Streamingsmodeller

Streamingsmodeller are computational models designed to process and analyze data that arrive in a continuous sequence, or data streams, with the aim of producing timely predictions or insights. They differ from traditional batch models, which work on fixed datasets processed offline.

Key requirements for streamingsmodeller include one-pass data processing, online learning capabilities, memory efficiency, and the ability to adapt to changing data distributions, a phenomenon known as concept drift.

Common approaches in streamingsmodeller include online regression and classification methods such as stochastic gradient descent, online ridge regression, and the passive-aggressive family; decision-tree methods like Hoeffding trees and ensemble variants; and incremental clustering and time-series forecasting tailored for streams.

Applications span real-time fraud detection, network monitoring, dynamic recommender updates, telemetry from IoT sensors, and anomaly detection in streaming data.

Evaluation typically uses prequential (predictive sequential) metrics, measures of latency and throughput, and assessments of memory usage and drift handling. Major challenges include concept drift, noisy or out-of-order data, and limited computational resources.

The term streamingsmodeller is used in Scandinavian contexts to describe models designed for streaming data and online learning in related literature. See also data stream mining, online learning, and streaming analytics.