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首页> 外文会议>International Conference on Intelligent Computing and Control Systems >A Novel approach to Data Visualization by supporting Ad-hoc Query and Predictive analysis : (An Intelligent Data Analyzer and visualizer)
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A Novel approach to Data Visualization by supporting Ad-hoc Query and Predictive analysis : (An Intelligent Data Analyzer and visualizer)

机译：通过支持即席查询和预测分析来实现数据可视化的新方法：（智能数据分析器和可视化器）

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Business Intelligence tools help to present a snapshot of the company by using graphical tools like pie charts, bar graphs, dashboards, etc. which facilitates easy understanding and decision-making. However, measures can be adopted to make BI tools more user-friendly. Our paper is an improvement over the existing BI tools as it supports predictive analytics along with the existing functionalities offered by any BI Tool. Our proposal also enables the user to ask queries in natural language format. This application analyses the query structure and categorizes it as a classification, regression, clustering, etc. problem. Once the query is categorized, it can then be processed by applying all different algorithms which are supported by Apache Spark’s machine learning library MLlib within each category. These algorithms are compared based on various evaluation metrics like accuracy, precision etc. and the most suitable algorithm is used to form the final predictive model. A labelled dataset ensures that our predictive analysis model needs to focus on Supervised learning algorithms only. The results computed are then represented in a graphical format for ease of comprehension of the management. The proposed solution exploits Apache Spark’s processing power, speed, its ability to handle huge datasets and its Machine learning support called Apache Spark MLlib. For implementation of the proposed solution we have used a MongoDB database of a windmill electricity generation plant. This proposal offers added functionalities to BI tools and improves user experience to accommodate the growing needs of the industry.
机译：商业智能工具通过使用图形工具（例如饼图，条形图，仪表板等）帮助呈现公司的快照，这有助于轻松理解和制定决策。但是，可以采取措施使BI工具更加用户友好。我们的论文是对现有BI工具的改进，因为它支持预测分析以及任何BI工具提供的现有功能。我们的建议还使用户能够以自然语言格式提出查询。此应用程序分析查询结构并将其分类为分类，回归，聚类等问题。对查询进行分类后，可以通过应用每个类别中Apache Spark机器学习库MLlib支持的所有不同算法来处理查询。这些算法是根据各种评估指标（如准确性，精度等）进行比较的，最适合的算法用于形成最终的预测模型。带有标签的数据集可确保我们的预测分析模型仅需要关注监督学习算法。然后，以图形格式表示计算出的结果，以便于理解管理。拟议的解决方案利用了Apache Spark的处理能力，速度，处理庞大数据集的能力以及其机器学习支持Apache Spark MLlib。为了实施建议的解决方案，我们使用了风车发电厂的MongoDB数据库。该提议为BI工具提供了附加功能，并改善了用户体验，以适应行业不断增长的需求。

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