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Raya Wittich

dblp:427/0791 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 100%
Computer networks
1 paper
Network management and operations · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction › large language model interaction › language-based interaction
natural language interface
1.012026
NOVAID: Natural-language Observability Visualization Assistant for ITOps Dashboard Widget Generation · AAAI 2026

Methods — techniques the papers use, named apart from their topics

semantic parsing · 2.0schema completion · 2.0large language model · 2.0fuzzy entity matching · 2.0
YearPublicationVenuePosition
2026 NOVAID: Natural-language Observability Visualization Assistant for ITOps Dashboard Widget Generation
abstract
Manual creation of IT monitoring dashboard widgets is slow, error-prone, and a barrier for both novice and expert users. We present NOVAID, an interactive chatbot that leverages Large Language Models (LLMs) to generate IT monitoring widgets directly from natural language queries. Unlike general natural language–to-visualization tools, NOVAID addresses IT operations–specific challenges: specialized widget types like SLO charts, dynamic API-driven data retrieval, and complex contextual filters. The system combines a domain-aware semantic parser, fuzzy entity matching, and schema completion to produce standardized widget JSON specifications. An interactive clarification loop ensures accuracy in underspecified queries. On a curated dataset of 271 realistic queries, NOVAID achieves promising accuracy (up to 94.10% in metric extraction) across multiple LLMs. A user study with IT engineers yielded a System Usability Scale score of 74.2 for NOVAID, indicating good usability. By bridging natural language intent with operational dashboards, NOVAID demonstrates clear potential and a path for deployment in enterprise ITOps monitoring platforms.
Pratik Mishra, Caner Gözübüyük, Seema Nagar, Prateeti Mohapatra, Raya Wittich, Arthur De Magalhaes
AAAI5