KOPS - The Institutional Repository of the University of Konstanz

Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution

Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution

Cite This

Files in this item

Checksum: MD5:1d5e300dc291d4b8afa94bfe71e64195

EL-ASSADY, Mennatallah, Fabian SPERRLE, Oliver DEUSSEN, Daniel KEIM, Christopher COLLINS, 2019. Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution. In: IEEE Transactions on Visualization and Computer Graphics. 25(1), pp. 374-384. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2018.2864769

@article{ElAssady2019-01Visua-43555, title={Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution}, year={2019}, doi={10.1109/TVCG.2018.2864769}, number={1}, volume={25}, issn={1077-2626}, journal={IEEE Transactions on Visualization and Computer Graphics}, pages={374--384}, author={El-Assady, Mennatallah and Sperrle, Fabian and Deussen, Oliver and Keim, Daniel and Collins, Christopher} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/43555"> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-10-17T07:29:56Z</dcterms:available> <dcterms:title>Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution</dcterms:title> <dc:creator>Keim, Daniel</dc:creator> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/43555"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:contributor>Sperrle, Fabian</dc:contributor> <dc:contributor>Keim, Daniel</dc:contributor> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/43555/1/El-Assady_2-1cwc6z3rv48981.pdf"/> <dc:contributor>El-Assady, Mennatallah</dc:contributor> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Deussen, Oliver</dc:creator> <dc:language>eng</dc:language> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/43555/1/El-Assady_2-1cwc6z3rv48981.pdf"/> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dc:contributor>Deussen, Oliver</dc:contributor> <dc:contributor>Collins, Christopher</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-10-17T07:29:56Z</dc:date> <dc:rights>terms-of-use</dc:rights> <dcterms:issued>2019-01</dcterms:issued> <dc:creator>Collins, Christopher</dc:creator> <dcterms:abstract xml:lang="eng">To effectively assess the potential consequences of human interventions in model-driven analytics systems, we establish the concept of speculative execution as a visual analytics paradigm for creating user-steerable preview mechanisms. This paper presents an explainable, mixed-initiative topic modeling framework that integrates speculative execution into the algorithmic decisionmaking process. Our approach visualizes the model-space of our novel incremental hierarchical topic modeling algorithm, unveiling its inner-workings. We support the active incorporation of the user's domain knowledge in every step through explicit model manipulation interactions. In addition, users can initialize the model with expected topic seeds, the backbone priors. For a more targeted optimization, the modeling process automatically triggers a speculative execution of various optimization strategies, and requests feedback whenever the measured model quality deteriorates. Users compare the proposed optimizations to the current model state and preview their effect on the next model iterations, before applying one of them. This supervised human-in-the-loop process targets maximum improvement for minimum feedback and has proven to be effective in three independent studies that confirm topic model quality improvements.</dcterms:abstract> <dc:creator>El-Assady, Mennatallah</dc:creator> <dc:creator>Sperrle, Fabian</dc:creator> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> </rdf:Description> </rdf:RDF>

Downloads since Oct 17, 2018 (Information about access statistics)

El-Assady_2-1cwc6z3rv48981.pdf 152

This item appears in the following Collection(s)

Search KOPS


Browse

My Account