Browsing by Subject "analytics"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item LIBFLOW: A PLATFORM TO SCHEDULE AND MANAGE WORKFLOWS USING DAGS(2019-01-01) Panhalkar, Shreyas; Nicholas, Charles; Computer Science and Electrical Engineering; Computer ScienceWith continuous user growth year-on-year, Internet companies are collecting user data on a massive scale. This raw data is in turn used for generating interesting insights and using those insights to perform better. Due to various use cases, companies typically use different data stores to store a different kind of data. To name a few, Apache Hive is often being used for large-scale bulk data processing while Amazon Redshift is being for fast and real-time analytical queries. Thus, owing to various business needs and the increasing complexity of underlying data, companies are moving away from a traditional one-for-all data warehousing solution. The heterogeneous nature of these platforms' API possesses difficulty for data engineers to write a series of transformations to process data from various sources. In this work, we propose a platform, to help data engineers easily write workflows to process large-scale data involving multiple data warehouses, without much rudimentary work. To address the data dependency issues, this platform uses Directed Acyclic Graphs to define workflows and Johnson's algorithm to detect elementary cycles.Item Using IMS Standards to Advance Next-Generation Digital Learning Environments (NGDLE)(2019-02-26) Suess, JohnThe NGDLE is a vision for a learning ecosystem not a detailed technical specification. As such, the NGDLE will evolve over time as technology matures and the research in teaching & learning identifies effective practices. This talk focuses on what Jack Suess (UMBC) believes to be three essential elements for success: 1. The role of standards in building a learning ecosystem, the most important standards to follow, and why standards such as Open Badges, Comprehensive Learning Record, and LTI-Advantage are essential for interoperability and integration; 2. The importance of learning analytics in advancing effective practices in course design and pedagogy, student graduation and retention, and personalization of learning; and 3. The importance of universal design principles to think more broadly about accessibility of learning resources and to move beyond a course-centric view of learning.