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Record W3212344365

Information Engineering: Conceptual Elements Related Information Management and Information Systems

2014· article· en· W3212344365 on OpenAlexaff
Alex Volnei Teixeira, María do Carmo Duarte Freitas

Bibliographic record

VenueSSRN Electronic Journal · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsFirst Nations University of CanadaUniversity of Regina
Fundersnot available
KeywordsComputer scienceInformation systemInformation engineeringKnowledge managementManagement information systemsInformation managementCompetitive advantageData scienceInformation integrationPersonal information managementStrategic information systemInformation flowManagement scienceData miningEngineeringBusiness
DOInot available

Abstract

fetched live from OpenAlex

The study addresses the challenges and transformations generated by access to the data and information management activity, considering the time to retrieve the correct information for decision-making. The greater pain organization, the more complex nature of the problems arising from the volume of data and information. In this scenario, the flow of information plays an important role in generating competitive advantages on products and services and the Engineering Information ( EI ) , comes with an interlocking set of formal techniques for creating , planning, analysis, design and construction of systems Information ( SI ) , reflecting the organizational efficiency and Information Management ( IM ) . This work aims to conceptually relate the Information Management, Information System with Information Engineering, through the investigation of its structural elements. The triangulation of the concepts offers an understanding of how Information Engineering participates in the process of creation of information as a strategic element for decision-making. Methodologically, the study is classified as exploratory, qualitative non-probabilistic. We also use the literature and documents to build a theoretical map with elements related to information management, information systems, and information engineering research. As a result of the study, EI is conceptually presented as an integrated set of formal techniques by which business models, data models, and process models are constructed from a knowledge base far-reaching, to create and maintain information systems focusing on strategy and competitive advantage, generated by suitable and qualified manipulation of the processes described. Therefore, one starts with the assumption of the Information Engineering, occurring systemic observation of the life cycle of information effectively in the organizational environment. Begins by generating (conception, creation), processing, custody, disposal, and reuse (tomb) closes the cycle of Information Engineering.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.020
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.016
Science and technology studies0.0030.027
Scholarly communication0.0200.022
Open science0.0020.005
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.201
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2014
Admission routes1
Has abstractyes

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