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Spatial Online Analytical Processing (SOLAP)

2007· book-chapter· en· W2490635813 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIGI Global eBooks · 2007
Typebook-chapter
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOnline analytical processingGeospatial analysisComputer scienceSpatial analysisGeographic coordinate systemGeolocationGeographic information systemPoint (geometry)Data miningQuadtreeComponent (thermodynamics)GeographyCartographyInformation retrievalData warehouseWorld Wide WebRemote sensingArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

It is recognized that 80% of data have a spatial component (ex. street address, place name, geographic coordinates, map coordinates). Having the possibilities to display data on maps, to compare maps of different phenomena or epochs, and to combine maps with tables and statistical charts allows one to get more insights into spatial datasets. Furthermore, performing fast spatio-temporal analysis, interactively exploring the data by drilling on maps similarly to drilling on tables and charts, and easily synchronizing such operations among these views is nowadays required by more and more users. This can be done by combining Geographical Information Systems (GIS) with On-Line Analytical Processing (OLAP), paving the way to “SOLAP” (Spatial OLAP). The present chapter focuses on the spatial characteristics of SOLAP from a geomatics engineering point of view: concepts, architectures, tools and remaining challenges.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.289
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it