基于查询扩展技术和内容过滤的大数据个性化分析

Menaceur Sadek, M. Derdour, Abdelkrim Bouramoul
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引用次数: 1

摘要

最近关于大数据环境下个性化分析的争论是商业智能(BI)管理员面临的最大挑战之一。大数据的海量、多品种、高速度给传统系统的数据存储、处理和分析带来了困难。这3v(体积、速度和种类)带来了许多新的挑战,使他们难以提取用户的特定需求。此外,用户可能会面临迷失方向的问题;他不知道什么信息真正符合他的需要。信息个性化系统旨在通过使用用户档案来克服这些迷失方向的问题。个性化系统在大数据环境下的有效性是通过根据用户需求和研究背景所获得的结果内容的相关性和准确性来证明的。然而,最近的大多数研究都集中在关系数据仓库的个性化上,而忽略了将用户上下文集成到OLAP多维数据集的分析中,这是执行用户多维查询的第一个关注点。为了解决这个问题,作者在本文中提出了一种基于基于内容的过滤和查询扩展技术的大数据环境下使用OLAP多维数据集的动态个性化方法。该方案的第一步是利用浓缩技术处理用户查询,将用户概要信息与其搜索上下文集成在一起,减少OLAP多维数据集中的搜索空间;利用扩展技术扩展OLAP多维数据集中的分析范围。与用户的初始请求相比,检索结果是“尽可能相关”的。然后,他们使用信息过滤技术,如基于内容的过滤,根据术语频率和余弦相似度对简化数据立方体中的分析进行个性化。最后,他们提出了一个案例研究和经验结果来评估和验证他们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Query Expansion Techniques and Content-Based Filtering for Personalizing Analysis in Big Data
The recent debates on personalizing analyses in a Big Data context are one of the most solicited challenges for business intelligence (BI) administrators. The high-volume, the high-variety, and the high-velocity of Big Data have produced difficulty in storing, processing, and analyzing data in traditional systems. These 3Vs (volume, velocity, and variety) created many new challenges and make them difficult to extract the specific needs of the users. In addition, the user may be faced with the problem of disorientation; he does not know what information really corresponds to his needs. The information personalization systems aim to overcome these problems of disorientation by using a user profile. The effectiveness of the personalization system in a Big Data context is to demonstrate by the relevance and accuracy of the content of the results obtained, according to the needs of the user and the context of the research. Nevertheless, most of the recent research focused on the relational data warehouse personalizing and ignored the integration of the user context into the analysis of OLAP cubes, which is the first concerned to execute the user's multidimensional queries. To deal with this, the authors propose in this article a dynamic personalizing approach in Big Data context using OLAP cubes, based on the Content-Based Filtering, and the Query Expansion techniques. The first step in the proposal consists of processing the user queries by an enrichment technique in order to integrate the user profile and his searching context to reduce the searching space in the OLAP cube, and use the expansion technique to extend the scope of the analysis in the OLAP cube. The retrieved results are: “as relevant as possible” compared to the user's initial request. Afterward, they use information filtering techniques such as content-based filtering to personalize the analysis in the reduced data cube according to the term frequency and cosine similarity. Finally, they present a case study and experiences results to evaluate and validate their approach.
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