The document proposes translating rules expressed in Spatio-Temporal Reach and Escape Logic (STREL) to streaming-based monitoring applications. STREL allows expressing properties over attributes that vary in space and time. The contribution is defining streaming operators whose semantics enforce STREL rules by composing base streaming operators. An evaluation on Apache Flink shows the approach can achieve throughput of 1000-500 tuples/second and sub-millisecond latency depending on spatial and temporal resolution of the data. Future work includes further evaluation, additional temporal operators, path-based spatial analysis, and compilation optimizations.

2. Agenda
• Motivation
• Preliminaries: data streaming in a nutshell
• Contribution: translation of Spatio-Temporal Logics to
Streaming-Based Monitoring Applications
• Evaluation
• Conclusion / Future work
2

3. 95
95
95
• Monitoring rules are essential to detect anomalies /
dangerous situations / broken agents...
• Challenges: the # of devices, their heterogeneity,
their complex interactions...
• Wishlist:
• easy-to-express rules  “for the masses”
• efficient analysis  for timely detection
UAVs should never get
closer than 10 meters to cars

4. easy-to-express rules + efficient analysis
• Ingredients:
• High-level language  Spatio-temporal logics
• Continuous analysis paradigm  Data streaming
• Distributed / Parallel analysis framework  Stream Processing Engines
How do we take his knowledge...
... and transfer it here?

5. Agenda
• Motivation
• Preliminaries: data streaming in a nutshell
• Contribution: translation of Spatio-Temporal Logics to
Streaming-Based Monitoring Applications
• Evaluation
• Conclusion / Future work
5

6. 7
data stream: unbounded sequence of tuples sharing the same schema
95
A 8:00 20
Ff Ff Ff
A 8:03 15
Ff Ff Ff
<UAV id, ts, speed, dest>

7. 8
95
A 8:00 20
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A 8:03 15
Ff Ff Ff
Join
Aggregate
stateful
continuous query: Directed Acyclic Graph of streams and operators Map
Filter
Union
Multiplex
stateless
Compute the average speed
in the last 10 minutes
(group by UAV)
Add to each UAV report
its average speed in the
last 10 minutes
Report UAVs close to
the destination that are
not decelerating
Stream Processing Engine (SPE)

8. Agenda
• Motivation
• Preliminaries: data streaming in a nutshell
• Contribution: translation of Spatio-Temporal Logics to
Streaming-Based Monitoring Applications
• Evaluation
• Conclusion / Future work
9

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STREL
(Spatio-Temporal Reach and Escape Logic)
• Spatio-temporal modal logic
• Allows expressing properties over
attributes which vary over space and
time
𝕊2
-Operators
(streaming-STREL operators)
can be ported to the streaming
paradigm by means of
Base streaming operator
whose semantics can be
enforced composing
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10. STREL
(Spatio-Temporal Reach and Escape Logic)
• Spatio-temporal modal logic
• Allows expressing properties over
attributes which vary over space and
time
𝕊2
-Operators
(streaming-STREL operators)
can be ported to the streaming
paradigm by means of
Base streaming operators
whose semantics can be
enforced composing
What’s the novelty?
Can be automated!(1)
Any of the existing
SPEs can be used
(2)
Their distribution / parallelization
/ adaptiveness / fault-tolerance
solutions can be leveraged!
(3)

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STREL
(Spatio-Temporal Reach and Escape Logic)
• Atomic propositions
(True/False/Undefined)
• Logical operations
(AND/OR/NOT)
• Temporal operators:
• Since
• Sometimes in the past
• Always in the past
• Spatial operators:
• Somewhere
• Everywhere
Join
Aggregate
stateful
Map
Filter
Union
Multiplex
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Base streaming operators
𝕊2
-MAP
𝕊2
-MERGE
𝕊2
-TAGGR (𝕊2
-time-aggregate)
𝕊2-LAGGR (𝕊2-location-aggregate)
𝕊2-Operators
(streaming-STREL operators)
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STREL
(Spatio-Temporal Reach and Escape Logic)
Any combination of STREL
operators...
Base streaming operators
𝕊2
-Operators
(streaming-STREL operators)
A tree of 𝕊2
-Operators
Each node of a tree
of 𝕊2
-Operators...
A DAG of base operators

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= 𝑔( )
STREL
(Spatio-Temporal Reach
and Escape Logic)
Base streaming operators
𝕊2
-Operators
(streaming-STREL operators)
the UAV should not have remained farther than 20 meters from its destination for longer than 10
minutes
95

14. Agenda
• Motivation
• Preliminaries: data streaming in a nutshell
• Contribution: translation of Spatio-Temporal Logics to
Streaming-Based Monitoring Applications
• Evaluation
• Conclusion / Future work
15

15. Evaluation
• Performance evaluation of the monitoring query:
• Implemented using Apache Flink
• Testbed: Odroid-XU4
• Synthetic data, discretized over
• Time: sampling frequency f (Hz)
• 2D space: number of cells c
the UAV should not have remained farther than 20
meters from its destination for longer than 10 minutes
95

16. Throughput,
latency and CPU
consumption for
increasing f values
and fixed c values
Evaluation

17. Evaluation
Throughput,
latency and CPU
consumption for
increasing c values
and fixed f values

18. Evaluation
For small to medium
spatial resolution,
throughput ~ 1000 t/s
(max f = 8), for high
spatial resolution,
throughput ~ 500 t/s
0

19. Evaluation
If the system is not
overloaded,
sub-millisecond latency

20. Agenda
• Motivation
• Preliminaries: data streaming in a nutshell
• Contribution: translation of Spatio-Temporal Logics to
Streaming-Based Monitoring Applications
• Evaluation
• Conclusion / Future work
21

21. Conclusions / future work
What’s next?
• Extended experimental evaluation
• Future temporal operators
• Spatial analysis based on paths
• Compilation optimizations
STREL
(Spatio-Temporal Reach and Escape Logic)
• Spatio-temporal modal logic
• Allows expressing properties over
attributes which vary over space and
time
𝕊2
-Operators
(streaming-STREL operators)
can be ported to the streaming
paradigm by means of
Base streaming operator
whose semantics can be
enforced composing