What is it and why?

The VSS Taxonomy defines what data "entities" (Signals and Attributes) we can deal with, and are used in the protocol(s) defined by W3C Automotive Working group, as well as other initiatives inside and outside the vehicle.

But in addition to VSS itself, we need to define the data-exchange formats for measured values of those Signals.  This starts by defining terms, but quickly develops into defining one or several variants of the actual message content format, whether in JSON or other.

Relationship to Protocols

Data formats sometimes overlap protocol definitions because some protocols (but not all) define the data format in its specification.  VISS / W3C Gen2 is an example of a protocol definition that defines both the protocol interactions between client and server, and the data exchange format that fits the VSS model.  Ultimately, any chosen (stack of) protocols must at some point clarify the transferred data formats, otherwise no understandable exchange can be had.

Looking at a wider set of protocols it is clear that we have some more work remaining.
The data exchange protocols we discuss fall into different categories, each requiring some more work on defining value exchange data formats:

  1. A protocol does not (yet) cover all variations of data exchange.
    1.  VISS / W3C Gen2 covers a lot of usage, but supports primarily fetching a single "latest value" measurement, and transferring instant updates according to subscriptions.  Exchanging a set of historically measured values has been discussed for future extension.  The picture we are looking at here should include also exchanging sets of previously measured data between systems.  We can benefit from adding transfer of historically measured data, or derived statistics (e.g. get the average value over time instead of transferring all values), for tying measurements to physical location in addition to the timestamp, and similar extensions. 
      This analysis is hopefully useful also for protocols like W3C "Gen 2" to cover these bases.
  2. A protocol defines only a "transport"
    1. We often discuss protocols that define some behavior of data transfer, such as pub/sub semantics, but they are designed to be generic and therefore support any type of information to be transferred by the protocol.  This means they do not (can not) define the format of the content of the data container (payload), and frequently are set up to transfer just any arbitrary sequence of bytes.   This makes those technologies widely applicable, but choosing them is not enough without also defining the payload format.  Examples of some such protocols would be MQTT or WAMP, but the principle extends to many "generic" protocols or frameworks.
  3. A protocol defines transport, query semantics, and even a few expectations for the exchanged data format, but is still generic and requires additional definitions to become unambiguous for a particular case.
    1. Example:  GraphQL is a generic technology that clarifies a bit more about expected data semantics and formats but still requires a schema to be defined to indicate the exact underlying data model, what types of queries can be made using the GraphQL language, and other details such as the datatypes that are expected to be returned.   A schema must be defined for GraphQL, and for other similar situations, and that schema might also be derived from this generic analysis.

Update: As a similar example, to consume and process data in Apache Spark, Kafka and presumably for many other generic data-handling frameworks we also need to define schemas that define the format and content of the transferred data, in a similar fashion.  These are also in category 2/3.


Definitions

(proposal, open for discussion)

Signal:

Request:

Job:  

Observation:

Data Package:

=   A delivery of data sent at a particular time.

(think of it as the whole Message that is in response to a Request)

This will likely need to include some metadata regarding the request:

Additional Metadata

Following input given in the W3C data TF:


Record:


(question) Why not just use a single record type (superset of all functionality)?  

A: The reason would be to optimize the performance and bandwidth.  In other words, don't transfer what is not needed for a certain case.  If a timestamp is not needed, we should make sure we support transferring data without providing a timestamp, for example.


Record Subtypes:

+ Record types which specify the signal name inside: 

Record types which specify the geospatial position in addition to the time value:

(N.B. Geospatial records also always include time stamp, because it seems to be the overwhelmingly dominant usage)

DerivedRecord and StatisticsRecord



TimeStamp

NOTE:  The exact format of all of these may differ when these concepts are translated to different protocols or languages.
As a starting point however, let's propose to use ISO 8601 standard format, with fractional seconds (e.g. microseconds) and always UTC (Zulu) time zone, if a TimeStamp is given in text format.  For other purposes such as binary formats, more efficient encodings should be considered. 

Real, "Wall clock time"

Relative to a previously predefined time stamp reference:


Bundle

TimeSeries

Snapshot:

Note that values in a Snapshot need a record type that specifies the signal, since different signals are included in the same message.


Stream:




Examples, in JSON


(Plain) Record:

{
   "value" : " 100.54"
}

TimestampedRecord:

{
   "ts" : "2020-01-10T02:59:43.491751Z   # Zulu time, ISO std with microseconds
   "value" : "42 "
}

GeospatialRecord:

{
   "pos" : "[format tbd]"
   "ts" : "2020-01-10T02:59:43.491751Z   # Zulu time, ISO std with microseconds
   "value" : "42 "
}


SpecifiedRecord:

{
   "signal" : "vehicle.Chassis.Axle2.WheelCount"
   "value" : "2"
}

SpecifiedTimestampedRecord:

{
   "signal" : "vehicle.Body.ExteriorMirrors.Heating.Status"
   "ts" : "2020-01-10T02:59:43.491751"
   "value" : "false"
}

TimeSeries:

{ 
    "signal"  :  "vehicle.body.cabin.temperature"
    "count"   : "132"  # Might be redundant information, optional.
    "values" : {
        { 
            "ts" : "2020-01-10T02:59:43.491751"
            "value" : "42.5"
        },
        { 
            "ts" : "2020-01-10T02:59:43.491751"
            "value" : "43.0"
        },
        ... 130 more records
    }
}

AVRO-schema example (not complete, and of course the "#comments" are not allowed to be there)
# First attempt at AVRO schema for TimeSeries

{
  "type": "record",       # This is the AVRO meaning of record (i.e. kind of struct), not the Record type in our type hierarchy.
  "name": "TimeSeries",   # We are defining the TimeSeries value format
  "fields" : [
    {"name": "signal", "type": "string"},   # Full path VSS with dot-notation, like in example above
    {"name": "count", "type": "int" },      # as above
    {"name": "values", "type": "array",
         "items" : {
            "type" : "record",              # ***
            "name" : "TimeStampedRecord",
            "fields" : [ 
              { "name" : "ts", "type" : "string"   # ISO formatted timestamp, maybe use int later for efficiency  },
              # "value" can be any VSS data type, so here we must define a "union" of types.  Also arrays are possible in that union.
              { "name" : "value", "type" : [ "int", "long", "float", "double", "string", "boolean", 
                 { "type" : "array", "items" :  
                    # This gets a bit complex because the array primitive type could also be any type (i.e. another union, except we don't do arrays in arrays)
                    [ "int", "long", "float", "double", "string", "boolean"
                    ] # End of union definition for the items in an array
                 } # End of the VSS Array type definition
               ] # End of union definition for the field "value" in the TimeStampedRecord
            ] # End of list of fields
         } # End of "items"
    } # end of "values"
  ] # end of "fields"
} # end of TimeSeries record

*** Note that we should define the TimeStampedRecord schema separately, and refer to it instead of having it inline like this...



Snapshot:

{ 
    "timeperiod" { 
        "start" : "2020-01-10T02:00:00Z",
        "end" : "2020-01-11T01:59:59Z"
    },
    "values" : {
     {
        "signal" : "vehicle.body.cabin.temperature",
        "value" : "22.0",
        "ts" : "2020-01-10T02:59:43.491751"
     },
     {
        "signal" : "vehicle.drivetrain.engine.rpm.average",
        "value" : "3200",
        "ts" : "2020-01-10T02:59:44.100403"
     }
}

todo: Examples of Derived/Statistics Records