Special Session 1:

2nd Special Session on Intelligent Data Mining

Dec.5-8, 2016 Washington D.C., USA

Recent developments in processing, storing, and sharing huge amount of data become problem due to the lack of new approaches, techniques, methods, algorithms and technologies. Researchers try to find proper solutions based on their experiences and make contributions to current data mining and classification knowledge. This approach actually causes new set of problems due to the missing theoretical notions, lack of necessary disciplines and insufficient awareness on data security, information retrieval, social networking within behavioral and social and ethical issues. This special session seeks solutions on interdisciplinary gaps between data and intelligent approaches.

The AI & ML fields are interdisciplinary, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience. There is no software or hardware available to solve problems like human's do. Researchers only make assumptions to replicate biological architectures. So, the brain is capable of change and recognizes experiences. Every day, there are more than 7 billion people works for processing limitless number of data like a single computer through sophisticated cloud networks. Each brain has own big data warehouse and biological CPU.

Nowadays, researchers use interdisciplinary way to understand knowledge among all types of resource including data, document, tool, device, experience, process and people. This approach may help to understand biological evolution to propose robust and powerful approaches between human nature and big data processing.

Intelligent Data Mining term is not only related to Computer Science. This special session opens to every researcher as well as industrial partners to make contribution.

The aims of this Special Session on Intelligent Data Mining are to:

  • Bring researchers and experts together to discuss and share their experiences
  • Share the current and new research topics and ideas,
  • Improve and enhance personal, enterprise, national and international awareness,
  • Provide a platform to present and discuss recent advancements,
  • Increase international collaborations among university–industry-institutions,

in the fields of theory and applications of data mining, artificial intelligence, computer science, mathematics, psychology, linguistics, philosophy, neuroscience and other disciplines to discuss better understanding of big data and intelligence.
The papers submitted to this special session might be in a large range of topics that include theory, application and implementation of artificial intelligence, machine learning and data mining including the topics given below,

Use of Artificial Intelligence | Machine Learning in Big Data as

  • Data Mining, Data Science and Big Data,
  • Data Warehouse, Clustering, Visualization
  • Security, Privacy,
  • Big DaaS
  • Scalable Computing, Cloud Computing,
  • Knowledge Discovery, Integration, Transformation
  • Information Retrieval,
  • Data Classification, Regression, Cleaning,
  • Smart Cities & Energy
  • Social Media, Social Networking, Social Data,
  • Semantics,
  • IoT
  • Multimedia
  • Mobile Computing
  • Sensors, Networks, Devices
  • Mathematics,
  • Psychology,
  • Linguistics,
  • Philosophy,
  • Neuroscience,
  • Biometric,
  • Sustainability
  • Human Biology,
  • Bioinformatics,
  • Cognitive Science,
  • Surveillance,
  • Business Intelligence,
  • Recent Theory, Trends, Technologies and Applications,
  • Future Directions and Challenges in Intelligent Data Mining ,
  • Industrial Challenges in Intelligent Data Mining ,
  • Demo Applications,

Papers should be submitted for this special session by Sept 23, 2016, at the conference special session submission system (http://cci.drexel.edu/bigdata/bigdata2016/PaperSubmission.html). Papers should be submitted as a pdf in 2-column IEEE format. Detailed instructions for the authors can be found at the conference website. Accepted papers will be published in the conference proceedings. All accepted papers must be presented by one of the author/s in the conference to include the article in the proceedings.

If you have any question about this special session, please do not hesitate to direct your question to the special session organizer Dr. Uraz Yavanoglu (urazyavanoglu@gmail.com, uraz.yavanoglu@asu.edu)

Special Session Organizer:
Dr. Uraz Yavanoglu
Department of Computer Engineering
Gazi University, Turkey

Postdoctoral Scholar
School of Computing, Informatics, and Decision Systems Engineering
Arizona State University, USA

The important dates for this special session are:
Full Paper Submission Deadline : Sept 23, 2016 11:59pm PST
Notification of Acceptance : Oct 24, 2016
Camera-ready papers & Pre-registration : Nov 5, 2016, 11:59pm PST
Conference Dates : Dec 5-8, 2016

Click Here for PDF Version

For Paper Submission, please click here

Special Session 1 Accepted Papers

Paper List
Francisco Padillo, José María Luna, and Sebastián Ventura
 Subgroup Discovery on Big Data: pruning the search space on exhaustive search algorithms
Abdulkadir KAYIKLI, Zehra Aysun ALTIKARDES, Hayriye KORKMAZ, Ali Serdar FAK, Hasan ERDAL, and Ahmet Fevzi BABA
A Novel Method for Prediction Dipper/Non-dipper Pattern In Hypertension Patients
Hsiao-Wei Hu, Hao-Chen Chang, and Wen-Shiu Lin
From Big Data to Big Challenge: An optimized frequent pattern mining algorithm with multiple minimum supports
Thamarai Selvi Somasundaram, Kannan Govindarajan, and Vivekanandan Suresh Kumar
 Swarm Intelligence (SI) based Profiling and Scheduling of Big Data Applications
Chenyu Zhai and jing peng
 Mining Latent Features from Reviews and Ratings for Item Recommendation
Sachin Kumar, Saibal K Pal, and Rampal Singh
 Smart Home: Accurate Prediction of Energy Consumption with Extreme Learning Machine Variants
Busra Mutlu, Merve Mutlu, Kasim Oztoprak, and Erdogan Dogdu
 Identifying Trolls and Determining Terror Awareness Level in Social Networks Using a Scalable Framework
Quanzhi Li
 Classifying Tables in Financial Documents
Kai Zhao, Sasu Tarkoma, Siyuan Liu, and Huy Vo
 Urban Human Mobility Data Mining: An Overview
Murat Ozbayoglu, Gokhan Kucukayan, and Erdogan Dogdu
 A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence
Jenq-Haur Wang and Jia-Zhi Lin
 Improving Clustering Efficiency by SimHash-based K-Means Algorithm for Big Data Analytics
Sercan Kulcu and Erdogan Dogdu
 A Survey on Semantic Web and Big Data Technologies for Social Network Analysis
Paul Raff and Ze Jin
 The Difference-of-Datasets Framework: A Statistical Method to Discover Insight
Ammar Jakabji and Hasan Dağ
Improving item-based recommendation accuracy with user's preferences on Apache Mahout
Omer Berat Sezer, Erdogan Dogdu, Murat Ozbayoglu, and Aras Can Onal
 An Extended IoT Framework with Semantics, Big Data, and Analytics
Madhu Shashanka, Min-Yi Shen, and Jisheng Wang
 User and Entity Behavior Analytics for Enterprise Security
Nora Alkhamees and Maria Fasli
 Event Detection from Social Network Streams Using Frequent Pattern Mining with Dynamic Support Values
Ozlem Yavanoglu
 Intelligent Authorship Identification with using Turkish Newspapers Metadata
Brahim Hnich, Ata Sasmaz, O¨ zkan Sayın, Faisal R. Al-Osaimid, and Amine Lamine
 Smart Online Vehicle Tracking System for Security Applications
Sampath Jayarathna and Faryaneh Poursardar
 Change Detection and Classification of Digital Collections
Feyza Yıldırım Okay and Suat Özdemir
 A Secure Data Aggregation Protocol for Fog Computing Based Smart Grids
Yerzhan Kerimbekov and Hasan Şakir Bilge
 A feature selection method based on Lorentzian metric
ismail duru, Gülüstan Doğan, and Banu Diri
 An Overview Of Studies About Students' Performance Analysis and Learning Analytics in MOOCs
Aparna Oruganti, Fangzhou Sun, Hiba Baroud, and Abhishek Dubey
 DelayRadar: A Multivariate Predictive Model for Transit Systems
Giuseppe Bruno
 Text Mining and Sentiment Extraction in Central Bank Documents
Xiao Li, Reza Sharifi Sedeh, Liao Wang, and Yang Yang
Jianbo Yuan, Walid Shalaby, Mohammed Korayem, David Lin, and Khalifeh AlJadda
 Solving Cold Start Problem in Large-scale Recommendation Engines: A Deep Learning Approach
Yehezkel Resheff
 Online Trajectory Segmentation and Summary With Applications to Visualization and Retrieval
Yiheng Zhou, Numair Sani, and Jiebo Luo
 Fine-grained Mining of Illicit Drug Use Patterns Using Social Multimedia Data from Instagram
Chengcheng Wan, Yanmin Zhu, Jiadi Yu, and Yanyan Shen
 SMOPAT: Mining Semantic Mobility Patterns from Trajectories of Private Vehicles
Bingchuan Liu, Yudong Tan, and Huimin Zhou
 A Bayesian Predictor of Airline Class Seats Based on Multinomial Event Model
Ali Sekmen, Akram Aldroubi, Ahmet Bugra Koku, and Ahmet Faruk Cakmak
 Skeleton Decomposition Analysis for Subspace Clustering
Yuchen Wu, Jianbo Yuan, Quanzeng You, and Jiebo Luo
 The Effect of Pets on Happiness: A Data-Driven Approach via Large-Scale Social Media
Omair Shafiq
 Event Segmentation using Parallel MRK-Means Clustering based on MapReduce
Victor Perazzolo Barros and Pollyana Notargiacomo
Big Data Analytics in Cloud Gaming: Players' Patterns Recognition using Artificial Neural Networks
Nada Basit, Yutong Zhang, Hao Wu, Haoran Liu, Jieming Bin, Yijun He, and Abdeltawab Hendawi
 MapReduce-based Deep Learning With Handwritten Digit Recognition Case Study
Soukaina Filali Boubrahimi, Berkay Aydin, Dustin Kempton, and Rafal Angryk
 Visualizing Solar Event Data Using Heterogenous Data Sources
Jaroslav Cechak, Philip Thruesen, Blandine Seznec, Roel Castano, and Nattiya Kanhabua
 To Link or Not to Link: Ranking Hyperlinks in Wikipedia using Collective Attention

Special Session 2:

GrC 2016: Data Science and Computing

Dec.5-8, 2016 Washington D.C., USA

Granular Computing (GrC) is a general computation theory for effectively using granules such as classes, clusters, subsets, groups and intervals to build an efficient computational model for complex applications with huge amounts of data, information and knowledge. Though the label is relatively recent, the basic notions and principles of granular computing, though under different names, have appeared in many related fields, such as information hiding in programming, granularity in artificial intelligence, divide and conquer in theoretical computer science, interval computing, cluster analysis, fuzzy and rough set theories, neutrosophic computing, quotient space theory, belief functions, machine learning, databases, and many others.

GrC 2016 Special Symposium: Data Science and Computing will continue to address the issues related to Granular Computing and its applications. it will provide researchers from universities, laboratories and industry to present state-of-the-art research results and methodologies in theory and applications of granular computing, including rough sets as special form for granulation. The conference will also make it possible for researchers and developers to highlight their new research directions and new interactions with novel computing models. To broaden more impact on granular computing and its applications, It will focus on currently important major research tracks such as social network computing, cloud computing, computer security, data mining, soft computing ( neural networks, fuzzy systems, evolutionary computation, rough sets), e-Intelligence (Web intelligence, semantic Web, Web informatics), bioinformatics, medical informatics


  • The conference will take place in Hyatt Regency Washington on Capitol Hill.
  • The meeting is intended to be a forum for discussions of concepts, issues, and methods by leading researchers in the fundamental problems of granular computing in an atmosphere intended to promote the exchange of ideas and viewpoints.

For Paper Submission, please click here