AUTOML (short for "Automated Machine Learning") is a type of software tool that automates the process of selecting and fine-tuning machine learning (ML) models. The goal of AUTOML is to make it easier for people with little or no ML experience to build and deploy ML models by automating many of the tasks that are involved in the model selection and optimization process.
 
In a Big Data context, AUTOML tools can be used to build and deploy ML models on large datasets. These tools can help you automate the process of building and tuning ML models by automating tasks such as feature selection, model selection, and hyperparameter optimization.
 
To use an AUTOML tool, you typically start by providing the tool with your dataset and specifying your goals for the analysis. The AUTOML tool will then automatically select and fine-tune an ML model that is optimized for your data and your specific goals
.
 
There are many different AUTOML tools available, and the specific features and capabilities of these tools can vary. Some common features of AUTOML tools include
·        Automated feature selection: The ability to automatically select the most relevant features from your dataset to use in the model.
 
·        Automated model selection: The ability to automatically select the best ML model for your data based on your specific goals.
 
·        Automated hyperparameter optimization: The ability to automatically tune the hyperparameters of the ML model to optimize its performance.
 
·        Visualization tools: The ability to visualize the results of the analysis and the performance of the model.

There are many different types of machine learning (ML) models, and the best model for a particular problem will depend on the specific characteristics of the data and the goals of the analysis. Here are some common types of ML models:
 
·        Supervised learning models: These models are trained on labeled data, which means that the data includes both input features and corresponding output labels. Supervised learning models are used to predict an output label for a given input feature. Examples include linear regression, logistic regression, and support vector machines (SVMs).
 
·        Unsupervised learning models: These models are trained on unlabeled data, which means that the data includes only input features and no corresponding output labels. Unsupervised learning models are used to identify patterns or relationships in the data. Examples include clustering algorithms (such as k-means) and dimensionality reduction techniques (such as principal component analysis).
 
·        Semi-supervised learning models: These models are trained on a mixture of labeled and unlabeled data. They can be used in situations where it is difficult or expensive to label a large dataset, but there is still some labeled data available for training.
 
·        Reinforcement learning models: These models are trained to take a series of actions in an environment in order to maximize a reward. They are commonly used in robotics and control systems.
 
·        Deep learning models: These models are based on artificial neural networks and are used to analyze complex data, such as images or natural language. They are often used in image recognition, natural language processing, and speech recognition tasks.

var comp = sdk.getWidget( 'datagrid1' );
comp.m_exporttopdfgrid.exportastable = true; // (if false, it will be exported as image in pdf)
comp.m_exporttopdfgrid.theme = "striped"; // ('striped', 'grid' or 'plain')
comp.m_exporttopdfgrid.orientation = "l"; // ( l for landscape and p for portrait mode )

Real-time data refers to data that is generated and processed in real-time, as it is being produced. This means that the data is processed as soon as it is available, rather than being stored and processed at a later time.
 
Batch data orchestration refers to the process of scheduling and executing data processing tasks in batch mode, rather than in real-time. In batch mode, data is collected over a period of time and then processed all at once, rather than being processed as it is being produced.
 
There are advantages and disadvantages to both real-time data processing and batch data orchestration. Real-time data processing can provide faster results and enable more immediate decision-making, but it can also be more resource-intensive and may require more complex infrastructure. Batch data orchestration can be more efficient and less resource-intensive, but it may result in a delay between the time the data is generated and the time the results are available.
 
The choice between real-time data processing and batch data orchestration will depend on the specific needs of the application and the resources available.

var comp = sdk.getWidget('timeline1');
//left
comp.m_customaxis.enable = true;
comp.m_customaxis.axis.left.data.min = 400;
comp.m_customaxis.axis.left.data.max = 1600;
comp.m_customaxis.axis.left.data.noOfMarkers = 9;
comp.m_customaxis.axis.left.data.step = 400;

//right
comp.m_customaxis.enable = true;
comp.m_customaxis.axis.right.data.min = 200;
comp.m_customaxis.axis.right.data.max = 1600;
comp.m_customaxis.axis.right.data.noOfMarkers = 9;
comp.m_customaxis.axis.right.data.step = 200;

By setting values as below,
enableCustomAxis= true;
minPoint-> the axis starts from min point
maxPoint-> the axis ends at max point
noOfMarkers-> noofmarkers to be drawn in axis
step-> step is interval or difference between two markers.

This feature will work in Area, Bar, Bubble and Timeline charts.

var comp1 = sdk.getWidget( 'timeline3' ); comp1 .m_repeatercommonmarker="true";

new Date() will give the complete current date with time..

var today = new Date();
var today1=today.getFullYear()+'-'+(today.getMonth() + 1)+'-'+today.getDate();

function getParam(name,url)
{
if(!url)url = location.href;
name = name.replace(/[\[]/,"\\\[").replace(/[\]]/,"\\\]");
var regexS = "[\\?&]"+name+"=([^&#]*)";
var regex = new RegExp( regexS );
var results = regex.exec(url);
if( results == null )
return "";
else
return results[1];
}
var Years = getParam("Year",referrer);
alert(Years);

By default want to show 3 tick and 1 untick on load.

setTimeout(function(){
var a = sdk.getWidget( 'Bar1' ).m_seriesVisibleArr["CalcField1"] = false;
sdk.getWidget( 'Bar1' ).draw();
}, 100);

It should be given in dashboard and the component's shadow property should be on..

//shadow
Widget.prototype.setDraggableDivShadow = function() {
if(IsBoolean(this.getShowShadow())) {
var shadow = hex2rgb(convertColorToHex(this.m_shadowcolor), this.m_shadowopacity);
$("#draggableDiv" + this.m_objectid).css({
"box-shadow": "0 8px 16px 0 rgba(0, 0, 0, 0.2)" ,
"-webkit-box-shadow": "0 8px 16px 0 rgba(0, 0, 0, 0.2)",
"-moz-box-shadow": "0 8px 16px 0 rgba(0, 0, 0, 0.2)"
});
} else {
/** remove the css property so designMode shadow can be aplied if set from preference **/
$("#draggableDiv" + this.m_objectid).css({
"box-shadow": "",
"-webkit-box-shadow": "",
"-moz-box-shadow": ""
});
}
};