3rd International Conference on Big Data Analysis and Data Mining September 26-27,2016 London,UK

Dr. Zev Leifer has an M.A. from Harvard University and a Ph.D. from New York University in Microbiology. He has been the course director of the Pathology Laboratory for over 30 years. Recently, he has published and spoken at international meetings in the area of pathology education. His work was written up in a featured section in the Springer monograph, “Digital Pathology”. His current research specializes in the adaptation of commercial software for use in digital pathology education. A prime example is the work described here in the area of data mining.

Diagnostic Pathology is a critical aspect in determining the nature of the disease process. Typically, a biopsy sample is converted to a slice of tissue on a glass slide, which is analyzed by a pathologist using a microscope. Today, we are in the world of digital pathology. The slide is digitized. The digital image is stored and retrievable. The image is viewed on a computer functioning as a microscope. Now, multiply this by hundreds or thousands of slides per day in a large medical center. Each slide may contain between 50 and 400 MB of data. In parallel, there is an obvious need to train medical students in the analysis of many pathologies, more so for the training of residents and the review of new or uncommon conditions by senior clinicians. Enter “Zev Leifer’s Lab” using Quartzy.com. This system, using a commercial product designed to track lab chemicals and supplies, has been adapted to deal with the data mining challenge of the massive storage of digital images. It is a listing of links to images stored in the collections of numerous medical institutions. The unique aspect lies in the metadata tags and the sorting capacity. One may search and organize by tissue type, pathology type, etc. This mined data of digital images can be used for study, testing and research.

Wolfgang Orthuber is orthodontist and mathematician and has interdisciplinary experience.

It is well known that data representation on the web can be improved very much. There are a lot of proposals, but if we want maximal efficiency, there is not so much freedom. Maximal efficiency of the basal data structure is desirable to minimize costs. In this short contribution we want to recall http://arxiv.org/abs/1406.1065 which shows that on the web efficient and uniform definition of searchable information is possible using the basal data structurernURL (of online definition) plus sequence of numbers rnwhich is called \"Domain Vector\" (DV). At this the \"online definition\" defines in standardized (machine readable) way a \"Domain Space\" (DS) which is a metric space whose elements are the DVs. A DV can precisely represent every definable information, from a simple word to complex multidimensional information e.g. in science, medicine, industry. http://numericsearch.com shows a few examples and demonstrates searchability. The online definition can be multilingual, but the meaning of DVs is language independent. DVs are internationally uniform and comparable, they allow well defined similarity search. The users create the online definitions and with this the search criteria. The URL locates the definition and can be abbreviated. Existing online definitions can be reused in new definitions, so that search over multiple DSs is possible. One of the next steps is determination of the exact standard for DS definitions. Everyone who recognizes the potential of the above data structure and who wants to maximize efficiency of data representation on the web is invited to contribute.rn

Anneline Adams is a Digital Strategist at Africa’s largest Mobile Operator. Having graduated at the age of 21 in the field of Computer Science, she spent the first 4 years modelling defense simulation scenarios, thereafter pursing her business studies at the University of South Africa, whilst starting a digital career in banking and technology. She has served as the digital channel architect to numerous Blue Chip organizations, and recently delivered the first Data Science Academy to Africa’s largest Telecommunications operator, implementing Real Time campaign management. She has published and presented articles on Bayesian analysis, statistical modelling and digital disruption.

Business Analytics employs big data together with digital marketing that drive unique, real time customer value propositions with the resulting Call to Action being the Transaction (take-up).rnDigital channels and social media has changed the marketplace for all. Sales and Service are no longer streams that follow traditional operations. There are many different consumer segments in the market and although most adopt digital solutions to the traditional sales and service models, Millennials vastly differ. The Millennial generation is the largest in history and as they reach their prime working and spending years, their impact on the economy is huge.rnMillennials have come of age during a time of technological change, globalization and economic disruption. Their affinity for technology has reshaped the marketplace.rnThey’re also the first generation of digital natives, and their affinity for technology helps shape how they shop. Millennials are turning to brands that can offer maximum convenience at the lowest cost. Digital disruption would be an ongoing activity for them.rnSegmentation and analytics is key in understanding the dataset in its foundation layer. Predictive and propensity modelling define the customer journeys for the millennial versus other consumer types. Predicting and preventing churn has never been more critical, as millennials are not loyal to brand. Campaign derivation and execution addresses this challenge with a platform for not just marketing from one organization to a consumer but for an eco-system of multiple engagements that cross pollinate between partners and consumer. Thousands of executions yield an evolutionary digital marketplace.