Doctor of Philosophy, The Ohio State University, 1992, Graduate School

Committee: Not Provided (Other) Subjects: Business Administration

MS, University of Cincinnati, 2014, Engineering and Applied Science: Computer Science

A main purpose of a database is to provide requested data efficiently. Query performance can be improved in many ways. One of the efficient ways to handle multiple queries posted simultaneously to the database is to distribute the database across several sites and instead of querying the entire database, only the site that contains the data related to the query is accessed. Distribution of a database involves fragmentation of the data and allocating the fragmented data across various sites. Several research works address the issue of fragmentation of databases based on workload, since the aim of fragmentation is to optimize query response time [MD08]. In particular, clustering the data according to query predicates or attributes is shown to perform well for fragmentation. Mahboubi and Darmont propose the use of a k-means based fragmentation approach [MD08]. The authors do not consider the similarity of query predicates in the workload before performing the k-means clustering in their approach. We cluster similar selection predicates involved in the workload as a pre-processing step for the fragmentation; we expect to further improve the query performance. We investigate clustering techniques and study the resulting performance for a selected case study. We conclude that in general for our workloads and for our experimental parameters, the final clusters obtained using our predicate preprocessing system are tighter and more meaningful. As the number of similar values in the workload decreases, the relative savings of the predicate preprocessing system is reduced. If there are no similar values in the workload, the original fragmentation system is more efficient.

Committee: Karen Davis Ph.D. (Committee Chair); Raj Bhatnagar Ph.D. (Committee Member); Carla Purdy Ph.D. (Committee Member) Subjects: Computer Science

MS, University of Cincinnati, 2006, Engineering : Computer Science

Advances in database and storage technology and need to manage constant flow of information have necessitated use of databases for every organization. These databases are independently owned and operated by respective organizations and privacy of data prevents complete data sharing between entities. Combined data from multiple sources potentially can contribute to mutually beneficial computations. This thesis perceives independent databases as single logical database partitioned and distributed over a number of locations and aims to compute complex mathematical functions over vertically distributed databases while preserving privacy of data. A Global function to be computed over single logical database, as we perceive it, is broken into a set of Local functions that operate on individual data sites. We developed an iterative algorithm and it's variation that performs global computation using summaries of local computations. The working of the algorithms was shown through software simulations and effectiveness of the suggested approach was demonstrated.

Committee: Dr. Raj Bhatnagar (Advisor) Subjects: Computer Science

MS, University of Cincinnati, 2003, Engineering : Computer Science

We work with temporal data stored in distributed databases that are spread over a region. We have considered a sensor network where a lot of sensor nodes are spread in a grid like manner. These sensor nodes are capable of storing data and thus act as a separate dataset. The entire network of these sensors act as a set of distributed datasets. An algorithm is introduced that mines global temporal patterns from these datasets and results in the discovery of linear trajectories of moving objects under supervision. Each of these datasets has its local temporal dataset along with spatial data and the geographical coordinates of a given object or target. The main objective here is to perform in-network aggregation between the data contained in the various datasets to discover global spatio-temporal patterns; the main constraint is that there should be minimal communication among the participating nodes. We present the algorithm and analyze it in terms of the communication costs. The cost of our algorithm is much smaller than that of the alternative in which the data must be transferred to a single site and then mined. In addition to this, we vary the requirements of our algorithm slightly and present a variant of it that enhances its performance in terms of the overall complexity of computations. We go on to show that the while the efficiency of the algorithm increases in terms of the number of messages exchanged between nodes, the amount of information available to all the nodes in the system decrease. The advantages and drawbacks of this variant of our algorithm is also presented.

Committee: Dr. Raj Bhatnagar (Advisor) Subjects: Computer Science