BIGDATA: F: DKM: DKA: Big Data Modeling and Analysis with Depth and Scale

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An important step in understanding large volumes of data is the construction of a model: a succinct but abstract representation of the phenomenon that produced the data. In order to understand a phenomenon, a data analyst needs to be able to propose a model, evaluate how the proposed model explains the data, and refine the model as new data becomes available. Statistical models, which specify relationships among random variables, have traditionally been used to understand large volumes of noisy data. Logical models have been used widely in databases and knowledge bases for organizing and reasoning with large and complex data sets. This project is aimed at developing a programming language and system for the creation, evaluation and refinement of combined statistical and logical models for the express purpose of understanding very large and complex data sets. Apart from its direct effect on model development for Big Data problems, the semantic foundations and scalable computing infrastructure resulting from this project is expected to directly impact the areas of system development and verification, planning, and optimization, with broad application in Science and Engineering. The tools developed in this project will facilitate the training of a new generation of scientists capable of transforming data into knowledge for use across disciplines. The project's education and outreach component is designed to train select undergraduate students on Big Data modeling and analysis via annual workshops and research mentorship; and graduate students via curriculum modifications including a specialization in Data Science.The project will develop Px, a language with well-defined declarative semantics, to support high-level model construction and analysis. Px will be capable of expressing generative and discriminative probabilistic and relational models, and the Px system will support complex queries over such models. The project will encompass three significant and complementary research directions, aimed at developing: (1) semantic foundations, including language constructs needed for succinct specification of complex models with rich logical and statistical structure; (2) scalable inference techniques combining exact and approximate methods, and query optimizations over combined logic/statistical models; and (3) programming extensions as well as static and dynamic analyses to support the creation and refinement of complex models. The Px language and system will be evaluated using two important and diverse application problems: (1) analysis and verification of infinite-state probabilistic systems, including parameterized systems, and (2) construction of phylogenetic trees from phenomic data, used in the Tree of Life project, for mapping the evolutionary history of organisms. The project is expected to make significant contributions towards creating a unifying framework combining probabilistic inference, logical inference, and constraint processing, with an emphasis on semantic clarity, efficiency, and scalability. The project will also demonstrate the practical utility of the proposed integrated framework by developing complex models from big data that take advantage of this technology in fundamental ways.