Reasoning about Independence in Probabilistic Models of Relational Data

We extend the theory of d-separation to cases in which data instances are not independent and identically distributed. We show that applying the rules of d-separation directly to the structure of probabilistic models of relational data inaccurately infers conditional independence. We introduce relational d-separation, a theory for deriving conditional independence facts from relational models. We provide a new representation, the abstract ground graph, that enables a sound, complete, and computationally efficient method for answering d-separation queries about relational models, and we present empirical results that demonstrate effectiveness.Comment: 61 pages, substantial revisions to formalisms, theory, and related wor

Performance Optimizations of NoSQL Databases in Distributed Systems

Databases store information about a system and provide a mechanism for data to be accessed and manipulated. While advancements in the 1970s provided a relational database model that has persisted to this day, web-scale era mass data needs surfacing in the 1990s and the early 2000s revealed limitations in the scalability of the relational model. As systems grew and transitioned into distributed architectures to support mass data storage and parallel processing, a complete overhaul of distributed computing technologies evolved that fundamentally departed from the relational data model in favor of the NoSQL data model. The course of this research details the scaling problems encountered by relational databases and the NoSQL solutions that made web-scale systems possible

The Effects of Conceptual and Logical Interfaces On Visual Query Performance of End Users

To the end users, the interface is the system. A better interface not only facilitates end user interaction with the database, it also enables them to formulate queries more efficiently and effectively. Two of the most important user-database interfaces are the conceptual and logical interfaces. With the conceptual interface, the user communicates with the database system in terms of entities, objects and relationships. On the other hand, the current user-database interaction is mainly based on the logical interface where the user expresses the queries in terms of relations and join operations. Because the concepts at the logical interface are abstract and convoluted to ordinary users, many researchers argue that end users will be better off with the conceptual interface. This research will test this claim by comparing the effects of coneeptual and logical interfaces on the visual query performance of end users. The experimental study involves three tests: an initial test, a retention test and a relearning test. This allows us to assess the learning effect over time. The results show that users of the conceptual interface achieve higher accuracy, are more confident in their answers, and spend less time on the queries than users of the logical interface in all three tests