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Efficient Query Refinement for View Recommendation in visual data exploration

IEEE ACCESS 2021年 9卷 76461-76478页

作者： Sharaf, Mohamed A. Ehsan, Humaira United Arab Emirates Univ Dept Comp Sci & Software Engn Coll Informat Technol Abu Dhabi U Arab Emirates

The need for efficient and effective data exploration has resulted in several solutions that automatically recommend interesting visualizations. The main idea underlying those solutions is to automatically generate all possible views of data, and recommend the top-k interesting views. However, those solutions assume that the analyst is able to formulate a well-defined query that selects a subset of data, which contains insights. Meanwhile, in reality, it is typically a challenging task to pose an exploratory query, which can immediately reveal some insights. To address that challenge, in this work we propose utilizing query refinement as one technique that allows to automatically adjust the analyst's input query to discover such valuable insights. However, a naive query refinement, in addition to generating a prohibitively large search space, also raises other problems such as deviating from the user's preference and recommending statistically insignificant views. In this paper, we address those problems and propose a novel suit of schemes, which efficiently navigate the refined queries search space to recommend the top-k insights that meet all of the analyst's pre-specified criteria.

关键词： data visualization Education Aggregates visualization Statistics Sociology Probability distribution visual data exploration data visualization query refinement view recommendation

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FlowSense: A Natural Language Interface for visual data exploration within a dataflow System

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2020年第1期26卷 1-11页

作者： Yu, Bowen Silva, Claudio T. NYU New York NY 10003 USA

dataflow visualization systems enable flexible visual data exploration by allowing the user to construct a dataflow diagram that composes query and visualization modules to specify system functionality. However learning dataflow diagram usage presents overhead that often discourages the user. In this work we design FlowSense, a natural language interface for dataflow visualization systems that utilizes state-of-the-art natural language processing techniques to assist dataflow diagram construction. FlowSense employs a semantic parser with special utterance tagging and special utterance placeholders to generalize to different datasets and dataflow diagrams. It explicitly presents recognized dataset and diagram special utterances to the user for dataflow context awareness. With FlowSense the user can expand and adjust dataflow diagrams more conveniently via plain English. We apply FlowSense to the VisFlow subset-flow visualization system to enhance its usability. We evaluate FlowSense by one case study with domain experts on a real-world data analysis problem and a formal user study.

关键词： Natural language interface dataflow visualization system visual data exploration

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Collaborative filtering over evolution provenance data for interactive visual data exploration

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INFORMATION SYSTEMS 2021年 95卷 101620-101620页

作者： Ben Lahmar, Houssem Herschel, Melanie IPVS Univ Stuttgart Stuttgart Germany

In interactive visual data exploration, users rely on recommendations on what data to explore next. EVLIN is a system that recommends queries to retrieve these data for the next exploration step, paired with suited visualizations. This paper extends EVLIN by combining its content-based recommendations with recommendations leveraging collaborative filtering to improve the effectiveness of recommendation-based visual data exploration. The recommendations rely on evolution provenance, which tracks users' interactions during interactive visual data exploration. As more users explore a dataset, the evolution provenance of individual user explorations is incrementally integrated into a multi-user graph, for which we present match and merge algorithms. To compute collaborative-filtering recommendations, we present a search algorithm and optimizations to efficiently search queries similar to a current user's query in the multi-user graph and give preference to queries that have been previously explored in an exploration step succeeding those similar queries. Our experimental evaluation studies the efficiency and effectiveness of the solutions proposed in this paper and demonstrates that using the full system with both content-based and collaborative-filtering recommendations enabled allows for effective interactive visual data exploration. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： visual data exploration Provenance Recommendations

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Towards Integrating Collaborative Filtering in visual data exploration Systems 23rd

Towards Integrating Collaborative Filtering in Visual Data E...

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23rd European Conference on Advances in databases and Information Systems (ADBIS)

作者： Ben Lahmar, Houssem Herschel, Melanie Univ Stuttgart IPVS Stuttgart Germany

ISBN: (纸本)9783030302788;9783030302771

visual data exploration assists users in investigating data by providing recommendations. These recommendations take the form of queries that retrieve these data for the next exploration step, paired with suited visualizations. This paper extends the content-based recommendation techniques adopted so far in EVLIN for query recommendations with collaborative-filtering recommendation techniques. For that, we propose a merge approach that fuses evolution provenance graphs representative of individual user's exploration session into a global multi-user graph. This merged graph is then used by our collaborative recommendation approach that searches similar queries for a given user query in the multi-user graph to then recommend queries that were previously explored in exploration steps adjacent to these similar queries.

关键词： visual data exploration Provenance Recommendations

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visualization by Demonstration: An Interaction Paradigm for visual data exploration

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2017年第1期23卷 331-340页

作者： Saket, Bahador Kim, Hannah Brown, Eli T. Endert, Alex Georgia Inst Technol Atlanta GA 30332 USA Depaul Univ Chicago IL 60604 USA

Although data visualization tools continue to improve, during the data exploration process many of them require users to manually specify visualization techniques, mappings, and parameters. In response, we present the visualization by Demonstration paradigm, a novel interaction method for visual data exploration. A system which adopts this paradigm allows users to provide visual demonstrations of incremental changes to the visual representation. The system then recommends potential transformations (visual Representation, data Mapping, Axes, and View Specification transformations) from the given demonstrations. The user and the system continue to collaborate, incrementally producing more demonstrations and refining the transformations, until the most effective possible visualization is created. As a proof of concept, we present VisExemplar, a mixed-initiative prototype that allows users to explore their data by recommending appropriate transformations in response to the given demonstrations.

关键词： visualization by Demonstration visualization Tools visual data exploration

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Provenance-based recommendations for visual data exploration 17

Provenance-based recommendations for visual data exploration

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Proceedings of the 9th USENIX Conference on Theory and Practice of Provenance

作者： Houssem Ben Lahmar Melanie Herschel IPVS University of Stuttgart Stuttgart Germany

visual data exploration allows users to analyze datasets based on visualizations of interesting data characteristics, to possibly discover interesting information about the data that users are a priori unaware of. In this context, both recommendations of queries selecting the data to be visualized and recommendations of visualizations that highlight interesting data characteristics support users in visual data exploration. So far, these two types of recommendations have been mostly considered in isolation of one *** present a recommendation approach for visual data exploration that unifies query recommendation and visualization recommendation. The recommendations rely on two types of provenance, i.e., data provenance (aka lineage) and evolution provenance that tracks users' interactions with a data exploration system. This paper presents the provenance data model as well as the overall system architecture. We then provide details on our provenance-based recommendation algorithms. A preliminary experimental evaluation showcases the applicability of our solution in practice.

关键词： provenance application visual data exploration

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MING: An interpretative support method for visual exploration of multidimensional data

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INFORMATION visualIZATION 2022年第3期21卷 197-219页

作者： Colange, Benoit Vuillon, Laurent Lespinats, Sylvain Dutykh, Denys Univ Grenoble Alpes INES 50 Ave Lac Leman F-73375 Le Bourget Du Lac France Univ Grenoble Alpes Univ Savoie Mt Blanc CNRS LAMA Chambery France

Dimensionality reduction enables analysts to perform visual exploration of multidimensional data with a low-dimensional map retaining as much as possible of the original data structure. The interpretation of such a map relies on the hypothesis of preservation of neighborhood relations. Namely, distances in the map are assumed to reflect faithfully dissimilarities in the data space, as measured with a domain-related metric. Yet, in most cases, this hypothesis is undermined by distortions of those relations by the mapping process, which need to be accounted for during map interpretation. In this paper, we describe an interpretative support method called Map Interpretation using Neighborhood Graphs (MING) displaying individual neighborhood relations on the map, as edges of nearest neighbors graphs. The level of distortion of those relations is shown through coloring of the edges. This allows analysts to assess the reliability of similarity relations inferred from the map, while hinting at the original structure of data by showing the missing relations. Moreover, MING provides a local interpretation for classical map quality indicators, since the quantitative measure of distortions is based on those indicators. Overall, the proposed method alleviates the mapping-induced bias in interpretation while constantly reminding users that the map is not the data.

关键词： Dimensionality reduction visual data exploration interpretative support distortion visualization neighborhood retrieval quality evaluation

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VERB: visualizing and Interpreting Bias Mitigation Techniques Geometrically forWord Representations

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ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS 2024年第1期14卷 1-34页

作者： Rathore, Archit Dev, Sunipa Phillips, Jeff M. Srikumar, Vivek Zheng, Yan Yeh, Chin-Chia Michael Wang, Junpeng Zhang, Wei Wang, Bei Univ Utah Salt Lake City UT 84112 USA Univ Calif Los Angeles Los Angeles CA USA VISA Res Palo Alto CA USA

Word vector embeddings have been shown to contain and amplify biases in the data they are extracted from. Consequently, many techniques have been proposed to identify, mitigate, and attenuate these biases in word representations. In this article, we utilize interactive visualization to increase the interpretability and accessibility of a collection of state-of-the-art debiasing techniques. To aid this, we present the visualization of Embedding Representations for deBiasing (VERB) system, an open-source web-based visualization tool that helps users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties. In particular, VERB offers easy-to-followexamples that explore the effects of these debiasing techniques on the geometry of high-dimensional word vectors. To help understand how various debiasing techniques change the underlying geometry, VERB decomposes each technique into interpretable sequences of primitive transformations and highlights their effect on the word vectors using dimensionality reduction and interactive visual exploration. VERB is designed to target natural language processing (NLP) practitioners who are designing decision-making systems on top of word embeddings and researchers working with the fairness and ethics of machine learning systems in NLP. It can also serve as a visual medium for education, which helps an NLP novice understand and mitigate biases in word embeddings.

关键词： Responsible XAI debiasing visual data exploration distributed representations bias mitigation model interpretation ethics

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Semantic Maps for Knowledge Graphs: A Semantic-Based Summarization Approach

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IEEE ACCESS 2024年 12卷 6729-6744页

作者： Camarillo-Ramirez, Pablo Cervantes-Alvarez, Francisco Gutierrez-Preciado, Luis Fernando Western Inst Technol & Higher Educ Guadalajara 45601 Jalisco Mexico

Knowledge Graphs (KGs) have emerged as a powerful tool for representing semantic structured information and enabling the development of intelligent systems. This paper focuses on the generation of semantic maps as summarization method for KGs. We propose a strategy that utilizes centroid-based clustering algorithms, namely Affinity Propagation and Partitioning Around Medoids (PAM), to capture the semantic distance between nodes in the KG and generate meaningful clusters. Our experiments demonstrate divergent results between the two clustering algorithms, with Affinity Propagation showing qualitative coherence and meaningfulness, while PAM performs well in terms of internal validation metrics. We leverage the computed centroids to infer a main term of the semantic map, which contributes to the visually appealing and informative representation of the KG. The combination of semantic distance capture, clustering algorithms, and centroid-based inference facilitates a comprehensive understanding of the KG. Our findings highlight the importance of considering both qualitative and quantitative evaluation measures in assessing clustering results. The effectiveness of semantic maps is showcased in visualizing KGs and advancing the field of knowledge graph visualization. The integration of centroid-based clustering algorithms, qualitative evaluation, and inference methods offers improved clarity and interpretability for complex KG analysis.

关键词： Semantics Knowledge graphs visualization Clustering algorithms data visualization Ontologies Measurement knowledge graph visualization semantic distance semantic mapping visual data exploration clustering

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Interactive visual facets to support fluid exploratory search

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JOURNAL OF visualIZATION 2023年第1期26卷 211-230页

作者： He, Chen Micallef, Luana Serim, Baris Tung Vuong Ruotsalo, Tuukka Jacucci, Giulio Univ Helsinki Dept Comp Sci Helsinki Finland Univ Copenhagen Dept Comp Sci Copenhagen Denmark

Exploratory search starts with ill-defined goals and involves browsing, learning, and formulating new targets for search. To fluidly support such dynamic search behaviours, we focus on devising interactive visual facets (IVF), visualising information facets to support user comprehension and control of the information space. We reviewed existing faceted search interfaces and derived two design requirements (DR) that have not been fully addressed to support fluid interactions in exploratory search. We then exemplified the requirements through devising an IVF tool, which coordinates a linear and a categorical facet representing the distribution and summarisation of items, respectively, and providing context for faceted exploration (DR1). To support rapid transitions between search criteria (DR2), the tool introduces a novel design concept of using facets to select items without filtering the item space. Particularly, we propose a filter-swipe technique that enables users to drag a categorical facet value sequentially over linear facet bars to view the items in the intersection of the two facets along with the categorical facet dynamically summarising the items in the intersection. Three applications with various datasets demonstrate how the features support information discovery with ease. A comparison with a baseline system provided evidence that the task performance of the IVF tool was comparable to the typical query search interface. Another study of 11 participants with realistic email search tasks shows that dynamic suggestions through the timeline navigation can help discover useful suggestions for search;the novel design concept was favoured over using facet values as filters. Based on these practices, we derive IVF design implications for fluid, exploratory searches.

关键词： Exploratory search Information facets Fluid interaction visual data exploration

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