Venkat N. Gudivada

Renaissance in Database Management: Navigating the Landscape of Candidate Systems

Big data requirements are motivating new database-management models that can process billions of data requests per second, and established relational models are changing to keep pace. The authors provide practical tools for navigating this shifting product landscape and finding candidate systems that best fit a data managers application needs.

Big Data Driven Natural Language Processing Research and Applications

Abstract Due to the inherent complexity of natural languages, many natural language tasks are ill-posed for mathematically precise algorithmic solutions. To circumvent this problem, statistical machine learning approaches are used for natural language processing (NLP) tasks. The emergence of Big Data enables a new paradigm for solving NLP problems—managing the complexity of the problem domain by harnessing the power of data for building high quality models. This chapter provides an introduction to various core NLP tasks and highlights their data-driven solutions. Second, a few representative NLP applications, which are built using the core NLP tasks as the underlying infrastructure, are described. Third, various sources of Big Data for NLP research are discussed. Fourth, Big Data driven NLP research and applications are outlined. Finally, the chapter concludes by indicating trends and future research directions.

Understanding Search-Engine Optimization

Because users rarely click on links beyond the first search results page, boosting search-engine ranking has become essential to business success. With a deeper knowledge of search-engine optimization best practices, organizations can avoid unethical practices and effectively monitor strategies approved by popular search engines.

A Machine Learning Based Framework for Verification and Validation of Massive Scale Image Data

Big data validation and system verification are crucial for ensuring the quality of big data applications. However, a rigorous technique for such tasks is yet to emerge. During the past decade, we have developed a big data system called CMA for investigating the classification of biological cells based on cell morphology that is captured in diffraction images. CMA includes a group of scientific software tools, machine learning algorithms, and a large scale cell image repository. We have also developed a framework for rigorous validation of the massive scale image data and verification of both the software systems and machine learning algorithms. Different machine learning algorithms integrated with image processing techniques were used to automate the selection and validation of the massive scale image data in CMA. An experiment based technique guided by a feature selection algorithm was introduced in the framework to select optimal machine learning features. An iterative metamorphic testing approach is applied for testing the scientific software. Due to the non-testable characteristic of the scientific software, a machine learning approach is introduced for developing test oracles iteratively to ensure the adequacy of the test coverage criteria. Performance of the machine learning algorithms is evaluated with the stratified N-fold cross validation and confusion matrix. We describe the design of the proposed framework with CMA as the case study. The effectiveness of the framework is demonstrated through verifying and validating the data set, software systems and algorithms in CMA.

Data quality issues in big data

Though the issues of data quality trace back their origin to the early days of computing, the recent emergence of Big Data has added more dimensions. Furthermore, given the range of Big Data applications, potential consequences of bad data quality can be for more disastrous and widespread. This paper provides a perspective on data quality issues in the Big Data context. it also discusses data integration issues that arise in biological databases and attendant data quality issues.

Data Analytics: Fundamentals

This chapter provides a comprehensive and unified view of data analytics fundamentals. Four functional facets of data analytics—descriptive, diagnostic, predictive, and prescriptive—are described. The evolution of data analytics from SQL analytics, business analytics, visual analytics, big data analytics, to cognitive analytics is presented. Data science as the foundational discipline for the current generation of data analytics systems is explored in this chapter. Data lifecycle and data quality issues are outlined. Open source tools and resources for developing data analytics systems are listed. Future directions in data analytics are indicated. The chapter concludes by providing a summary. To reinforce and enhance the reader’s data analytics knowledge and tools, questions and exercise problems are provided at the end of the chapter.