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Record W2017309315 · doi:10.1109/icter.2011.6075030

NoSQL query processing system for wireless ad-hoc and sensor networks

2011· article· en· W2017309315 on OpenAlexfundno aff
T. A. M. C. Thantriwatte, Chamath Keppetiyagama

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsnot available
FundersUniversity of ColomboUniversity of British Columbia
KeywordsNoSQLComputer scienceSQLWireless sensor networkSargableWireless ad hoc networkQuery optimizationQuery languageDatabaseComputer networkDistributed computingWirelessWeb search queryInformation retrievalOperating systemSearch engine

Abstract

fetched live from OpenAlex

TinyDB and TikiriDB are query processing systems, which have been used successfully in wireless sensor networks (WSN). These query processing systems provide a SQL query interface to extract data from sensors and it has proven to be a convenient programming interface in these environments. However, a relational database model that guarantees ACID properties is not a good match for a wireless sensor network since consistent connectivity or the uninterrupted operation of the sensor nodes cannot be expected. We noted that the NoSQL approach which does not rely on the ACID properties is a better match for a query processing systems for WASNs. We developed a NoSQL based query processing system on the Contiki operating system that is popular among the WSN community.

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.

How this classification was reachedexpand

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.897

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 science0.0010.000
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 head0.021
GPT teacher head0.210
Teacher spread0.190 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2011
Admission routes1
Has abstractyes

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