High-value sample program

An impact assessment study was prepared for the Commission, detailing the list of high-value datasets that were to be made available. The starting point of the study was a map of all relevant EU legislation, presenting the datasets that were already available from all EU Member States.

Interviews with stakeholders then took place to develop a preliminary wish list of datasets considered to be of the highest value from an economic and social reuse perspective.

Moreover, the Commission provided the inception impact assessment , aiming to inform citizens and relevant stakeholders about its plans and open up to feedback. The document stressed the importance of high-value datasets and the need to have harmonisation rules to improve the availability of public data and its reuse.

These characteristics help overcome a series of barriers that often restrict the free circulation of information, such as high-use fees, non-machine-readable content, restrictive licences, poor interoperability or poor accessibility due to scattered data holders.

As a result of this process, a limited and well-defined group of datasets were identified. These aim to provide maximum value to their users and will be able to be used free of any technical, legal or financial barrier.

These datasets are listed in the relevant i mplementing regulation and are grouped in a list of six high-value datasets thematic categories: geospatial, earth observation and environment, meteorological, statistics, companies and company ownership, and mobility.

In this context, the current EU legislation provides an important guide in the choice of datasets in all six thematic categories. The first guidelines concerning PSI in the EU were produced in , and since then several policy documents, studies and further legislation have followed.

More specifically, PSI was regulated by the first PSI directive in , the directive on establishing an infrastructure for spatial information in the European Community INSPIRE , the second PSI directive in , the general data protection regulation and, lastly, by the latest and third PSI directive of , renamed as open data directive.

The PSI directives were instrumental in harmonising the PSI available to the public, increasing transparency and introducing a set of measures such as the use of machine-readable formats or central repositories to facilitate the discovery and reuse of information produced by the public administration.

This new implementing act establishing high-value datasets will be the culmination of a process developed over several years. Macro characteristics of high-value datasets. The literature review conducted on those thematic categories found several macro characteristics that give them potential value.

These macro characteristics include:. Each of these dimensions can help in its own way. Climate change and environment data is about exploiting information to improve environmental conditions and address climate change.

High-quality, decent jobs can be created by the private sector using economic data, while innovation and AI data can help develop new applications related to algorithmic decision-making.

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Work with Us. Claim samples Speak to an expert. Preparing a more informative but smaller dataset to reduce labelling efforts has been a vital research problem.

Although existing techniques can assess the value of individual data samples, how to represent the value of a sample set remains an open problem. In this research, the aggregation value is defined using a novel representation for the value of a sample set by modelling the invisible redundant information as the overlaps of neighbouring values.

The sampling problem is hence converted to the maximisation of the submodular function over the aggregation value. The comprehensive analysis of several manufacturing datasets demonstrates that the proposed method can provide sample sets with superior and stable performance compared with state-of-the-art methods.

The research outcome also indicates its appealing potential to reduce labelling efforts for more data-scarcity scenarios. With the trend of rapid digitisation and intelligentisation in the manufacturing industry, process modelling has become the fundamental technology for extracting industrial knowledge and revealing hidden laws [ 1 ].

Many non-linear multi-physics dynamics accompanying manufacturing processes bring significant difficulties in traditional mechanism-based modelling [ 2 ].

With the substantial development of multi-sensors and machine learning techniques, data-driven modelling, which can build a low-demand end-to-end solution for domain knowledge, has shown promising potential in diagnostics, decision-making and many other aspects of manufacturing [ 3 ].

However, the significant performance of data-driven modelling heavily relies on a large amount of labelled data for training, while generating labelled data in manufacturing is usually expensive and time-consuming either computationally or experimentally. For example, it would normally take several hours or days to conduct the complete three-dimensional 3D thermo-chemical analysis of a typical aerospace composite part e.

the wing skin of the Boeing using commercial simulation software [ 4—6 ]. Consequently, the high computational cost limits the application of data-driven thermo-chemical models.

Therefore, establishing data-driven models using substantially reduced labelled data is one of the most challenging tasks for intelligent manufacturing [ 7 ]. Previous research reported that, by leveraging auxiliary rich labelled data from one or multiple relevant tasks, supervised transfer learning [ 7 ], few-shot learning [ 8 ] or meta-learning [ 9 ] can enhance the performance of the target task when only a few labelled data are available.

Furthermore, integrating physical knowledge can also reduce the data requirement [ 10 , 11 ]. A series of physics-informed or theory-guided machine learning methods are developed for different manufacturing scenarios, including milling stability analysis [ 12 ], composite curing [ 13 ] and tool wear monitoring [ 14 ].

To tackle the challenge of data scarcity, another essential issue emerged of how to determine the distribution of the limited labelled dataset. Since the distribution of the training data influences the performance of algorithms, sampling an informative data set that preserves the characteristics of the task can significantly reduce the required amount of training data [ 15—17 ].

Representativeness is the most common consideration for unsupervised sample selection problems, where the selected samples are expected to represent the characteristics that should be preserved [ 17 ].

Clustering sampling or probabilistic sampling methods can provide a reasonable sample set to approximate the probabilistic distribution of the potential total dataset [ 18 , 19 ]. Low-rank-based methods can select the fewest samples to preserve the patterns or basis for high-dimensional samples [ 15 , 20 ].

An important underlying presupposition of representativeness-based sampling methods is that we believe representative samples might provide more valuable information for the model [ 16 ].

Although reasonable, the presupposition is insufficient because representativeness is only the indirect characterisation of the value of samples.

Thus, some core samples that can reflect the characteristic of the models might not be captured. This problem is further exacerbated in highly imbalanced real-world datasets where the representative sample set, with high probability, may miss the dominant samples [ 19 ].

To directly quantify the contribution of each sample during the model training, recent researchers proposed another interesting indicator, the value of samples.

The first attempt at data valuation was leave one out and the subsequent influence function method [ 22 ], in which a specific value was determined for each sample according to the performance difference when the sample was removed from the data pool.

From the perspective of cooperative game theory, training a dataset can be treated as a coalitional game, in which all data samples are players working for a common goal. Based on cost-sharing theory, Ghorbani et al. The Shapley value of each sample was represented as the average marginal gains of all potential subsets.

Then highest-ranking samples were selected as the satisfied sample set based on the standard greedy algorithm in submodular function maximisation [ 23 , 24 ]. A series of improved versions and accelerating algorithms were further proposed to boost the development of the Shapley value in the machine learning field [ 21 ].

Further analysis in multiple datasets revealed the high-value samples' clustering phenomenon in feature space. Neighbouring samples from the same high-value cluster might carry similar or redundant feature information, which cannot bring a proportional contribution to the model training.

Therefore, close or similar samples can only provide very small additional contributions for machine learning tasks, regardless of regression, classification or structural learning tasks [ 26 , 27 ].

This means that the sum of the values of samples in the selected set cannot represent the actual value of the set. Therefore, defining the actual value of a sample set considering redundant information becomes the critical challenge for sample selection problems. Comprehensive experiments on several manufacturing datasets demonstrate the influence of data distribution on model performance and the enormous potential of data sampling.

On the one hand, the selected optimised samples can provide more accurate and robust prediction results under the exact size of labelled data.

In addition, detailed analysis reveals the high-value samples' clustering phenomenon and interprets the advancement of the proposed aggregation-value-based sampling.

The general illustration of the proposed aggregation-value-based sampling method is given in Fig. The Shapley values of these samples can be evaluated based on game theory to represent their average contribution during curve regression Fig.

Illustration of the aggregation-value-based sampling. a Shapley value in a regression task; the sizes of the circles represent the Shapley values of each point. A larger circle means a more valuable sample.

b Shapley value function. c Value aggregation function, representing the neighbouring influence of the value function. d Aggregation-value-based sampling via greedy maximisation. To represent the values of neighbouring samples, a value aggregation function VAF is constructed by aggregating the values of its neighbouring samples using a kernel filter Fig.

Therefore, the close samples would share significant overlaps in their VAFs, which can explicitly represent the redundant information carried by these samples. Based on this, the aggregation value, defined as the expectation of the united VAFs, can be the intuitive target to assess the sampling results.

Maximising the aggregation value can effectively reveal the most contributing samples while mitigating redundant information. Figure 1 d shows the procedure of greedy sampling, which queries the new potential sample by iteratively maximising the increment of the aggregation value.

The implementation procedure of the proposed aggregation-value-based sampling is shown in Fig. The purpose of aggregation-value-based sampling is to reduce the labelling efforts for industrial applications by designing an optimal but smaller sample set; thus, it is less meaningful if the establishment of the value function requires too much labelled data.

Although the proposed method is derived from the Shapley value function, the basic idea of the aggregation value can be generalised to other forms of the value function as long as it is positively correlated with the real contribution of samples. Therefore, we generalise the proposed method to more practical scenarios in the case studies by introducing four value function schemes.

Scheme A: evaluate the value function from direct labelled data. Sufficient direct labelled data could provide a more accurate value function but increase the labelling burden.

Therefore, the case study for this scheme aims to demonstrate that the proposed method could find the optimal sample set rather than focusing on comparing the labelling efforts. Scheme B: reuse the value function from similar tasks. Just as transfer learning and meta-learning can utilise data from similar or relevant tasks to assist the target task, the value function from similar tasks, such as different manufacturing systems or cutting conditions, could also provide a reference for the target task.

Scheme C: reuse the value function from low-fidelity data. High-fidelity manufacturing process simulation is expensive and time-consuming, while simplified low-fidelity models are far more efficient.

Although not accurate enough, the low-fidelity data can still provide an effective value function to design the optimal samples for the following high-fidelity simulations. Scheme D: define the value function from prior knowledge.

With a broad and in-depth understanding of the prior knowledge of various manufacturing processes, researchers and engineers can define specific value functions according to the sample requirements. From this point of view, aggregation-value-based sampling can be extended to various engineering-based sampling scenarios, such as curvature-based sampling for surface measurement[ 28 ] and adaptive sampling for aerodynamic modelling[ 29 ].

The implementation procedure of the proposed aggregation-value-based sampling method. The value function can be evaluated from four schemes: evaluate the value function from direct labelled data; reuse the value function from similar tasks; reuse the value function from low-fidelity data; define the value function from prior knowledge.

In the following section we report the case studies for the four schemes. Scheme A demonstrates that aggregation-value-based sampling could find the optimal sample sets for various engineering problems, including classification and regression. Schemes B, C and D show that the proposed method can reduce the labelling efforts while achieving similar prediction accuracy.

The sensitivity analysis in the Results section shows that aggregation-value-based sampling is robust to the accuracy of value functions. This property provides the guarantee for reusability of value functions.

Figure 3 a—d reports the detailed results of different sampling methods on four datasets. A different number of samples is selected from the potential data pool. A machine learning model is then trained on the selected samples and evaluated on the test set. The detailed data processing and model training are reported in the online supplementary material S2, S3.

A brief description of these tasks is summarised as follow. Cifar10 [ 30 ] is a widely used classification dataset in the image process field. A small dataset is constructed from Cifar10 to evaluate the generalisability of the proposed method, and the result is shown in Fig.

The bearing fault dataset from Case Western Reserve University CWRU [ 31 ] is a famous benchmark dataset in the fault diagnosis field. Predicting the thermal lag of temperature from curing parameters is an important task for the quality control of composite parts.

Six hundred combinations of curing parameters are generated from a reasonable range, and the corresponding thermal lags are simulated using finite element FE software [ 5 , 6 ]. The mean absolute error MAE results are shown in Fig. The tool wear dataset from the Prognostics and Health Management Society [ 32 ] consists of collected monitoring signals during milling and the corresponding tool wear values for three blades of cutting tools.

Two regression tasks are formulated: blade No. The MAE results are shown in Fig. Comparison of different sampling methods. The value functions in a—d are evaluated from direct labelled data.

The value functions in e and f are reused from similar tasks. a Experimental results of Cifar b Experimental results of CWRU HP1.

c Experimental results of thermal lag prediction of the composite. d Experimental results of Tool wear B3C6. e Results of reusing the value function of task CWRU HP0 on HP1.

f Results of reusing the value function of task B2C4 on B3C6. Figure 3 a and d illustrates that HighAV can consistently achieve superior performance, especially when the number is limited.

Under most circumstances, HighAV outperforms the uncertainty boundary of Random grey region while Cluster is only better than Random occasionally but far more unstable.

The Cluster results fluctuate sharply because similar sample sizes e. In Fig. Theoretically, minimising the aggregation value can also provide the worst sample set. As seen in Fig. Although the low valuable sample set seems meaningless for real application, it does reveal the importance of the distribution of training data, as well as the magic of aggregation-value-based sampling.

Table 1 summaries the regression and classification results with training data from different sampling methods under different sample sizes 30, 50, 80, It is clear that the proposed aggregation-value-based sampling method can provide better sample sets compared to other sampling methods.

Summary of performance with training data from different sampling methods Random, Cluster, HighSV, HighAV. The best results are highlighted bold. This table shows that HighAV can achieve better performance under the same number of samples.

To avoid data labelling for the value function, in this section we investigate the possibility of reusing the value function learnt from a similar task on the target task without training a new one. It can be observed that the accuracy of HighSV is even lower than Random, but HighAV can consistently achieve leading performance.

This phenomenon reveals that the effectiveness of HighSV relies heavily on the accuracy of the value function. However, HighAV is more robust, meaning that a less accurate value function can still provide helpful value information. The same conclusions can also be drawn from Fig.

In this section we investigate Scheme C for the composites curing case, in which the value function is first calculated from the simplified low-fidelity finite difference FD model, and then reused for parameters designed in high-fidelity FEM simulations. An illustration of the curing of a 1D composite-tool system is shown in Fig.

The actual temperature of the composite part always lags behind the designed cure cycle Fig. Thus, the thermal lag is defined as the maximum difference between the cure cycle and the actual temperature of any point in the thickness during the heat-up step [ 5 , 6 ]. The objective here is to establish the data-driven prediction model of thermal lag from the simulation results, where the input features include the heating rate, the cooling rate, the hold temperature, the hold time and the heat transfer coefficients of both sides Fig.

Since the labelled data comes from the time-consuming high-fidelity FEM simulation, a better sampling method should reduce the number of simulations but maintain the required accuracy of the data-driven model.

Experimental results of Scheme C, the thermo-chemical analysis of the composite. a Illustration of the 1D composite-tool curing system. b The cure cycle and the thermal lag in composite curing.

c The defined data-driven task from the curing parameters to the corresponding thermal lag. d The full workflow of sampling curing parameters for composite simulation. e MAEs of 10 repeated trails of different sample selection methods with 40 samples.

f Required samples of different sample selection methods to achieve an MAE of 5 K. The detailed procedure of aggregation-value-based sampling for this case is shown in Fig. An optimal parameter sample set S is then determined based on the proposed sampling method for the subsequent complete high-fidelity FEM simulations.

A Gaussian process regression model is then trained on the simulation results of the selected samples and evaluated on the test set. The MAEs of 10 repeated trials for four methods are shown in Fig.

It can be observed that HighAV can achieve a superior and stable performance with an MAE around 5 K. Conversely, Cluster is slightly better than Random, and HighSV is very unstable, even worse than Random.

These results show that the distribution of the designed curing parameter combinations significantly influences the performance of data-driven models, and the proposed HighAV can provide a better sample set stably. Figure 4 f reports how many samples are required to achieve an MAE of 5 K for different sample selection methods.

In each independent experiment, a sample set is constructed by increasing instances one by one from an empty set until the MAE becomes less than 5 K stably.

The size of the final sample set is recorded as the required size of this trial. As shown in the scatter and box plots of 10 repeated tests in Fig.

Table 2 reports the detailed required samples for different sampling methods to stably achieve MAEs of 5 and 6 K. These results demonstrate that the proposed sampling method can reduce the data-collecting effort of FEM simulations in the composite curing problem while maintaining the required accuracy.

The required number of samples M for different sampling methods to achieve a predefined required MAE.

1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss


Sample Size Calculation using R

High-value sample program - The High Value Care (HVC) curriculum for educators & residents helps train physicians to be good stewards of limited healthcare resources. View curriculum 1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss

If your company does this, you should look at the customers who are sharing your loyalty program the most. These people are extremely valuable to your company because they generate word-of-mouth marketing and potential leads for your business.

One of the most common misconceptions about customer journey maps is that businesses think they only need to create one map for all of their customers' journeys. In reality, one customer's experience with your company can be drastically different than another customer's.

Here's an example. One customer discovers your business online and buys your product through your website. This customer loves how user-friendly your website is and is impressed by how fast you delivered their order. They quickly become a repeat customer because of the convenience that your brand offers.

The next customer finds out about your company through a friend, who purchased your product and immediately fell in love with it. This customer goes to one of your brick-and-mortar stores and demos your product with the help of a sales rep.

They appreciate how durable your product is and how easy it is to use. They also become a repeat customer because of the reliability of your product and the competency of your sales team.

Both customers are high-value, but each one had a very different journey with your business. If you only have one journey map to represent all of your customers, then you might overlook some high-value ones and improperly categorize them.

The more you can track these experiences and map them for your team, the easier it will be to identify your most valuable customers. As mentioned earlier, sometimes it's not about how much a customer is spending on your products, but who they're sharing their experiences with.

Social media and third-party review sites are incredibly powerful channels for word-of-mouth marketing, and all it takes is one post to go viral to generate some serious attention for your brand. One of the most relevant examples we can look at today is the partnership between social media influencer, Charli D'melio, and Dunkin'.

Recognizing D'melio's impressive million followers on Tik Tok, the coffee brand, Dunkin', offered her a partnership to promote its products — which she does using videos like the one below. got my own song and it just hits different.

show me how you "do the charli" while you drink 'the charli' using charlirunsondunkin!! dunkin ad. Even if D'melio doesn't spend a cent on Dunkin's coffee, she's still one of the brand's highest-value customers. Because she generates tons of revenue for the business just by posting videos online.

Even though Dunkin' is likely paying her to do so, this is a much more effective way of attracting customers compared to traditional advertising methods.

If you don't track KPIs like CLV or ARR, you can always survey your customers to learn more about their purchasing habits. While the downside of this is that it's up to the customer to supply information, the benefit is that you can ask direct questions and find out specific information about how people feel about your brand.

One survey that you can use is Net Promoter Score, or NPS ®, which asks customers how likely they are to recommend your company to a friend.

This survey asks participants to rank their likelihood to refer on a scale of and it provides them with a comment box where they can justify their answer or provide additional context. With this survey, you can quickly identify who's most likely to recommend your brand and who's most likely to churn after interacting with your company.

Identifying high-value customers is only the first step. Once you know who is making the biggest impact on your company's bottom line, your next task is to maximize their value and develop long-lasting, mutually-beneficial relationships with them.

Not only do you want your company to feel secure in your partnership with these customers, but you also want your customers to be so delighted with their experience that they're compelled to tell other people about your company.

For more ways to keep high-value customers happy, read about customer retention and loyalty. Free email, survey, and buyer persona templates to help you engage and delight your customers. Service Hub provides everything you need to delight and retain customers while supporting the success of your whole front office.

What Is a High-Value Customer? Updated: June 15, Published: February 09, charlidamelio got my own song and it just hits different. Topics: Customer Retention Ticketing System. Don't forget to share this post!

Are You Losing Customers? Find Out Why. Customer Loyalty vs. Brand Loyalty: Everything You Need to Know. No value is higher. The default value for HIGH-VALUES is X'FF'. HIGH-VALUES is a figurative-constant, When you MOVE HIGH-VALUES TO somwhere, all receiving positions of somewhere will be filled with X'FF'.

Note, there is no difference between HIGH-VALUE and HIGH-VALUES. For any type of matched-key processing, high-values can be useful, since nothing can be higher.

It can also be useful for "trailer" records, as binary-ones are the highest in the collating sequence. If you want a non-default value for high-values, take some time with the manuals and see if you can work it out.

Post by Pragya » Wed Sep 30, am Thanks William for the great explnation. What is "collating sequence"? Post by enrico-sorichetti » Wed Sep 30, am What is "collating sequence"? Post by William Collins » Wed Sep 30, pm Collating sequence starts from the lowest value, and continues, in sequence, by each subsequent higher value.

ABCDEFG A is lowest, B is greater than A, C is greater than B so also greater than A , D is greater than C so also greater than B and A etc. Without a collating sequence, you can do no "greater than" or "less than" comparisons.

The collating sequence also determines in what order data will be sorted. At the basic level data "collates" from X'00' thru X'FF, in sequence.

In EBCDIC, all "displayable" characters have a hexadecimal value. This is also true in ASCII, but, the hexadecimal values of, for instance, the alphabet and the numbers is different between the two character sets, so the collating sequence is different in EBCDIC, letters collate lower than numbers, in ASCII the reverse.

At the basic level, in COBOL LOW-VALUES is the lowest hexadecimal value in the collating sequence, and HIGH-VALUES is the highest, and that is X'00' and X'FF' respectively.

However, in a COBOL program and elsewhere outside COBOL you can use a different collating sequence for a specific purpose. In a COBOL program running on a Mainframe you could process ASCII data using an ASCII collating sequence, or some custom collating sequence where LOW-VALUES and HIGH-VALUES still contain the lowest and highest in the sequence, but do not contain X'00' and X'FF'.

It is very rare that you would need to do this "for real", but you could do some little tests anyway. Post by Robert Sample » Wed Sep 30, pm Moving High-values will move all 1's in the variable. zLog Board index About TM Jobs Contact us.

Sample selection Lrogram Mehdi-Souzani. Product trial opportunities of the Discounted Barbecue Supplies High-value sample program. Here A±B represents prorgam mean A and Higgh-value deviation B of the required number Highvalue in 10 Product trial opportunities trials. Prograj of the most common misconceptions about customer journey maps is that businesses think they only need to create one map for all of their customers' journeys. It is clear that HighAV can reduce the error of areas with high curvatures, which plays a similar role as traditional curvature-based sampling. PrimeEx A program with various approaches to determine if an int is prime or not.

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