Adaptive Storage Intelligence & Context-Aware Maintenance System

[SMOKESCREEN2]

Is your feature request related to a problem?

Yes.
While SD Maid 2/SE is highly effective at detecting and cleaning unwanted files, its current approach is mostly rule-based and snapshot-oriented. This limits its ability to understand long-term storage behavior, user patterns, and system-level changes over time.

As a result, the app can identify “what exists”, but not “how it evolves” or “why it grows”.

Describe the solution you'd like

I would like SD Maid 2/SE to introduce an adaptive storage intelligence and context-aware maintenance layer that extends beyond static scans and manual scheduling.

This system could include:

Tracking storage usage trends over time (per app, folder, and category)
Detecting abnormal growth patterns (e.g. logs, caches, media accumulation spikes)
Learning from repeated user cleaning actions to refine future recommendations
Introducing context-aware automation triggers such as:
storage threshold-based cleaning (e.g. below X% free space)
idle-state maintenance routines
post-uninstall deep cleanup workflows
Providing explanations, not only results (e.g. “this folder is growing due to X behavior”)

The goal is not to replace existing tools, but to add a higher-level intelligence layer on top of them.

Describe alternatives you've considered

I considered using manual periodic cleaning with the current tools (AppCleaner, Analyzer, Scheduler), but this requires constant user intervention and does not provide any insight into long-term storage behavior.

I also considered relying on third-party storage monitoring apps, but they lack integration with deep system-level cleaning capabilities that SD Maid already has.

Describe why this would be in the interest of all users

This would benefit a wide range of users because storage issues are universal across Android devices, especially with modern apps that continuously generate cache, logs, and media files.

Instead of requiring users to manually analyze and decide what to clean, the app would proactively:

prevent storage buildup before it becomes a problem
reduce manual maintenance effort
improve transparency of what consumes storage over time
make the cleaning process more intelligent and less reactive

This is not a niche feature—it addresses a core problem of Android usage: unmanaged storage growth.

Additional context

SD Maid 2/SE is already extremely strong in detection, compatibility, and low-level system access. This proposal focuses on evolving it from a powerful cleaning tool into a predictive and adaptive storage maintenance system, leveraging its existing strengths (root/Shizuku/Analyzer/AppCleaner) into a unified intelligence layer.

[MarkyMarkDE]

[...] SD Maid 2/SE is already extremely strong in detection, compatibility, and low-level system access. This proposal focuses on evolving it from a powerful cleaning tool into a predictive and adaptive storage maintenance system, leveraging its existing strengths (root/Shizuku/Analyzer/AppCleaner) into a unified intelligence layer.

@SMOKESCREEN2
Interesting idea, but I wonder whether something like this is still technically feasible on modern Android.

Android has become increasingly restrictive and unpredictable over the years, especially regarding background execution, permissions, accessibility services, and inter-process communication. In practice, even relatively simple states are not always stable or reliably observable over longer periods of time.

For example, there are situations where apps can temporarily lose access to functionality they previously had, services may become unavailable, or system states may not be reported consistently across devices and Android versions.

Because of that, I am not sure how reliable a long-term intelligence layer could be in reality. Features such as trend analysis, adaptive recommendations, behavioral learning, or context-aware automation would depend on having access to stable and trustworthy historical data over extended periods.

My concern is not whether such a system would be useful—it certainly could be—but whether the Android platform still provides a sufficiently stable foundation for this kind of long-term observation and decision-making.

I would be interested in your opinion on whether Android, in its current state, still allows something like this to be implemented in a reliable and maintainable way.

[SMOKESCREEN2]

I understand the concerns regarding Android’s increasing restrictions, and I agree that long-term, always-on observation is not reliably possible in a fully continuous way across all devices.
However, I think a hybrid approach could still make this concept viable within current Android constraints.
Instead of relying on persistent background monitoring, the system could combine:
Event-driven signals (e.g. app install/uninstall, storage threshold changes, charging state, periodic system callbacks)
Scheduled lightweight analysis using WorkManager within allowed execution windows
UsageStats-based trends for coarse but stable behavioral patterns
On-demand context evaluation when the user interacts with SD Maid SE
This would not require continuous tracking or perfect system visibility, but rather opportunistic data collection within what Android already allows.
The goal would not be “perfect long-term awareness”, but rather:
probabilistic and best-effort insight generation within platform limits
Even with current restrictions, apps can still build meaningful adaptive behavior if they accept:
delayed updates instead of real-time tracking
partial visibility instead of full system awareness
device-specific variation instead of uniform behavior
So I agree with the limitation in principle, but I believe a reduced-scope version of the idea could still be both stable and useful in practice, especially if framed as “assistive insights” rather than continuous intelligence.