| Name | chrontext JSON |
| Version |
0.8.16
JSON |
| download |
| home_page | None |
| Summary | Hybrid SPARQL query engine for timeseries data |
| upload_time | 2024-04-30 11:56:32 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
rdf
graph
arrow
sparql
timeseries
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# chrontext: High-performance hybrid query engine for knowledge graphs and time-series
Chrontext allows you to use your knowledge graph to access time-series data. It uses a commodity SPARQL Triplestore and your existing infrastructure for time-series.
It currently supports time-series stored in Google Cloud BigQuery (SQL) and OPC UA HA, but can easily be extended to other APIs and databases.
Chrontext forms a semantic layer that allows self-service data access, abstracting away technical infrastructure.
Users can create query-based inputs for data products, that maintains these data products as the knowledge graph is maintained, and that can be deployed across heterogeneous on-premise and cloud infrastructures with the same API.
Chrontext is a high-performance Python library built in Rust using [Polars](https://www.pola.rs/), and relies heavily on packages from the [Oxigraph](https://github.com/oxigraph/oxigraph) project.
Chrontext works with [Apache Arrow](https://arrow.apache.org/), prefers time-series transport using [Apache Arrow Flight](https://arrow.apache.org/docs/format/Flight.html) and delivers results as [Polars](https://www.pola.rs/) DataFrames.
Please reach out to [Data Treehouse](https://www.data-treehouse.com/contact-8) if you would like help trying Chrontext, or require support for a different time-series backend.
## Installing
Chrontext is in pip, just use:
```shell
pip install chrontext
```
The API is documented [HERE](https://datatreehouse.github.io/chrontext/chrontext/chrontext.html).
## Queries in python
We can make queries in Python. The code assumes that we have a SPARQL-endpoint and BigQuery set up with time-series.
```python
from chrontext import *
import os
SCHEMA = os.getenv("SCHEMA")
BIGQUERY_CONN = os.getenv("BIGQUERY_CONN")
tables = [
TimeseriesTable(
resource_name="nist",
schema=SCHEMA,
time_series_table="nist2",
value_column="VALUE",
timestamp_column="TIMESTAMP",
identifier_column="external_id",
),
]
bq_db = TimeseriesBigQueryDatabase(key=os.getenv("BIGQUERY_CONN"), tables=tables)
engine = Engine(timeseries_bigquery_db=bq_db, sparql_endpoint=SPARQL)
engine.init()
df = engine.query("""
PREFIX xsd:<http://www.w3.org/2001/XMLSchema#>
PREFIX ct:<https://github.com/DataTreehouse/chrontext#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX rds: <https://github.com/DataTreehouse/solar_demo/rds_power#>
SELECT ?inv_path WHERE {
# We are navigating th Solar PV site "Metropolis", identifying every inverter.
?site a rds:Site .
?site rdfs:label "Metropolis" .
?site rds:functionalAspect+ ?inv .
?inv a rds:TBB . # RDS code TBB: Inverter
?inv rds:path ?inv_path .
# Find the timeseries associated with the inverter
?inv ct:hasTimeseries ?ts_pow .
DT {
timestamp= ?t,
labels= (?ts_pow:"InvPDC_kW"),
interval= "10m",
from= "2018-12-25T00:00:00Z",
aggregation = "avg" }
}
ORDER BY ?inv_path ?t
""")
```
This produces the following DataFrame:
| inv_path | t | ts_pow_value_avg |
|---------------------------| --- | --- |
| str | datetime[ns] | f64 |
| =<Metropolis>.A1.RG1.TBB1 | 2018-12-25 00:00:00 | 0.0 |
| … | … | … |
| =<Metropolis>.A5.RG9.TBB1 | 2019-01-01 04:50:00 | 0.0 |
Not much power being produced at night in the middle of winter :-)
## API
The API is documented [HERE](https://datatreehouse.github.io/chrontext/chrontext/chrontext.html).
## References
Chrontext is joint work by Magnus Bakken and Professor [Ahmet Soylu](https://www.oslomet.no/om/ansatt/ahmetsoy/) at OsloMet.
To read more about Chrontext, read the article [Chrontext: Portable Sparql Queries Over Contextualised Time Series Data in Industrial Settings](https://www.sciencedirect.com/science/article/pii/S0957417423006516).
## License
All code produced since August 1st. 2023 is copyrighted to [Data Treehouse AS](https://www.data-treehouse.com/) with an Apache 2.0 license unless otherwise noted.
All code which was produced before August 1st. 2023 copyrighted to [Prediktor AS](https://www.prediktor.com/) with an Apache 2.0 license unless otherwise noted, and has been financed by [The Research Council of Norway](https://www.forskningsradet.no/en/) (grant no. 316656) and [Prediktor AS](https://www.prediktor.com/) as part of a PhD Degree. The code at this state is archived in the repository at [https://github.com/DataTreehouse/chrontext](https://github.com/DataTreehouse/chrontext).
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