Tutorial 1: Mobile Crowdsourcing for Smart City Applications

Abstract: This tutorial will discuss mobile crowdsourcing issues for smart city applications. In particular, it will motivate the use of incentives for better crowd participation from people carrying mobile devices, data quality, data analytics and trust issues among others. It will also discuss some open research problems in that domain.

Sanjay Kumar Madria received his Ph.D. in Computer Science from Indian Institute of Technology, Delhi, India in 1995. He is a full tenured Professor, Department of Computer Science, at the Missouri University of Science and Technology (formerly, University of Missouri-Rolla), USA. Earlier he was Visiting Assistant Professor in the Department of Computer Science, Purdue University, West Lafayette, USA. He has published more than 230 Journal and conference papers in the areas of mobile computing, sensor networks, security, cloud computing, etc. He won five IEEE best paper awards in conferences including IEEE SRDS 2015, IEEE MDM in 2011 and 2012. He co-authored a book entitled “Web Data Management: A Warehouse Approach” published by Springer-verlag. He guest edited WWW Journal, several Data and Knowledge Engineering Sp. Issues on Web data management and Data warehousing. He was founding Program Chair for EC&WEB conference series. He served as a general co-chair of Mobile Data Management conference in 2010, IEEE Symposium on Reliability in Distributed Systems in 2012 and PC co-chair of MDM 2015. He serves in steering committees of IEEE SRDS and IEEE MDM. He is serving/served as PC member of various conferences such as VLDB, MDM, CIKM, ICDCS, and reviewer for many reputed journals such as IEEE TKDE, IEEE Computer, ACM Internet Computing, IEEE TMC etc. Dr. Madria has given tutorials on mobile data management in many international conferences like Middleware, MDM and SRDS. He is regular invited panelist in NSF, NSERC (Canada), Hong Kong Research Council and Sweden Council of Research. He received UMR faculty excellence award in 2007, 2009, 2011, 2013 and 2015, Japanese Society for Promotion of Science invitational fellowship in 2006, and Air Force Research Lab’s visiting faculty fellowship from 2008 to 2016. He was honored with NRC fellowship in 2012-2013 by National Academies. His research is supported by multiple grants from several agencies such as NSF, DOE, NIST, AFRL, ARL, UM research board and from industries such as Boeing. He is IEEE Senior Member, served as IEEE Distinguished speaker and currently he is a speaker under ACM Distinguished Visitor program. He is ACM Distinguished Scientist and IEEE Golden Core Awardee.


Tutorial 2: Adaptivity in Database Kernels

Adaptivity addresses a class of problems related to database physical design optimization for scenarios where the workload is unknown and immediate availability is a requirement. The general strategy is to improve physical design by means of incremental changes, each guided by the current workload request. For instance, adaptive indexing builds partial in- dexes through steps during query processing rather than building full indexes. Adaptivity can also be applied on data storage layout in order to optimize relevant data exchange between memory hierarchy layers. Instead of having a fixed layout, adaptive storage re- designs data organization to answer incoming queries incrementally based on the current requests or recent workload pattern.

Javam C. Machado is a full professor at the Federal University of Ceara (UFC), Brazil. He obtained a MsC in Computer Science from the Federal University of Rio Grande do Sul, Brazil and a PhD degree in Computer Science from the University of Grenoble, France. For 8 years, he was the manager of the UFC’s IT infrastructure and, in 2011, he became the vice-director of the College of Science at the same University. Since 1995, he has coordinated research projects on the area of Database and Distributed Systems and he has advised MsC and PhD candidates. Javam is the coordinator of the Database Special Committee of the Brazilian Computer Society (2017) and he was the chair of the 2016 edition of the Brazilian Databases Systems Symposium. He has served as a member of the program committee of different conferences on database and on cloud computing. In 2010 Javam founded the Databases and Systems Laboratory (LSBD) of the Computer Science Department which he coordinates since than. Lately he is interested more particularly in data management and data privacy but also in adaptive database systems and cloud computing.

Paulo Roberto Pessoa Amora is a Computer Science MSc. student at UFC – Federal University of Ceara ́ under Prof. Javam Machado. He is a member of the Databases and Systems Laboratory (LSBD) currently working on adaptive storages in main-memory databases. Paulo earned a BSc. in Computer Engineering at IFCE – Instituto Federal do Ceara ́ with a sandwich year at University of Pittsburgh. He has interest in the following research topics: Adaptive Databases, Main-Memory Databases, Storage management.

 Elvis Marques Teixeira is a Computer Science MSc. student at UFC – Federal University of Ceara ́ under Prof. Javam Machado. He is also a member of the Databases and Systems Laboratory (LSBD) currently working on adaptive indexing techniques. Elvis earned a BSc. in Physics at UFC – Federal University of Ceara ́, meanwhile conducted research on mineralogy data processing. He has interest in the following research topics: Adaptive Indexes, data analysis and applications of machine learning on the design and adaptation of data access methods.


Tutorial 3: Social Professional Networks: Taxonomy, Metrics and Analyses of Relationship Strength

Social professional networks provide features not available in other networks. For example, LinkedIn facilitate professional networking, and GitHub enables committing and sharing code. Such networks also provide data on users, behaviors and interactions. Here, we foster a deeper understanding of the social professional networks types, definitions, features, analyses and applications while providing a useful taxonomy about their use. We also study the strength of ties, a central aspect that allows studying the roles of relationships. Therefore, besides analyzing the strength of co-authorship ties, we also present a set of metrics and algorithms to measure such strength in different contexts.

Michele A. Brandão has recently finished her PhD in Computer Science at UFMG (Belo Horizonte, Brazil), with title Tie strength in co-authorship social networks: analyses, metrics and a new computational model . She holds a Masters degree from UFMG, and a Bachelor from Universidade Estadual de Santa Cruz, both in Computer Science. She is currently at UFMG as staff ( “professor substituto” ). Her research interests are mostly in data science, social networks, recommender systems and link prediction. Recent publications include papers: at journals Computer Communications, Journal of the Brazilian Computer Society, and JIDM; as well as at conferences IEEE/WIC/ACM Web Intelligence, BRASNAM, AMW, DEXA, and SBBD.

Mirella M. Moro is associate professor at the Computer Science department at UFMG (Belo Horizonte, Brazil). She holds a Ph.D. in Computer Science (University of California Riverside – UCR, 2007), and MSc and BSc in Computer Science as well (UFRGS, Brazil). She is a member of the ACM Education Council, ACM SIGMOD, ACM SIGCSE, ACM-W, IEEE, IEEE WIE, SBC, and MentorNet. She was the Education Director of SBC (Brazilian Computer Society, 2009-2015), the editor-in-chief of the electrocnic magazine SBC Horizontes (2008-2012), and associated editor of JIDM (2010-2012). Mirella has been working with research in Computer Science in the area of Databases since 1997. Her research interests include social networks analysis, query optimization, and hybrid SQL/NoSQL modeling. Her recent publications include papers on prestigious venues such as Scientometrics, Data & Knowledge Engineering, ACM Hypertext, IEEE/WIC/ACM Web Intelligence, TPDL, JCDL as well as JIDM and SBBD.