis and visualization is alternatively seen as a paradigm for computation, for interaction, for data transmission, or for visual presentation. It is thus not surprising that PVA-related research is distributed over multiple disciplines, motivated by various underlying problems, described in different, sometimes overloaded terms at different levels of detail for different audiences.

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Section: Handling Fluctuating or Even Diverging Progressionsmentioning

confidence: 99%

A Review and Characterization of Progressive Visual Analytics

et al. 2018

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“…• We implemented our techniques in a prototype Interactive Data Exploration Accelerator [7] (IDEA), and our benchmarks use real world datasets to demonstrate that we can achieve interactive latencies in many cases where alternative approaches cannot.…”

Section: Female Malementioning

confidence: 99%

“…As an illustrative example of this process, consider the sample exploration session shown in Figure 1, which is drawn from a past user study [7]. In the exploration session, the user is analyzing data from the 1994 US census [22] using Vizdom [6] in order to understand which attributes affect an individual's annual salary.…”

Section: Overviewmentioning

confidence: 99%

Revisiting reuse for approximate query processing

et al. 2017

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Visual data exploration tools allow users to quickly gather insights from new datasets. As dataset sizes continue to increase, though, new techniques will be necessary to maintain the interactivity guarantees that these tools require. Approximate query processing (AQP) attempts to tackle this problem and allows systems to return query results at "human speed." However, existing AQP techniques start to break down when confronted with ad hoc queries that target the tails of the distribution. We therefore present an AQP formulation that can provide low-error approximate results at interactive speeds, even for queries over rare subpopulations. In particular, our formulation treats query results as random variables in order to leverage the ample opportunities for result reuse inherent in interactive data exploration. As part of our approach, we apply a variety of optimization techniques that are based on probability theory, including new query rewrite rules and index structures. We implemented these techniques in a prototype system and show that they can achieve interactivity where alternative approaches cannot.

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“…However, none of the existing tools can guarantee interactive latencies [53]. Previous work by Crotty et al [23] approached this problem for structured data, but we are not aware of any work focusing on unstructured data or use cases in the humanities.…”

Section: Interactive System Supportmentioning

confidence: 99%

Interactive Data Analytics for the Humanities

Gurevych

Meyer

Binnig

et al. 2018

Computational Linguistics and Intelligent Text Processing

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In this vision paper, we argue that current solutions to data analytics are not suitable for complex tasks from the humanities, as they are agnostic of the user and focused on static, predefined tasks with large-scale benchmarks. Instead, we believe that the human must be put into the loop to address small data scenarios that require expert domain knowledge and fluid, incrementally defined tasks, which are common for many humanities use cases. Besides the main challenges, we discuss existing and urgently required solutions to interactive data acquisition, model development, model interpretation, and system support for interactive data analytics. In the envisioned interactive systems, human users not only provide annotations to a machine learner, but train a model by using the system and demonstrating the task. The learning system will actively query the user for feedback, refine its model in real-time, and is able to explain its decisions. Our vision links natural language processing research with recent advances in machine learning, computer vision, and data management systems, as realizing this vision relies on combining expertise from all of these scientific fields. 1 Challenges in Analyzing Humanities Data Automated data analytics, aka. data mining and machine learning, is a key technology for enriching and interpreting data, making informed decisions, and developing new data-driven scientific methods across many disciplines in industry and academia. Although the potential of interactive problem solving was recognized early on [16], this field has not progressed very far beyond the initial work. In particular, interactive machine learning and data analytics have only recently received increased attention [98]. Current data analytics solutions focus predominantly on well-defined tasks that can be solved by processing large, homogeneous datasets available in a structured form. Consider for example recommender systems, which suggest new products based on the product's properties, the products that the customer has previously bought, and the collective behavior of the customer database [43]. The state-of-the-art relies on huge amounts of data-over one billion pairs of users and news items passively gathered-to train a deep neural network [28]. This may explain, why data analytics is conceived in a rather impersonal way, with algorithms working autonomously on passively collected data, although practice is quite the opposite. Most of the influence practitioners have, comes through interacting with data, including crafting the data and examining results. In the late 1990s, digitized data became widely available in the humanities as well. Since then, there has been a clear demand for data analytics approaches to tap into these

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The case for interactive data exploration accelerators (IDEAs)

Cited by 34 publications

References 13 publications

A Review and Characterization of Progressive Visual Analytics

A Review and Characterization of Progressive Visual Analytics

Revisiting reuse for approximate query processing

Interactive Data Analytics for the Humanities

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