Digital Signal Processing Computer Science Perspective Pdf' title='Digital Signal Processing Computer Science Perspective Pdf' />Center for Data Science and Big Data Analytics The Research Office. Kathy Bates Weight Loss. The research focus of this center is on healthcare operations analytics, industrial and financial analytics, genome and evolutionary biology research, sensor networks and the internet of things. The center and its faculty researchers also partner closely with industry and other institutions to address current and trending issues. Healthcare Operations Analytics. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. Easily share your publications and get. The demand for computer specialists grows every year. As a graduate in Computer Science from Western Illinois University, youll be wellprepared for a variety of. Traditional healthcare analytics involves using patient as well as operational data to conduct statistical and quantitative analysis, build explanatory and predictive models, and fact based management to drive healthcare decisions and actions. It is broadly concerned with the use, study, creation or synthesis of information artifacts such as databases, knowledge bases, mathematicalstatistical models, data integration and transformation tools and entire decision support systems. The primary aim of healthcare analytics is to improve managerial decision making through access to better information. However, the amount of medical data generated and the heterogeneity of that data makes traditional analytics inefficient particularly given the fact that much of the data is non numerical. Digital Signal Processing Computer Science Perspective Pdf' title='Digital Signal Processing Computer Science Perspective Pdf' />For example, notes written by physicians and nurses, images, and videos contain valuable information that need to be factored into the analysis. However, current tools do not have adequate mechanisms to integrate different types of data. As part of this research stream, the center would focus on developing an infrastructure for acquiring, integrating and analyzing the healthcare data to support decision making. I/41uLeeygkVL._SR600%2C315_PIWhiteStrip%2CBottomLeft%2C0%2C35_PIStarRatingFOURANDHALF%2CBottomLeft%2C360%2C-6_SR600%2C315_ZA(5%20Reviews)%2C445%2C286%2C400%2C400%2Carial%2C12%2C4%2C0%2C0%2C5_SCLZZZZZZZ_.jpg' alt='Digital Signal Processing Computer Science Perspective Pdf' title='Digital Signal Processing Computer Science Perspective Pdf' />Anticipated Outputs Architecture for healthcare data acquisition and integration from disparate sources. Privacy preserving data analytics methods. Proof of concept prototype demonstration. Industrial Analytics. Digital Signal Processing Computer Science Perspective Pdf' title='Digital Signal Processing Computer Science Perspective Pdf' />With auto industry and its primary and secondary supplier industries around, there are streams of big datasets that await the analysis. While larger companies have some sort of technical research centers, albeit inadequate for the purpose, smaller industries completely lack resources or manpower to handle their big data. The Center for Data Science and Big Data Analytics at Oakland University would act as a bridge between different disciplines and industries and provide analytics services. Anticipated Outputs Collaboration with auto and other allied industry on research problems of shared interests. A university based consulting service center guiding the auto industry with statistical experimentation, data analytics and quantitative methods. Develop short training programs in quantitative data analysis for local and national industries. Develop student internship programs in collaboration with industries. Financial Analytics. This research stream focuses on using the multivariate and Bayesian methods in big data problems with special reference to finance. These datasets are huge, over thousands of stocks, mutual funds, Exchange traded funds and other financial instruments and are collected over years at the intervals of day, hour or minute and even at further higher frequencies. The sheer volume of data on various financial instruments and indexes collected over years on per minute frequency or on per stock price change commonly called tick basis and the interconnectedness of various stock price changes thereof pose a great challenge. The complexity is further compounded by events such as stock splits, mergers, stocks leaving the space as some companies die and new stocks entering the space as new companies are formed. The challenges of studying the market behavior or predictions can only be handled by looking at the data together rather than doing so on per stock basis. Such correlated data can be analyzed only through appropriate techniques and in view of the complexity of the data, these techniques are bound to be computer intensive. With Bayesian and Markov Chain Monte Carlo methods of modelling these data, new ways of analyzing these data would be developed. Further, such problems inevitably require special expertise, intensive computational power and special analytics. A specific objective of this research is the efficient and effective analysis of financial data. Anticipated Outputs Development of new techniques which can reliably predict financial market behavior. Development of techniques for statistical arbitrage where one can make decision as to when and which financial instruments are going to perform better. Providing the financial advice to outside firms. Development of new courses possibly cross listed between the department of mathematics and statistics and SBA and exploration of developing new degree or certificate programs. Genome Research. This research stream focuses on studyingidentifying gene mutations that lead to cardiovascular diseases. Specifically, this research will use mouse sensitized whole genome ENU mutagenesis screens to identify genes involved in the pathogenesis of several cardiovascular diseases including venous thromboembolism, heart attacks and other vascular occlusive diseases such as sickle cell anemia. The whole genome sequencing is used to identify the mutations, thus this research generates terabytes worth of genomic sequencing data per experiment. The high volume of genomic sequence data produced necessitates computationally intensive analyses and data storage. The Center for Data Science and Big Data Analytics aims to conduct cutting edge research in genomics and provide critical research and training opportunities for OU faculty and students. Hollywood Cartoon Movies In Hindi Dubbed Hd For Pc'>Hollywood Cartoon Movies In Hindi Dubbed Hd For Pc. Anticipated Outputs The cardiovascular genome research project will produce genes involved in cardiovascular diseases. This information will be used for the improved diagnosis and treatment of cardiovascular diseases. New methodologies for the analysis of whole genome sequencing data will likely be developed. The proposed work will result in publications and applications for external funding. Intellectual property is likely to be generated as a result of this work, enabling the university to gain the revenue necessary to provide funding for future research endeavors or centers. Evolutionary Biology Research. This stream focuses broadly on fully computational evolutionary research using large datasets. It consists of two applied research areas supported by strong theoretical investigations. One of the major goals of this research is to explore evolution of life through phylogenetic trees, with special attention to evolution of early microbial life. It requires working with thousands of sequenced genomes that are obtained from available databases and reconstructing large evolutionary histories. The second goal of this stream of research is to explore the correlation between genotype and phenotype with particular attention to pathogenic species. This requires the use of fully sequenced genomes and of techniques to reconstruct past evolutionary steps ancestral state reconstruction, which can be calculated by intense computational applications. Both these goals are supported by large scale simulations that allow testing of the accuracy of obtained estimates within a controlled environment and the optimization of methodologies and software implementations. This research would greatly benefit from a venue in which expertise from other Big Data scientists could be tapped to design new and innovative ways to analyzevisualize data, and statistically evaluate the significance of the results.