Issues, Challenges and Solutions : Big Data Mining

Jaseena, K U; David, Julie M.

doi:10.5121/csit.2014.41311

Cited by 13 publications

(8 citation statements)

References 6 publications

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“…For big data analytics, several IE approaches can be used such as statistical, machine learning, and rule-based, but interpretability, simplicity, accuracy, speed, and scalability are important characteristics that should be considered while selecting an appropriate approach for the solution. 135 Heterogeneity, scale, timeliness, complexity, and privacy are important challenges of big data analysis pipeline 128 where heterogeneity, timeliness, and complexity are more relevant to the IE from unstructured big data. Accuracy, coverage, and scalability are challenges to big data IE, whereby, accuracy and coverage are particular to IE and scalability is related to big data.…”

Section: Preconditions To Improve Unstructured Big Data Analyticsmentioning

confidence: 99%

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.

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Section: Preconditions To Improve Unstructured Big Data Analyticsmentioning

confidence: 99%

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Adnan

Akbar

2019

International Journal of Engineering Business Management

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“…In health sector-as an example of big data application (K. U & M. David, 2015), (Li, 2016) data are shared around the world in order to make a research to save our live,a although this area suffer from the privacy issues (Benjelloun, & Lahcen,2018) and there is lows grantee the patient privacy,to save data is also important because security breach of Big Data cost a lot, company may lose about $1.1 billion (Garg & Sharma, 2014) but the data from this sector is very important and it needs to be studied in suitable platforms to make this health systems and health tools much stronger (Zandi, Reis, Vayena & Goodman, 2019), also data mining and extracting data may give us good health knowledge and avoid the ambiguity in this area. (Li, 2016), this is the main aim of big data management.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Big Data Analytics Frameworks, Data Resources and Challenges

Abuqabita¹,

Al-Omoush²,

Alwidian³

2019

MAS

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Recently, huge amount of data has been generated in all over the world; these data are very huge, extremely fast and varies in its type. In order to extract the value from this data and make sense of it, a lot of frameworks and tools are needed to be developed for analyzing it. Until now a lot of tools and frameworks were generated to capture, store, analyze and visualize it. In this study we categorized the existing frameworks which is used for processing the big data into three groups, namely as, Batch processing, Stream analytics and Interactive analytics, we discussed each of them in detailed and made comparison on each of them.

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“…It may take a lot of time for its processing if we do not use appropriate analytics tools and algorithms. This is also called timeliness [2]. There are many patterns that cannot be found by the computer easily but, humans can detect them.…”

Section: Processing Issuesmentioning

confidence: 99%

“…Analytics algorithms accept only structured data. Even after cleaning the data, there may exist some errors and incompleteness that can make its management more difficult [2] [27]. This issue is one of the major areas of concern.…”

Section: Management Issues and Affordabilitymentioning

confidence: 99%

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A Review of Big Data Analytics in Sector of Higher Education

Vatsala¹,

Jadhav²,

Sathyaraj³

2017

IJERA

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This paper is about the use of big data analytics in higher education. In this paper, we see what the big data is and where does it come from. We will also try to find why the big data analytics has become a buzzword in almost every sector today through our literature review on the big data analytics and its applications in higher education sector. Then we see what the big educational data is, how it is generated and analyzed. We found that the two most important types of analytics are-Learning and academic analytics which will be discussed. Several papers describe the benefits of implementation of analytics in the education sector and the opportunities provided which will be discussed in this paper. We also found that the basic characteristics such as size, speed, variety and some other factors are responsible for some issues and challenges to the use of analytics in this sector. We will discuss those issues and challenges and discuss some proposed solutions to address them.

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Issues, Challenges and Solutions : Big Data Mining

Abstract: ABSTRACT

Cited by 13 publications

References 6 publications

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

Limitations of information extraction methods and techniques for heterogeneous unstructured big data

A Comparative Study on Big Data Analytics Frameworks, Data Resources and Challenges

A Review of Big Data Analytics in Sector of Higher Education

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