Scientific Program

Conference Series Ltd invites all the participants across the globe to attend 7th International Conference on Big Data Analytics & Data Mining Chicago, Illinois, USA.

Day 2 :

OMICS International Data Analytics 2018 International Conference Keynote Speaker Morgan C Wang photo
Biography:

Morgan C Wang received his PhD from Iowa State University in 1991. He is the funding Director of Data Mining Program and Professor of Statistics at the University of Central Florida. He has published one book (Integrating Results through Meta-Analytic Review Using SAS Software, SAS Institute, 999), and over 80 papers in refereed journals and conference proceedings on topics including interval analysis, meta-analysis, computer security, business analytics, healthcare analytics and data mining. He is the elected member of International Statistical Association and member of American Statistical Association and International Chinese Statistical Association.

Abstract:

Prescriptive analytics can be used to improve business operation, however, many constraints factors including (i) the shortage of high-quality data analysts; (ii) the time to develop a useful prescriptive model takes very long time; (iii) the lifespan of the prescriptive model is relative short prevent the usage of prescriptive analytics. Automatic intelligent model building system which is capable (a) of building prescriptive model automatically with relatively short time (hours instead of weeks or months); (b) being used effectively by IT personnel with adequate knowledge of data sources; and (c) deploying easily can be used to overcome all the constraints. Thus, it overcomes all shortages of traditional modeling approach and it can be used to improve business operation. A portal type of automatic intelligent model building system has been developed. It is capable of fixing data problems such as missing values, skewness, and high cardinality. It supports neural network, decision trees, gradient boosting, rand forest and many regression algorithms. This system also attempts to open the black box to allow the user to see some insight of the modeling results such as interaction among predictors, important predictors, how to alter predictors to change the predicted values. Two case studies will be discussed to demo the capability of how to use this system to enhance business operation. The first case study is to a precision marketing system. The second case study is on employ management system. The results from both cases studies are very positive and encouraging.

OMICS International Data Analytics 2018 International Conference Keynote Speaker Ching Y Suen photo
Biography:

Ching Y Suen, Hon. Chair in Artificial Intelligence and Pattern Recognition, Director of CENPARMI (Centre for Pattern Recognition and Machine Intelligence).Concordia University, Montreal, Canada Section Editor and Emeritus Editor-in-Chief of Pattern Recognition, Elsevier Editor of Book Series on Language Processing, Pattern Recognition, and Intelligent Systems, World Scientific Publishing Co. General Chair, Int. Conf. on Pattern Recognition and Artificial Intelligence, Fellow of the Royal Society of Canada, Fellow IEEE, Fellow IAPR Author of 14 books and more than 500 technical papers.

Abstract:

Graphology is a scientific study and analysis of handwriting. It is a practical way of interpreting behavior from examining the peculiarities in handwriting, such as determining people's psychological, social, occupational and medical attributes, as well as their moral stature. Handwriting Analysis has been shown as an effective and reliable indicator of personality and behavior and has become a useful tool for many organizational processes, e.g. recruitment, interviewing and selection, team-building, counseling, and career-planning. This talk will show how handwriting analysis is computerized, what features to look for, methods of investigating the formation of some characters, connectivity between letters, spacing and slant, pen pressure, letter size and placement of strokes, and the presentation and structure of the handwritten document. We shall touch on both computational and psychological aspects in the processing of large volumes of data. The handwriting of famous people and of diversified groups of professionals will be presented, across different languages and over long periods of time. Also, life demos will be given.

OMICS International Data Analytics 2018 International Conference Keynote Speaker Shikharesh Majumdar, photo
Biography:

Shikharesh Majumdar is a Full Professor and Director of the Real-Time and Distributed Systems Research Centre at the Department of Systems and Computer Engineering in Carleton University, Ottawa, Canada. He is a member of the board for Carleton University Institute for Data Science and of the faculty team associated with Carleton University’s Canada-India Centre for Excellence. He holds a PhD (Computational Science) from University of Saskatchewan, Saskatoon, Canada. His research interests are in the areas of cloud computing, smart systems, high-performance data analytics platforms, operating systems and performance evaluation. He actively collaborates with the industrial sector and has performed his sabbatical research at Nortel and Cistech. He has been the area editor for the Simulation Modelling Practice and Theory journal published by Elsevier (2009-2017). He is a member of ACM, a senior member of IEEE and was a Distinguished Visitor for the IEEE Computer Society (1998-2001).

Abstract:

Enterprises, social networks and smart systems that leverage the Internet of Things technology often lead to large datasets. Data analytics concerns the extraction of knowledge from such raw data. The challenges underlying the processing of such data sets are captured in the 3V characteristics of BigData: Volume, Velocity, and Variety. The first refers to the large size of stored data sets, the second to data in motion streaming from social networks or sensor-based smart systems for example while the third concerns the large variety in data types and formats. High-performance computing platforms such as clusters and clouds are often deployed to address these challenges. Enabling technology that includes parallel processing frameworks and platforms, as well as algorithms for the management of resources in the cloud/cluster, is crucial for performing data analytics in a timely manner. Focusing on such enabling technology this talk will address the various challenges and potential solutions in the context of cloud-based systems for supporting Big Data analytics and smart systems. Issues to be discussed include (a) Management of resources in the context of latency-sensitive data analytics applications such as deadline driven MapReduce jobs and mobile object tracking (video analytics) algorithms. (b) Scheduling techniques for supporting streaming data analytics. (c) Edge-computing based platforms for performing complex event processing in the context of sensor-based streaming applications such as remote patient monitoring. (d) A cloud-based middleware for the unification of geographically dispersed resources required in the management of smart systems such as sensor-based bridges and aerospace machinery.

OMICS International Data Analytics 2018 International Conference Keynote Speaker Gurdip Singh photo
Biography:

Gurdip Singh is the Associate Dean for Research and Graduate Programs at Syracuse University. He was a Program Director at National Science Foundation from 2014 to 2016. From 2009 and 2014, he was the Head of Computer Science Department at Kansas State University. His research interests include real-time embedded systems, sensor networks, network protocols and distributed computing. His research has been funded by NSF, ARO, DARPA and Lockheed Martin. He received his PhD in 1991 for Stony Brook University and BTech from IIT Delhi in 1986.

Abstract:

Development of Smart and Connected Communities will require novel approaches to design reliable and robust infrastructure systems. In addition, to provide resilient services, the interactions and interdependence of infrastructure systems in different domains (e.g., energy, transportation, and public health) must be addressed. This is also resulting in the accumulation of large amounts of data, which can be analyzed, interpreted, and appropriately leveraged. In this presentation, we provide our perspectives on data-driven infrastructure systems in the context of smart and connected communities. We will discuss the need to integrate data from multiple infrastructure systems, and a multidisciplinary approach to address problems in smart communities. We will discuss this in the context of the management of water and road infrastructure systems in a city.

Keynote Forum

Xiaofeng Shao

University of Illinois at Urbana-Champaign, USA

Keynote: Martingale difference divergence and its applications to contemporary statistics
OMICS International Data Analytics 2018 International Conference Keynote Speaker Xiaofeng Shao photo
Biography:

Xiaofeng Shao is a professor of Statistics and PhD program director at the Department of Statistics, the University of Illinois at Urbana-Champaign. His main research interests include time series analysis, high dimensional statistics, resampling methods, spatial statistics, and functional data analysis. He is a recipient of The Tjalling C Koopmans Econometric Theory Prize in 2009, Econometric Theory Multa Scripist Award in 2011 and was named as UIUC LAS Centennial Scholar in 2013. He is also an associate editor for the Journal of the American Statistical Association, Journal of Multivariate Analysis and Journal of Time Series Analysis.

Abstract:

Martingale difference divergence is a metric that quantifies the conditional mean dependence of a random vector Y given another random vector X and it can be viewed as an extension of distance covariance, which characterizes the dependence and has recently much attention in the literature. We shall present applications of martingale difference divergence and its variant to several contemporary statistical problems: high dimensional variable screening, dependence testing and dimension reduction for multivariate time series.