List scheduling and simulated annealing in a HW/SW co-design environment

For decades combinatorial problems have been studied in numerous disciplines. The scheduling problem, in which finding the optimal solution cannot be defined in a polynomial time, belongs to this type of problem. Several methodologies for the scheduling problem have been described in order to obtain efficient results. This thesis employs a combination of two methods to solve the task-scheduling problem. The first method, known as list-based heuristics, finds a feasible solution to the problem quickly, but with no guarantee of obtaining the optimal solution. The second method is a very well known search technique called simulated annealing. The simulated annealing technique explores all feasible solutions and obtains better solutions. Also, simulated annealing accepts worse solutions with a probability. This acceptance probability avoids local minimums in the algorithm. Furthermore, this thesis introduces the concept of hardware acceleration in order to improve the final algorithm's overall execution time. By transferring software functionality to dedicated hardware, the design achieves a significant reduction in execution time. (Published By University of Alabama Libraries

Knowledge explorer:exploring the 12-billion-statement KnowWhereGraph using faceted search (demo paper)

Knowledge graphs are a rapidly growing paradigm and technology stack for integrating large-scale, heterogeneous data in an AI-ready form, i.e., combining data with the formal semantics required to understand it. However, toolchains that support data synthesis and knowledge discovery through information organization, search, filtering, and visualization have been developed at a pace lagging knowledge graph technology. In this paper, we present Knowledge Explorer, an open-source faceted search interface that provides environmentally intelligent services for interactively browsing and navigating KnowWhereGraph. Currently one of the largest open knowledge graphs, KnowWhereGraph contains over 12 billion statements with rich spatial and temporal information from more than 30 data layers. With an extensive collection of facets, Knowledge Explorer enables spatial, temporal, full-text, and expert search with dereferencing functionality to support "follow-your-nose"exploration, and it allows users to narrow their search by selecting facets. Given the size of the underlying graph and dependency on GeoSPARQL, we have improved query performance by implementing Elasticsearch indexing, spatial query generation, and caching. Knowledge Explorer is capable of retrieving information within seconds, answering a wide variety of competency questions posed by researchers, humanitarian relief organizations, and the broader public, thus helping better perform tasks such as cross-gazetteer place retrieval and disaster assessment from global to local geographic scales.</p