Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ukusanyaji wa Data kwa Kutumia API za Simu za Mkononi× | Uchimbaji wa Wavuti× | |
|---|---|---|
| Nyanja | Metodolojia ya Dodoso | Metodolojia ya Dodoso |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2007–2010 (mainstream smartphone era) | Late 1990s–2000s |
| Mwanzilishi≠ | Emerged from mobile computing and REST/web API proliferation (Fielding, 2000; widespread adoption ~2007–2010 with smartphone ecosystem) | Early internet practitioners; systematised in research contexts from the late 1990s onward |
| Aina≠ | Digital data collection technique | Automated digital data collection technique |
| Chanzo asilia≠ | Luce, M. F., Kahn, B. E., & Malhotra, N. K. (2016). Capturing consumer experiences with mobile research methods. Journal of Consumer Research, 42(6), 949–965. link ↗ | Mitchell, R. (2018). Web Scraping with Python: Collecting More Data from the Modern Web (2nd ed.). O'Reilly Media. ISBN: 978-1491985571 |
| Majina mbadala | mobile API data collection, smartphone API data harvesting, mobile app API research data collection, API-driven mobile data collection | web harvesting, screen scraping, web crawling, automated data extraction |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Mobile API-based data collection uses mobile devices (smartphones, tablets) to query application programming interfaces — structured web endpoints that return machine-readable data — enabling researchers to gather behavioral, contextual, sensor-enriched, or platform-generated data in real time from participants in their natural environments. It combines the ubiquity of mobile hardware with the scalability and standardization of RESTful or GraphQL APIs. | Web scraping is a computational data collection technique in which software automatically retrieves and extracts structured or semi-structured content from websites. Widely used in social science, computational linguistics, economics, and information science, it enables researchers to assemble large datasets from publicly accessible web sources — such as news archives, social media platforms, government portals, and online marketplaces — that would be impractical to collect manually. |
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