Day 1 :
NASS, US Department of Agriculture,USA
Keynote: Big Data, Data Hubs, Sensors, IoT and Precision Agriculture – Their Effect on Data Collection, Analysis, Forecasting and Survey Processes
Time : 09:00-09:30
Michael Valivullah is currently serving as the Chief Technology Officer (CTO) at US Department of Agriculture’s (USDA) National Agricultural Statistics Service (NASS). He served as Director of Information Technology (IT) and Chief Information Officer (CIO) at NASS and the Directorate of Science and Technology in the US Department of Homeland Security (DHS). He is a member of the Senior Executive Service (SES) in the US Federal Government. He has worked for 14 years in the public sector and over 15 years in the private sector and non-profits.
The National Agricultural Statistics Agency (NASS) of the United States Department of Agriculture (USDA) is in a mission to provide timely, accurate and useful statistics on US agriculture. Precision agriculture (PA) is a farming management principle that measures and responds to variability in crop conditions and animal health using sensors, robots, satellites and global positioning systems (GPS). The advent of precision agriculture has provided agricultural producers an unprecedented amount of data for use in data mining and big data analytics in farming operations. As more and more farmers are using automatic and remote sensing tools to collect data to be more productive, efficient and profitable, it is in the best interest of NASS to collect data from these sources (sensors, agribots, farm data hubs, drones, etc.,) and not burden farm producers by asking for the same data in NASS surveys. Automatic data collection (machine to machine) will also eliminate manual data entry errors. NASS needs to develop new survey and data collection processes, algorithms to validate, and analyze and process this data. Data analysis will require data scientists familiar with sophisticated algorithms, artificial intelligence, decision models, predictive analytics, etc. NASS also needs data dissemination tools with sophisticated big data processing capabilities and data visualization abilities. This presentation will discuss opportunities and challenges in dealing with big data in precision agriculture.
Central Michigan University,USA
En-Bing Lin is a Professor of Mathematics at Central Michigan University, USA. He has been associated with several institutions including Massachusetts Institute of Technology, University of Wisconsin-Milwaukee, University of California, Riverside, University of Toledo, UCLA, and University of Illinois at Chicago. He received his PhD from Johns Hopkins University. His research interests include Data Analysis, Applied and Computational Mathematics, and Mathematical Physics. He has supervised a number of graduate and undergraduate students. He serves on the editorial boards of several journals. He has organized many special sessions at regional IEEE conference.
With the increasing use of advanced technology, the amount of data in our world has been exploding. Big data analytics can examine large data sets and uncover hidden patterns. On the other hand, poor quality of big data results in some inaccurate insights or compliance failures that give rise to partially complete information systems. In order to obtain complete information systems, we use Rough Set Theory (RST), which was introduced by Pawlak in 1982 as a way to deal with data analysis based on approximation methods in information systems. The theory has many applications in a number of different areas, such as engineering, environment, banking, medicine, bioinformatics, pattern recognition, data mining, machine learning and others. RST is intrinsically a study of equivalence relations on the universe (a set of object). In fact, rough sets can be used to represent ambiguity, vagueness and general uncertainty. Given some relations between objects in the set, we can construct lower and upper approximations of the objects. We intend to use some advanced computing methods to determine lower and upper approximations and find several properties of the characteristics of objects within RST, as well as to extend RST to generalized RST. This line of research has to do with some developments in big data analytics. Traditional algorithm cannot satisfy the needs of big data computing. In this presentation, we will show some advanced computing methods that can solve our problems effectively. We will also present several examples to illustrate the concepts introduced in this presentation
Institute of Computer Vision and applied Computer Sciences,Germany
Time : 10:00-10:30
Petra Perner (IAPR Fellow) is the director of the Institute of Computer Vision and Applied Computer Sciences IBaI. She received her Diploma degree in electrical engineering and her PhD degree in computer science for the work on “Data Reduction Methods for Industrial Robots with Direct Teach-in-Programing”. Her habilitation thesis was about “A Methodology for the Development of Knowledge-Based Image-Interpretation Systems". She has been the principal investigator of various national and international research projects. She received several research awards for her research work and has been awarded with 3 business awards for her work on bringing intelligent image interpretation methods and data mining methods into business. Her research interest is image analysis and interpretation, machine learning, data mining, big data, machine learning, image mining and case-based reasoning.
The ubiquitous availablity of high quality data European industry gathers, allows to optimize manafacturing processes even more and to stay competititve. However, while the data are rich enough to include those elements needed for optimization, the even encreasing volume, veloctiy and variety of the data make mining it effectively increasingly difficult. The paper addresses the special challenges in developing scalable algorithm and infrastructures for creating responsive analytical capabilities that produce timely prediction and monitoring alerts in industrial environments. We will describe a platform that can handle the special needs of the data and has a reach enough tool of data mining techniques. Case-Based Reasoning is used to combine streaminig data of different types ( sensor data, time series, maintenance logs etc.) as well. Special time series algorithm will be developed allowing the efficient analyisis of the machine data. It will be deploded and validated in three industrial cases where data-driven maintenances is expected is expected to have a significant impact: high-tech medical equipment, high-tech manufacturing of hard disks and structural health monitoring.
University of Huddersfield, UK
Keynote: Data Mining in Big Data Analytics: Exploiting Resolution Scale, Addressing Bias, Having Analytical Focus
Time : 10:50-11:20
Fionn Murtagh is Professor of Data Science and was Professor of Computer Science, including Department Head, in many universities. Fionn was Editor-in-Chief of the Computer Journal (British Computer Society) for more than 10 years, and is an Editorial Board member of many journals. With over 300 refereed articles and 30 books authored or edited, his fellowships and scholarly academies include: Fellow of: British Computer Society (FBCS), Institute of Mathematics and Its Applications (FIMA), International Association for Pattern Recognition (FIAPR), Royal Statistical Society (FRSS), Royal Society of Arts (FRSA).Elected Member: Royal Irish Academy (MRIA), Academia Europaea (MAE). Senior Member IEEE.
The benefits and also the challenges of Big Data analytics can be addressed in innovative ways. It is known that analytical focus is important. Considering just as an analogy for our analytics, how a microscope or a telescope bring about observation and measurement at very fine scales and at very gross scales, we can take that analogy as being associated with the resolution scale of our analysis. Another challenge is the bias in Big Data. But we may calibrate our analytical process with a Big Data framework or infrastructure. A further challenge of an ethical nature, is how respresentativity replaces the individual. So we want "to rehabilitate the individual". Important opportunities arise from contextualization. That can be associated with the resolution scale of our analytics, and it can also be supported by full account taken of appropriate contexts. The innovation that stems from the different facets of our analytical procedures can be of great benefit. Here we seek to discuss many such themes that are always in the context of interesting and important case studies. The main case studies for us here include the following: analytics of mental health and associated well-being; social media analytics based on Twitter; questionnaire and survey analytics with many respondents. Ultimately what is sought is not just scalability alone, but also new and insightful, revealing and rewarding, perspectives, returns and benefits. A book of ours, to be published in April 2017: Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics.