Browsing by Subject "Database"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Database management system support for collaborative filtering recommender systems
    (2014-08) Sarwat, Mohamed
    Recommender systems help users identify useful, interesting items or content (data)from a considerably large search space. By far, the most popular recommendation technique used is collaborative filtering which exploits the users' opinions (e.g., movie ratings) and/or purchasing (e.g., watching, reading) history in order to extract a set of interesting items for each user. Database Management Systems (DBMSs) do not provide in-house support for recommendation applications despite their popularity. Existing recommender system architectures either do not employ a DBMS at all or only uses it as a data store whereas the recommendation logic is implemented in-full outside the database engine. Incorporating the recommendation functionality inside the DBMS kernel is beneficial for the following reasons: (1) Many recommendation algorithms take as input structured data (users, items, and user historical preferences) that could be adequately stored and accessed using a database system. (2) The In-DBMS approach facilitates applying the recommendation functionality and typical database operations(e.g., Selection, Join) side-by-side. That allows application developers to go beyond traditional recommendation applications, e.g., "Recommend to Alice ten movies", and flexibly define Arbitrary Recommendation scenarios like "Recommend ten nearby restaurants to Alice" and "Recommend to Bob ten movies watched by her friends". (3) Once the recommendation functionality lives inside the database kernel, the recommendation application takes advantage of the DMBS inherent features (e.g., query optimization, materialized views, indexing) provided by the storage manager and query execution engine.This thesis studies the incorporation of the recommendation functionality inside the core engine of a database management system. This is a major departure from existing recommender system architectures that are implemented on-top of a database engines using either SQL queries or stored procedures. The on-top approach does not harness the full power of the database engine (i.e., query execution engine, storage manager)since it always generates recommendations first and then performs other database operations. Ideas developed in this thesis are implemented inside RecDB ; an opensource recommendation engine built entirely inside PostgreSQL (open source relational database system).
  • Thumbnail Image
    Item
    Multivariate DNA taphonomy: evaluating the effects of environmental context, specimen properties, and laboratory strategies on the preservation and detection of DNA in ancient and challenging specimens.
    (2012-05) Alveshere, Andrea Joanna
    Within their diminutive structures, DNA molecules hold tantalizing potential to address myriad questions about human history, prehistory, and the evolution and dispersal of all forms of life. When accessible and accurate, DNA from ancient and degraded specimens can elucidate many topics of interest to researchers in a variety of fields including archaeology, biological anthropology, forensics, conservation and evolutionary biology, agronomy, and medicine. Despite the great informational potential of genetic studies, the high cost and destructive nature of DNA analyses discourage many researchers from submitting archaeological specimens for testing. A diversity of DNA detection protocols, the limited scope of individual research projects, and a bias toward publishing successful results make it difficult to evaluate the comparative influence of different preservation factors, field methods, and laboratory strategies on the recovery of useful genetic information from ancient and degraded specimens. The work presented in this manuscript is predicated upon the contention that the opportunity to conduct ancient DNA research entails an obligation to make the most of every specimen fragment consumed, every data point collected, and every funding dollar spent. The scope of this project is to develop a system for evaluating whether DNA testing might be appropriate for a given specimen; for determining which steps can be taken to increase the chances of recovering useful data; and for maximizing the contribution of individual research projects, conducted across disparate fields, to the greater body of knowledge on DNA preservation and detection. This endeavor involved: (1) inventorying variables having potential to influence DNA preservation and/or detection; (2) investigating subsets of the candidate variables through case studies of archaeological materials from Kromdraai, Wonderwerk Cave, and Border Cave, South Africa, Silvernale Village, Minnesota, and UV-irradiated forensic-type samples; and (3) the development and validation (via case study data) of the Biomolecular Preservation and Detection Information System (BIOPADIS™), a standardized system for synthesis, management, and analysis of biomolecular taphonomy data. BIOPADIS™ (\bī-’op-ad-is\) comprises a relational database that accommodates all manner of relevant data, a querying capability that makes these data accessible, and a set of statistical approaches appropriate for identifying and evaluating correlations within these multivariate, multi-study data.
  • Thumbnail Image
    Item
    R Code, Data, and Output Supporting: Nutrient Data from U.S. Manure Systems
    (2024-06-27) Bohl Bormann, Nancy; Wilson, Melissa; Cortus, Erin; Silverstein, Kevin; Janni, Kevin; Gunderson, Larry; nancy.bohl.bormann@gmail.com; Bohl Bormann, Nancy
    This repository contains R code, processed data, and associated outputs supporting the results reported in: Bohl Bormann, 2024. Manure Nutrient Data Compilation and Analysis for Agronomic and Environmental Applications. PhD Dissertation.
  • Thumbnail Image
    Item
    Transaction and data consistency models for cloud applications
    (2014-02) Padhye, Vinit A.
    The emergence of cloud computing and large-scale Internet services has given rise to new classes of data management systems, commonly referred to as NoSQL systems. The NoSQL systems provide high scalability and availability, however they provide only limited form of transaction support and weak consistency models. There are many applications that require more useful transaction and data consistency models than those currently provided by the NoSQL systems. In this thesis, we address the problem of providing scalable transaction support and appropriate consistency models for cluster based as well as geo-replicated NoSQL systems. The models we develop in this thesis are founded upon the snapshot isolation (SI) model which has been recognized as attractive for scalability. In supporting transactions on cluster-based NoSQL systems, we introduce a notion of decoupled transaction management in which transaction management functions are decoupled from storage system and integrated with the application layer. We present two system architectures based on this concept. In the first system architecture all transaction management functions are executed in a fully decentralized manner by the application processes. The second architecture is based on a hybrid approach in which the conflict detection functions are performed by a dedicated service. Because the SI model can lead to non-serializable transaction executions, we investigate two approaches for ensuring serializability. We perform a comparative evaluation of the two architectures and approaches for guaranteeing serializability and demonstrate their scalability. For transaction management in geo-replicated systems, we propose an SI based transaction model, referred to as causal snapshot isolation (CSI), which provides causal consistency using asynchronous replication. The causal consistency model provides more useful consistency guarantees than the eventual consistency model. We build upon the CSI model to provide an efficient transaction model for partially replicated databases, addressing the unique challenges raised due to partial replication in supporting snapshot isolation and causal consistency. Through experimental evaluations, we demonstrate the scalability and performance of our mechanisms.