2016 IEEE/ACM 9th International Conference on Utility and Cloud Computing

2016 IEEE/ACM 3rd International Conference

on Big Data Computing, Applications and Technologies

6-9 December 2016 Shanghai, China

Conference Program

Message from the UCC 2016 General and Organizing Chairs

It is our great pleasure to welcome you to the 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2016, held at Tongji University, Shanghai, China, from 6–9 December 2016.

The IEEE/ACM International Conference on Utility and Cloud Computing (UCC) is a premier IEEE/ACM conference covering all areas related to cloud computing as a utility and provides an international forum for leading researchers and practitioners in this important and growing field. As with the previous successful instances of this conference series, UCC 2016, to be held in Shanghai, brings academics and industrial researchers together to discuss leading innovations in cloud computing research and novel uses of this technology in applications. We received 85 submissions this year, of which 22 were accepted, leading to an acceptance rate of ~26%. We received papers from 30 countries, with most submissions being from China, the United Kingdom, and the United States. The country with the highest acceptance rate (given the number of submissions) is the United Kingdom.

An international conference requires the hard work and dedication of many people. First, we would like to thank Program Committee Chairs Josef Spillner (Zurich University of Applied Sciences, Switzerland) and Alan Sill (Texas Tech, USA) together with all Program Committee members and reviewers for their considerable time and effort. Their dedication and commitment to coordinating the review process and ensuring that reviews were of high quality has been essential to once again provide a high-quality program. We would like to thank Honorary Chair Prof. Rajkumar Buyya and the Steering Committee members for their valuable help and support for UCC 2016. We would like to express our appreciation to Workshop Chairs Khalid Elgazzar (Carnegie Mellon University, USA) and Shangguang Wang (BUPT, China) for coordinating the workshops. We would also like to thank Proceeding Chairs Samee Khan (North Dakota State University, USA), Ashiq Anjum (University of Derby, UK), and Bo Yuan (Tongji University, China) for preparing the proceedings for the conference. Special thanks go to the Publicity Chairs George Papadopoulos (University of Cyprus, Cyprus), Zhiyi Huang (University of Otago, New Zealand), Zhangxi Lin (Texas Tech, USA), Yan Wu (Jiangsu University, China), and Luiz Bittencourt (UNICAMP, Brazil) for their efforts to distribute the Call for Papers in their respective regions around the world. We would like to thank Poster Chair Rafael Tolosana-Calasanz, (University of Zaragoza, Spain) for arranging the poster session for the conference, and Doctoral Symposium Chairs Rami Bahsoon (University of Birmingham, UK) and Zhihui Du (Tsinghua University, China) for organizing a PhD Symposium. Collectively, their efforts have produced the peer-reviewed, high-quality program that you will see at UCC 2016.

We also express our gratitude to the Registration and Financial Chair Richard Hill (University of Derby, UK) for managing the registration system and other technical assistance and to Web Chair John Panneerselvam (University of Derby, UK) for maintaining the conference website. A very special appreciation goes out to the UCC 2016 local organizing team for their hard work and local arrangements for the event.

Finally, we would like to thank the authors for choosing UCC 2016 as the venue at which to present their research and all of the participants attending this event. We hope that the conference fosters interaction among researchers and provides a stimulating forum for exchanging new ideas and sharing development experiences in the rapidly changing field of cloud computing and utility computing.

We would like to single out one individual who has played a major role in coordinating activities this year. His hard work, people skills, and unending support for virtually all UCC activities has enabled us to meet IEEE/ACM deadlines and produce the high-quality program you will see this year. This is Prof. Lu Liu from the University of Derby. It has been a pleasure to interact with him over the last year, and to work alongside him with other members of the organizing committee.

We hope you will all enjoy the conference and enjoy your stay in Shanghai China.

Changjun Jiang, Tongji University, China Omer Rana, Cardiff University, UK Nick Antonopoulos, University of Derby, UK UCC 2016 General Chairs Zhijun Ding, Tongji University, China Yaying Zhang, Tongji University, China Cheng Wang, Tongji University, China Lu Liu, University of Derby, UK UCC 2016 Organizing Chairs

Message from the UCC 2016 Technical Program Committee Co-Chairs

We are delighted to see UCC return to China this year. As a strongly community-driven conference, the 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2016) has the ambition to bring together researchers and practitioners from all parts of the world. This ambition leads to constant changes in conference locations, formats, topics, and trends. And yet, the topics do not diverge too much from one year to another, suggesting that cloud and utility computing challenges remain interesting everywhere. This year, the carefully selected TPC members, many of whom are previous authors to UCC, have selected 15 full papers and 6 short papers out of 85 submissions in total. While acceptance rates are poor metrics for conference quality, they are nevertheless a useful indicator, and in this case are determined to be 18% for full papers and 26% overall. This highly selective process with 193 reviews in total confirms the commitment by the UCC TPC and the steering committee to deliver a high-quality conference that reports on significant works. Five interesting full-paper sessions and one packed short-paper session are representing current global cloud research. Submissions have been received from 30 countries. The host country, China, saw 56 submissions, followed by the United Kingdom with 26, and the United States with 20. The topics cover the whole cloud stack from hardware and infrastructure to middleware, scheduling and monitoring up to the applications, and their software design and tuning. Enjoy the conference with all of its tracks and the wonderful city of Shanghai, which will give you a good time in connection with the social programme offered by the local host. Shanghai’s history is one of cultural encounters, debates about difficult situations and how to proceed, and which technologies to use to advance quickly—the perfect setting for serious debates among academic and industrial researchers and other attendees at UCC 2016. Josef Spillner, Zurich University of Applied Sciences, Switzerland Alan Sill, Texas Tech, USA UCC 2016 Technical Program Committee Co-Chairs

Message from the IEEE/ACM BDCAT 2016 Program Co-Chairs

On behalf of the program committee, it is our pleasure to welcome you to the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, being held in Shanghai, China. Rapid advances in digital sensors, networks, storage, and computation along with their availability at low cost is leading to the creation of huge collections of data—dubbed “Big Data.” As a result, a Big Data computing paradigm has emerged, enabling new insights that can change the way business, science, and governments deliver services to their consumers, and can impact society as a whole. BDCAT provides an international forum for researchers and practitioners to present and discuss new discoveries, developments, and results, as well as the latest trends in big data computing, technologies, and applications. Since the 1st Big Data Computing conference (BDC 2014 held in London, UK), the conference has been continuously growing. This year we reviewed 100 submissions from 23 countries. The conference accepted 24 papers as regular papers, leading to an acceptance rate of 24%. The conference also accepted an additional 15 papers as short papers. For this we would like to acknowledge the dedication and tremendous efforts of the program committee and reviewers, who gave their time and expertise as we handled these submissions. An event such as BDCAT 2016 is not possible without the coordinated efforts of many dedicated individuals who volunteer their time and expertise. We would like to acknowledge the leadership of the conference Honorary Chairs, Prof. Geoffrey Fox at the Indiana University, Prof. Rajkumar Buyya at the University of Melbourne, and Prof. Beng Chin Ooi at the National University of Singapore. We are also grateful for the dedication and hard work of the Local Organizing Chair, Prof. Cheng Wong at the Tongji University. We also acknowledge the Publicity Chairs, Prof. Yaser Jararweh at the Jordan University of Science and Technology and Prof. Shruti Kohli at the University of Birmingham. We hope that you will find the BDCAT 2016 technical program interesting and thought provoking, and that it provides you with a valuable opportunity to share ideas with researchers and practitioners from academia and industry from around the world.

Prof. Ashiq Anjum Department of Computing and Mathematics University of Derby, UK

Prof. Xinghui Zhao School of Engineering and Computer Science Washington State University Vancouver, USA

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Program at A Glance

Date/Time ActivityRoom/LocationC201 C301 C401 A401

5-Dec14:00-18:00 Registration (Location:Tongji Sino French Center, Siping Road Campus, Tongji University)

6-Dec08:00 -- 18:00 Registration (Location:Tongji Sino French Center, Siping Road Campus, Tongji University)9:00 - 9:20 Opening Opening (Location:C201)9:20- 10:20 Plenary Keynote 1 Minyi Guo, "Platform Development for Collaborative Computing with Urban Big Data"(Location: C201)10:20- 10:40 Tea/Coffee break (JCR)10:40 - 12:10 Parallel Sessions UCC 1 BDCAT 1 SCCTSA 1 BIUC 112:10 - 13:10 Lunch: Tongji Sanhaowu Restaurant13:10 - 14:10 Plenary Keynote 2 Jae Kyu Lee, "Can the Bright Cloud be a Business Model?"14:10 - 15:40 Parallel Sessions UCC 2 BDCAT 2 SCCTSA 2 BIUC 215:40 - 16:00 Tea/Coffee break (JCR)16:00 - 17:30 Parallel Sessions UCC 3 BDCAT 3 Posters Session BIUC 318:00 - 20:30 Reception (Location:Kingswell Hotel Tongji)

7-Dec9:00 - 10:00 Plenary Keynote 3 Xinbing Wang, "Paperbook: Design and Implementation"(Location: C201)10:00 - 10:30 Tea/Coffee break (JCR)10:30 - 12:00 Parallel Sessions UCC 4 BDCAT 4 SD3C 112:00 - 13:00 Lunch:Tongji Sanhaowu Restaurant13:00 - 14:00 Plenary Keynote 4 Geyong Min, "Cloud-Assisted and Data-Driven Knowledge Discovery for Future Internet"(Location: C201)14:00 - 15:30 Parallel Sessions UCC 5 BDCAT 5 CloudAM 115:30 - 15:50 Tea/Coffee break (JCR)15:50 - 17:20 Parallel Sessions PhD Symposium BDCAT 6 CloudAM 2

8-Dec9:00 - 10:00 Plenary Keynote 5 Hui Lei, "When Big Data Meets Cognitive Computing ... on the Cloud"(Location: C201)10:00 - 10:30 Tea/Coffee break (JCR)10:30 - 12:00 Parallel Sessions UCC 6 BDCAT 7 RTDPCC 112:00 - 13:00 Lunch:Tongji Sanhaowu restaurant13:00 - 14:00 Plenary Keynote 6 Jiannong Cao, "Performance Modeling and Optimization in Mobile Cloud Computing Environment"(Location: C201)14:00 - 15:30 Parallel Sessions BDCAT 12 BDCAT 8 BDCAT 1115:30 - 15:50 Tea/Coffee break (JCR)15:50 - 17:30 Plenary Panel UCC and BDCAT (Location:C201)18:00 - 20:30 Best Paper Awards/Planning for 2017/Banquet(Location: Kingswell Hotel Tongji)

9-Dec9:00 - 10:00 Plenary Keynote 7 Dharma Rajan, "The Art of Transforming Traditional Utilities to the Cloud Model"(Location: C201)10:00 - 10:30 Tea/Coffee break (JCR)10:30 - 12:00 Parallel Sessions IDP 1 BDCAT 9 RTDPCC 212:00 - 13:00 Lunch:Tongji Sanhaowu Restaurant13:00 - 14:30 Parallel Sessions IDP 2 BDCAT 10 RTDPCC 314:30 - 14:50 Tea/Coffee break (JCR)14:50-17:30 Parallel Sessions Tutorial 1 Tutorial 2 Tutorial 317:30 Closing (Location:C201)

Conference Venues:Registration, Plenary Keynotes and Technical Sessions Tongji Sino French Center (同济中法中心, 四平路校区)Lunch Tongji Sanhaowu Restaurant (同济三好坞餐厅, 四平路校区)Reception and Banquet Kingswell Hotel Tongji (同济君禧大酒店, 四平路校区)

9th IEEE/ACM International Conference on Utility and Cloud ComputingProgram

Room/Location: C201Date/Time Activity Program

December 6th, 2016

10:40-12:10Session 1: Hardware-as- a-Service and Energy Session Chair: Gleb Radchenko

full paperAnca Iordache, Guillaume Pierre, Peter Sanders, Jose Gabriel De F. Coutinho and Mark Stillwell: "Democratizing High Performance in the Cloud with FPGA Groups"

full paperPeter Garraghan, Yaser Al-Anii, Jon Summers, Harvey Thompson, Nik Kapur and Karim Djemame: "A Unified Model for Holistic Power Usage in Cloud Datacenter Servers"

full paperMauro Canuto, Mario Macias and Jordi Guitart: "A Methodology for Full-System Power Modeling in Heterogeneous Data Centers"

14:10-15:40

Session 2: Emerging Topics in Cloud Computing Session Chair: Radu Prodan

short paperMohan Baruwal Chhetri, Quoc Bao Vo and Ryszard Kowalczyk: "CL-SLAM: Cross-Layer SLA Monitoring Framework for Cloud Service-Based Applications"

short paperMichael Borkowski, Stefan Schulte and Christoph Hochreiner: "Predicting Cloud Resource Utilization"

short paper Wei Wang: "Towards an Emerging Cloudware Paradigm for Transparent Computing"

short paperKuan-Hsin Lee, I-Cheng Lai and Che-Rung Lee: "Optimizing Back-and-forth Live Migration"

short paperThiago A. L. Genez, Luiz F. Bittencourt, Rizos Sakellariou and Edmundo Madeira: "A Flexible Scheduler for Workflow Ensembles"

short paperWilliam Tarneberg, Vishal Chandrasekaran and Marty Humphrey: "Experiences Creating a Framework for Smart Traffic Control using AWS IOT"

16:00-17:30Session 3: Scheduling and Scalability Session Chair: Luiz Bittencourt

full paperVahid Arabnejad, Kris Bubendorfer and Bryan Ng: "Deadline Distribution Strategies for Scientific Workflow Scheduling in Commercial Clouds"

full paperCarlos Mera-Gómez, Rami Bahsoon and Rajkumar Buyya: "Elasticity Debt: A Debt-Aware Approach to Reason About Elasticity Decisions in the Cloud"

full paperHamid Mohammadi Fard, Sasko Ristov and Radu Prodan: "Handling the Uncertainty in Resource Performance for Executing Workflow Applications in Clouds"

December 7th, 2016

10:30-12:00 Session 4: Virtualisation Session Chair: Fahimeh Farahnakian

full paperDinuni Fernando, Hardik Bagdi, Yaohui Hu, Ping Yang, Kartik Gopalan, Charles Kamhoua and Kevin Kwiat: "Quick Eviction of Virtual Machines Through Proactive Live Snapshots"

full paperVincenzo De Maio, Gabor Kecskemeti and Radu Prodan: "An Improved Model for Live Migration in Data Centre Simulators"

full paper

Muyang He, Paul Pang, Denis Lavrov, Ding Lu, Yuan Zhang and Abdolhossein Sarrafzadeh: "Reverse Replication of Virtual Machines (rRVM) for Low Latency and High Availability Services"

14:00-15:30Session 5: Monitoring and Tuning Session Chair: Mohan Baruwal Chhetri

full paperDániel Géhberger, Péter Mátray and Gábor Németh: "Data-Driven Monitoring for Cloud Compute Systems"

full paperBernhard Primas, Peter Garraghan, Karim Djemame and Natasha Shakhlevich: "Resource Boxing: Converting Realistic Cloud Task Utilization Patterns for Theoretical Scheduling"

full paperOleg Sukhoroslov, Sergey Volkov and Alexander Afanasiev: "Program Autotuning as a Service: Opportunities and Challenges"

December 8th, 2016

10:30-12:00Session 6: Services and Federation Session Chair: Dirk Habich

full paperYash Khandelwal, Suresh Purini and Puduru Reddy: "Fast Algorithms for Optimal Coalition Formation in Federated Clouds"

full paperPhilipp Leitner, Jürgen Cito and Emanuel Stöckli: "Modelling and Managing Deployment Costs of Microservice-Based Cloud Applications"

full paperRichard Sinnot, Natasha Thomas, Himanshu Bansal and Zeyu Zhao: "My Ever Changing Moods: Sentiment-based Event Detection on the Cloud"

3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

ProgramRoom/Location: C301 (exceptions: Session 11 in C401, Session 12 in C201)

Date/Time Activity ProgramDecember 6th, 2016

10:40-12:10Session 1: Big Data & Machine Learning Session Chair: Ying Xie

full paperLinh Le, Jie Hao, Ying Xie and Jennifer Priestley: "Deep Kernel: Learning Kernel Function from Data Using Deep Neural Network"

full paperSalman Salloum and Joshua Zhexue Huang: "Empirical Analysis of Asymptotic Ensemble Learning for Big Data"

full paperMuhammad Usman Yaseen: "Cloud-based Blur and Illumination Invariant Object Classification"

14:10-15:40Session 2: Hadoop & Spark Session Chair: Iman Elghandour

full paperOrazio Tomarchio, Giuseppe Di Modica, Marco Cavallo and Carmelo Polito: "H2F: a Hierarchical Hadoop Framework for big data processing in geo-distributed environments"

full paperShashank Gugnani, Xiaoyi Lu and Dhabaleswar Panda: "Performance Characterization of Hadoop Workloads on SR-IOV-enabled Virtualized InfiniBand Clusters"

full paperYi Chen and Behzad Bordbar: "DRESS: A Rule Engine on Spark for Event Stream Processing"

16:00-17:30Session 3: Visualization & Social Networks Session Chair: Mohsen Farid

full paperChris Muelder, Robert Faris and Kwan-Liu Ma: "A Visual Analytics Approach to Author Name Disambiguation"

short paperYing Xie, Pooja Chenna, Jing Selena He, Lihn Le and Jacey Planteen: "Visualization of Big High Dimensional Data in a 3 Dimensional Space"

short paperAqsa Hameed, Saqib Ali, Roger Cottrell and Bebo White: "Applying Big Data Warehousing and Visualization Techniques on PingER Data"

short paperBo Yuan, Lu Liu and Nick Antonopoulos: "Efficient Service Discovery in Decentralized Online Social Networks"

December 7th, 2016

10:30-12:00Session 4: Health Applications Session Chair: Richard Sinnott

full paper

Arjun Athreya, Kee Yuan Ngiam, Zhaojing Luo, Tai E Shyong, Zbigniew Kalbarczyk and Ravishankar Iyer: "Towards Longitudinal Analysis of a Population’s Electronic Health Records using Factor Graphs"

full paperChunxiao Xing, Fengjing Shao, Shunyao Wu and Rencheng Sun:"Disease gene discovery of single-gene disorders based on Complex Network"

short paperMohammed Bahja and Mark Lycett: "Identifying Patient Experience from Online Re-sources via Sentiment Analysis and Topic Moddeling Approach "

14:00-15:30Session 5: Data Model & Information Retrieval Session Chair: Xinghui Zhao

full paperAmir Sinaeepourfard, Jordi Garcia, Xavier Masip-Bruin and Eva Marín-Tordera: "Towards a Comprehensive Data LifeCycle model for Big Data Environments"

full paperChristina Lioma, Birger Larsen, Wei Lu and Yong Huang: "A Study of Factuality, Objectivity and Relevance: Three Desiderata in Large-Scale Information Retrieval?"

short paper

Francisco J. Clemente-Castelló, Bogdan Nicolae, M. Mustafa Rafique, Rafael Mayo Gual and Juan Carlos Fernandez: "On Exploiting Data Locality for Iterative MapReduce Applications in Hybrid Clouds"

15:50-17:20Session 6: Spatial Data Analytics Session Chair: Yong Xue

full paperMariam Malak Fahmy, Iman Elghandour and Magdy Nagy: "CoS-HDFS: Co-Locating Geo-Distributed Spatial Data in Hadoop Distributed File System"

full paper

Lau Pik Lik, Tanmay Chaturvedi, Kai Kiat Benny Ng, Kai Li, Marakkalage Sumudu Hasala and Chau Yuen: "Spatio and Temporal Analysis of Urban Space Utilization Renewable Wireless Sensor Network"

short paperVinutha Magal Shreenath and Sebastiaan Meijer: "Spatial Big Data for designing large scale infrastructure"

December 8th, 2016

10:30-12:00Session 7: Scalability & Performance Session Chair: Radu Prodan

full paperJingcai Guo: "An Improved Incremental Training Approach for Large Scaled Dataset based on Support Vector Machine"

full paperLinlin You and Bige Tunçer: "SAPAM: a Scalable “Activities in Places” Analysis Mechanism for Informed Place Design"

full paper

Mohamed Hassaan and Iman Elghandour: "DAMB: A Real-Time Big Data Analysis Framework on a CPU/GPU Heterogeneous Cluster\\ A Meteorological Application Case Study"

14:00-15:30Session 8: Pattern Detection & Recognition Session Chair: Philipp Leitner

full paperChristophe Courtin and Miguel Tomasena: "A Benchmarking Platform for Analyzing Corpora of Traces: The recognition of the users' involvement in fields of competencies"

full paperAbdul Razaq, Huaglory Tianfield and Peter Barrie: "A Big Data Analytics Based Approach to Anomaly Detection "

short paperJingjiao Zhang, Jiaqing Fu, Chunhong Zhang and Zheng Hu: "Not Too Late to Identify Potential Churners: Early Churn Prediction in Telecommunication Industry"

14:00-15:30Session 11: Big Data Applications 1 Session Chair: Nick Antonopoulos (Room:C401)

full paper

Patrick Glauner, Jorge Meira, Lautaro Dolberg, Radu State, Franck Bettinger, Yves Rangoni and Diogo Duarte: "Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets"

short paper

Qing Sun, Niu Jianwei, Zhong Yao and Qiu Dongmin: "Research on Semantic Orientation Classification of Chinese Online Product Reviews Based on Multi-aspect Sentiment Analysis"

short paperAnne Tall, Jun Wang and Dezhi Han: "Survey of Data Intensive Computing Technologies Application to Security Log Data Management"

short paperMohammed Nasser, Ibrahim Kamel and Zaher Al Aghbari: "Social Community Detection based on Node Distance and Interest"

14:00-15:30Session 12: Big Data Applications 2 Session Chair: Rafael Tolosana (Room:C201)

full paperGuoying Zhang, Min He, Hao Wu, Guanghui Cai and Jianhong Ge: "Non-negative Multiple Matrix Factorization with Social Similarity for Recommender Systems"

short paperJian Li, Guanjun Liu, Changjun Jiang and Chungang Yan: "A Hybrid Method of Recommending POIs Based on Context and Personal Preference Confidence"

short paper Jie Hou and Ya Zhang:"Synergy and Antagonism in Online Advertising"

short paperManxing Du, Radu State, Mats Brorsson and Tigran Avanesov: "Behavior Profiling for Mobile Advertising"

December 9th, 2016

10:30-12:00Session 9: Visual and Graph Analytics Session Chair: Luiz Fernando Bittencourt

full paperTim Kiefer, Dirk Habich and Wolfgang Lehner: "Penalized Graph Partitioning based Allocation Strategy for Database-as-a-Service Systems"

full paperPhuong-Hanh Du and Ngoc-Hoa Nguyen: "Optimizing the shortest path query on large-scale dynamic directed graph"

short paperZhenglong Yan, Wenjian Luo, Chenyang Bu and Li Ni: "Clustering Spatial Data by the Neighbors Intersection and the Density Difference"

13:00-14:30Session 10: Memory & Storage Session Chair: Ashiq Anjum

full paperJie Zhou, Yanyan Shen, Sumin Li and Linpeng Huang: "NVHT: An Efficient Key-Value Storage Library for Non-Volatile Memory"

full paperAhsan Javed Awan, Mats Brorsson, Vladimir Vlassov and Eduard Ayguade: "Node Architecture Implications for In-Memory Data Analytics in Scale-in Clusters"

short paperMehnuma Tabassum Omar and K. M Azharul Hasan: "A Scalable Storage System for Structured Data based on Higher Order Index Array"

3rd International Workshop on Smart City Clouds: Technologies, Systems and Applications (SCCTSA 2016)

ProgramRoom/Location: C401Date/Time Activity Program6th December10:40 - 12:10 Systems and Application Session Chair: Zaheer Khan

OpeningGuest Talk: Richard McClatchey - Emerging Technologies for Supporting Smarter CitiesAntorweep Chakravorty, Bikash Agrawal, Tomasz Wiktorski and Chunming Rong:"Enrichment of Machine Learning based Activity Classification in Smart Homes using Ensemble Learning"Kamran Soomro, Zaheer Khan and Khawar Hasham:"Towards Provisioning of Real-time Smart City Services Using Clouds"Rawad Hammad and David Ludlow:"Towards A Smart Learning Environment for Smart City Governance"

14:10 - 15:40 Security and Safety Session Chair: Zaheer KhanAljawharah Almuaythir and Mohammad Anwar Hossain:"Cloud-Based Parametrized Publish/Subscribe System for Public Safety Applications in Smarter Cities"Shohreh Hosseinzadeh, Samuel Laurén and Ville Leppänen:"Security in Container-based Virtualization Through Vtpm"Yu Lei, Philip S. Yu:"Service Topic Model with Probability Distance"

5th International Workshop on Clouds and (eScience) Applications Management - CloudAM 2016

ProgramRoom/Location: C401Date/Time Activity Program7th December

14:00 - 15:30Service Composition, Scheduling & Performance Session Chair: Luiz Bittencourt

OpeningKuo-Chan Huang, Yu-Chun Lu, Meng-Han Tsai, Ying-Jhih Wu and Hsi-Ya Chang: "Performance-Efficient Service Deployment and Scheduling Methods for Composite Cloud Services"Bilkisu Larai, Muhammad-Bello, Masayoshi Aritsugi: "TCloud: A Transparent Framework for Public Cloud Service Comparison"Jorge Mario Cortés-Mendoza, Andrei Tchernykh, Alexander Drozdov and Loic Didelot: "Robust Cloud VoIP Scheduling with VMs Startup Time Delay Uncertainty"Vojtech Uhlir, Ondrej Tomanek, Lukas Kencl: "Latency–based Benchmarking of Cloud Service Providers"

15:50 - 17:20 Resource Management Session Chair: Ashiq AnjumLuiz Henrique Nunes, Julio Cezar Estrella, Stephan Reiff-Marganiec, Alexandre Claúdio Botazzo Delbem and Charith Perera: "The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study"Victor Medel, Omer Rana, Jose Angel Bañares and Unai Arronategui: "Modelling Performance and Resource Management in Kubernetes"Rafael Tolosana-Calasanz, Javier Diaz-Montes, Luiz F. Bittencourt, Omer Rana and Manish Parashar: "Capacity Management for Streaming Applications over Cloud Infrastructures with Micro Billing Models"

Closing

5th International Workshop on Bright Internet-based Utility Computing BIUC 2016

ProgramRoom/Location: A401Date/Time Activity Program6th December

10:40-12:10Session 1: Big data & bright Internet Session Chair: Zhangxi Lin

full paperDazeng Yuan, Mingxing He, Shengke Zeng, Xiao Li, and Long Lu:"(t,p)-Threshold Point Function Secret Sharing Scheme Based on Polynomial Interpolation And Its Application"

full paperShengke Zeng, Shuangquan Tan, Yong Chen, Mingxing He, Meichen Xia, and Xiao Li:"Privacy-preserving Location-based Service based on Deniable Authentication"

full paperMeina Song, Xuejun Zhao, Haihong E, Zhonghong Ou:"Statistic-based CRM approach via time series segmenting RFM on large scale data"

full paperHu Yang and Yu He:"The Penalized Weighted Clustering Algorithm for Missing and Noisy Data"

14:10-15:40Session 2: Social network analytics Session Chair: Zhu Jian Ming

full paperJianfeng Li, Zhangxi Lin and Jiaxian Qiu:"A social network-based evaluation of credit in online P2P lending market"

full paperGuofang Ma, Yuexuan Wang, Xiaolin Zheng and Litao Xiao:"Leveraging Social Trust Relations to Improve Cross-domain Recommendation"

full paperRui Gu and Kanliang Wang:"Empirical Study of Mobile Social Network Users’ Dissemination of Health-Threatening Information"

full paperYuhao Li and Kanliang Wang:"Avoid It in a private and social space? An Empirical Study of Marketers-generated Content Avoidance"

full paper Wenping Zhang and Wei Xu:"Is a Hospital Reliable? Its Website Tells"

16:00-17:30 Session 3: Emerging Topics Session Chair: Wei Xu

full paperArodh Lal Karn, Niranjan Sapkota and Muhammad Rafiq: "Incorporating News in Real Time Trading:A High Frequency Trading perspective"

full paperZhang Ge, Zhang Shuo and Yang Yiping: "Analysis of Hotelling Model in Enterprise Cloud Computing Competition based on User Participation"

full paperYufan Wang and Yingjing Wu: "Research on Determinants of E-commerce Blend Degree on Sustained Competitive Abilities of SME in Inner Mongolia"

full paperFu Yong Gui and Zhu Jian Ming: "Operation Mechanism and Data Value Analysis for G2B System Based on Blockchain"

full paperNing Zhang and Shan Zhong: "Using Blockchain to Protect Personal Privacy in the Scenario of Internet Car Rental"

2nd Workshop on Sustainable Data Centers and Cloud Computing SD3C 2016

ProgramRoom/Location: C401Date/Time Activity Program7th December

10:30 - 12:00Sustainable Data Centers and Cloud Computing Session Chair: Robert Birke

OpeningBob Duncan, Andreas Happe and Alfred Bratterud:"Enterprise IoT Security and Scalability: How Unikernels can Improve the Status Quo"Mstapha Ait-Idir and Nazim Agoulmine:"Enhancing Cloud capabilities for SLA enforcement of Cloud scheduled applications"Petteri Mäki, Sampsa Rauti, Shohreh Hosseinzadeh, Lauri Koivunen and Ville Leppänen:"Interface Diversification in IoT Operating Systems"Alessandro Carrega and Matteo Repetto:"Exploiting Novel Software Development Paradigms to Increase the Sustainability of Data Centers"Xiangyue Huang, Zhifeng Zhao and Honggang Zhang:"Latency Analysis of Cooperative Caching with Multicast for 5G Wireless Networks"

5th International Workshop on Intelligent Data Processing IDP 2016Program

Room/Location: C201Date/Time Activity Program9th December10:30 - 12:00 Recognition and Prediction Session Chair: Haolan Zhang

Keynote: Sonya Zhang "The Use of Machine Learning in Business"Wen Xu, Jing He and Hao Lan Zhang:"Real-Time Target Detection and Recognition with Deep Convolutional Networks for Intelligent Visual Surveillance"Valentina Franzoni, Giulio Biondi, Alfredo Milani and Yuanxi Li:"Web-based Similarity for Emotion Recognition in Web Objects"Bilal Mehboob, Muzamal Liaqat and Nazar Abbas:"Student Performance Prediction and Risk Analysis by Using Data Mining Approach"

13:00 - 14:30 Intelligent Processing Session Chair: Haolan ZhangXiaoyun Li, Shizhong Huang, Huanyu Zhao, Xueyan Guo, Libo Xu, Xingsen Li and Youjia Li:"Image Compression Based on Restricted Wavelet Synopses with Maximum Error Bound"Mengyuan Pan, Yang Yang and Zhenqiang Mi:"Research on an extended SVD Recommendation algorithm based on user’s neighbor model"Wang Suzhen and Zhou Haowei:"The research of MapReduce load balancing based on multiple partition algorithm"

International Symposium on Real-time Data Processing for Cloud Computing (RTDPCC 2016)

ProgramRoom/Location: C401Date/Time Activity Program8th December 10:30-12:00 Data Processing Session Chair: Lu Liu

Keynote: Prof Yong Xue, 'Big Earth Data – a New Dimension for Digital Earth'Nan Guo, Yuan He, ChunGang Yan, Lu Liu, Cheng Wang: "Multi-level Topical Text Categorization with Wikipedia"Jun Yu and Zengfu Wang: "A Monocular Video-Based Facial Expression Recognition System by Combining Static and Dynamic Knowledge"Paul Comerford, John N. Davies and Vic Grout: "Reducing packet delay through filter merging"

9th December

10:30 - 12:00 Strategy and Applications Session Chair: Xiaojun ZhaiKeynote: Dr Liangxiu Han, “Meeting Society Challenges: Big Data Driven Approaches”Guilin Shao and Jiming Chen: "A Load Balancing Strategy Based on Data Correlationin Cloud Computing"Xingzhen Bai, Maoyong Cao, Lu Liu and John Panneerselvam: "Collaborative Estimation and Actuation of Wireless Sensor and Actuator Networks for the Greenhouse Environment"Li Song, Hong Zhong and Jie Cui: "A Certificateless Public Auditing Scheme for Cloud-based Wireless Body Area Network with Revocation and Privacy Preserving"

13:00 - 14:30 Network Access and Control Session Chair: Xiaojun ZhaiJie Cui, Hong Zhong and Xuan Tang: "A Fined-grained Privacy-Preserving Access Control Protocol in Wireless Sensor Networks"Mohammad Al-Athamneh, Fatih Kurugollu, Danny Crookes and Mohsen Farid: "Video Authentication Based on Statistical Local Information"Shoukun Wang, Kaigui Wu and Changze Wu: "Attribute-Based Solution with Time Restriction Delegate for Flexible and Scalable Access Control in Cloud Computing"Bksp Kumar Raju, Bhupendra Moharil and G Geethakumari:"FaaSeC: Enabling Forensics-as-a-Service for Cloud Computing Systems"

PhD SymposiumProgram

Room/Location: C201Date/Time Activity Program7th December15:50 - 17:20 PhD Symposium Session Chair: Zhihui Du

Carlos Ruiz, Hector A. Duran-Limon, Nikos Parlavantzas:"Towards a Software Product Line-based approach to adapt IaaS cloud configurations"Salim Saay, Alex Norta:"A Reference Architecture for a National e-Learning Infrastructure"Yatheendraprakash Govindaraju, Hector Duran-Limon:"A QoS and Energy aware Load Balancing and Resource Allocation Framework for IaaS Cloud"Moeez Masroor Subhani, Ashiq Anjum:"Clinical and Genomics Data"Bilal Arshad, Ashiq Anjum:"Graph Based Data Integration for System Integrity and Scalable Analytics"

Posters SessionProgram

Room/Location: C4016th December

16:00 - 17:30 Posters Session Session Chair: Rafael Tolosana-Calasanz

Poster Title Poster Authors

16:00QuRAM Service Recommender: A Platform for IaaS Service Selection Sima Soltani, Khalid Elgazzar and Patrick Matin

16:15Adaptive Application Scheduling under Interference in Kubernetes

Victor Medel, Omer Rana, Jose A. Bañares and Unai Arronategui

16:30 Testing-as-a-Service Approach for Cloud Applications Gleb Radchenko, Dmitry Savchenko and Nikita Ashikhmin

16:45Titian2: A Scalable System-level Emulator with All Programmability for Datacenter Servers in Cloud Computing

Ke Zhang, Ran Zhao, Hongxia Zhang, Lei Yu, Yisong Chang, Zhao Zhang and Mingyu Chen

17:00Reducting the VM Instance Rental Cost in the Cloud Spot Market Jianxiong Wan, Gefei Zhang, Xiang Gui and Ran Zhang

TutorialsProgram

Date/Time Activity Program Room9th December

14:50-17:30 Tutorial 1Patrick Glauner and Radu State, "Deep Learning on Big Data Sets in the Cloud with Apache Spark and Google TensorFlow" C201

14:50-17:30 Tutorial 2Fionn Murtagh and Mohsen Farid, "Survey Analytics from Questionnaires and Textual Social Media Analytics" C301

14:50-17:30 Tutorial 3 Jiming Wu, "Use Amazon Elastic MapReduce to Process Big Data" C401

Plenary Keynote: Platform Development for Collaborative Computing with Urban Big data

Professor Minyi Guo, Shanghai Jiao Tong University, China

Abstract: Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety

of big data in urban spaces, e.g. human mobility, air quality, traffic patterns, and geographical data. The big data implies rich knowledge about a city and can help tackle these challenges when used correctly. We believe this is the right time to research on holistic urban big data which has been made possible due to recent advances in communication technologies that allow wireless connection and untethered data exchange among vast urban sensing and computing devices, as well as advanced data and computing science that provides us necessary methods and computing power to understand, model, and reason the urban data and people. In this talk, we give some properties for processing urban big data, introduce a system for urban big data processing, and discuss how the collaborative computing bridges the data and computation in the cyber space and the environment, systems, people and things in the physical world. Biography:

Minyi Guo is currently Zhiyuan Chair professor and chair of the Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU), China. Before joined SJTU, Dr. Guo had been a professor of the school of computer science and engineering, University of Aizu, Japan. Dr. Guo received the national science fund for distinguished young scholars from NSFC in 2007, and was supported by “1000 recruitment program of China” in 2010. His present research interests include parallel/distributed computing, compiler optimizations, embedded systems, pervasive computing, and cloud computing.

He has more than 300 publications in major journals and international conferences in these areas, including the IEEE Transactions on Parallel and Distributed Systems, the IEEE Transactions on Computers, the ACM Transactions on Autonomous and Adaptive Systems, INFOCOM, IPDPS, ICS, ISCA, HPCA, SC, WWW, PODC, etc. He received 5 best paper awards from international conferences. He is on the editorial board of IEEE Transactions on Parallel and Distributed Systems and Journal of Parallel and Distributed Computing.

Plenary Keynote: Can the Bright Cloud be a Business Model?

Professor Jae Kyu Lee, Korea Advanced Institute of Science & Technology, South Korea

Abstract: The Bright Internet aims a safer Internet platform where the origination of malicious behaviors can

be deterred because their origins can be identified. As such, the primary goal of the Bright Internet is the establishment of Preventive Security paradigm in contrast with the current paradigm of protective security of its own system.

The current cloud computing service providers have no choice but to adopt the protective security paradigm. In this talk, the benefit of adopting the Bright Internet platform will be presented in the cloud service provisioning. A question is how to motivate the individual Cloud Service Providers (CSPs) to adopt the Bright Internet platform.

For this purposes, we analyze the benefits of adopting the Bright Internet platform in terms of marketing, economy, and compliance to regulation.

1) Marketing Advantage: Suppose that the Bright Internet Global Governance Center certifies the cleanness level of outgoing messages which will upgrade their trustworthiness to their online business partners. If the clients of a CSP need such trustworthiness for their business creation, then the CSP needs to offer the Bright Internet based cloud services.

2) Economic Advantage: Suppose the Bright Internet Global Governance Center evaluates the levels of harms created by the originating companies such as CPSs. If the cost of preventive measure is more economical than the payment for the penalty, CSPs will be motivated to invest for preventive security for their clients.

3) Compliance Advantage: If the social value of preventive security is bigger than the sum of individual investments for it, the legislation that requires the preventive security measures will be socially justified. Then the CSPs will have a good reason to adopt the preventive measures like the Bright Internet.

We present the architecture of Bright Cloud that justifies these business models. To explain the concept of Bright Cloud, this talk will explain the three goals of Bright Internet (Preventive Security, Freedom of Anonymous Expression for the Innocent Netizens, and Privacy Protection) and Five Basic Principles (Origin Responsibility, Deliverer Responsibility, Identifiable Anonymity, Global Collaborative Search, and Privacy Protection). The specific Bright Cloud business models may adopt the essential principles that are most suitable for the specific business strategy. The first mover of Bright Cloud will be able to get the benefit of marketing advantage, and eventually the benefits of economic and compliance advantages.

Biography: Jae Kyu Lee was the HHI Chair Professor of Korea Advanced Institute of Science and Technology, and has become Professor Emeritus of KAIST since September 2016. He is currently the Director Emeritus of Bright Internet Research Center at KAIST, a Distinguished Visiting Professor at Heinz College of Carnegie Mellon University, and the Honorary Yingluo Wang Professor at School of Management at Xian Jiaotong University in China as a co-director of the Bright Internet Global Governance Research Center, China.

He is the Immediate Past President and Fellow of Association for Information Systems, and conference chair of International Conference on Information Systems 2017 in Seoul. He is also the chair of inaugurating Bright Internet Global Summit that will be held in Seoul as the pre-ICIS 2017.

He received a Ph.D. in Operations and Information Systems from the Wharton School, University of Pennsylvania (1985), and has been a Professor of Information Systems and Electronic Commerce at KAIST since then. He was the founding editor-in-chief of the journal, Electronic Commerce Research and Applications (Elsevier, SSCI and SCIE Accredited), and was the founding chair of the International Conference on Electronic Commerce. He was a chair of the International Conference on Electronic Commerce (ICEC 1998, and ICEC 2000) and Pacific Asia Conference on Information Systems (2001, 2006).

He was the President of Korea Society of Management Information Systems and Korea Society of Intelligent Information Systems, and served for the program committee of numerous international conferences in information systems, intelligent systems, and e-commerce.

He authored four English books and seven Korean books with many editions in the area of Electronic Commerce, Information System, and Intelligent Systems, including Electronic Commerce: A Managerial Perspective (2014 Springer; coauthored with Efraim Turban), Artificial Intelligence in Finance and Investing (Irwin). He published many international journal papers in journals such as MIS Quarterly, Information Systems Research, Decision Support Systems, Communications of ACM, Management Science, International Journal of Electronic Commerce, Expert System with Applications, European Journal of Information Systems, and many others. He presented many keynote speeches at ICIS, PACIS, AMCIS, and ICEC. He received the best paper awards ten times from the major conferences, and received a national decoration from the Korea Government for his contribution to the development of the IT industry.

His research interest has been the application of Artificial Intelligence for Managerial Decision Support, Electronic Commerce, and Green IT, and his current research interest is the establishment of the Bright Internet platform. He has conducted 45 granted projects on the topics of the Bright Internet, Green Business, eCommerce strategies for financial sectors, SCM and eProcurement Systems, case based project management systems, intelligent scheduling systems for ship building, power generation, and refinery.

Plenary Keynote: Paperbook: Design and Implementation

Professor Xinbing Wang, Shanghai Jiaotong University, China

Abstract:

In this keynote, we will introduce a novel academic system, paperbook or AceMap, to analyze the big scholarly data and present the results through a “map" approach. AceMap integrates several algorithms in the eld of network analysis and data mining, and then displays the information in a clear and intuitive way, aiming to help the researchers facilitate their work. After describing the big picture, we present achieved results and our work in progress. By far, AceMap has implemented the following functions: dynamic citation network display, paper clustering, academic genealogy, author and conference homepage, etc. We have also designed and performed distributed network analysis algorithms in a cutting-edge Spark system and utilized modern visualization tools to present the results. Finally, we conclude my keynote by proposing the future outlooks.

Biography:

Professor Xinbing Wang received the B.S. degree (with hons.) in Automation from Shanghai Jiao Tong University, Shanghai, China, in 1998, the M.S. degree in computer science and technology from Tsinghua University, Beijing, China, in 2001, and the Ph.D. degree with a major in electrical and computer engineering and minor in mathematics from North Carolina State University, Raleigh, in 2006. Currently, he is a Professor in the Department of Electronic Engineering, and Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China. Dr. Wang has been an Associate Editor for IEEE/ACM Transactions on Networking, IEEE Transactions on Mobile Computing, and ACM Transactions on Sensor

Networks. He has also been the Technical Program Committees of several conferences including ACM MobiCom 2012,2014, ACM MobiHoc 2012-2017, IEEE INFOCOM 2009-2017.

Plenary Keynote:Cloud-Assisted and Data-Driven Knowledge Discovery for Future Internet

Professor Geyong Min, University of Exeter, U.K.

Abstract: Autonomic Future Internet (AFI) coupled with the emerging SDN/NFV technologies is regarded as

a promising and viable solution for addressing many grand challenges faced by future 5G networks and Cloud computing systems. The ambition of AFI is to exploit an autonomic, intelligent and self-managing Future Internet with consequent improvement in system efficiency and performance, increased profitability, and reduced OPEX and CAPEX. Two key features of AFI are self-management and cognitive learning; the former is essential for complexity reduction and fast adaptation to changing situations and the latter can increase the intelligence through flexible knowledge utilization.

In this talk, we will present state-of-the-art network architecture for AFI that is seamlessly integrated with SDN and NFV. The core Knowledge Plane within this unified architecture is responsible for real-time network big data analysis and knowledge discovery in order to maintain high-level behaviors of how the system should be configured, managed, and optimized. To establish a powerful, flexible and scalable Knowledge Plane in AFI, we will present the innovative big data processing technologies and cost-effective platform developed in Cloud-assisted computational framework. This framework includes the unified representation of heterogeneous big data and real-time incremental data analysis tools for extracting valuable insights to support better decision making for system design, resource management and optimization. This talk offers the theoretical underpinning for efficient processing of big data, and also opens up a new horizon of research and development by exploiting the key intelligence and insights hidden in rich network big data for design and improvement of Future Internet and Cloud computing systems.

Biography:

Professor Geyong Min is a Chair in High Performance Computing and Networking with the Computer Science discipline in the College of Engineering, Mathematics and Physical Sciences at the University of Exeter, UK. His recent research has been supported by European FP6/FP7, UK EPSRC, Royal Academy of Engineering, Royal Society, and industrial partners including Motorola, IBM, Huawei Technologies, INMARSAT, and InforSense Ltd. Prof. Min is the Co-ordinator of two recently funded FP7 projects: 1) Quality-of-Experience Improvement for Mobile Multimedia across Heterogeneous Wireless Networks; and 2) Cross-Layer Investigation and Integration of Computing and Networking Aspects of Mobile Social

Networks. As a key team member and participant, he has made significant contributions to several EU funded research projects on Future Generation Internet. He has published more than 200 research papers in leading international journals including IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Multimedia, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, and at reputable international conferences, such as SIGCOMM-IMC, ICDCS, IPDPS, GLOBECOM, and ICC. He is an Associated Editor of several international journals, e.g., IEEE Transactions on Computers. He served as the General Chair/Program Chair of a number of international conferences in the area of Information and Communications Technologies.

Plenary Keynote: When Big Data Meets Cognitive Computing…on the Cloud

Hui Lei, Director and CTO, Watson Health Cloud IBM, U.S, IEEE Fellow

Abstract:

The cloud has turned into an important platform for business innovation and industry transformation, leveraging the rapid growth of big data and the emerging paradigm of cognitive computing. Specifically, big data is becoming the world’s new natural resource and is driving fundamental changes in technology, business and society. With its exponentially increasing volume, velocity and variety, big data promises to be for the 21st century what steam power was for the 18th century, electricity for the 19th, and gas and oil for the 20th. At the same time, the rise of cognitive systems represents the dawn of a new era of computing. A necessary and natural evolution of traditional programmable systems, cognitive systems are able to scale and extend human knowledge, reason with purpose, and learn and improve over time. More importantly, cognitive computing is a key enabling technology for turning big data into insights and delivering on the full value of big data.In this talk, I will draw upon our experience at IBM building the Watson Health Cloud, and discuss how big data and cognitive computing can come together to enable innovative health solutions that tackle many of the clinical, societal, and economic issues faced by today’s health industry. I will present use cases, highlight the challenges, describe our approaches, and relate to client experiences as appropriate.

Biography: Dr. Hui Lei is CTO, Watson Health Cloud at IBM. An IBM Distinguished Engineer, he provides leadership on the Watson Health Cloud technical strategy, and spearheads the design and development of the Watson Health Cloud platform. Prior to his current role, Dr. Lei was Senior Manager, Cloud Platform Technologies at the IBM T. J. Watson Research Center, where he led IBM’s worldwide research strategies in cloud infrastructure services and cloud managed services. Dr. Lei’s technical vision and creative contributions have influenced many commercial software products and services, which range

across big data solutions, cloud service offerings, middleware platform for mobile and pervasive computing, and e-business tooling. Dr. Lei is an active and recognized member of the international technical community. He is a Fellow of the IEEE, Editor-in-Chief of the IEEE Transactions on Cloud Computing, and Chair of the IEEE Computer Society Technical Committee on Business Informatics and Systems. He has taken part in many international conferences as a steering committee chair, general chair, technical program chair, or keynote speaker. He is also a prolific inventor and has over 70 patents to his credit. He received his PhD in Computer Science from Columbia University.

Plenary Keynote: Performance Modeling and Optimization in Mobile Cloud Computing Environment

Professor Jiannong Cao, Hong Kong Polytechnic University, China, IEEE Fellow

Abstract: Mobile cloud computing has emerged as a new paradigm in IT industry and led to many research

and development initiatives. High performance for both the end users and system providers remains to be an essential goal but is much more difficult to achieve in the new paradigm. Due to the diversity of applications in mobile cloud computing, there exists different performance models and thus various methodologies to enhance the application performance. In this talk, we focus on three types of mobile cloud applications, i.e., the workflow applications, data streaming applications, and the content delivery applications, and discuss how to model the performance of the applications. Based on the performance models, we then present our methods to optimize the application performance. In particular, for the workflow and data streaming applications, we will present a series of new solutions on computation partitioning to optimize the application performance, while for the content delivery application, we will present our recent work on load dispatching and service placement to minimize the overall latency of end users in accessing the content/services.

Biography:

Prof Jiannong Cao is currently a chair professor and head of the Department of Computing at Hong Kong Polytechnic University, Hung Hom, Hong Kong. His research interests include parallel and distributed computing, computer networks, mobile and pervasive computing, fault tolerance, and middleware. He has co-authored 3 books, co-edited 9 books, and published over 300 papers in major international journals and conference proceedings. He is a fellow of IEEE, a senior member of China Computer Federation, and a member of ACM. He was the Chair of the Technical Committee on Distributed Computing of IEEE Computer Society

from 2012 - 2014. Prof. Cao has served as an associate editor and a member of the editorial boards of many international journals, including ACM Transactions on Sensor Networks, IEEE Transacitons on Computers, IEEE Transactions on Parallel and Distributed Systems, IEEE Networks, Pervasive and Mobile Computing Journal, and Peer-to-Peer Networking and Applications. He has also served as a chair and member of organizing / program committees for many international conferences, including PERCOM, INFOCOM, ICDCS, IPDPS, ICPP, RTSS, DSN, ICNP, SRDS, MASS, PRDC, ICC, GLOBECOM, and WCNC.

Prof Cao received the BSc degree in computer science from Nanjing University, Nanjing, China, and the MSc and the Ph.D degrees in computer science from Washington State University, Pullman, WA, USA.

Plenary Keynote: The Art of Transforming Traditional Utilities to the Cloud Model

Dharma Rajan, Practice Solutions Architect – Vmware, U.S.

Abstract:

Public, private, and hybrid clouds are now a de facto industry model. New cloud services are being introduced by the industry at a very fast pace. This keynote session will take you through the journey of cloud evolution, from enterprise to utilities, and the industry transformation that is happening. We will drive through telco cloud with the advent of SDN and NFV, as well as look at how 5G and IoT cloud evolution will enable new service models. An artful transformation to software-defined smart cities with smart utilities operated from the cloud is becoming close to reality. With trends in automation, orchestration, and evolving technology like multi-cloud micro-services, Mobile Virtual Network Operators can offer new revenue generating cloud services that might transform the way we do business and research.

Biography:

Dharma Rajan is a leading expert in cloud technology working as lead Solution Architect at VMware, USA. His areas of expertise span infrastructure virtualization, hybrid cloud, NFV, and cloud security. Prior to joining VMware, Dharma has worked at Ericsson, USA for over a decade, building 4G platform architectures, carrier grade networks, and network management systems. He has also worked at Cisco Systems, USA on enterprise architecture. He has several technical publications and is an invited

speaker at major industry events and world conferences. He holds an MS in Computer Engineering from NCSU, USA and M.Tech in CAD from IIT-Kanpur, India.

IDP Keynote-1:

The Use of Machine Learning in Business

Sonya Zhang, California State Polytechnic University, U.S.

Abstract: Machine Learning is no doubt gaining momentum and reaching the top of

Gartner’s hype curve. As data analytics becomes a more common practice, businesses are now looking deeper into their data to increase efficiency and competitiveness using machine learning, which can learn from data, find hidden insights, and make predictions without being explicitly programmed. Today Machine learning can be found in many business applications, ranging from facial and object recognition, fraud detection, product or content recommendation, to effective web search and targeted ads. In this talk I will give a brief introduction on machine learning, and then focus on current applications and examples of machine learning in different business functions, business models, and industries, and finally, the opportunities and challenges.

Biography: Sonya Zhang is an Associate Professor of Computer Information Systems at the College of Business Administration, California State Polytechnic University, Pomona. She received her PhD in Information Systems and Technology from Claremont Graduate University. She also holds an M.S. in Computer Science, and an MBA from Illinois State University.

Sonya’s research specialties are: Web and Software Development, Digital Analytics, Internet Entrepreneurship, and Online Learning. She co-authored The Smarter Startup: A Better Approach to Online Business for Entrepreneurs. Her work also appeared in Journal of Computer Information Systems, ACM Interactions, Journal of Information Systems Education, Journal of Information Technology Education, International Journal of Healthcare Information Systems and Informatics, HICSS, AMCIS and IEEE conference proceedings.

Prior to joining academia, Sonya was a software engineer in health informatics and higher education for seven years, worked on ERP, Business Intelligence, CMS, eLearning and eHealth products/projects.

RTDPCC Keynote-1:

Big Earth Data – a New Dimension for Digital Earth

Professor Yong Xue, University of Derby, U.K.

Abstract: Digital Earth is a multi-resolution, three-dimensional representation of the planet,

into which we can embed vast quantities of geo-referenced data (Al Gore, 1998). As a new dimension of the Digital Earth, in addition to Computational Science, Mass Storage, Satellite Imagery, Broadband networks, Interoperability and Metadata, Big Data technologies provide a set of advanced tools that can improve development of Digital Earth. After a period of slow but steady scientific progress, this scientific area seems to be mature for new research and application breakthroughs. The rapid progress in the development of integrated Big Data and Earth observation tools has boosted this process (Goodchild et al. 2012, Guo et al. 2016).

As one of the Big Data fields, Earth observation Big Data is unleashing an interesting time of transition, driving the innovation and development of disciplines, becoming a new key to the cognition of nature and a new engine for Earth sciences. Based on widely collected Earth observation big data combined with models of the Earth system, the development of theory and methods for knowledge discovery related to big Earth data is an important scientific issue needing attention.

Bibliography: Professor Dr. Yong Xue (senior member of IEEE) is a Professor in Computation in University of Derby, United Kingdom. He received his BSc degree in Physics and his MSc degree in remote sensing and GIS from Peking University, China in 1986 and 1989, respectively. He received his PhD in remote sensing and GIS from University of Dundee, UK in 1995. His main research interests include Geocomputation, aerosol optical depth retrieval from remotely sensed data, thermal inertia modeling and heat exchange calculation for the boundary layer. Prof. Xue has published over 104 peer-reviewed journal papers

(with the highest Impact Factor at 7.885) and over 148 peer-reviewed conference papers. The overall citations of his publications are over 1330 times with one paper citations of over 130 times (Google Scholar). He has served as the technical programme committee members for several international conferences, such as IEEE/IGARSS conferences and the International Conferences on Computational Science (ICCS). Professor Xue is an Associate Editor of the International Journal of Remote Sensing published by Taylor and Francis, UK, a Chartered Physicist and a member of the Institute of Physics, UK, and the Chapter chair of the joint chapter of IEEE Aerospace Engineering Society/Oceanic Engineering Society/Geosciences and Remote Sensing Society since 2004 in United Kingdom. Contact him at: y.xue@derby.ac.uk.

RTDPCC Keynote-2:

Meeting Society Challenges: Big Data Driven Approaches

Dr. Liangxiu Han, Manchester Metropolitan University, U.K.

Abstract: This talk will be focusing on new developments and methods based on big data driven

approaches to address society challenges and their applications into application domains such as Health, Food, Smart Cities.

Biography: Dr. Liangxiu Han is a Reader in Computer Science, where she is a Deputy Director for two centres: Informatics Research Centre and the Man Met Crime and Well-Being Big Data Centre. Having worked in both industry and academia, Dr. Han has over 14 years research and practical experiences in developing intelligent ICT-enabled software solutions for large scale data processing and data analysis and mining in different application domains (e.g. Health, Smart Cities, Bioscience, Cyber Security, Energy, etc.) using various datasets including images, sensor data, and web pages (funded by innovate UK, EPSRC, EU-FP7, Government and Industry respectively). As a Principal Investigator (PI) or Co-PI, Han has been conducting research in relation to large-scale data

processing, data mining, cloud computing, software architecture (funded by EPSRC, BBSRC, Innovate UK, Horizon 2020, Industry, Charity, respectively, etc.). Dr. Han is a member of EPSRC Peer Review College, an independent expert for Horizon 2020 proposal evaluation/review and British Council Peer Review Panel. She is also a reviewer for IEEE computer society and Journal of Parallel and Distributed Computing, Journal of Information Science from Elsevier science, IEEE Transaction on Service Computing, Brain Computing, IEEE Transaction on Biomedical Imaging engineering, Bioinformatics, Brain Informatics, Clustering Computing, etc. and various international conferences and programme committee member of various International Conferences. She had been also involved in number of professional activities in UK and China.

Deep Learning on Big Data Sets in the Cloud with Apache Spark and Google TensorFlow

Patrick GLAUNER and Radu STATE

Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg

{first.last}@uni.lu

September 12, 2016

Abstract

Machine learning is the branch of artificial intelligence giving computers the ability to learn patterns from data without being explicitly programmed. Deep Learning is a set of cutting-edge machine learning algorithms that are inspired by how the human brain works. It allows to self- learn feature hierarchies from the data rather than modeling hand-crafted features. It has proven to significantly improve performance in challenging data analytics problems. In this tutorial, we will first provide an introduction to the theoretical foundations of neural networks and Deep Learning. Second, we will demonstrate how to use Deep Learning in a cloud using a distributed environment for Big Data analytics. This combines Apache Spark and TensorFlow, Google's in-house Deep Learning platform made for Big Data machine learning applications. Practical demonstrations will include character recognition and time series forecasting in Big Data sets. Attendees will be provided with code snippets that they can easily amend in order to analyze their own data. A related, but shorter tutorial focusing on Deep Learning on a single computer was given at the Data Science Luxembourg Meetup in April 2016. It was attended by 70 people making it the most attended event of this Meetup series in Luxembourg ever since its beginning.

1. Intended audience

This tutorial assumes no prior experience with Apache Spark, machine learning, Deep Learning or TensorFlow. Attendees will be able to acquire both, the theoretical foundations and hands-on experience, in this tutorial. Attendees with prior experience in machine learning will benefit from this part by experiencing a comprehensive rehearsal of the theoretical foundations. However, this tutorial will include advanced topics of Deep Learning such as new regularization methods or long-short term memories (LSTM) which primarily focus on attendees with prior experience in this domain. This part can be skipped by beginners and will not be crucial to their overall learning experience. In order to fully benefit from this tutorial, attendees should bring their own laptop. This will allow them to perform experiments on their computer at the same time and to discuss practical questions.

2. Learning outcome

Attendees will get an understanding of what machine learning is and how Deep Learning, its cutting-edge flavor, works. They will not only learn how to apply a distributed environment to Big Data analytics that can be deployed in a cloud. Rather, they will experience it using Apache Spark and Google TensorFlow on real and Big Data sets. This knowledge will allow them to apply these techniques and infrastructure to their own analytics problems in a cloud.

3. Description 3.1 Motivation

Machine learning allows computers to learn from data without being explicitly programmed. However, hand-crafting features from raw data input is a major challenge in machine learning. Deep Learning allows to self-learn increasingly more complex feature hierarchies from the raw data input. Deep Learning builds on top of the theory of neural networks, which are celebrating a comeback under this

new term. Deep Learning has proven to significantly outperform other learning algorithms in a variety of tasks, such as image recognition1, speech recognition2 or winning the game of Go3. However, Deep Learning is not an easy-to-use silver bullet and requires intensive training. To date, there is no comprehensive book on this topic and expertise must be painfully collected from many different sources. Therefore, the goal of this tutorial is to provide a comprehensive introduction to the foundations of Deep Learning. Another shortcoming of Deep Learning is the potentially long training time of a deep neural network. TensorFlow is Google's in-house Deep Learning platform that allows to efficiently train deep neural networks on GPUs. A different approach is to use map reduce architectures such as Apache Spark or GPUs. In this tutorial, this effectiveness of a combination of both will be shown on real Big Data sets and how to deploy it in a cloud.

3.2 Outline of the proposed content

The proposed structure of this tutorial is as follows:

1. This tutorial will begin with a quick introduction to the most relevant foundations of machine learning.

2. It will then provide a comprehensive introduction to neural networks, a learning algorithm that is inspired by how the human brain works. This also includes a discussion of the limitations of backpropagation, the traditional neural network training method.

3. Neural networks are the foundation of Deep Learning, which are basically a neural network with many layers of neurons. In this section, Deep Learning will be presented to the audience and how these new training methods overcome the limitations of backpropagation in order to efficiently train powerful deep neural networks.

4. Training Deep Learning architectures is time-consuming. However, training neural networks is basically a series of matrix multiplications. Matrix multiplications can be efficiently distributed. Typical distribution methods include map reduce and training on GPUs.

5. Apache Spark4 uses map reduce in order to distribute computations among nodes. In contrast, Google TensorFlow5 allows to distribute training on one or multiple GPUs. Both approaches will be presented and also how they can be combined to take advantage of both concepts to achieve the most efficient outcome in a cloud.

6. In the first practical demonstration, multiple deep neural networks are trained to recognize characters using the notMNIST dataset6, which are characters A-J of different fonts. This will include a discussion of convolutional neural networks (CNN), which are inspired by how the human vision system works.

7. In the second practical demonstration, multiple deep neural networks are trained to forecast a time series. This will include a discussion of recurrent neural networks (RNN) which are able to process temporal information. Furthermore, long-short term memories (LSTM) will be discussed, a modular and highly effective type of RNN. Furthermore, advanced time series forecasting such as for electricity load coreacasting using a dataset of the "Global Energy Forecasting Competition 2012 - Load Forecasting" Kaggle challenge7 will be discussed.

4. Prior tutorials

A related tutorial was given at the Data Science Luxembourg Meetup8 in April 2016 under the title "Deep Learning with TensorFlow"9. That 1-hour tutorial assumed expertise in machine learning and focused on the theoretical foundations of Deep Learning and how to apply regular deep feed-forward

1Y. LeCun, Y. Bengio and G. E. Hinton, "Deep Learning", Nature, vol. 521, pp. 436-444, 2016. 2G. Hinton, L. Deng, D. Yu, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, G. Dahl and Brian Kingsbury, "Deep Neural Networks for Acoustic Modeling in Speech Recognition", IEEE Signal Processing Magazine, 29 (6), 82-97, 2012. 3D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel and D. Hassabis, "Mastering the game of Go with deep neural networks and tree search", Nature, vol. 529, pp. 484-489, 2016. 4http://spark.apache.org/ 5http://www.tensorflow.org/ 6http://yaroslavvb.blogspot.lu/2011/09/notmnist-dataset.html 7http://www.kaggle.com/c/global-energy-forecasting-competition-2012-load-forecasting 

neural networks in TensorFlow to the notMNIST dataset for character recognition. It was attended by approximately 70 people, who asked many questions and their feedback was consistently positive. This was the most popular Data Science Luxembourg Meetup event ever since this monthly meet up series was started in November 2012. A tutorial on Deep Learning for load forecasting10

was accepted at IEEE PES Innovative Smart Grid Technologies (ISGT), Europe11

and will be given in October 2016. However, the focus will be on Deep Learning on a single computer using TensorFlow for time series forecasting.

This 3-hour tutorial will be different in the following ways:

Use of Apache Spark combined with TensorFlow taking advantage of a distributed environment in order to efficiently process Big Data sets in a cloud.

The length of this tutorial allows to also cover CNNs and not just regular feed-forward architectures for the image recognition example.

It will include not only RNNs in the time series example but also provide a comparison to other state-of-the-art models such as Hidden Markov Models.

Prior machine learning experience will not be assumed and the theoretical foundations will be covered in the beginning.

It will focus on Deep Learning and skip the last part on the future of artificial intelligence and the technological singularity.

5. Materials

A comprehensive tutorial slide deck will be provided, which contains figures, definitions, explanations, relevant parts of code snippets and annotated bibliography. The complete and functional code snippets will be provided. In order to make them work, a list of dependencies to required libraries will also be provided, so that attendees can easily install them. All code snippets will be able to be deployed in a cloud to speed up training time.

6. Bio sketch

Patrick GLAUNER graduated as valedictorian with a B.Sc. degree in computer science from Karlsruhe University of Applied Sciences in 2012 and received the M.Sc. degree in machine learning from Imperial College London in 2015. He was a Fellow at CERN, the European Organization for Nuclear Research, worked at SAP and is an alumnus of the German National Academic Foundation (Studienstiftung des deutschen Volkes). He is currently a Ph.D. student in machine learning in the Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, under the supervision of Dr. Radu STATE. He also holds an adjunct faculty appointment at Karlsruhe University of Applied Sciences where he teaches artificial intelligence. His interests include anomaly detection, big data, computer vision, deep learning, time series. Radu STATE received the M.Sc. degree from the Johns Hopkins University, Baltimore, MD, USA, and the Ph.D. degree and a HDR from University of Lorraine, Nancy, France. He is a Senior Researcher with the Interdisciplinary Center on Security and Trust in Luxembourg, where he heads the SEDAN research group. He was a Professor at the University of Lorraine and a Senior Researcher at INRIA Nancy, Grand Est. Having authored more than 100 papers, his research interests cover network and system security and management.

8http://www.meetup.com/LuxRgroup/events/229662811/ 9P. Glauner, "Introduction to Deep Learning and Google TensorFlow", Data Science Luxembourg Meetup, Luxembourg, Luxembourg, 2016. 10P. Glauner and R. State, "Load Forecasting with Artificial Intelligence on Big Data", Sixth IEEE Conference on Innovative Smart Grid Technologies, Europe (ISGT Europe 2016), tutorial session, Ljubljana, Slovenia, 2016. 11http://sites.ieee.org/isgt-europe-2016/ 

Survey Analytics from Questionnaires and Textual Social Media Analytics. With Accompanying Practical Sessions,

examples and case studies in English.

Prof Fionn Murtagh Professor, Big Data Lab, University of Derby;

and Goldsmith University of London.

fmurtagh@acm.org

Dr Mohsen Farid Associate Professor, Big Data Lab, University

of Derby.

m.farid@derby.ac.uk)

1. Course Description

The work of the celebrated social scientist Pierre Bourdieu (1930-2002) includes the thoughtful and creative use of the Correspondence Analysis, published in English in 1984, with title Distinction. It is on such a geometric data analysis approach that this course is based.

The focus is: (1) interpretation of results, graphical displays and other outputs, (2) practical implementation using the R statistical and visualization environment, and (3) providing intuition, and full understanding, relating to the geometry and statistical processing. We use data collected in various questionnaires, starting from work by Bourdieu on cultural taste. Other questionnaire analysis case studies will be related to transport, cooking and lifestyle, student experience, consumer behavior, and music appreciation.

Next the questionnaire outcomes express both closed, fixed format questions, and, conjointly analyzed, free text responses.

Finally studied will be data sourced from social media micro-blogging, i.e. Twitter.

Data Sources: Questionnaire Numerical Scoring Responses, Free Text Responses, and Twitter Data Sources.

2. Syllabus Tools

The course uses the R programming and visualization language

Topics

In accompanying online course materials, there will be a practical introduction to the R language and environment. This is for participants who have not used R before.

Part 1: Questionnaire analysis case study: taking the Bourdieu taste data, detailed discussion of output, detailing the R code used.

Part 2: Geometric intuition: the methodology used for graphical display, hierarchical clustering, and putting it all together.

Part 3: Carrying out geometric data analysis, including clustering, using R. Including publication/presentation outputs, storing data for later work, and maintaining the R scripts that are used.

Part 4: Further case studies of questionnaire analysis.

Part 5: Questionnaire analysis, using conjoint, or integrally related, analysis of closed questions, and open or free text questions.

Part 6: Coverage of social media data sources, will be especially centered on Twitter. All sessions will be associated with practical exercises, using case studies.

Final Part: Concluding short debate and discussion on potential and scope for analytics, and statistical treatment of data, and text mining.

3. Target Audience

Practitioners and researchers related to any domains that are encompassed in the case studies, and practical exercises. Students who are undertaking, or who are planning to undertake, any and all such work.

Domains of general relevance include:

Health and medical surveys, Marketing, Security and forensics, Information and data sourcing through web-based questionnaires, Lifestyle and wellbeing analytics, Legal studies, Political studies, Language and literature, Digital humanities.

The presentation language of the short course is English. Case studies will also be in English as well, however issues related other languages such as Arabic may be addressed.

4. Facilities Required Classrooms equipped with a computer (with the complete software environment) connected to

an overhead projector and screen, plus a writing board. Computers for participants. Course participants’ own laptops are also feasible (with the

complete software environment). Software:

o R, open source and openly available with pertinent toolboxes as required., for all computer platforms.

Course Material o All course materials, including the data and examples of software use for the case

studies, will be made available for course participants, on a password protected web site.

Use Amazon Elastic MapReduce to Process Big Data Jiming Wu

Associate Professor of California State University, East Bay

Abstract:

This tutorial is to teach audience how to use Amazon Elastic MapReduce (Amazon EMR) to analyze large amount of data. Amazon EMR is a web service that provides a managed Hadoop framework to simplify big data processing. Topics will include 1) create an Amazon Web Service Account, 2) employ Amazon cloud storage service, 3) run an EMR cluster, 4) set up an EMR job, and 5) examine EMR job output.

Intended Audience: graduate students with a concentration on business analytics, data analytics, or data science.

Learning outcome: audience will be able to use Amazon EMR to process Big Data.

Description: This is an introduction to Amazon Elastic MapReduce system. Topics include MapReduce features, Hadoop distributed filesystem, input/output, Amazon storage system, and EMR cluster. Students will have opportunity to use Amazon MapReduce system to process Big Data. The objective of this tutorial is to impart working knowledge and skills associated with Big Data technologies and to let students better understand how companies leverage these technologies to analyze Big Data.

Outline of the content: 1) learn how to create an Amazon Web Service Account, 2) discuss how to employ Amazon cloud storage service, 3) explain how to create and run an EMR cluster, 4) describe how to set up an EMR job, and 5) show how to access and interpret EMR job output.

An example of finding the maximum temperature:

1. Set up a Hadoop cluster on Amazon Elastic MapReduce (EMR) 2. Submit max-temperature.jar to EMR. Please refer to the following website about how to

submit a customer Jar: http://docs.aws.amazon.com/ElasticMapReduce/latest/DeveloperGuide/emr-launch-custom-jar-cli.html

3. Set up the input file folder on Amazon storage service – S3 4. In Amazon console, set up Jar (max-temperature.jar) and then set up arguments:

MaxTemperature s3://chapter2/input s3://chapter2/output

5. Run the Jar on Amazon Elastic MapReduce 6. In Amazon SSH command interface: Amazon local drive folder is /home/hadoop; Amazon

HDFS folder is /user/hadoop 7. Copy file from S3 to Local Disk: aws s3 cp s3://chapter2/MaxTemperatureMapper.java . 8. Copy file from Local Disk to HDFS: hdfs dfs -copyFromLocal file.gz . 9. Compile Java files:

javac -cp src/:hadoop-common-2.6.1.jar:hadoop-mapreduce-client-core-2.6.1.jar:commons-cli-2.0.jar -d . MaxTemperature.java MaxTemperatureReducer.java MaxTemperatureMapper.java

10. Create a Jar file: jar -cvf max-temperature.jar MaxTemperature*.class 11. Run a Jar file:

hadoop jar max-temperature.jar MaxTemperature /user/hadoop/input0/sample.txt /user/hadoop/output01

12. Display the output on screen: hadoop fs -cat /user/hadoop/output01/part-r-00001

Statement: this tutorial has never been given before.

Materials: PowerPoint slides and Word documents will be provided to attendees.

Bio-sketch: Jiming Wu is an Associate Professor in the Department of Management at California State University, East Bay. He received his B.S. from Shanghai Jiao Tong University, M.S. from Texas Tech University, and Ph.D. from the University of Kentucky. His research interests include knowledge management, IT adoption and acceptance, and computer and network security. His work has appeared in MIS Quarterly, Journal of the Association for Information Systems, European Journal of Information Systems, Information & Management, Decision Support Systems, and elsewhere.

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