Ontology-assisted provenance visualization for supporting enterprise search of engineering and business files

Self Cite

100

While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely "VA-assisted ML". The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly.

Section: Using Vis4mlmentioning

confidence: 99%

VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning

Sacha

Kraus

Keim

et al. 2019

Self Cite

100

“…Their SemViz system exemplified this process, using three ontologies to automatically visualize music chart data. Khan et al [21] used an ontology in an enterprise search system to capture search provenance, using the ontology to visualize collaborative search graphs.…”

Section: Ontology-supported Visualizationmentioning

confidence: 99%

Propagating Visual Designs to Numerous Plots and Dashboards

Khan

Nguyêñ

Abdul‐Rahman

et al. 2022

Self Cite

Fig. 1. Our novel propagation workflow makes it easy to propagate visual designs to numerous datasets. Reference visualizations are created for data streams, which are associated with several keywords in our ontology. A search and activate process is used to propagate the reference visualisation to other appropriate data streams. (1) Ontology keywords are used to construct a query in our search UI for suitable data stream combinations. (2) Search results consist of ranked data stream combinations that match query parameters, although some results may not be suitable for propagation. (3) A quality assurance step carried out by an expert ensures the visual design is only propagated to suitable data, resulting in new visualizations that are immediately deployed as web pages.

“…Khan et al [18] propose a framework that records users search activities in an enterprise file repository. Users can explore the evolving search provenance graph by setting a time range and exploring the collection of matching files in a detail view.…”

Section: Index and Provenancementioning

confidence: 99%

KnowledgePearls: Provenance-Based Visualization Retrieval

Stitz

Gratzl

Piringer

et al. 2019

Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.