Location Data Analytics in the Business Value Chain: A Systematic Literature Review
Bibliographic record
Abstract
Context information has become a significant asset to optimize the value obtained from information systems. Location is an important type of context information that refers to the place in which an event occurs. In business environments, the implementation of location-based analytics systems to aid decision making processes is of paramount importance for business development. However, after an exhaustive literature review, we found that researchers and practitioners still lack a comprehensive characterization of location-based data analytics systems that have been effectively applied to business processes. This paper presents the results of a systematic literature review (SLR), in which we characterized a total of 168 location-based and business oriented analytics solutions that were published between 2014 and 2019. To conduct this SLR we defined three characterization dimensions: business aspects, through which we identified value chain business processes or activities that may be benefited with the proposed solution; data source, which allowed us to report on the data used in each of the studies; and data analytics, through which we report on the analytics techniques and validation strategies implemented by the studied approaches. The contribution of our SLR is twofold. First, it provides business and data analytics practitioners with a comprehensive catalog of location-based data analytics approaches that could be applied to improve value generation, at different levels, along their businesses' value chains. And second, it provides researchers with a complete landscape of recent advancements and open challenges in the field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".