Integrated Digital Library System for Long Documents and their Elements

Abstract

We describe a next-generation integrated Digital Library (DL) system that addresses the numerous goals associated with long documents such as Electronic Theses and Dissertations (ETDs). Our extensible workflow-centric design supports a variety of users/personas (e.g., researchers, curators, and experimenters) who can benefit from improved access to ETDs and the content buried therein. Our approach leverages natural language processing, deep learning, information retrieval, and software engineering methods. The services cover ingesting, storing, curating, analyzing, detecting, extracting, classifying, summarizing, topic modeling, browsing, searching, retrieving, recommending, visualizing/reporting, and interacting with ETDs and derivative text/image-based elements/objects. Workflows connect the services and their APIs, along with UI-based access. We believe our approach can guide others to combine tailored user support, research, and education by way of extensible DLs. CCS CONCEPTS• Information systems → Digital libraries and archives; Information retrieval; Document representation; Retrieval tasks and goals.

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

“…Furthermore, the cloud task scheduling process is highly uncertain because of multiple factors triggering the unpredictable cloud environment, including network connectivity [23], resource usage [24], peak network demands [25], and web service performance inherent to service models of the cloud [26]. Artificial intelligence and machine learning techniques offer intelligent and adaptive solutions by analyzing patterns and predicting future demands, leading to proactive load management [27,28].…”