Seedlabs

FocusFilter: Predicted-Relevance Decluttering for Progressive Dashboards

A filter for live dashboards that dims irrelevant data points so the analyst can focus on what matters.

A rendering layer for progressive analytics dashboards that predicts how relevant each not-yet-processed record is to the analyst's task and dims or hides low-relevance marks from the first partial results onward — a clean, task-tailored view at interactive speed, with no changes to the query engine.

Computer ScienceData Visualization and Analytics
Cognitive-Adaptive Visual AnalyticsBig-data BI dashboards (e.g., fraud/AML analysts exploring hundreds of millions of records through progressively loading visuals)

Concept

A front-end rendering layer for progressive analytics tools — engines that stream query results in chunks and paint partial results as they arrive. As each chunk lands, the layer fits a regression-tree model of degree of interest (DOI) to the data seen so far and predicts DOI for the not-yet-processed remainder. Visual encoding then follows predicted relevance: high-interest marks are emphasized in the foreground, low-interest marks are dimmed or hidden (information hiding), and encodings smoothly refine as each new chunk updates the predictions. The analyst gets a task-tailored, uncluttered view from the very first partial results, at interactive update rates, with zero changes to the underlying query engine — so it can ship as a plugin to existing progressive BI dashboards.

Why now

Paper [0] identifies the exact blocker that has kept relevance-driven decluttering out of progressive tools: DOI functions need the context of all data, and their lengthy computations break the analyst's flow in progressive visualization. It then demonstrates the fix — a model that computes interest for available data and predicts it for the rest, implemented with regression trees and evaluated in benchmarks — making DOI values available at interactive update rates for progressive visualization. That demonstrated capability (predicting interest over unseen data cheaply enough to keep up with chunked updates) is precisely what this product packages into focus+context and information hiding for partial results.

Written by GLM · 11 Oct 2026

AI assessment

Backed by 1 paper89

A clever front-end optimization for progressive BI that uses lightweight prediction to declutter partial results, offering a high-value 'plugin' wedge for existing big-data platforms.

Evidence strength
5/5
The idea directly implements the specific technical solution (regression-tree based DOI prediction) demonstrated in the cited research to solve the latency bottleneck of progressive visualization.
Market pull
4/5
High-volume data analysts (AML/Fraud) suffer from 'visual noise' in big-data dashboards, and the named beneficiaries are the primary vendors of these high-cost tools.
Novelty & moat
4/5
While other catalog ideas focus on backend query prioritization (SteerQueue, SculptSteer), this is a distinct front-end rendering approach that manages cognitive load without altering the query engine.
Feasibility
4/5
Since it is a rendering layer that doesn't require backend changes, a prototype can be built as a wrapper or plugin using the regression-tree logic described in the paper.
Wedge clarity
5/5
The 'plugin for existing progressive dashboards' is a sharp, low-friction entry point that avoids the 'rip-and-replace' risk of a full BI platform.
Simplicity / focus
5/5
The product is focused on a single, clear function: predicting relevance to dim/hide marks in a streaming data view.

Scored by Gemma 4 · 11 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.

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Who benefits

  • Microsoftcompany

    Power BI's incremental refresh and large semantic models already deliver results progressively over huge tables; a predicted-relevance layer would surface the interesting marks in early partial loads instead of an undifferentiated wall of points.

  • Salesforcecompany

    Tableau extracts routinely hold billions of rows; DOI-predicted emphasis and hiding would let analysts read the task-relevant subset immediately while the extract finishes loading.

  • Databrickscompany

    Databricks notebooks already stream partial results of long Spark queries; turning those partial result sets into relevance-ranked, decluttered views is a direct upgrade to the in-notebook experience.

  • Qlikcompany

    Qlik's associative engine explores large in-memory datasets interactively; predicted-interest filtering adds focus+context over partial results without re-querying.

Research it builds on

  1. Combining Degree of Interest Functions and Progressive Visualization
    Marius Hogräfer, Dominik Moritz, Adam Perer et al. · 2023 · 1 citations
    All ideas from this paper →
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feasibility