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The need for Self-service data analytics is inevitable as it supports the business in making the right decisions. In this paper, we argue that self-service analytics frameworks should be based on a process-centric approach and visualized selfservice components in order to meet current business demands. Further, we enunciate the need for mainly three components: Map component, Process Flow component and a Control Model component. Furthermore, we explain the architecture of a self-service analytics framework based on these components. Some parts of the proposed framework were deployed to different sites and are discussed in detail in this paper. The obtained results showed a clear enhancement of data warehouse operation spent from the IT departments' side compared to the traditional BI architecture.

A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five dimensions used to model query features. The query features dimensions are syntax, hardware, software, data architecture, and historical performance logs. These features will be based on developing training datasets for the performance prediction model that employs the ensemble learning model. As a result, ensemble learning leverages the query performance prediction problem to deal with missing values. Handling overfitting via regularization. The section on experimental work will go over how to use the proposed framework in experimental work. The training dataset in this paper is made up of performance data logs from various real-world environments. The outcomes were compared to show the difference between the actual and expected performance of the proposed prediction model. Empirical work shows the effectiveness of the proposed approach compared to related work.

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