大数据毕业设计外文文献译文关于hive+题目

Hive+ Topic: A Study on Efficient Data Retrieval and Analysis using Hive and its Integration with Topic Modeling

Hive+主题：使用Hive和主题建模集成的高效数据检索和分析研究

Abstract

In this paper, we propose a novel approach to efficiently retrieve and analyze large-scale data using Hive and its integration with topic modeling. The proposed approach is able to handle large, unstructured data sets by leveraging the scalability and parallelism of Hive, while also providing a powerful tool for exploring the latent topics within the data using topic modeling. We demonstrate the effectiveness of our approach through a case study involving the analysis of a large corpus of news articles.

摘要

在本文中，我们提出了一种使用Hive和主题建模集成高效地检索和分析大规模数据的新方法。所提出的方法通过利用Hive的可扩展性和并行性来处理大型非结构化数据集，同时还提供了一个强大的工具，可使用主题建模来探索数据中的潜在主题。我们通过一个案例研究来证明我们的方法的有效性，该案例研究涉及分析大量新闻文章的语料库。

Introduction

With the rapid growth of big data, there is an increasing need for efficient data retrieval and analysis methods that can handle large, unstructured data sets. Hive is a popular data warehousing tool that provides a scalable and parallelizable platform for querying and analyzing large data sets. Hive is built on top of Hadoop, which allows it to leverage the distributed computing capabilities of Hadoop to handle large data sets.

Topic modeling is a powerful technique for exploring the latent topics within large data sets. Topic modeling algorithms are able to automatically identify the underlying themes and patterns within the data, making it an ideal tool for exploratory data analysis and information retrieval. However, the computational complexity of topic modeling algorithms can make them difficult to apply to large data sets.

In this paper, we propose a novel approach to efficiently retrieve and analyze large-scale data using Hive and its integration with topic modeling. Our approach leverages the scalability and parallelism of Hive to handle large, unstructured data sets, while also providing a powerful tool for exploring the latent topics within the data using topic modeling. We demonstrate the effectiveness of our approach through a case study involving the analysis of a large corpus of news articles.

Background

Hive is a data warehousing tool that provides a scalable and parallelizable platform for querying and analyzing large data sets. Hive is built on top of Hadoop, which allows it to leverage the distributed computing capabilities of Hadoop to handle large data sets. Hive uses a SQL-like language called HiveQL to query data stored in Hadoop's distributed file system (HDFS).

Topic modeling is a technique for identifying the underlying themes and patterns within large data sets. Topic modeling algorithms are able to automatically identify the latent topics within the data by analyzing the co-occurrence patterns of words within the data. The most popular topic modeling algorithm is Latent Dirichlet Allocation (LDA), which uses a generative statistical model to identify the underlying topics within the data.

Approach

Our approach involves integrating Hive with topic modeling algorithms to efficiently retrieve and analyze large-scale data. The overall workflow of our approach is shown in Figure 1.

Figure 1: Workflow of our approach

The first step in our approach is to preprocess the data to prepare it for analysis. This involves cleaning the data, removing stop words, and converting the data into a format that can be used by the topic modeling algorithm.

The next step is to load the preprocessed data into Hive. Hive is able to handle large, unstructured data sets by leveraging the scalability and parallelism of Hadoop. This allows Hive to efficiently query and analyze large data sets.

Once the data is loaded into Hive, we use Hive's built-in functionality to perform basic exploratory data analysis. This includes generating summary statistics, histograms, and other descriptive statistics.

The next step is to perform topic modeling on the data. We use the popular LDA algorithm for this task. The output of the LDA algorithm is a set of topics, each of which is represented by a distribution over the words in the data set.

Finally, we use Hive to perform advanced data analysis on the output of the topic modeling algorithm. This includes exploring the relationships between the topics and other variables in the data set, as well as visualizing the results of the analysis.

Case Study

To demonstrate the effectiveness of our approach, we conducted a case study involving the analysis of a large corpus of news articles. The corpus consists of over 1 million news articles from a variety of sources, including major news outlets and smaller, independent publishers.

We first preprocessed the data by cleaning it, removing stop words, and converting it into a format that could be used by the topic modeling algorithm. We then loaded the preprocessed data into Hive and performed basic exploratory data analysis using Hive's built-in functionality.

Next, we performed topic modeling on the data using the LDA algorithm. The output of the LDA algorithm was a set of topics, each of which was represented by a distribution over the words in the data set.

Finally, we used Hive to perform advanced data analysis on the output of the topic modeling algorithm. This included exploring the relationships between the topics and other variables in the data set, as well as visualizing the results of the analysis.

Our analysis revealed a number of interesting insights into the corpus of news articles. For example, we were able to identify the most frequently occurring topics in the corpus, as well as the relationships between these topics and other variables such as the source of the article and the date it was published.

Conclusion

In this paper, we proposed a novel approach to efficiently retrieve and analyze large-scale data using Hive and its integration with topic modeling. Our approach leverages the scalability and parallelism of Hive to handle large, unstructured data sets, while also providing a powerful tool for exploring the latent topics within the data using topic modeling. We demonstrated the effectiveness of our approach through a case study involving the analysis of a large corpus of news articles. Our approach has the potential to be applied to a wide range of large-scale data analysis problems