A survey on scholarly data: From big data perspective

Khan, Samiya; Liu, Xiufeng; Shakil, Kashish Ara; Alam, Mansaf

doi:10.1016/j.ipm.2017.03.006

Cited by 116 publications

(68 citation statements)

References 109 publications

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“…In collaboration networks, the information reflects the collaboration patterns and academic social relationships among scholars in different disciplines. Community detection is traditionally considered as a kind of graph partition to discover exhaustive and disjoined node clusters in a given network (Khan et al, 2017). The discovery of structures in networks has attracted scholars' attentions for a long time.…”

Section: Related Workmentioning

confidence: 99%

COMICS: a community property-based triangle motif clustering scheme

Feng

Zhang

et al. 2019

PeerJ Computer Science

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With the development of science and technology, network scales of various fields have experienced an amazing growth. Networks in the fields of biology, economics and society contain rich hidden information of human beings in the form of connectivity structures. Network analysis is generally modeled as network partition and community detection problems. In this paper, we construct a community property-based triangle motif clustering scheme (COMICS) containing a series of high efficient graph partition procedures and triangle motif-based clustering techniques. In COMICS, four network cutting conditions are considered based on the network connectivity. We first divide the large-scale networks into many dense subgraphs under the cutting conditions before leveraging triangle motifs to refine and specify the partition results. To demonstrate the superiority of our method, we implement the experiments on three large-scale networks, including two co-authorship networks (the American Physical Society (APS) and the Microsoft Academic Graph (MAG)), and two social networks (Facebook and gemsec-Deezer networks). We then use two clustering metrics, compactness and separation, to illustrate the accuracy and runtime of clustering results. A case study is further carried out on APS and MAG data sets, in which we construct a connection between network structures and statistical data with triangle motifs. Results show that our method outperforms others in both runtime and accuracy, and the triangle motif structures can bridge network structures and statistical data in the academic collaboration area. massive scale related data, such as digital libraries and scholarly networks . As collaboration behaviors among scholars are becoming frequent, collaboration networks are generally in large-scale and contain rich collaboration information, reflecting the cooperation patterns among scholars in different research areas. Bordons et al. (1996) regard the academic teams as scientists communities, in which scholars can share research methods, materials, and financial resources rather than institutions organized by fixed structures (Barjak & Robinson, 2008). Furthermore, the ternary closures in social networks constitute a minimal stable structure; that is, a loop with three nodes. The number of ternary closures in social networks changes over time, which reveals the evolvement of human social behaviors. Besides, the definition of a clustering coefficient is based on the distributions of ternary closures. Milo et al. (2002) defined small network structures as motifs to present interconnections in complex networks by numbers that are significantly higher than those in randomized networks. Motifs can define universal classes of networks, and researchers are carrying on the motif detection experiments on networks from different areas, such as biochemistry, neurobiology, and engineering, to uncover the existence of motifs and the corresponding structure information in networks (Ribeiro, Silva & Kaiser, 2009;Bian & Zhang, 2016). Hence, triangle mo...

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Section: Related Workmentioning

confidence: 99%

COMICS: a community property-based triangle motif clustering scheme

Feng

Zhang

et al. 2019

PeerJ Computer Science

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“…Big data [4] is a huge amount of data that is generated from various sources. It may be structured when data come from flat files or relational databases, and unstructured or semi structured when data come from the web activities or equipment sensors.…”

Section: Big Data Analyticsmentioning

confidence: 99%

Real-Time Analysis of Students’ Activities on an E-Learning Platform based on Apache Spark

Chaffai¹,

Hassouni²,

Anoun³

2017

ijacsa

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Abstract-Real time analytics is the capacity to extract valuables insights from data that comes continuously from activities on the web or network sensors. It is largely used in web based business to drive decisions based on user's experiences, such dynamic pricing and personalized advertising. Many universities have adopted web based learning in their learning process. They use data-mining techniques to better understand students' behavior, and most of the tools developed are based on historical and stored data, and do not allow real time reactivity. Online activities of learners generate at high speed a huge amount of data in form of users' interactions which have all characteristics to be considered as Big data. Deal with volume and velocity of these data in order to inform and enable decisions-makers to act at right time lead us to use new methods to capture E-Learning data, and process it in real time.This paper focuses on the design and implementation of modern and hybrid real time data pipeline architecture using Apache Flume to collect data, Apache Spark as an unified engine computation for performing analytics on students' activities data and Apache Hive as a data warehouse for storing the processed data and for use by various reporting tools. To conceive this platform we conduct an experiment on Moodle database source.

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“…As knowledge is being amassed rapidly, domain knowledge entities accumulate as data sets in scholarly documents. These scholarly documents have become huge reservoir of data that contain diverse information such as authors, references, and keywords (Khan, Liu, Shakil, & Alam, ; Xia, Wang, Bekele, & Liu, ). The data are encapsulated as strings in the literature (Ding et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the strong potential of discovering latent knowledge embedded in literature, scholarly data is one of the lesser‐explored application areas of data analytics (Khan et al, ). The primary focus of previous efforts was evaluative entities (Ding et al, ; Swanson, ).…”

Section: Introductionmentioning

confidence: 99%