Using a Rich Context Model for Real-Time Big Data Analytics in Twitter

In this paper we present an approach for contextual big data analytics in social networks, particularly in Twitter. The combination of a Rich Context Model (RCM) with machine learning is used in order to improve the quality of the data mining techniques. We propose the algorithm and architecture of our approach for real-time contextual analysis of tweets. The proposed approach can be used to enrich and empower the predictive analytics or to provide relevant context-aware recommendations.

[1]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[2]  Nam Khanh Tran,et al.  Time-aware topic-based contextualization , 2014, WWW.

[3]  Xue-wen Chen,et al.  Big Data Deep Learning: Challenges and Perspectives , 2014, IEEE Access.

[4]  Marcelo Milrad,et al.  Implementing and Validating a Mobile Learning Scenario Using Contextualized Learning Objects , 2014, ICCE 2014.

[5]  David Taniar,et al.  Progressive Methods in Data Warehousing and Business Intelligence: Concepts and Competitive Analytics , 2009 .

[6]  Sean Owen,et al.  Advanced Analytics with Spark: Patterns for Learning from Data at Scale , 2015 .

[7]  Florian Boudin,et al.  Effective Tweet Contextualization with Hashtags Performance Prediction and Multi-Document Summarization , 2013, CLEF.

[8]  Zhexue Huang,et al.  CLUSTERING LARGE DATA SETS WITH MIXED NUMERIC AND CATEGORICAL VALUES , 1997 .

[9]  Marcelo Milrad,et al.  Using a Rich Context Model for People-to-People Recommendation , 2015, 2015 3rd International Conference on Future Internet of Things and Cloud.

[10]  Saeed Shahrivari,et al.  Beyond Batch Processing: Towards Real-Time and Streaming Big Data , 2014, Comput..

[11]  Rim Faiz,et al.  Towards Events Tweet Contextualization Using Social Influence Model and Users Conversations , 2015, WIMS.

[12]  Patrice Bellot,et al.  Overview of INEX Tweet Contextualization 2014 track , 2014, CLEF.

[13]  Meriem Amina Zingla Using Association Rules between Terms and Nominal Syntagms for Tweet Contextualization , 2015, CORIA.

[14]  Rim Faiz,et al.  User-Tweet Interaction Model and Social Users Interactions for Tweet Contextualization , 2015, ICCCI.

[15]  Maarten de Rijke,et al.  Query-Dependent Contextualization of Streaming Data , 2014, ECIR.

[16]  Chunhua Ju,et al.  A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm , 2013, TheScientificWorldJournal.

[17]  Shrikanth S. Narayanan,et al.  A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle , 2012, ACL.

[18]  Rajiv Ranjan,et al.  Streaming Big Data Processing in Datacenter Clouds , 2014, IEEE Cloud Computing.

[19]  Nick Pentreath,et al.  Machine Learning with Spark , 2015 .

[20]  Steve Chan,et al.  Context-Based Analytics in a Big Data World : Better Decisions , 2013 .

[21]  K. Maghooli,et al.  Missing Data Analysis: A Survey on the Effect of Different K-Means Clustering Algorithms , 2014 .

[22]  Cherif Chiraz Latiri,et al.  Statistical and Semantic Approaches for Tweet Contextualization , 2015, KES.

[23]  Hamid Haidarian Shahri,et al.  A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses , 2008, Handbook of Research on Fuzzy Information Processing in Databases.

[24]  Iria da Cunha,et al.  Tweet Contextualization: a Strategy Based on Document Retrieval Using Query Enrichment and Automatic Summarization , 2013, CLEF.

[25]  Ivor W. Tsang,et al.  Maximum Margin Clustering Made Practical , 2009, IEEE Trans. Neural Networks.

[26]  Tadeusz M. Szuba,et al.  Computational Collective Intelligence , 2001, Lecture Notes in Computer Science.