Patent Description:
The present specification generally relates to mobile networks in which data (e.g. historical data) accumulate, are to be stored, and to be are to be retrieved.

With the increasing need in e.g. the <NUM>rd Generation Partnership Project (3GPP) <NUM>th Generation (<NUM>) and beyond mobile networks e.g. to use artificial intelligence (AI) / machine learning (ML), a multiplicity of ML tasks will be executed, potentially leading to an explosion of amount of (historical) data to be stored. It will in general be important to save or limit resources for data storage while maintaining quality of the data stored.

With an analytics data repository function (ADRF) being the 5GC network function (NF) deputed to store historical data and analytics, it will be important for the ADRF to save or limit resources for historical data storage while maintaining quality of historical data stored.

This is limitation may be addressed by using techniques focusing on reducing the volume of data to be stored, by efficiently managing the data storage, by utilizing better existing storage hardware, or by utilizing storage equipment that consumes less energy.

Compression is a reduction in the number of bits needed to represent data. Reduction techniques can bring an acceptable trade-off between data quality, data quantity and resource saving or limiting for the ADRF. However, such approach may be accompanied with loss of data quality.

Hence, the problem arises that there are no approaches for handling an expected increase in data to be transmitted and stored without accompanying data loss or data quality loss.

Hence, there is a need to provide for optimized data storage in mobile network scenarios.

Prior art which relates to this field can be found in document "<NPL>", disclosing storing and exchanging a machine learning (ML) model via analytics data repository function (ADRF), and in particular a mechanism to store the ML model by a model training logical function (MTLF) by using an enhanced ADRF service, where a consumer uses the ADRF service to fetch the ML model.

Further prior art can be found in document <CIT>, disclosing data model generation using generative adversarial networks and a fully automated machine learning system which generates and optimizes solutions given a dataset and a desired outcome.

Further prior art can be found in document "<NPL>".

Various embodiments aim at addressing at least part of the above issues and/or problems and drawbacks.

Objects of the present disclosure are achieved by what is defined in the appended independent claims. Advantageous modifications thereof are set forth in the appended dependent claims.

Any one of aspects of the invention enables an efficient and loss-less or at least loss-reduced storage, transmission, and re-use of data, to thereby solve at least part of the problems and drawbacks identified in relation to the prior art.

By way of example embodiments, there is provided optimized data storage in mobile network scenarios. More specifically, by way of example embodiments, there are provided measures and mechanisms for realizing optimized data storage in mobile network scenarios.

Thus, improvement is achieved by methods and apparatuses enabling/realizing optimized data storage in mobile network scenarios.

In the following, the present disclosure will be described in greater detail by way of non-limiting examples with reference to the accompanying drawings, in which.

The present disclosure is described herein with reference to particular non-limiting examples and to what are presently considered to be conceivable embodiments. A person skilled in the art will appreciate that the disclosure is by no means limited to these examples, and may be more broadly applied.

It is to be noted that the following description of the present disclosure and its embodiments mainly refers to specifications being used as non-limiting examples for certain exemplary network configurations and deployments. Namely, the present disclosure and its embodiments are mainly described in relation to 3GPP specifications being used as non-limiting examples for certain exemplary network configurations and deployments. As such, the description of example embodiments given herein specifically refers to terminology which is directly related thereto. Such terminology is only used in the context of the presented non-limiting examples, and does naturally not limit the disclosure in any way. Rather, any other communication or communication related system deployment, etc. may also be utilized as long as compliant with the features described herein.

Hereinafter, various embodiments and implementations of the present disclosure and its aspects or embodiments are described using several variants and/or alternatives. It is generally noted that, according to certain needs and constraints, all of the described variants and/or alternatives may be provided alone or in any conceivable combination (also including combinations of individual features of the various variants and/or alternatives).

According to example embodiments, in general terms, there are provided measures and mechanisms for (enabling/realizing) optimized data storage in mobile network scenarios.

Network data analytics function (NWDAF) is a <NUM> network function that collects data from <NUM> core (5GC) network functions, performs network analytics, ML-based inference, and provides insights with closed loop automation to authorized data consumers.

Located at core and edge, central and distributed NWDAF instances serve use cases which do not have real-time requirements and use cases which do have real-time, respectively. These are interfaced with functions such as a data and machine learning models repository for continuous training of AI/ML models.

The NWDAF may contain the following logical functions:.

The <NUM> system (5GS) architecture allows ADRF to store and retrieve the collected data and/or analytics sent by the consumer. The following options are supported:.

<FIG> shows a schematic diagram of an example of a system environment, and in particular illustrates an architecture of a connection between an NWDAF and an ADRF as discussed above.

Historical data is data related to a past time period. The period may be fixed by the parameter "Time Window" that is the start and stop time when the requested data or analytics was collected.

After a consumer obtains data and/or analytics, the consumer may store historical data and/or analytics in an ADRF.

<FIG> shows a schematic diagram of signaling sequences, and in particular illustrates communication in relation to historical data and analytics storage.

The consumer may directly contact the ADRF or may go via the DCCF or via a messaging framework for storing the historical data and/or analytics in the ADRF.

In particular, the consumer may send data and/or analytics to the ADRF by invoking the Nadrf_DataManagement_StorageRequest (collected data, analytics, service operation, analytics specification or data specification and time window) service operation. The ADRF may, based on implementation, determine whether the same data and/or analytics is already stored or being stored based on the information sent by the consumer NF, and, if the data and/or analytics is already stored or being stored in the ADRF, the ADRF may decide not to store again the data and/or analytics sent by the consumer. The ADRF may send a Nadrf_DataManagement_StorageRequest response message to the consumer, indicating that data and/or analytics is stored, including when the ADRF may have determined that data or analytics is already stored. This process can also be done via notifications.

On the other hand, for a consumer it may be advantageous to retrieve historical data and/or analytics from an ADRF.

This may be used by data consumers (NWDAF, DCCF) to obtain historical data. Heretofore, the ADRF may be requested by the NWDAF or indirectly by DCCF to retrieve data.

In particular, the consumer may send a Nadrf_DataManagement_RetrievalRequest request to the ADRF to retrieve data or analytics for a specified data or analytics collection time window. The ADRF may determine the availability of the data or analytics in its repository and send a success/failure indication in the response to the consumer. If success, the ADRF may send in the response to the consumer either the data or analytics, or instructions for fetching the data or analytics using Nadrf_DataManagement_RetrievalNotify.

A consumer may also send a Nadrf_DataManagement_RetrievalSubscribe request to the ADRF to retrieve data or analytics for a specified data or analytics collection time window and to receive future notifications containing the corresponding data or analytics received by ADRF using Nadrf_DataManagement_RetrievalNotify. If the time window includes the future and the ADRF has subscribed to receive the data or analytics, subsequent notifications received by the ADRF are sent by the ADRF to the consumer. The Nadrf_DataManagement_RetrievalNotify service operation provides consumers with either data or analytics from an ADRF, or instructions to fetch the data or analytics from an ADRF.

<NUM>-advanced is the step before <NUM>th Generation <NUM> which will open the door for pervasive AI and therefore, for many new ML-based services.

Thus, as mentioned above, with the increasing need in <NUM> and beyond mobile networks to use AI/ML, a multiplicity of ML tasks will be executed potentially leading to an explosion of collected data and thus, amount of historical data to be stored, and with the ADRF being the 5GC NF deputed to store historical data and analytics in <NUM> network, it will be important for it to save or limit resources for historical data storage while maintaining quality of historical data stored.

Hence, in brief, according to example embodiments, an NWDAF can use generative models (GM) to produce input data for inference and/or training, and the usage of the GM, and possibly the GM instance too, is included in the services exposed by ADRF, to reduce the volume of transferred and stored data.

GM is a deep learning-based tool to generate synthetic data that has the same statistical properties as the source data. By using GMs which size in amount of bit may be smaller than the size of a source data, the size of historical data stored can be significantly reduced.

Moreover, using GMs instead of raw data provides a higher flexibility, as e.g. ML tasks (such as those run by NWDAF) can locally generate an exact amount of data they need.

A data consumer may accept to use synthetic-data instead of real-data, for example for performing an initial model training.

GMs may address numerical, categorical, data, text.

Implicit distribution GMs as a generative adversarial network (GAN) are particularly referred to for example embodiments. Implicit distribution GMs as a GAN do not require an explicit definition for their model distribution. Instead, these models train themselves by indirectly sampling data from their parameterized distribution. In other words, implicit distribution GMs as a GAN do not require any information on data distribution to enable the generation of synthetic data which statistics are close to the data source statistics.

<FIG> shows a schematic diagram of an example of a GAN architecture.

GAN is a type of deep learning techniques. In the scope of generative models, GAN has emerged recently as a powerful tool for learning the probability and modelling complex data distribution. GAN is an unsupervised learning method for estimating density function via an adversarial process. GAN is capable of training in a completely unsupervised and unconditional fashion, meaning no labels are involved in the training process, and consequently, no process of label generation is required for operators that want to use them. This task is based on a combination of two adversarial models, a generator G and a discriminator D, that work together or simultaneously train. G and D models are neural networks with weight and bias parameters denoted as θ. G is used to generate data samples, while D tries to discriminate between real or fake data as seen in <FIG>. From a simple noise source, the GAN technique is able to generate various data distributions while directly learning the joint distribution of multiple random variables.

By appropriately training the two models, it is possible to obtain a generator model that takes sampling vectors from random or targeted distribution as input and generates a sample in the problem domain as output. Thus, an opportunely trained generator model can generate data with a desired distribution. In the context of mobile networks this means data with same distribution of data collected from the network.

As mentioned above, according to example embodiments, an NWDAF can use generative models (GM) to produce input data for inference and/or training, and the usage of the GM, and possibly the GM instance too, is included in the services exposed by ADRF, to reduce the volume of transferred and stored data.

Considering this, according to example embodiments, historical data storage management is provided for NWDAF.

Namely, when a NWDAF makes a request to retrieve data from ADRF, according to example embodiments, the NWDAF indicates its capability to use GMs. Then, according to example embodiments, the ADRF provides the requested data along with a GM (GMs) and its descriptor for the NWDAF to use it to generate further data.

On the other hand, when the NWDAF requests the ADRF to store data, according to example embodiments, the NWDAF also indicates the GMs used and/or requests to store the GM. This way, less data can be stored by the ADRF, and when subsequent data retrieval requests are issued by NWDAFs for the same dataset, according to example embodiments, the ADRF sends the GM along with the stored data, which are expected to be significantly less than the data actually needed by the requesting NWDAF for its operations.

Summarizing, according to example embodiments, the ADRF is the data storage used by NWDAF to store and retrieve collected data and analytics for subsequent lookups/collections.

The collected data may come from the sources like Core, radio access network (RAN), from external like Core, operations, administration and maintenance (OAM) (e.g. performance management (PM), key performance indicators (KPI)), and from user equipments (UE) (e.g. in Rel-<NUM>).

Only data that is requested may be stored in ADRF.

According to example embodiments, the ADRF can use GMs to reduce the amount of data stored (i.e., at rest). Then, the GMs can be used to reduce the amount of data in transit in anticipation to explosion of historical data to be stored.

In particular, according to example embodiments, when an NWDAF retrieves data from an ADRF, the NWDAF.

Further, according to example embodiments, when an NWDAF requests an ADRF to store data, the ADRF.

Some embodiments are specified below in more detail. <FIG> is a block diagram illustrating an apparatus according to example embodiments. The apparatus is a network node or entity <NUM> such as a network data analytics function (NWDAF) or a network node or entity <NUM> implementing an NWDAF, the apparatus comprising a transmitting circuitry <NUM> and a receiving circuitry <NUM>. The transmitting circuitry <NUM> transmits a data storage request requesting storage of data, said data storage request including conveyed information and an indicator indicative of a degree of involvement of generative models in said conveyed information which represents said data. The receiving circuitry <NUM> receives a data storage acknowledge response. <FIG> is a schematic diagram of a procedure according to example embodiments. The apparatus according to <FIG> may perform the method of <FIG> but is not limited to this method. The method of <FIG> may be performed by the apparatus of <FIG> but is not limited to being performed by this apparatus.

As shown in <FIG>, a procedure according to example embodiments comprises an operation of transmitting (S71) a data storage request requesting storage of data, said data storage request including conveyed information and an indicator indicative of a degree of involvement of generative models in said conveyed information which represents said data, and an operation of receiving (S72) a data storage acknowledge response. <FIG> is a block diagram illustrating an apparatus according to example embodiments. In particular, <FIG> illustrates a variation of the apparatus shown in <FIG>. The apparatus according to <FIG> may thus further comprise a creating circuitry <NUM>.

In an embodiment at least some of the functionalities of the apparatus shown in <FIG> (or <FIG>) may be shared between two physically separate devices forming one operational entity. Therefore, the apparatus may be seen to depict the operational entity comprising one or more physically separate devices for executing at least some of the described processes.

According to further example embodiments, said indicator is indicative of that said conveyed information includes a full data set, and said conveyed information includes said full data set.

According to further embodiments, said indicator is indicative of that said conveyed information includes a partial data set and at least one generative model configured to create a full data set based on said partial data set, and said conveyed information includes said partial data set and said at least one generative model.

According to further example embodiments, said indicator is indicative of that said conveyed information includes at least one generative model configured to create a full data set without reference to a part of said data set, and said conveyed information includes said at least one generative model. According to further example embodiments, said conveyed information includes a generative model description specifying utilization of said at least one generative model.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of creating said data utilizing said at least one generative model.

<FIG> is a block diagram illustrating an apparatus according to example embodiments. The apparatus is a network node or entity <NUM> such as an analytics data repository function (ADRF) or a network node or entity <NUM> implementing an ADRF, the apparatus comprising a receiving circuitry <NUM>, a storing circuitry <NUM>, and a transmitting circuitry <NUM>. The receiving circuitry <NUM> receives a data storage request requesting storage of data, said data storage request including conveyed information and an indicator indicative of a degree of involvement of generative models in said conveyed information which represents said data. The storing circuitry <NUM> stores said conveyed information. The transmitting circuitry <NUM> transmits a data storage acknowledge response. <FIG> is a schematic diagram of a procedure according to example embodiments. The apparatus according to <FIG> may perform the method of <FIG> but is not limited to this method. The method of <FIG> may be performed by the apparatus of <FIG> but is not limited to being performed by this apparatus.

As shown in <FIG>, a procedure according to example embodiments comprises an operation of receiving (S81) a data storage request requesting storage of data, said data storage request including conveyed information and an indicator indicative of a degree of involvement of generative models in said conveyed information which represents said data, an operation of storing (S82) said conveyed information, and an operation of transmitting (S83) a data storage acknowledge response.

In an embodiment at least some of the functionalities of the apparatus shown in <FIG> may be shared between two physically separate devices forming one operational entity. Therefore, the apparatus may be seen to depict the operational entity comprising one or more physically separate devices for executing at least some of the described processes.

According to further example embodiments, said indicator is indicative of that said conveyed information includes a partial data set and at least one generative model configured to create a full data set based on said partial data set, and said conveyed information includes said partial data set and said at least one generative model.

According to further example embodiments, said indicator is indicative of that said conveyed information includes at least one generative model configured to create a full data set without reference to a part of said data set, and said conveyed information includes said at least one generative model.

According to further example embodiments, said conveyed information includes a generative model description specifying utilization of said at least one generative model.

<FIG> is a block diagram illustrating an apparatus according to example embodiments. The apparatus may be a network node or entity <NUM> such as a network data analytics function (NWDAF) or a network node or entity <NUM> implementing an NWDAF, the apparatus comprising a transmitting circuitry <NUM> and a receiving circuitry <NUM>. The transmitting circuitry <NUM> transmits a data retrieval request requesting data, said data retrieval request including an indicator indicative of a degree of involvement of generative models in information to be conveyed which represents said data. The receiving circuitry <NUM> receives said information to be conveyed. <FIG> is a schematic diagram of a procedure according to example embodiments. The apparatus according to <FIG> may perform the method of <FIG> but is not limited to this method. The method of <FIG> may be performed by the apparatus of <FIG> but is not limited to being performed by this apparatus.

As shown in <FIG>, a procedure according to example embodiments comprises an operation of transmitting (S91) a data retrieval request requesting data, said data retrieval request including an indicator indicative of a degree of involvement of generative models in information to be conveyed which represents said data, and an operation of receiving (S92) said information to be conveyed.

<FIG> is a block diagram illustrating an apparatus according to example embodiments. In particular, <FIG> illustrates a variation of the apparatus shown in <FIG>. The apparatus according to <FIG> may thus further comprise a creating circuitry <NUM>.

According to further example embodiments, said indicator is indicative of that said information to be conveyed includes a full data set, and said information to be conveyed includes said full data set.

According to further example embodiments, said indicator is indicative of that said information to be conveyed includes a partial data set and at least one generative model configured to create a full data set based on said partial data set, and said information to be conveyed includes said partial data set and said at least one generative model.

According to further example embodiments, said indicator is indicative of that said information to be conveyed includes at least one generative model configured to create a full data set without reference to a part of said data set, and said information to be conveyed includes said at least one generative model.

According to further example embodiments, said information to be conveyed includes a generative model description specifying utilization of said at least one generative model.

According to further example embodiments, said data retrieval request includes a generative model support indicator indicating support of generative models for creation of data sets.

According to a variation of the procedure shown in <FIG>, exemplary additional operations are given, which are inherently independent from each other as such. According to such variation, an exemplary method according to example embodiments may comprise an operation of creating said data utilizing said information to be conveyed.

<FIG> is a block diagram illustrating an apparatus according to example embodiments. The apparatus may be a network node or entity <NUM> such as an analytics data repository function (ADRF) or a network node or entity <NUM> implementing an ADRF, the apparatus comprising a receiving circuitry <NUM>, a fetching circuitry <NUM>, and a transmitting circuitry <NUM>. The receiving circuitry <NUM> receives a data retrieval request requesting data, said data retrieval request including an indicator indicative of a degree of involvement of generative models in information to be conveyed which represents said data. The fetching circuitry <NUM> fetches said information to be conveyed based on said data retrieval request. The transmitting circuitry <NUM> transmits said information to be conveyed. <FIG> is a schematic diagram of a procedure according to example embodiments. The apparatus according to <FIG> may perform the method of <FIG> but is not limited to this method. The method of <FIG> may be performed by the apparatus of <FIG> but is not limited to being performed by this apparatus.

As shown in <FIG>, a procedure according to example embodiments comprises an operation of receiving (S101) a data retrieval request requesting data, said data retrieval request including an indicator indicative of a degree of involvement of generative models in information to be conveyed which represents said data, an operation of fetching (S102) said information to be conveyed based on said data retrieval request, and an operation of transmitting (S103) said information to be conveyed.

Example embodiments outlined and specified above are explained below in more specific terms.

<FIG> shows a schematic diagram of signaling sequences according to example embodiments, and in particular illustrates a procedure according to which an NWDAF requests the ADRF to store historical data with a GM according to example embodiments.

Here, while <FIG> shows an example in which the NWDAF requests the ADRF to store a partial data set and the GM used to create the full dataset, example embodiments are not limited thereto, as specified above and explained further below.

In a step <NUM> of <FIG>, according to example embodiments, the NWDAF uses a GM to create the input data set needed for its operations, i.e., analytics and/or training.

In a step <NUM> of <FIG>, according to example embodiments, the NWDAF requests the ADRF to store partially the input data set, including a ShapeData indicator to inform that the data is synthetic along with the GM used to create the data set.

In a step <NUM> of <FIG>, according to example embodiments, the ADRF sends a response to the NWDAF to acknowledge the operation.

<FIG> shows a schematic diagram of signaling sequences according to example embodiments, and in particular illustrates a procedure according to which an NWDAF requests the ADRF to retrieve historical data with a GM according to example embodiments.

Here, while <FIG> shows an example in which the NWDAF retrieves from the ADRF a partial data set and the GM used to create the full dataset, example embodiments are not limited thereto, as specified above and explained further below.

In a step <NUM> of <FIG>, according to example embodiments, the NWDAF sends a request to the ADRF in order to retrieve data, including a ShapeData indicator to inform that the data can be used to feed a GM, along with the GM support indicator.

In a step <NUM> of <FIG>, according to example embodiments, the ADRF sends the requested data to the NWDAF, along with the supported GM.

In a step <NUM> of <FIG>, according to example embodiments, the NWDAF uses the GM to create the input data set needed for its operations, i.e., analytics and/or training.

According to example embodiments, a GM can be created by a new logical function of an NWDAF, namely an MTLF dedicated to create GMs, referred to as generative model training logical function (GMTLF), which adds support to create GMs.

Further, according to example embodiments, a GM can be created by another NWDAF including only an MTLF dedicated to create GMs, which adds support to create GMs as well.

In both cases, signaling and specific exchanges are established between NWDAF and GMTLF.

An NWDAF according to example embodiments.

An ADRF according to example embodiments.

The ShapeData indicator (i.e., being an example for the indicator indicative of a degree of involvement of generative models in conveyed information/information to be conveyed) according to example embodiments is an indicator that informs whether the input data is sent.

According to example embodiments, the ShapeData indicator is a numerical value X. If X = "<NUM>", then full data. If X = "<NUM>", then partial data and GMs. If X= "<NUM>", then full GMs set.

However, the ShapeData indicator is not limited to such type nor structure nor logical denotation.

The GM support indicator (i.e., being an example for the generative model support indicator indicating support of generative models for creation of data sets) according to example embodiments is an indicator that informs about support to run GM to create data sets.

The GM descriptor (i.e., being an example for the generative model description specifying utilization of said at least one generative model) according to example embodiments is a message/field that contains all information/metadata/overhead that fixes e. g the number of GMs, the architecture and model of GM (HyperParameters and training parameters) and an execution file (optional), e.g. Docker file (Image = libraries + file) or MLApp (application programming interface (API)).

<FIG> shows a schematic diagram of an example of a generative model descriptor scheme according to example embodiments.

In case of GAN, according to example embodiments, only the generator model may be transferred. The generator model is comprises (<NUM>) HyperParameters, (<NUM>) Training Parameters, and (<NUM>) a Data Generation Execution file.

<FIG> shows a schematic diagram of an example of a generative adversarial network generator model according to example embodiments, and in particular illustrates a generator model entity of GAN, particularly a sequence of N+<NUM> data, where N is the number of data that GM can generate.

According to example embodiments, advantageously, less data can be stored by the ADRF, i.e., less data can be necessary to be stored by the ADRF. In other words, the amount of historical data to store and to deliver in case of ML tasks multiplicity is limited.

Further, according to example embodiments, advantageously, enhancements can be provided for storage of data and/or analytics in ADRF, NWDAF and/or data source NF.

Still further, according to example embodiments, advantageously, enhancements can be provided to further reduce signaling and data traffic and the impact of obtaining data on data sources related to network analytics.

Fast and flexible access can be provided in particular for big historical data processing. For ML tasks (as MLTF or AnLF), local flexibility to generate an exact amount of data required can be provided. Further, for providers, control of the amount of data GMs can generate can be provided.

In the foregoing exemplary description of the network entity, only the units that are relevant for understanding the principles of the disclosure have been described using functional blocks. The network entity may comprise further units that are necessary for its respective operation. However, a description of these units is omitted in this specification. The arrangement of the functional blocks of the devices is not construed to limit the disclosure, and the functions may be performed by one block or further split into sub-blocks.

In <FIG>, an alternative illustration of apparatuses according to example embodiments is depicted. As indicated in <FIG>, according to example embodiments, the apparatus (network entity) <NUM>', <NUM>' (corresponding to the network entity <NUM>, <NUM>) comprises a processor <NUM>, a memory <NUM> and an interface <NUM>, which are connected by a bus <NUM> or the like. Further, the apparatus (network entity) <NUM>', <NUM>' (corresponding to the network entity <NUM>, <NUM>) comprises a processor <NUM>, a memory <NUM> and an interface <NUM>, which are connected by a bus <NUM> or the like. The apparatuses may be connected via link <NUM>, respectively.

The processor <NUM>/<NUM> and/or the interface <NUM>/<NUM> may also include a modem or the like to facilitate communication over a (hardwire or wireless) link, respectively. The interface <NUM>/<NUM> may include a suitable transceiver coupled to one or more antennas or communication means for (hardwire or wireless) communications with the linked or connected device(s), respectively. The interface <NUM>/<NUM> is generally configured to communicate with at least one other apparatus, i.e. the interface thereof.

The memory <NUM>/<NUM> may store respective programs assumed to include program instructions or computer program code that, when executed by the respective processor, enables the respective electronic device or apparatus to operate in accordance with the example embodiments.

According to example embodiments, an apparatus representing the network entity <NUM> comprises at least one processor <NUM>, at least one memory <NUM> including computer program code, and at least one interface <NUM> configured for communication with at least another apparatus. The processor (i.e. the at least one processor <NUM>, with the at least one memory <NUM> and the computer program code) is configured to perform transmitting a data storage request requesting storage of data, said data storage request including conveyed information and an indicator indicative of a degree of involvement of generative models in said conveyed information which represents said data (thus the apparatus comprising corresponding means for transmitting), and to perform receiving a data storage acknowledge response (thus the apparatus comprising corresponding means for receiving).

According to example embodiments, an apparatus representing the network entity <NUM> comprises at least one processor <NUM>, at least one memory <NUM> including computer program code, and at least one interface <NUM> configured for communication with at least another apparatus. The processor (i.e. the at least one processor <NUM>, with the at least one memory <NUM> and the computer program code) is configured to perform receiving a data storage request requesting storage of data, said data storage request including conveyed information and an indicator indicative of a degree of involvement of generative models in said conveyed information which represents said data (thus the apparatus comprising corresponding means for receiving), to perform storing said conveyed information (thus the apparatus comprising corresponding means for storing), and to perform transmitting a data storage acknowledge response (thus the apparatus comprising corresponding means for transmitting).

According to example embodiments, an apparatus representing the network entity <NUM> comprises at least one processor <NUM>, at least one memory <NUM> including computer program code, and at least one interface <NUM> configured for communication with at least another apparatus. The processor (i.e. the at least one processor <NUM>, with the at least one memory <NUM> and the computer program code) is configured to perform transmitting a data retrieval request requesting data, said data retrieval request including an indicator indicative of a degree of involvement of generative models in information to be conveyed which represents said data (thus the apparatus comprising corresponding means for transmitting), and to perform receiving said information to be conveyed (thus the apparatus comprising corresponding means for receiving).

According to example embodiments, an apparatus representing the network entity <NUM> comprises at least one processor <NUM>, at least one memory <NUM> including computer program code, and at least one interface <NUM> configured for communication with at least another apparatus. The processor (i.e. the at least one processor <NUM>, with the at least one memory <NUM> and the computer program code) is configured to perform receiving a data retrieval request requesting data, said data retrieval request including an indicator indicative of a degree of involvement of generative models in information to be conveyed which represents said data (thus the apparatus comprising corresponding means for receiving), to perform fetching said information to be conveyed based on said data retrieval request (thus the apparatus comprising corresponding means for fetching), and to perform transmitting said information to be conveyed (thus the apparatus comprising corresponding means for transmitting).

For the purpose of the present disclosure as described herein above, it should be noted that.

Generally, any method step is suitable to be implemented as software or by hardware without changing the idea of the present disclosure. Devices and means can be implemented as individual devices, but this does not exclude that they are implemented in a distributed fashion throughout the system, as long as the functionality of the device is preserved. Such and similar principles are to be considered as known to a skilled person.

Claim 1:
A method of a network entity (<NUM>) implementing a network data analytics function, the method comprising
transmitting (S71), towards a network entity (<NUM>) implementing an analytics data repository function, a data storage request requesting storage of historical data, said data storage request including conveyed information and an indicator indicative of a degree of involvement of generative models in said conveyed information which represents said historical data, and
receiving (S72) a data storage acknowledge response, wherein
said indicator is indicative of that said conveyed information includes a partial data set and at least one generative model configured to create a full data set based on said partial data set, and
said conveyed information includes said partial data set and said at least one generative model.