Making a Graph Database from Unstructured Text

Jeon, Songhee; Khosiawan, Yohanes; Hong, Bonghee

doi:10.1109/cse.2013.144

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…Kako smo rano identificirali nedostatke, upravo iz tog razloga smo samostalno implementirali određena proširenja o kojima ćemo govoriti u ostatku rada. Radova u kojima se također uvode proširenja ili se istražuju grafovske baze ima podosta: (Kaliyar, 2015), (Thompson, Personick, & Cutcher, 2014), (Bayerl & Granitzer, 2017), (Reutter, Romero, & Vardi, 2017), (Robinson, Webber, & Eifrem, 2013), (Jeon, Khosiawan, & Hong, 2013), (Chen, Song, Zhao, & Li, 2020), (de Oliveira, de Souza, Moreira, & Seraphim, 2020), itd.…”

Section: Uvodunclassified

Istraživanja u području grafovskih baza podataka

Rabuzin

Kudelić

2020

Politehnika (Online)

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Kako smo unatrag nekoliko godina dobili određena sredstva (tzv. sveučilišne potpore) kojima smo istraživali određene aspekte grafovskih baza podataka, odlučili smo rezultate istraživanja predstaviti i hrvatskoj akademskoj i stručnoj zajednici. Smatramo kako su istraživanja bila kvalitetna i da rezultati istih imaju potencijal za širu primjenu i implementaciju u dostupnim sustavima za upravljanje grafovskim bazama podataka. Također, postoje i određene mogućnosti za proširenje implementiranih rješenja, kao i prostor za dodatna istraživanja i preslikavanja relacijskih u koncepte grafovskih baza podataka

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Section: Uvodunclassified

Istraživanja u području grafovskih baza podataka

Rabuzin

Kudelić

2020

Politehnika (Online)

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“…Xiaotong (Liu et al, 2013) developed a visual search engine based on CompactMap to stream the text data for visual analytics. Seunggwoo (Jeon et al, 2013) transformed unstructured email texts into a graph database and visualized them. Richard and Ralph (Lomotey, R.K. and Dters, R. 2013) extracted the topics and terms from unstructured data by using the TouchR2 tool they created.…”

Section: Related Workmentioning

confidence: 99%

Data behaviours model for Big Data visual analytics

Zhang

Huang

2016

IJBDI

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“…Jinglan Zhang et al [5] analyzed the national bird"s audio dataset, and used time-frequency, tags-linking and GeoFlow as the visualizing techniques for BigData audio data visualization. Seunggwoo Jeon et al [6] transformed unstructured email texts into a graph database. Yinglong Xia et al [7] visualized large scale graph data using "System G", the graph processing system they developed.…”

Section: Fig 2yuri Demchenko Et Al [4] Demonstrated Bigdata"s 5vs Cha...mentioning

confidence: 99%

Visual analytics for BigData variety and its behaviours

Zhang

Huang

Meng

2015

ComSIS

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BigData, defined as structured and unstructured data containing images, videos, texts, audio and other forms of data collected from multiple datasets, is too big, too complex and moves too fast to analyze using traditional methods. This has given rise to a few issues that must be addressed; 1) how to analyze BigData across multiple datasets, 2) how to classify the different data forms, 3) how to identify BigData patterns based on its behaviours, 4) how to visualize BigData attributes in order to gain a better understanding of data. It is therefore necessary to establish a new framework for BigData analysis and visualization. In this paper, we have extended our previous works for classifying the BigData attributes into the "5Ws" dimensions based on different data behaviours. Our approach not only classifies BigData attributes for different data forms across multiple datasets, but also establishes the "5Ws" densities to represent the characteristics of data flow patterns. We use additional non-dimensional parallel axes in parallel coordinates to display the ?5Ws? sending and receiving densities, which provide more analytic features for BigData analysis. The experiment shows that our approach with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.

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Making a Graph Database from Unstructured Text

Cited by 9 publications

References 10 publications

Istraživanja u području grafovskih baza podataka

Istraživanja u području grafovskih baza podataka

Data behaviours model for Big Data visual analytics

Visual analytics for BigData variety and its behaviours

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