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Instructional Systems Design is the practice of creating of instructional experiences that make the acquisition of knowledge and skill more efficient, effective, and appealing [18]. Specifically in designing courses, an hour of training material can require between 30 to 500 hours of effort in sourcing and organizing reference data for use in just the preparation of course material. In this paper, we present the first system of its kind that helps reduce the effort associated with sourcing reference material and course creation. We present algorithms for document chunking and automatic generation of learning objectives from content, creating descriptive content metadata to improve content-discoverability. Unlike existing methods, the learning objectives generated by our system incorporate pedagogically motivated Bloom's verbs. We demonstrate the usefulness of our methods using real world data from the banking industry and through a live deployment at a large pharmaceutical company. [For the full proceedings, see ED593090.]

The need to analyze huge amount of data for various business intelligence applications is well known. However, the rate at which enterprise data is generated now demands periodic migration of older data from the operational data warehouse to magnetic tapes. In this paper, we propose an "Active data archival service" in which the data is seamlessly archived on the cloud while ensuring that the archived data can be queried without any perceptible change to the end-user. This takes the burden of maintaining the archive off the user and shifts it to the archival service. We discuss the architecture of the service, challenges arising therein due to the federation of data brought on by the archival and how we handle these issues. Specifically, we investigate how the relational data needs to be transformed so that storing and retrieving the data from the cloud is efficient and seamless to the end user. We present our insights through an experimental study using TPC-DS benchmark. [ABSTRACT FROM PUBLISHER]

Enterprise datasets are often noisy. Several columns can have non-standard, erroneous or missing information. Poor quality data can lead to incorrect reporting and wrong conclusions being drawn. Data cleansing involves standardizing such data to improve its quality. Often data cleansing tasks involve writing rules manually. The step involves understanding the data quality issues and then writing data transformation rules to correct these issues. This is a human intensive task. In this study we propose a method to identify noisy subsets of huge unlabelled textual datasets. This is a two step process where in the first step we develop an estimation tool to predict the data quality on an unlabelled text dataset as produced by a segmentation model. The accuarcy of the proposed method is shown on a real life dataset. [ABSTRACT FROM PUBLISHER]

Record Linkage is an essential but expensive step in enterprise data management. In most deployments, blocking techniques are employed which can reduce the number of record pair comparisons and hence, the computational complexity of the task. Blocking algorithms require a careful selection of column(s) to be used for blocking. Selection of appropriate blocking column is critical to the accuracy and speed-up offered by the blocking technique and requires intervention by data quality practitioners who can exploit prior domain knowledge to analyse a small sample of the huge database and decide the blocking column(s). However, the selection of optimal blocking column(s) can depend heavily on the quality of data and requires extensive analysis. An experienced data quality practitioner is required for the selection of optimal blocking columns. In this paper, we present a data-driven approach to automatically choose blocking column(s), motivated from the modus operandi of data quality practitioners. Our approach produces a ranked list of columns by evaluating them for appropriateness for blocking on the basis of factors including data quality and distribution. We evaluate our choice of blocking columns through experiments on real world and synthetic datasets. We extend our approach to be employed in scenarios where more than one column can be used for blocking. [ABSTRACT FROM PUBLISHER]