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Upcoming Events

Ontology Engineering Fall 2018
Description:
This course provides an introduction to ontologies, their uses, and an overview of their application in semantically enabled systems. Ontologies encode term meanings. Ontologies with their declarative encodings of meaning can be used to improve communications between people and can enable computer programs to function more effectively. They provide the foundation for clear and unambiguous interaction. Ontologies have become increasingly common on the web, and class participants will not only learn about the use of ontologies in web-based applications but how to evaluate ontologies for reuse in such applications. Participants will read relevant papers, learn how to critically review ontology papers as well as ontologies themselves, and will participate in at least one group project designing, using, and evaluating ontologies.
To learn how to build computer understandable definitions of terms for usage in automated systems.

Dates: August 1, 2018 - December 31, 2018
Concepts: Taxonomy, Schema, Linked Data, Semantic Foundation, Controlled Vocabulary, Rule Modeling, Ontology, Semantic Web, Semantic Web Services, Information Model, Provenance, Inference, Vocabulary, Semantic Reasoning


Recent Events

Data Analytics 2018
Description:
Introduce students to relevant methods to recognize and apply quantitative algorithms, techniques and interpretation To develop students' strategic thinking skills, combined with a solid technical foundation in data and model-driven decision-making. Develop ability to apply critical and analytical methods to formulate and solve science, engineering, medical, and business problems Students will examine real-world examples using modern cyberinfrastructure to place statistical and data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science. By the end of the course, students can effectively communicate analytic findings to non-specialists
Data and Information analytics extends analysis (descriptive and predictive models to obtain knowledge from data) by using insight from analyses to recommend action or to guide and communicate decision-making. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with an entire methodology. The world at-large is confronted with increasingly larger and complex sets of structured/unstructured information; from sensors, instruments, and generated by computer simulations; data is "hidden" in websites, application servers, social networks and on mobile devices. As a nation, assimilating information across disparate domains (e.g., intelligence, economics, science) has the potential to provide improved capabilities for decision makers. In commerce and industry, analytics-driven enterprises are becoming mainstream. Yet, there is a shortfall in the key education skills needed to meet the growing needs. Traditional enterprises are moving toward analytics-driven approaches for core business functions. In the government and corporations, cybersecurity problems are prevalent. The investment in advanced analytics capabilities could potentially be more broadly leveraged today and greater than any prior government investments in computing. Emphasis is now placed on disruptive data and information sources on the Web and Internet: using Web Science and informatics to explore social networks, platform competition, the "long tail" and economic or resource impacts of the search for new findings. Key topics include: advanced statistical computing theory, multivariate analysis, and application of computer science courses such as data mining and machine learning and change detection by uncovering unexpected patterns in data. Introduce students to relevant methods to recognize and apply quantitative algorithms, techniques and interpretation To develop students' strategic thinking skills, combined with a solid technical foundation in data and model-driven decision-making. Develop ability to apply critical and analytical methods to formulate and solve science, engineering, medical, and business problems Students will examine real-world examples using modern cyberinfrastructure to place statistical and data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science. By the end of the course, students can effectively communicate analytic findings to non-specialists

Dates: January 18, 2018 - May 3, 2018
Concepts:
Xinformatics 2018
Description:
This course will introduce informatics, each of its components and ground the material that students will learn in discipline areas by coursework and project assignments. In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems.
To instruct future information architects how to sustainably generate information models, designs and architectures To instruct future technologists how to understand and support essential data and information needs of a wide variety of producers and consumers For both to know tools, and requirements to properly handle data and information Will learn and be evaluated on the underpinnings of informatics, including theoretical methods, technologies and best practices.

Dates: January 16, 2018 - May 1, 2018
Concepts:
GIS for Science 2018
Description:
  1. To provide students an opportunity to learn geospatial applications and tools.
  2. To introduce relational analysis and interpretation of spatial data and presentation on maps.
  3. Introduce spatial database concepts and technical aspects of query languages and geographic integration of graphic and tabular data.
  4. To introduce intermediate aspects of geospatial analysis: map projections, reference frames, multivariate analysis, correlation analysis, regression, interpolation, exptrapolation, and kriging.
  5. To gain experience in an end-to-end GIS application via a term project.
Introduction to relational analysis and interpretation of spatial data and their presentation on static and interactive maps using PostGIS, qGIS, Leaflet.js and Geoserver. Geographic spatial data concepts covered are map projections, vectors and geoprocessing, raster analsysis, interpolation, collaborative mapping, GIS on the cloud and web mapping. Database concepts of building and manipulating a spatial database, SQL, spatial queries, and integration of graphic and tabular data are also covered. During each class we will discuss topics and do examples. Related take-home exercises will be assigned. Depending on class size, students may be asked to present assignments to the rest of the class. Each student will do a semester-long project on some topic of particular interest to them, but also of relevance to the class. These projects will be presented to the class during the last week. 4 credit hours. Each Topic will meet for 3 to 4 hours per week, comprising 1.5 hours of instruction and approximately 2 hours of lab.

Dates: January 16, 2018 - May 4, 2018
Concepts: Geoinformatics, Geoscience, Geographic Information System