Deploying Spatial-Stream Query Answering in C-ITS Scenarios

Tracking #: 2395-3609

Patrik Schneider
Thomas Eiter
Josiane Xavier Parreira
Ryutaro Ichise

Responsible editor: 
Guest Editors EKAW 2018

Submission type: 
Full Paper
Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatio- temporal data via V2X between vehicles and the infrastructure, which will be used for the deployment of (semi)-autonomous vehicles. The Local Dynamic Map (LDM) is a key concept for integrating static and streamed data in a spatial context. The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. We show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, events detection, and advanced driving assistance systems. We define for them requirements derived from necessary domain-specific features and report, based on them, on the extension of our query language with temporal relations, delaying, numeric predictions and trajectory predictions. An experimental evaluation of queries that reflect the requirements, using the real-world traffic simulation tool provides evidence for the feasibility/efficiency of our approach in the new scenarios.
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Review #1
By Maxime Lefrançois submitted on 17/Feb/2020
Review Comment:

I do recommend to accept this new version for publication. The authors greatly improved the article regarding:
- clarity of the contributions wrt previous work of the authors;
- presentation of the results (self containment);
- amount of new contributions.

1. As requested, the authors did clarify what content is genuine in the article, and what is taken from previous work.
2. The article is self contained, Section 5 now contains
3. The authors added new contributions regarding:
- an other LDM ontology better aligned with the SOSA/SSN standard;
- complexity assessment of the query answering;
- discussion and evaluation of optimization strategies: query rewriting and query evaluation optimization with caching and parallelization.

Overall this article represents an important contribution to the field of spatial-stream query answering. It has a good balance between: application-driven, theoretical, and system implementation and evaluation.

I only have minor comments and detected a few typos:

Figure 2: The top concepts in the text are not the ones depicted in the figure. In particular, Event is not in the figure.

The semantics of role ldm:observes (LDM ontology) seems to be different from the one of sosa:observes.
sosa:observes links a sensor to a property
ldm:observes links an actor (system, sensor, ...) to a result
so when mentioning the role *observe* in p.5:1, it should be clarified which ontology is considered

p3:2:46: a separation between of the top concepts -> delete *of*
p5:1:3-5: This sentence is written twice (also p4:1:40-42)
p5:2:39: v happens during s -> should be p happens during s.
p12:1:10 atomic attributes E -> should be Uc
p12:2:51 B_A -> the indice is
p13: The notation D_k[...] should be explained somewhere
p14:1 (around Eq 8) check use of variables z, w, in Eq and text
p14:2: The use of symbol \boxplus is unclear: sometimes with one indice \psi which is meant to be a query atom (explained lower in the text), sometimes with an indice T (not explained) and a suffix L (window size), sometimes alone.
p16:1:6: or be extracted -> deleted *be*
Example 5.3: the line breaks in the query do not correspond to what is explained in the text
p17:2:39 Datalag -> Datalog
p17:2:42 are not connected the rest -> to the rest
p18:1:1 can not directly lifted -> be lifted
p20:2:44 this line should be deleted

Review #2
By Manolis Koubarakis submitted on 10/Mar/2020
Review Comment:

The paper introduces ontology-based reasoning in stream query processing in the context of Cooperative Intelligent Transportation Systems. This is a very interesting area given the current emphasis in smart cars and smart transportation systems.

The paper has interesting application, implementation and some theoretical contributions.

The revised version has addressed my comments.

I recommend acceptance of this version.

Review #3
Anonymous submitted on 12/Mar/2020
Review Comment:

The paper provides an interesting investigation of how to use the Local Dynamic Map in Cooperative Intelligent Transport Systems. This revised version of the paper addresses all the issues identified by reviewer #3 in a satisfactory manner.
As such I recommend accept.

As a minor comment please add the abstract to the current version of the paper.