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Energyworx Glossary

Glossary​

  • Annotation​

A flag that is added to a datapoint in timeseries data that gives additional information about that datapoint. Commonly created by validation rules to signify that the validation of a particular datapoint failed and why. Annotations should only be created in exception cases and should not be used as a default way of storing data.

  • Chained flow​

A flow that was triggered from a previous flow (normal; ingestion; or chained) via the use of the 'chain_flow'; 'conditional_chain_flow'; or 'bulk_chain_flow' rules. Chained flows contain all the timeseries and tag data that was stored in the flow that triggered this flow, even if this data is not available yet in our databases. Often used to separate parts of a business process in such a way that it has multiple entry points or to delay the execution of a part of the business process. An example of the former would be a forecasting process: You need to create the forecast -> check the forecast -> approve the forecast. But the approval part of this could be both done automatically after creating and checking the forecast; or manually if the check step failed. In the latter case, the "approve the forecast" step must be executable separately and thus needs its own entry point.

The channels take care of your time/series data, and it’possible to have multiple channels set up for each datasource. A perfect example would be a meter that measures the usage during the day and during the night. It delivers two sets of timeseries values, and therefore would consist of two channels. The channels themselves are each of a specific channel classifier.

  • Channel classifier​

The classifier of a channel. Serves as an indication as to what the timeseries data stored on the channel should represent. An example would be "ACTIVE_DELIVERY_INT30_RAW", which indicates that the data on a channel with this classifier is active delivery data in intervals of 30 minutes and is raw data that came directly from the meter. Channel classifiers specify a set of metdata fields, among which are ‘Unit type’ and `Datapoint type’. ‘Unit types’ can be defined as kW, kWh or ‘seconds’. And ‘Datapoint types’ can be defined as ‘REGISTER’, ‘INTERVAL’, or ‘GAUGE’. REGISTER displays a cumulative value, and always increases. INTERVAL displays a unit measured over time, for instance kWh. GAUGE displays a unit measured at a specific moment. It can increase or decrease, compared to previous points in time. Sometimes shortened to: CC.

  • Channel flow​

Channel flows represent the old way of data processing, which requires a source and destination channel for each sequence in the flow. The data of the source channel in the first sequence is automatically loaded upon execution of the rule. Channel flows can only be executed on datasources that have the first sequence's source channel as a channel on them.

The Python code that is associated with a rule, implementing a rule class that inherits AbstractRule or AbstractTransformRule. It can be viewed and modified through the Energyworx platform.

  • Continuous flow​

Continuous flows store all their timeseries data results on the same version. Each flow execution overrides the values stored previously and the old values can no longer be retrieved. Commonly used in situations where the history of the data on a channel holds no value or simply serves no purpose, like measurement data from a meter that has gone through the VEE process (only the latest version of this has value).

  • Datapoint attribute​

A tag-like added to a datapoint in timeseries data that gives additional information about that datapoint. Commonly used to store additional metadata on timeseries data that has no purpose being stored separately. Unlike annotations, storing a datapoint attribute on a datapoint is not an exception. Very commonly shortened to: DPA.

  • Datasource​

A reference to a single object, which could be either physical (like a smart meter) or virtual (like an imaginary concept). Sometimes shortened to: DS

  • Datasource Classifier​

Grouping mechanisms for datasources holding homogeneous timeseries data

  • Datasource Flow​

Data source flows represent the preferred way of data processing, these flows don't require predefined source or destination channels. It allows for more flexible data processing, accommodating use cases where multiple input channels are needed, or none at all. It requires the user to load their data manually from their rules

  • Decision tree​

A piece of code that can be used in the 'conditional_chain_flow' rule to dynamically assign a different flow ID to be chained depending on the state of the current flow. Allows for more dynamic flow and business execution. Often shortened to: DT

Datapoint attribute: A tag-like added to a datapoint in timeseries data that gives additional information about that datapoint. Commonly used to store additional metadata on timeseries data that has no purpose being stored separately. Unlike annotations, storing a datapoint attribute on a datapoint is not an exception.

The library that can be found at https://gitlab.com/energyworx/solutions/ewx-tools/ewx-cli. Commonly used by Solutions and externals for managing configurations with its built-in resource manager. Implements a command-line interface with a large number of utility commands that make it easier to work with the Energyworx platform, with the resource manager being the most important. Sometimes referred to as "the CLI".

  • File Manager​

The space where all the files that have been pushed into our platform can be easily found.

A collection of sequences, which themselves are collections of logic-based rules. Should ideally represent one business process. Sometimes referred to as Flow Design

  • Flow sequence​

The sequence is a grouping of business rules that applies a set of operations on the data. In channel flows, each flow sequence has a source and a destination channel that is available within the rule. Besides this, flow sequences have no influence on the execution of a flow. They are commonly used to separate logic within a flow for organizational purposes.

  • Identity and Access Management​

A set of business processes, policies and technologies that help manage digital identities. Sometimes shortened to: IAM

  • Ingestion flow​

A flow that is executed upon the ingestion of data in a transformation configuration. Ingestion flows are special in that they automatically contain the timeseries and tag data that was ingested, meaning that they can contain data or datasources that do not exist yet in our databases. Additionally, ingestion flows are considered part of the ingestion process and thus are executed as part of "transform" instead of "crunch".

  • Market Adapter​

Component that parses raw data into comprehensible format for Energyworx. Often shortened to: MA

  • Meter Data Management​

A process of collecting, managing, and presenting meter data. Sometimes shortened to: MDM

An isolated instance of datasources; timeseries data; tags; and configurations on the Energyworx platform. Namespaces can only access data or configurations stored under that namespace.

  • Payloadtype​

A type that is given to incoming files from external systems that tells the platform what type of file it is. Used in combination with the 'market_adapters' namespace property to map that file to a market adapter to automatically ingest the file with.

  • Pluggable Rule​

The rule can be written by the end user in the platform. You can plug it into the system or into the flow(pluggable rule framework)

  • Point of Delivery​

A location or medium where a particular service or product is delivered to the customer. Sometimes shortened to: POD

  • Prepared datasource​

A datasource that has specifically been prepared in the 'prepare_context' of a rule. Only prepared datasources can have their timeseries retrieved within the 'apply' of a rule.

  • Process Monitoring​

An observability service in the Energyworx platform that gathers execution details about business processes executions. Sometimes shortened to: ProMo

  • Resource manager​

The 'Manager' class found within the ewx-cli library. This class allows users to manage resource configurations across multiple different namespaces and transfer configurations between them. The resource manager automatically takes care of resolving dependencies that any configuration may have and when transferring, it also automatically remaps all IDs (which are unique per namespace) to be correct across all transferred configurations. The resource manager is available both through the library itself as well as via its CLI.

Python class which extends AbstractRule; used in flows. Implements the execution of a single step in a business process.

Slowly Changing Dimensions: SCDs are techniques in data management that deal with how to handle changes in the attribute values of dimensions over time in a data warehouse. In Energyworx, this is often used for tags whose values dynamically change over time, but not frequent enough to be stored as timeseries data. An example would be contract information.

  • Scenario flow​

Scenario flows store all their timeseries data results as separate versions. Each flow execution creates a new version and each version can be retrieved separately. Commonly used in situations where the history of the data on a channel needs to be preserved or the previous versions have value, like the raw measurement data coming from a meter.

  • Slowly Changing Dimensions​

Slowly Changing Dimensions are techniques in data management that deal with how to handle changes in the attribute values of dimensions over time in a data warehouse. In Energyworx, this is often used for tags whose values dynamically change over time, but not frequent enough to be stored as timeseries data. An example would be contract information. Often shortened to: SCD.

A simple key-value table that stores data not suited for storage as timeseries data. This includes metadata like location or hardware details; or data like contract information. If the tag's data can slowly change value over time (for example with contracts), then the tag can be set as "slowly changing dimensions", which keeps track of multiple different versions of the same tag and can also state when which version of the tag is active.

Task Management V2 Sometimes shortened to: Tasks

  • Temporal Aggregation​

An aggregation within one datasource, used to indicate a frequency change / downsampling.

Alternative term used for the ewx-cli library, which can be found at https://gitlab.com/energyworx/solutions/ewx-tools/ewx-cli. Commonly used by Solutions and externals for managing configurations with its built-in resource manager.

  • This datasource​

In the context of rule development, refers to the datasource the rule is being executed on. This datasource can have its tags and timeseries retrieved within 'apply' of the rule without any preparation.

Part of day; part of week. Sometimes shortened to: Timeslice

  • TimeSlice Group​

A collection of different periods of time that can be used to calculate different tariffs. Sometimes shortened to: TSG

Reformats a tabular structure into Datasources, Tags, and timeseries.

  • Transformation Configuration​

A configuration that states how to transform the data coming from a market adapter into Energyworx' datamodel entities like datasources; tags; and channels. Often shortened to: TC

  • Transform Rule​

Python class which extends AbstractTransformRule; used in transformation configurations. Special rule that is used to transform incoming data.

  • Transport Adapter​

Feature to retrieve your data automatically from an external source such as SOAP, FTP, URL

Object meant to start process. Sometimes shortened to: Trigger

  • Trigger Schedule​

Cron timer for triggers. Sometimes shortened to: CronJob, Cron Trigger

Virtual datasource: Reference to a single object that has no physical counterpart, like a contract; an address (of a company); an aggregation (for a city); etc. Virtual datasources have a filter that filters out specific other (virtual) datasources based on their tags.

Short for "Validation, Estimation, Editing". The procedures used to check and correct meter data. "Validation" checks if the data makes sense and aligns with certain rules or constraints (e.g., negative energy usage might be flagged during validation, Holidays). "Estimation" steps in when data is missing or deemed unreliable; it generates a reasonable substitute based on established methods (e.g., interpolation or using data from similar time periods). "Editing" involves correcting detected errors in the data before it's used for billing, analysis, or other purposes.

  • Virtual datasource​

Reference to a single object that has no physical counterpart, like a contract; an address (of a company); an aggregation (for a city); etc. Often shortened to: VDS