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VisExPreS: A Visual Interactive Toolkit for User-Driven Evaluations of Embeddings

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第7期28卷 2791-2807页

作者： Ghosh, Aindrila Nashaat, Mona Miller, James Quader, Shaikh Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada IBM Toronto Software Lab Toronto ON L6G 1C7 Canada

Although popularly used in big-data analytics, dimensionality reduction is a complex, black-box technique whose outcome is difficult to interpret and evaluate. In recent years, a number of quantitative and visual methods have been proposed for analyzing low-dimensional embeddings. On the one hand, quantitative methods associate numeric identifiers to qualitative characteristics of these embeddings;and, on the other hand, visual techniques allow users to interactively explore these embeddings and make decisions. However, in the former case, users do not have control over the analysis, while in the latter case. assessment decisions are entirely dependent on the user's perception and expertise. In order to bridge the gap between the two, in this article, we present VisExPreS, a visual interactive toolkit that enables a user-driven assessment of low-dimensional embeddings. VisExPreS is based on three novel techniques namely PG-LAPS, PG-GAPS, and RepSubset, that generate interpretable explanations of the preserved local and global structures in embeddings. In the first two techniques, the VisExPreS system proactively guides users during every step of the analysis. We demonstrate the utility of VisExPreS in interpreting, analyzing, and evaluating embeddings from different dimensionality reduction algorithms using multiple case studies and an extensive user study.

关键词： Measurement visualization Quality assessment data visualization Stress Distortion Linear programming data and knowledge visualization interactive data exploration and discovery knowledge and data engineering tools and techniques

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C-TREND: Temporal cluster graphs for identifying and visualizing trends in multiattribute transactional data

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2008年第6期20卷 721-735页

作者： Adomavicius, Gediminas Bockstedt, Jesse Univ Minnesota Carlson Sch Management Dept Informat & Decis Sci Minneapolis MN 55455 USA

Organizations and firms are capturing increasingly more data about their customers, suppliers, competitors, and business environment. Most of this data is multiattribute ( multidimensional) and temporal in nature. data mining and business intelligence techniques are often used to discover patterns in such data;however, mining temporal relationships typically is a complex task. We propose a new data analysis and visualization technique for representing trends in multiattribute temporal data using a clustering-based approach. We introduce Cluster-based Temporal Representation of EveNt data (C-TREND), a system that implements the temporal cluster graph construct, which maps multiattribute temporal data to a two-dimensional directed graph that identifies trends in dominant data types over time. In this paper, we present our temporal clustering-based technique, discuss its algorithmic implementation and performance, demonstrate applications of the technique by analyzing data on wireless networking technologies and baseball batting statistics, and introduce a set of metrics for further analysis of discovered trends.

关键词： clustering data and knowledge visualization data mining interactive data exploration and discovery temporal data mining trend analysis

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Interactive entity resolution in relational data: A visual analytic tool and its evaluation

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2008年第5期14卷 999-1014页

作者： Kang, Hyunmo Getoor, Lise Shneiderman, Ben Bilgic, Mustafa Licamele, Louis Univ Maryland Inst Adv Comp Studies College Pk MD 20742 USA Univ Maryland Dept Comp Sci College Pk MD 20742 USA

databases often contain uncertain and imprecise references to real-world entities. Entity resolution, which is the process of reconciling multiple references to underlying real-world entities, is an important data cleaning process required before accurate visualization or analysis of the data is possible. In many cases, in addition to noisy data describing entities, there is data describing the relationships among the entities. This relational data is important during the entity resolution process;it is useful both for the algorithms that determine likely database references to be resolved and for visual analytic tools that support the entity resolution process. In this paper, we introduce a novel user interface, D-Dupe, for interactive entity resolution in relational data. D-Dupe effectively combines relational entity resolution algorithms with a novel network visualization that enables users to make use of an entity's relational context for making resolution decisions. We describe resolution strategies based on pairs or sets of references and show appropriate visualizations for each. Since resolution decisions often are interdependent, D-Dupe facilitates understanding this complex process through animations that highlight combined inferences and a history mechanism that allows users to inspect chains of resolution decisions. An empirical study with 12 users confirmed the benefits of the relational context visualization on the performance of entity resolution tasks in relational data in terms of time as well as users' confidence and satisfaction.

关键词： information visualization visual analytics data and knowledge visualization data mining graphical user interfaces user-centered design

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A natural language interface to a graph-based bibliographic information retrieval system

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data & knowledge ENGINEERING 2017年 111卷 73-89页

作者： Zhu, Yongjun Yan, Erjia Song, Il-Yeol Drexel Univ Coll Comp & Informat Philadelphia PA 19104 USA

With the ever-increasing volume of scientific literature, there is a need for a natural language interface to bibliographic information retrieval systems to retrieve relevant information effectively. In this paper, we propose one such interface, NLI-GIBIR, which allows users to search for a variety of bibliographic data through natural language. NLI-GIBIR makes use of a novel framework applicable to graph-based bibliographic information retrieval systems in general. This framework incorporates algorithms/heuristics for interpreting and analyzing natural language bibliographic queries via a series of text- and linguistic-based techniques, including tokenization, named entity recognition, and syntactic analysis. We find that our framework, as implemented in NLI-GIBIR, can effectively represent and address complex bibliographic information needs. Thus, the contributions of this paper are as follows: First, to our knowledge, it is the first attempt to propose a natural language interface for graph-based bibliographic information retrieval. Second, we propose a novel customized natural language processing framework that integrates a few original algorithms/heuristics for interpreting and analyzing bibliographic queries. Third, we show that the proposed framework and natural language interface provide a practical solution for building real-world bibliographic information retrieval systems. Our experimental results show that the presented system can correctly answer 39 out of 40 example natural language queries with varying lengths and complexities.

关键词： Information retrieval Natural language interface Graph database data and knowledge visualization Digital libraries

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Cardinality constraints and functional dependencies over possibilistic data

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data & knowledge ENGINEERING 2018年 117卷 339-358页

作者： Roblot, Tania Link, Sebastian Univ Auckland Dept Comp Sci Auckland New Zealand

Modern applications require advanced techniques and tools to process large volumes of uncertain data. For that purpose we study cardinality constraints and functional dependencies as a declarative mechanism to control the occurrences and interrelationships of uncertain data. Uncertainty is modeled qualitatively by assigning to each object a degree of possibility by which the object occurs in an uncertain instance. Cardinality constraints and functional dependencies are assigned a degree of certainty that stipulates on which objects they hold. Our framework empowers users to model uncertainty in an intuitive way, without the requirement to put a precise value on it. Our class of cardinality constraints and functional dependencies enjoys a natural possible world semantics, which is exploited to establish several tools to reason about them. We characterize the associated implication problem axiomatically and algorithmically in linear input time. Furthermore, we show how to visualize any given set of our cardinality constraints and functional dependencies in the form of an Armstrong sketch. Even though the problem of finding an Armstrong sketch is precisely exponential, our algorithm computes a sketch with conservative use of time and space. data engineers may therefore compute Armstrong sketches that they can jointly inspect with domain experts in order to consolidate the set of cardinality constraints and functional dependencies meaningful for a given application domain.

关键词： data and knowledge visualization data models database semantics Management of integrity constraints Requirements engineering

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Environmental chemistry through intelligent atmospheric data analysis

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ENVIRONMENTAL MODELLING & SOFTWARE 2010年第6期25卷 760-769页

作者： Gross, Deborah S. Atlas, Robert Rzeszotarski, Jeffrey Turetsky, Emma Christensen, Janara Benzaid, Sami Olson, Jamie Smith, Thomas Steinberg, Leah Sulman, Jon Ritz, Anna Anderson, Benjamin Nelson, Catherine Musicant, David R. Chen, Lei Snyder, David C. Schauer, James J. Carleton Coll Dept Chem Northfield MN 55057 USA Carleton Coll Dept Comp Sci Northfield MN 55057 USA Univ Wisconsin Dept Comp Sci Madison WI 53705 USA Univ Wisconsin Environm Chem & Technol Program Madison WI 53705 USA

Here we present a new open-source software package designed to facilitate the analysis of atmospheric data, with emphasis on data mining applications applied to single-particle mass spectrometry data from aerosol particles. The software package, Enchilada (Environmental Chemistry through Intelligent Atmospheric data Analysis), is designed to seamlessly handle large datasets, to allow for temporal aggregation of data from many instruments, and to integrate techniques such as clustering (K-means, K-medians, and Art-2a), labeling of peaks in mass spectra, and temporal correlations of multiple datasets from multiple instrument types. The software, which continues to be developed and improved, provides users with a single package to integrate data from multiple mass spectrometer systems (ATOFMS, PALMS, SPASS, Q-AMS) as well as any time-based data stream. A detailed description of the software and examples of analysis methods that are incorporated into it are described here. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Mass spectrometry Clustering Aerosol particle data mining database design data and knowledge visualization User interfaces

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Open Government data in Brazil

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IEEE INTELLIGENT SYSTEMS 2012年第3期27卷 45-49页

作者： Breitman, Karin Salas, Percy Casanova, Marco Antonio Saraiva, Daniel Gama, Vinicius Viterbo, Jose Magalhaes, Regis Pires Franzosi, Ednylton Chaves, Miriam Pontifical Catholic Univ Rio de Janeiro Dept Informat Rio De Janeiro Brazil Univ Fed Fluminense Dept Comp Sci BR-24220000 Niteroi RJ Brazil Univ Fed Ceara Fortaleza Ceara Brazil

This article discusses the current status of open government data in Brazil and summarizes the lessons learned from publishing Brazilian government data as linked data.

关键词： DH HEM Ts data Structures Boolean Functions Intelligent Systems Brazil Government Policies data Processing database Systems XML knowledge Management Information Services Government database Models database Design Modeling And Management data Translation data and knowledge visualization XML XS RDF

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Automated discovery of multi-faceted ontologies for accurate query answering and future semantic reasoning

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data & knowledge ENGINEERING 2013年 87卷 405-424页

作者： Gollapalli, Mohammed Li, Xue Wood, Ian Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld Australia Univ Queensland Sch Math & Phys Brisbane Qld Australia

There has been a surge of interest in the development of probabilistic techniques to discover meaningful data facts across multiple datasets provided by different organizations. The key aim is to approximate the structure and content of the induced data into a concise synopsis in order to extract meaningful data facts. Performing sensible queries across unrelated datasets is a complex task that requires a complete understanding of each contributing database's schema to define the structure of its information. Alternative approaches that use data modeling enterprise tools have been proposed, in order to give users without complex schema knowledge the ability to query databases. Unfortunately, data modeling-based matching is a content-based technique and incurs significant query evaluation costs, due to attribute level pairwise comparisons. We propose a multi-faceted classification technique for performing structural analysis on knowledge domain clusters, using a novel Ontology Guided data Linkage (OGDL) framework. This framework supports self-organization of contributing databases through the discovery of structural dependencies, by performing multi-level exploitation of ontological domain knowledge relating to tables, attributes and tuples. The framework thus automates the discovery of schema structures across unrelated databases, based on the use of direct and weighted correlations between different ontological concepts, using a h-gram (hash gram) record matching technique for concept clustering and cluster mapping. We demonstrate the feasibility of our OGDL algorithms through a set of accuracy, performance and scalability experimental tests run on real-world datasets, and show that our system runs in polynomial time and performs well in practice. To the best of our knowledge, this is the first attempt initiated to solve data linkage problems using a multi-faceted cluster mapping strategy, and we believe that our approach presents a significant advancement towards

关键词： Concept modeling Ontologies data and knowledge visualization Query processing Semantic reasoning

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Least square projection: A fast high-precision multidimensional projection technique and its application to document mapping

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2008年第3期14卷 564-575页

作者： Paulovich, Fernando V. Nonato, Luis Gustavo Minghim, Rosane Levkowitz, Haim Univ Sao Paulo Inst Ciencia Matemat & Computacao BR-13560970 Sao Carlos SP Brazil Univ Massachusetts Dept Comp Sci Lowell MA 01854 USA

The problem of projecting multidimensional data into lower dimensions has been pursued by many researchers due to its potential application to data analyses of various kinds. This paper presents a novel multidimensional projection technique based on least square approximations. The approximations compute the coordinates of a set of projected points based on the coordinates of a reduced number of control points with defined geometry. We name the technique Least Square Projections ( LSP). From an initial projection of the control points, LSP defines the positioning of their neighboring points through a numerical solution that aims at preserving a similarity relationship between the points given by a metric in mD. In order to perform the projection, a small number of distance calculations are necessary, and no repositioning of the points is required to obtain a final solution with satisfactory precision. The results show the capability of the technique to form groups of points by degree of similarity in 2D. We illustrate that capability through its application to mapping collections of textual documents from varied sources, a strategic yet difficult application. LSP is faster and more accurate than other existing high-quality methods, particularly where it was mostly tested, that is, for mapping text sets.

关键词： document and text processing visualization simulation modeling and visualization data and knowledge visualization information visualization visualization techniques and methodologies

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Input dependent prediction intervals for supervised regression

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INTELLIGENT data ANALYSIS 2014年第5期18卷 873-887页

作者： Pevec, Darko Kononenko, Igor Univ Ljubljana Fac Comp & Informat Sci Ljubljana 1000 Slovenia

In this article we compare and put to test two families of non-parametric approaches to constructing prediction intervals for arbitrary regression models in the supervised learning framework. It is often assumed for the errors to be independent and identically distributed, but we focus on the general case when the errors may be input dependent. The first family of approaches is based on the idea of explaining the total prediction error as a sum of the model's error and the error caused by noise inherent to the data, so the two are estimated independently. The second family is based on the assumption of similarity of the data and these approaches estimate the prediction intervals of the target regression variable by using sample's nearest neighbors. Results on a large set of artificial and real-world datasets show that one method from the second family is superior to other methods. Approaches from the first family always form valid, yet not necessarily confirmatory prediction intervals, whereas approaches from the second family prove to be more time efficient.

关键词： Prediction intervals regression model validation data and knowledge visualization methodologies and tools

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