A Big Data Model Supporting Information Recommendation in Social Networks

Han, Xiaoyue; Tian, Lianhua; Yoon, Minjoo; Lee, Minsoo

doi:10.1109/cgc.2012.125

Cited by 25 publications

(13 citation statements)

References 4 publications

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“…Menon et al [9] focused on data warehousing and analytics platform of Facebook to provide support for batch-oriented analytics applications. Han et al [10] introduced a big data model which used social network data for information recommendation related to a variety of social behaviors.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Organic streams: Data aggregation and integration based on individual needs

Zhou

Jin

et al. 2013

2013 International Joint Conference on Awareness Science and Technology &Amp; Ubi-Media Computing (iCAST 2013 &Amp; UMEDIA 2013

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With the high accessibility of the social media, more and more people have been accustomed to sharing their personal contents across the social networks, which results in an explosive increase of data scale. In this study, in order to support information and knowledge discovery in big data, we propose an approach to aggregation and integration of personal big data from life logs in accordance with individual needs, which can benefit the sustainable information utilization process. In details, the organic stream, which is designed as an extensible data carrier, is introduced and developed to formulize and organize the personal big data, in order to extract dynamical individual needs from the tremendous amount of data posted through social media, and further aggregate and integrate the related data in a meaningful way, which can also facilitate the personalized information retrieval and reuse process. The architecture of the system with the foundational modules is given, and the experiment result is presented to demonstrate the usability and effectiveness of our approach.

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Section: Related Workmentioning

confidence: 99%

“…Big data has drawn more and more researchers these years [5][6][7][8][9][10]. Alsubaiee et al [5] have built a parallel database system called ASTERIX, which tried to combine time-tested principles from parallel database systems with those of the Web-scale computing community, in order to deal with the "Big Data" management challenges.…”

Section: Related Workmentioning

confidence: 99%

Organic streams: Data aggregation and integration based on individual needs

Zhou

Jin

et al. 2013

2013 International Joint Conference on Awareness Science and Technology &Amp; Ubi-Media Computing (iCAST 2013 &Amp; UMEDIA 2013

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“…Moreover, the number of applications of scalable recommender systems has also grown in the last years and involves now many different areas. Sun et al, for instance, propose a MapReduce-based recommender system for e-commerce [10]; Han et al [11] have proposed a big data model for recommendation in social networks; and Jiang and Xu [12] have presented a big-data framework for doctor recommendation. There are many works using the Hadoop ecosystem for supporting item-based recommender systems.…”

Section: State Of the Artmentioning

confidence: 99%

An efficient and scalable recommender system for the smart web

Baldominos

Sáez

Albacete

et al. 2015

2015 11th International Conference on Innovations in Information Technology (IIT)

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This is a postprint version of the following published document: Abstract: This work describes the development of a web recommender system implementing both collaborative filtering and content-based filtering. Moreover, it supports two differ-ent working modes, either sponsored or related, depending on whether websites are to be recommended based on a list of ongoing ad campaigns or in the user preferences. Novel recommendation algorithms are proposed and imple-mented, which fully rely on set operations such as union and intersection in order to compute the set of recommendations to be provided to end users. The recommender system is deployed over a real-time big data architecture designed to work with Apache Hadoop ecosystem, thus supporting horizontal scalability, and is able to provide recommendations as a service by means of a RESTful API. The performance of the recommender is measured, resulting in the system being able to provide dozens of recommendations in few milliseconds in a single-node cluster setup.

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“…Big Data Analysis is an interdisciplinary topic which combines many different disciplines, such as social networking [14,15], computational social science [16], data integration and fusion [17,18], data mining [19], trend discovery, multimedia and business management, opinion analysis and sentiment analysis [20,21], machine learning [22], graph mining, knowledge-based systems [21], decision support system [23], healthcare and biomedical applications [2,24], cloud computing. Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value by capturing and analyzing process.…”

Section: Introductionmentioning

confidence: 99%

Bio-Inspired ICT for Big Data Management in Healthcare

Stefano

Corte

Lió

et al. 2015

Studies in Big Data

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In the future, life and death decisions will depend on having more data and more organized knowledge. These data will overcome traditional scale and dimensions, thus we will need to think about new kind of strategies which involve Information and Communication Technologies. Collect, organize and compute every aspect will be crucial for survival of patients and for healthcare management. Following a bio-inspired approach to ICT, we can relate Big Data and the data intensive computing issues in the future vision of a smart healthcare. The multidimensional approach to comorbidity and the introduction of a social dimension of analysis allow to find out correlations and causality relations between diseases and patients also considering the connectedness and social contagion processes. In this way, we obtain an evolution from data to multiagents through the introduction of personalized medicine in a multilayer architecture.

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A Big Data Model Supporting Information Recommendation in Social Networks

Cited by 25 publications

References 4 publications

Organic streams: Data aggregation and integration based on individual needs

Organic streams: Data aggregation and integration based on individual needs

An efficient and scalable recommender system for the smart web

Bio-Inspired ICT for Big Data Management in Healthcare

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