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| Pengumpulan Data Sensor× | Pengumpulan Data Berasaskan API× | |
|---|---|---|
| Bidang | Metodologi Tinjauan | Metodologi Tinjauan |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1990s–2000s (widespread deployment with IoT ~2000s) | 2000s–2010s (formalized as a research method) |
| Pengasas≠ | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward | Emerged from computational social science and web 2.0 platform practices |
| Jenis≠ | Quantitative / mixed data collection technique | Digital data collection technique |
| Sumber perintis≠ | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Alias | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Berkaitan | 5 | 5 |
| Ringkasan≠ | Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies. | API-based data collection is a systematic technique in which a researcher sends structured requests to an application programming interface to retrieve data automatically from digital platforms, databases, or services. It is the primary method used in computational social science to gather large-scale social media records, government open data, financial data streams, and scientific repository content in machine-readable formats such as JSON or XML, enabling reproducible and scalable data acquisition that manual collection cannot match. |
| ScholarGateSet data ↗ |
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