Physical Sciences and Mathematics Commons^™

Sort Vs. Hash Join On Knights Landing Architecture, Victor L. Pan, Felix Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

With the increasing amount of information stored, there is a need for efficient database algorithms. One of the most important database operations is “join”. This involves combining columns from two tables and grouping common values in the same row in order to minimize redundant data. The two main algorithms used are hash join and sort merge join. Hash join builds a hash table to allow for faster searching. Sort merge join first sorts the two tables to make it more efficient when comparing values. There has been a lot of debate over which approach is superior. At first, hash join …

Blockchain: Technical Review, Evan D. Poff

Marriott Student Review

This review of blockchain technology accompanies the article "Strategic Implications of Blockchain."

Environmental Restoration Database, Joao Nascimento

Graduate Theses & Non-Theses

Environmental restoration projects face many challenges. Public awareness, funding constraints, unpredictable weather, unknown biological/chemical factors and the uncertainty about how the targeted ecosystem will develop work against the planned and ideal restoration.

One way the projects’ efficiency can be improved is by using software tools for data and quality management systems, in order to share information, make field practice follow rules, keep track of maintenance tasks, measure results and, therefore, increase the rate of success by the amount of resources invested.

Since the conception of every project, all resources involved need to be focused and coherent to the final restoration …

Supporting Scientific Analytics Under Data Uncertainty And Query Uncertainty, Liping Peng

Doctoral Dissertations

Data management is becoming increasingly important in many applications, in particular, in large scientific databases where (1) data can be naturally modeled by continuous random variables, and (2) queries can involve complex predicates and/or be difficult for users to express explicitly. My thesis work aims to provide efficient support to both the "data uncertainty" and the "query uncertainty". When data is uncertain, an important class of queries requires query answers to be returned if their existence probabilities pass a threshold. I start with optimizing such threshold query processing for continuous uncertain data in the relational model by (i) expediting selections …