8DB | What Can Geometry Reveal That Data Points Alone Cannot?
Explore the ideas and implementation context behind this capability.
Explore the models / 8DB
Tensors
Work across the dimensions that matter.
Represent multidimensional arrays and explore supported factorization and multiscale analysis paths.
Factor · project · analyze
Model / TensorsConceptual diagram
Start with the operations
your question needs.
- TensorN/Tucker N-way array factorization
- Paired scalar transforms and reconstruction
- Trained per-series/window layers with staleness metadata
Documented product scope, reviewed 17 September 2026. The underlying build and deployment must be qualified for your application.
Tucker arrays and scalar multiscale layers are separate capabilities. The current evidence does not establish general 2-D/3-D MERA, lossless compression for every dataset or local I/O for every partial reconstruction.
Connected native models
Keep the next question within reach.
8DB’s tensor and multiscale work includes N-way factorization as well as a distinct scalar path with reconstruction and trained time-series layers. These representations let the application ask different questions of the same source information.
Keep the meaning in every observation.
Observe · window · analyze
Explore model ↗Preserve what can be compared.
Order · join · meet
Explore model ↗Follow the relationship to a better answer.
Connect · traverse · follow
Explore model ↗These connections show the models involved in the application design. Compare the supported combination and full workflow with a realistic alternative.
Input → usable value / interactive design example
Examine a sensor change at several resolutions
Receive source observations with time and units
Measured points, an interval bound and a visible gap retain different meanings along the same timeline.
Next: Preserve typed values
Conceptual application workflow. Qualify the supported operations, external tools, protection and persistence for the complete path. These diagrams are explanatory, not measured results.
Read the complete workflow and comparison scope
- Receive source observations with time and units
- Preserve their typed values and version
- Derive the supported multiscale projection
- Inspect coarse behaviour and retained detail/staleness
- Return a scale-aware view linked to source observations
Compare reconstruction error, retained information, stale-state behaviour and the complete path. A compact approximation should not be compared with full-fidelity raw data as though the outputs were identical.
Compare a realistic alternative.
Xarray provides labelled multidimensional arrays. It is an analysis-library alternative within a larger stack; compare axes, operations and persistence/context responsibilities explicitly.
Xarray overviewCompare equivalent outputs and required guarantees, including the simplest single-product alternative where one exists. A specialist may remain the better fit when its operators, ecosystem or operational maturity are decisive. Combining native models becomes valuable when your actual question crosses them.
Modality performance / Evidence reviewed 18 September 2026
Define the useful result. Measure the complete job.
A modality-specific timing is not yet linked to this guide.
There may be supporting engineering measurements in the internal archive. We are reconciling their workload, result quality and comparison conditions before presenting a public chart. A benchmark for an adjacent model does not establish this model’s performance.
01Shape, dtype & output correctness
02Contraction / slice latency
03Peak working memory
Include memory, storage, energy and hardware writes
Record build-time and serving memory separately, original and derived stored bytes, measured joules per completed job, and physical writes including indexing, compaction and recovery. A smaller representation does not by itself prove lower energy or longer device life.
Match correctness, freshness, durability and protection across alternatives. Encryption, authorization, adapters, network setup and output delivery belong in the complete-job comparison wherever required. No universal zero-overhead claim follows from these results.
Choose the path that fits your deployment.
Verify the supported API/SDK, data format, persistence and indexing behaviour for the release you plan to use. Match the operation-by-modality protection profile and device qualification to the workload.
Related articles
Take the next step into the thinking.
8DB | Native Multimodal Data: Connect the Dots Across Graphs, Maps and Time Series
Explore the ideas and implementation context behind this capability.
Reference / Reviewed 17 September 2026
Readable, shareable and traceable.
This reference explains 8DB’s public modality description with an explicit workload and selection criteria. It is a documentary review, not fresh execution of every operator or platform.
Public sources: 8DB modality scope; Xarray overview; the related articles above. Internal source leads and outstanding release checks remain in the editorial record.
Download this referenceEvaluate the question
your application needs to answer.
Bring the data shapes, input state, expected output and the systems you use today. Define a comparison around that complete job.
Discuss your workload