![]() ![]() The advancement in technology promoted the rapid growth of data volume in recent years. Numerous techniques have been introduced for different data types i.e. Therefore, the efficient and accurate transformation of unstructured data in the IE process improves the data analysis. The extracted information from unstructured data is used to prepare data for analysis. Information extraction (IE) process extracts useful structured information from the unstructured data in the form of entities, relations, objects, events and many other types. The outcome of the research and recommendations will help to improve the big data analytics by making it more productive. The research is significant in terms of recent trends and challenges related to big data analytics. Potential solutions are proposed giving future research directions in big data IE. Recent challenges of IE are also identified and summarized. ![]() 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. 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. ![]() Numerous studies have been conducted on IE, addressing the challenges and issues for different data types such as text, image, audio and video. 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. The volume and variety of big data demand to improve the computational capabilities of these IE systems. Traditional IE systems are inefficient to deal with this huge deluge of unstructured big data. Big data arise new challenges for IE techniques with the rapid growth of multifaceted also called as multidimensional unstructured data. Process of information extraction (IE) is used to extract useful information from unstructured or semi-structured data. ![]()
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