New trends on exploratory methods for data analytics

Mottin, Davide; Lissandrini, Matteo; Velegrakis, Yannis; Palpanas, Themis

doi:10.14778/3137765.3137824

Cited by 33 publications

(9 citation statements)

References 41 publications

(54 reference statements)

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“…The analyst provides examples of what she needs to get and the system explores other groups similar to those provided examples. Example-based exploration is beneficial where the analyst is not able to express her needs otherwise [40], [81]. Implementations of by-example exploration are as follows.…”

Section: By-example the Parameter θ Consists Of Group Examplesmentioning

confidence: 99%

User Group Analytics Survey and Research Opportunities

Omidvar-Tehrani¹,

Amer-Yahia²

2020

IEEE Trans. Knowl. Data Eng.

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User data can be acquired from various domains and is characterized by a combination of demographics such as age and occupation, and user actions such as rating a movie or recording one's blood pressure. User data is appealing to analysts in their role as data scientists who seek to conduct large-scale population studies, and gain insights on various population segments. It is also appealing to users in their role as information consumers who use the social Web for routine tasks such as finding a book club or choosing a physical activity. User data analytics usually relies on identifying group-level behaviors such as "Asian women who publish regularly in databases". Group analytics addresses peculiarities of user data such as noise and sparsity to enable insights. In this survey, we discuss different approaches for each component of user group analytics, i.e., discovery, exploration, and visualization. We focus on related work which arises from combining those components. We also discuss challenges and future directions of having an all-in-one system, where all those components are combined. This survey has been presented in the form of two tutorials [1], [2].

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Section: By-example the Parameter θ Consists Of Group Examplesmentioning

confidence: 99%

User Group Analytics Survey and Research Opportunities

Omidvar-Tehrani¹,

Amer-Yahia²

2020

IEEE Trans. Knowl. Data Eng.

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“…the model's semantics), before they run it. Therefore, it does not surprise us that data scientists advocate the process of exploratory data analytics (EDA) [34,35], which uses summary statistics and visualization in order to extract insights from the data which could help in modelling ML algorithms.…”

Section: Help?mentioning

confidence: 99%

Reliability of Training Data Sets for ML Classifiers: A Lesson Learned from Mechanical Engineering

Juric

Danilchanka²,

Mousavi³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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The popularity of learning and predictive technologies, across many problem domains, is unprecedented and it is often underpinned with the fact that we efficiently compute with vast amounts of data and data types, and thus should be able to resolve problems, which we could not in the past. This view is particularly common among scientists who believe that the excessive amount of data, we generate in real life, is ideal for performing predictions and training algorithms.However, the truth might be quite different.The paper illustrates the process of preparing a training data set for an ML classifier, which should predict certain conditions in mechanical engineering. It was not the case that it was difficult to define and choose classifiers, in order to secure safe predictions. It was our inability to create a safe, reliable and trustworthy training data set, from scientifically proven experiments, which created the problem. This places serious doubts on the way we use learning and predictive technologies today. It remains debatable what the next step should be. However, if in ML algorithms, and classifiers in particular, the semantic which is built-in data sets, influences classifier's definition, it would be very difficult to evaluate and rely on them, before we understand data semantics fully. In other words, we still do not know how the semantic, sometimes hidden in a data set, can adversely affect algorithms trained by them.

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“…Cohort exploration Various exploration approaches have been proposed in the literature as means of navigating in health-care data. Typically, data exploration strategies are classified as query-based [49], distribution-based [34], facetbased [50], and example-based [51]. Core is facet-based, where cohort demographics and actions are used as facets to explore other contrast cohorts.…”

Section: Related Workmentioning

confidence: 99%

Cohort analytics: efficiency and applicability

Omidvar-Tehrani¹,

Amer-Yahia

Lakshmanan

2020

The VLDB Journal

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The abundant availability of health-care data calls for effective analysis methods to help medical experts gain a better understanding of their patients and their health. The focus of existing work has been largely on prediction. In this paper, we introduce Core, a framework for cohort "representation" and "exploration." Our contributions are twofold: First, we formalize cohort representation as the problem of aggregating the trajectories of its patients. This problem is challenging because cohorts often consist of hundreds of patients who underwent medical actions of various types at different points in time. We prove that producing a representative cohort trajectory is NP-complete with a reduction in the multiple sequence alignment problem. We propose a heuristic that extends the Needleman-Wunsch algorithm for sequence matching to handle temporal sequences. To further improve cohort representation efficiency, we introduce "trajectory families" and "stratified sampling." Our second contribution is formalizing the problem of cohort exploration as finding a set of cohorts that are similar to a cohort of interest and that maximize entropy. This problem is challenging because the potential number of similar cohorts is huge. We prove NP-completeness with a reduction in the maximum edge subgraph problem. To address complexity, we develop a multi-staged approach based on limiting the search space to "contrast cohorts." To speed up the computation of cohort similarity, we use "event sets" that are inspired from the double dictionary encoding proposed for keyword search. Moreover, we explore the usefulness and efficiency of Core using an extensive set of qualitative and quantitative experiments on two real health-care datasets. In a user study with medical experts, we show that Core reduces time-to-insight from hours to seconds and helps them find better insights than baseline approaches. Moreover, we show that the obtained cohort representations offer the right trade-off between quality and performance. We study the benefits of trajectory families and stratified sampling for cohort representation and show their applicability on large and heterogeneous cohorts. We also show the benefit of event sets for cohort exploration in providing interactive performance. Keywords Health-care data analysis • Cohort analytics • Cohort representation • Cohort exploration IntroductionWith the increase in health-care data in various sectors (e.g., prognoses, treatments, hospitalizations, and compliances), medical experts need effective analysis methods to understand the evolution of their patients' health. Cohort analysis

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New trends on exploratory methods for data analytics

Cited by 33 publications

References 41 publications

User Group Analytics Survey and Research Opportunities

User Group Analytics Survey and Research Opportunities

Reliability of Training Data Sets for ML Classifiers: A Lesson Learned from Mechanical Engineering

Cohort analytics: efficiency and applicability

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