The mapping process of unstructured data to structured data

Abdullah, Mohammad Fikry; Ahmad, Kamsuriah

doi:10.1109/icriis.2013.6716700

Cited by 16 publications

(10 citation statements)

References 5 publications

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“…For example, IE techniques for text in biomedical domain are not adequate to extract information from images of the same domain. In this regard, a mapping process for unstructured data in terms of data management has been explained in [59] where unstructured data are managed using DC metadata element ‗subject'. But still the research is at very initial stage and facing scalability issues.…”

Section: E Feature Selection and Extraction Issuesmentioning

confidence: 99%

Information Extraction from Multifaceted Unstructured Big Data

2019

IJRTE

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In the era of digital globalization, huge volume and variety of data are being produced at a very high rate. Every day, the world is producing around 2.5 quintillion bytes of data. According to IDC, by 2020, over 40 zettabytes of data will be generated and reproduced. Digital data have become a deluge, overwhelming in every field of information technology (IT), business, science and engineering. These fields are shifting to smart and advanced technologies such as smart manufacturing industries, data-aware medical sciences, and other smart applications. These applications are facilitating the industries in context of data-driven decision making, big data storage, and complex analysis of large data sets. Also, these applications are contributing to generate big data deluge where a variety of data necessitate the industries to use advanced IT approaches. 95% of the digital universe is unstructured data. It is rich data as it contains information that can play a vital role to improve big data analytics. The heterogeneity, complexity, lack of structured information, poor quality and scalability of unstructured data generates difficulties in adapting traditional information extraction techniques. Information extraction can play a vital role in transformation of unstructured data into useful information. A multistep pipeline with data preprocessing steps, extraction methods and representation are utmost requirement to improve the unstructured data analytics. In this regard, this paper presents a short review of information extraction process w.r.t. input data type, extraction methods with their corresponding techniques, and representation of extracted information. The issues with unstructured data and the challenges to information extraction from multifaceted unstructured big data as well as the future research directions have also been discussed

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Section: E Feature Selection and Extraction Issuesmentioning

confidence: 99%

Information Extraction from Multifaceted Unstructured Big Data

2019

IJRTE

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“…Abdullah and Ahmad [1] suggested that this mapping should be according to the following four steps process:…”

Section: Using Relational Databasesmentioning

confidence: 99%

Visualizing Large Graphs Out of Unstructured Data for Competitive Intelligence Purposes

Boulouard

Koutti

Chouati³

et al. 2017

Lecture Notes in Networks and Systems

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In the information era, people's lives are deeply impacted by IT via exposure to social media, emails, RSS feed, chats, web pages, etc. Such data is considered very valuable nowadays since it may help companies to better their strategies. For example, companies can analyse their customers' trends or their competitors marketing interventions and adjust their strategies accordingly. Several decisional tools have been developed but most of them rely on relational databases. This makes it difficult for decision makers to take advantage of unstructured data which today represents more than 85% of the available data. Thus, there is a rising need for a suitable management process of unstructured data through collecting, managing, transferring and transforming it into a meaningful informed data. This paper will introduce a new tool for Big Unstructured Data for the Competitive Intelligence named Xplor EveryWhere (XEW). It will also describe the enhancement brought to its newest feature XEWGraph. This tool, or as described later on the paper, this "Service", offers the decision makers the possibility to have a better user experience regarding large graph visualization on their web browsers as well as their mobile devices.

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“…Unstructured data is any data that can be found through mediums, such as the internet, newspapers, and more [5]. This type of data can be in the form of the content in documents, texts, or social media posts.…”

Section: Introductionmentioning

confidence: 99%

“…This data can later be structured with the end goal in mind. According to the authors in [5], the process of mapping unstructured data is the following: 1) Extraction 2) Classification 3) Development of Repositories 4) Data Mapping Extraction is the first process for the mapping of unstructured data by identifying the source and format of the data. There are two types of extraction: fact and entity [4].…”

Section: Introductionmentioning

confidence: 99%

A Case Study on using Unstructured Data Analysis Methods to identify local Covid-19 Hotspots

et al. 2021

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The purpose of this work is to use unstructured data analysis methods to identify Covid-19 hotspots within local communities using publicly-available health and socioeconomic data. Consequently, a detailed analysis showing which local communities are most impacted by Covid-19 in the North Florida region is conducted based on zip code profiling. This work contributes to the knowledge and discovery of the impact of Covid-19 on lower income communities.

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The mapping process of unstructured data to structured data

Cited by 16 publications

References 5 publications

Information Extraction from Multifaceted Unstructured Big Data

Information Extraction from Multifaceted Unstructured Big Data

Visualizing Large Graphs Out of Unstructured Data for Competitive Intelligence Purposes

A Case Study on using Unstructured Data Analysis Methods to identify local Covid-19 Hotspots

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