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UID:pretalx-foss4g-europe-2026-MTBKC7@talks.osgeo.org
DTSTART;TZID=EET:20260629T143000
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DESCRIPTION:The two papers by Cannata et al. (2023) and Collombin et al. (2
 024) address a central paradox in open geospatial research: while geospati
 al web services (e.g. OGC-based services) foster data sharing in line with
  Open Science and FAIR principles\, they simultaneously challenge reproduc
 ibility. This core issue concerns particularly dynamic geospatial data. Un
 like static datasets stored in repositories with persistent identifiers (e
 .g. DOIs)\, data accessed through web services are continuously updated\, 
 corrected\, or reprocessed. As a result\, the exact dataset used in a stud
 y may no longer be retrievable in its original state\, making it difficult
  or impossible to reproduce results. This issue is particularly critical f
 or time-varying datasets such as environmental monitoring\, sensor observa
 tions\, or cadastral data. Even when workflows and computational environme
 nts are reproducible\, reproducibility ultimately fails if the underlying 
 data cannot be accessed in the same version used in the original analysis.
  Both works highlight that current geospatial infrastructures lack key mec
 hanisms such as data versioning\, persistent identification\, and temporal
  querying capabilities (“system-time”). Without these\, web services c
 annot guarantee access to historical data states. Addressing this limitati
 on requires moving beyond interoperability toward infrastructures that exp
 licitly manage data evolution over time\, enabling retrieval of past state
 s and supporting transparent and verifiable research. \n\nIn this context\
 , istSOS4Things (https://github.com/istSOS/istSOS4) is introduced as a Sen
 sorThings API compliant solution that tackles these challenges by integrat
 ing mechanisms for temporal versioning\, traceability\, and controlled acc
 ess directly into the data service layer. Rather than acting as a simple i
 nterface to mutable data\, the system is designed as a version-aware and p
 olicy-enabled service\, capable of preserving and exposing the evolution o
 f geospatial data streams. A core element of this approach is the implemen
 tation of system-time versioning at the database level\, where each observ
 ation is associated with temporal attributes capturing both its validity a
 nd its transaction history. This enables reconstruction of the dataset as 
 it existed at a specific point in time\, effectively introducing a “time
 -travel” capability. Users can therefore query not only the current stat
 e of the data\, but also past states\, addressing the reproducibility gap 
 identified in the literature. \n\nFrom an architectural perspective\, istS
 OS4Things adopts a container-based\, microservice-oriented design\, where 
 each component is deployed as an independent service and orchestrated thro
 ugh Docker. The core of the system is a PostgreSQL database extended with 
 PostGIS. On top of the database\, the API layer is implemented using SQLAl
 chemy ORM with asyncpg as query engine and FastAPI for routing logic\, and
  served through Uvicorn as an ASGI server. To support performance and scal
 ability\, the architecture integrates Redis as an in-memory data store\, u
 sed for caching request to query conversion workload. This combination of 
 FOSS ensures high performance\, asynchronous request handling\, and scalab
 ility of the SensorThings API endpoints. \n\nIn istSOS4Things\, the “tim
 e-travel” capability is exposed through an extension of the SensorThings
  API query model that introduces explicit temporal navigation parameters. 
 In particular\, an as_of parameter allows retrieving the state of the data
  at a specific timestamp\, while a from_to parameter enables exploration o
 f how data evolved over a defined time interval. These parameters extend s
 tandard OData-based filtering mechanisms and bring system-versioned data c
 oncepts\, commonly found in temporal databases\, into web-based geospatial
  services. \n\nA key innovation is the introduction of a commit-based vers
 ioning model. Each modification to the dataset is recorded as a Commit ent
 ity\, representing a discrete change event that groups one or more operati
 ons. Each commit is associated with metadata such as timestamp\, descripti
 on\, and context\, and is linked to a User entity\, capturing the identity
  of the actor responsible for the change. This explicit association enable
 s tracking of who performed what modification and when\, introducing accou
 ntability and traceability into the data lifecycle. Observations are there
 fore not only versioned in time\, but also logically grouped into commits\
 , forming a structured history of changes. This allows navigation across d
 ataset evolution both by timestamp (system-time) and by discrete change ev
 ents. In practice\, this enables reconstruction of the dataset at a given 
 point or commit\, inspection of differences between versions\, and underst
 anding of the sequence of transformations applied to the data. The combina
 tion of temporal versioning and commit-based tracking provides a comprehen
 sive provenance model that goes beyond simple versioning. \n\nImportantly\
 , the combination of service endpoint\, query definition\, and temporal re
 ference (e.g. via as_of) effectively defines a persistent and reproducible
  view of the dataset\, supporting reproducible data citation without requi
 ring static dataset snapshots. Building on this\, the system supports repr
 oducible data access through fully specified queries. By combining spatial
 \, temporal\, and thematic filters with temporal parameters\, users can re
 -execute the same query over a well-defined data state. This shifts reprod
 ucibility from static data publication toward reproducible data access pat
 terns\, where both the query and the temporal context define the dataset. 
 \n\nAnother key aspect is the integration of fine-grained access control a
 nd policy enforcement mechanisms directly at the data layer. Access to dat
 a is regulated through a Role-Based Access Control (RBAC) model implemente
 d using PostgreSQL roles combined with Row-Level Security (RLS) policies. 
 This enables permissions to be enforced not only at the table level\, but 
 also at the level of individual records\, allowing selective visibility an
 d editing of observations based on the querying user (e.g. restricting upd
 ates to specific sensor networks). \n\nOverall\, this work promotes a shif
 t in how reproducibility is approached in geospatial research. Rather than
  relying on static data publication\, it embraces the dynamic nature of da
 ta and provides mechanisms to reconstruct past states\, document their evo
 lution\, and control access over time. By combining temporal versioning\, 
 commit-based change tracking\, extended query capabilities\, provenance me
 tadata\, and policy-based access control\, istSOS4Things transforms geospa
 tial web services into reproducible\, auditable\, and governable data infr
 astructures\, directly addressing the limitations identified in prior rese
 arch.
DTSTAMP:20260604T220840Z
LOCATION:A01
SUMMARY:istSOS4Things: a reproducible\, auditable\, and governable sensor d
 ata infrastructures - Massimiliano Cannata
URL:https://talks.osgeo.org/foss4g-europe-2026/talk/MTBKC7/
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