Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. The volume and variety of big data demand to improve the computational capabilities of these IE systems. It is necessary to understand the competency and limitations of the existing IE techniques related to data pre-processing, data extraction and transformation, and representations for huge volumes of multidimensional unstructured data. Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. Very limited consolidated research work have been conducted to investigate the task-dependent and task-independent limitations of IE covering all data types in a single study. This research work address this limitation and present a systematic literature review of state-of-the-art techniques for a variety of big data, consolidating all data types. Recent challenges of IE are also identified and summarized. Potential solutions are proposed giving future research directions in big data IE. The research is significant in terms of recent trends and challenges related to big data analytics. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive.

中文翻译：

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从非结构化多维大数据中提取信息的分析研究

信息提取（IE）的过程用于从非结构化或半结构化数据中提取有用的信息。随着多方面（也称为多维非结构化数据）的快速增长，大数据对IE技术提出了新的挑战。传统的IE系统无法有效应对大量的非结构化大数据。大数据的数量和种类要求提高这些IE系统的计算能力。有必要了解与数据预处理，数据提取和转换以及大量多维非结构化数据表示有关的现有IE技术的能力和局限性。针对IE进行了大量研究，以解决不同数据类型（例如文本，图像，音频和视频）的挑战和问题。已经进行了非常有限的合并研究工作，以调查IE中与任务有关和与任务无关的局限性，其中涵盖了所有数据类型。这项研究工作解决了这一局限性，并提供了针对各种大数据的最新技术的系统文献综述，整合了所有数据类型。还确定并总结了IE的最新挑战。提出了潜在的解决方案，为大数据IE提供了未来的研究方向。就与大数据分析相关的最新趋势和挑战而言，这项研究具有重要意义。研究和建议的结果将通过提高生产力来帮助改善大数据分析。