Advanced Analytics in SAP HANA 189

Advanced Analytics in SAP HANA 189

Blog Article

Technology Background

In addition to the standard SQL database kernel, SAP HANA includes a variety of advanced analytics processing engines. These include spatial, series, text, and predictive analytics engines.

These engines rely on the same underlying database technologies as the core database, but require additional hardware and additional system management operations.

Feature Analysis

The COVID-19 Healthy Diet dataset contains the death status of people in different countries, taking into account the type of food they consume. This dataset contains 25 features that describe food categories and their relationship with the death rate.

The feature analysis of the dataset is carried out using scatter graphs. These charts help to observe patterns and identify strong correlations between the features. They also allow us to understand the effect of each individual feature on the model.

To improve the performance of the model, PCA is used to enhance and shape the features. Afterwards, the ReliefF method is applied to find more discriminatory features. The resulting features are normalized and reduced in dimension. The final ML model is trained on the new feature vector and evaluated using MSE, RMSE, MAE, and R2. The results show that the proposed HANA model can accurately predict the status of people with regards to their diet and nutrition during the COVID-19 pandemic.

Feature Selection

There is no one feature selection method that is optimal for all problem settings. Instead, different methods have strengths and weaknesses based on their underlying metrics and assumptions.

It is generally advisable to Www.gutterinstallationfortcollins.com consider both feature redundancy and complex epistatic interactions during feature selection. These considerations are motivated by the fact that LD and linkage disequilibrium create redundant SNPs at loci and that complex epistatic effects may account for some of the heritability of disease.

A common way to perform such analysis is to use a global sensitivity approach, which decomposes the target variable variance into summands of variances contributed by individual features in increasing dimensionality. The best-performing summands are then used to select the most relevant features. Such wrapper or embedded methods are often considered to be more effective than filters and hybrid approaches, but they can also be computationally expensive.

Feature Enhancement

Using PCA to enhance features is an important step in data preprocessing. By enhancing and improving the data, it becomes more meaningful and easier to understand. The results can also be used for prediction purposes. Moreover, the improved data can be applied to various regression and prediction models.

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Feature Reduction

Feature reduction techniques aim to mitigate the curse-of-dimensionality and improve model prediction accuracy and generalization ability. Supervised machine learning models can be trained with too many features which leads to overfitting and poor generalization ability. Feature reduction helps reduce feature complexity while preserving relevant information.

There are two main types of dimensionality reduction: feature selection and feature extraction. FS selects only a subset of the original features that contain relevant information while FX decreases feature dimensions by transforming the original features into a lower-dimensional space.

To overcome the limited number of techniques employed in this study, upcoming work should further explore other methods to widen FS and FSX, e.g. filter and wrapper techniques for FS; and nonlinear PCA, robust PCA, ICA, and LDA for FX. Combined techniques such as FS coupled with FX are also worth investigating.

Evaluation

With the help of PlanVisualizer, you can evaluate how a statement runs in HANA. Unlike the Executed Plan shown in HANA Studio, which shows only estimated records and execution times (see Fig 1.a), the Visualized Plan includes actual measured data.

The Tables Used list and the Operator List show the different tables that are used in the query. The Timeline tool allows you to visually analyze the operators in the SQL statement and see how they are processed. For example, you can find sequential bottlenecks that might have the same root cause and could be improved together.

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