# Real-time Stream-based Monitoring

arXiv: https://arxiv.org/abs/1711.03829 · HTML: https://mlsanity.com/paper/1711.03829/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 13 of 13)

- [Monitoring Algorithmic Fairness](https://mlsanity.com/paper/2305.15979/) (2023)
- [Monitoring Algorithmic Fairness under Partial Observations](https://mlsanity.com/paper/2308.00341/) (2023)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [Monitoring of Static Fairness](https://mlsanity.com/paper/2507.03048/) (2025)
- [Algorithmic Fairness: A Runtime Perspective](https://mlsanity.com/paper/2507.20711/) (2025)
- [Energy Shields for Fairness](https://mlsanity.com/paper/2605.24926/) (2026)

## Abstract (arXiv)

We introduce RTLola, a new stream-based specification language for the description of real-time properties of reactive systems. The key feature is the integration of sliding windows over real-time intervals with aggregation functions into the language. Using sliding windows we can detach fixed-rate output streams from the varying rate input streams. We provide an efficient evaluation algorithm of the sliding windows by partitioning the windows into intervals according to a given monitor frequency. For useful aggregation functions, the intervals allow a more efficient way to compute the aggregation value by dynamically reusing interval summaries. In general, the number of input values within a single window instance can grow arbitrarily large disallowing any guarantees on the expected memory consumption. Assuming a fixed monitor output rate, we can provide memory guarantees which can be computed a-priori. Additionally, for specifications using certain classes of aggregation functions, we can perform a more precise, better memory analysis. We demonstrate the applicability of the new language on practical examples.