East Tennessee State University recently adopted Alma & Primo and was suprised by the lack of an A-Z database discovery module. Frustrated by having to maintain electronic resources separately on our library website and in Alma, we embarked on a goal to eliminate redundancy and use Alma/Primo exclusively. This presentation will cover our entire workflow in both Alma & Primo and the issues we encountered along the way. I'll first go over our process in Alma including MARC record creation, electronic collection setup, and the top level collection module. Next, I'll cover our workflow in Primo including normalization rules, scoping, PNX display, facets, and code table changes. The last section will cover the Primo X-Services API and how it was developed into an A-Z Database list.

Databases and database technology have become an essential component of everyday life in modern society. As databases are widely used in every organization with a computer system, control of data resources and management of data are very important. Database Management System (DBMS) is the most significant tool developed to serve multiple users in a database environment consisting of programs that enable users to create and maintain a database. Windows Distributed Internet Applications (DNA) architecture describes a framework of building software technologies together in an integrated web and client-server model of computing. This thesis focuses on development of a general database management system, for small and medium sized manufacturing enterprises, by using Windows DNA technology. Defining, constructing and manipulating institutional, commercial and operational data of the company is the main frame of the work. And also by integrating &ldquo / Optimization&rdquo / and &ldquo / Agent&rdquo / system components which were previously developed in Middle East Technical University, Mechanical Engineering Department, Computer Integrated Manufacturing Laboratory (METUCIM) into the SME DBMS, a unified information system is developed. &ldquo / Optimization&rdquo / system was developed in order to calculate optimum cutting conditions for turning and milling operations. &ldquo / Agent&rdquo / system was implemented to control and send work orders to the available manufacturing cell in METUCIM. The components of these systems are redesigned to share a unique database together with the newly developed &ldquo / SME Information System&rdquo / application program in order to control data redundancy and to provide data sharing and data integrity.

XML has emerged as leading document format for exchanging data. Because of vast amounts of XML documents available and transfered, there is a strong need to store and query information in these documents. However, the most companies are still using a RDBMS for their data warehouses and it is often necessary to combine legacy data with the ones in XML format, so it might be useful to consider storage possibilities for XML documents in a relation database. In this thesis we focused on structured and semi-structured data-based XML documents, because they are the most common when exchanging data and they can be easily validated against an XML schema. We propose a slightly modified Hybrid algorithm to shred doc- uments into relations using an XSD scheme and we allowed redundancy to make queries faster. Our goal was not to provide an academic solution, but fully working system supporting latest standards, which will beat up native XML databases both by performance and vertical scalability.

Many relational databases exhibit complex dependencies between data attributes, caused either by the nature of the underlying data or by explicitly denormalized schemas. In data warehouse scenarios, calculated key figures may be materialized or hierarchy levels may be held within a single dimension table. Such column correlations and the resulting data redundancy may result in additional storage requirements. They may also result in bad query performance if inappropriate independence assumptions are made during query compilation. In this paper, we tackle the specific problem of detecting functional dependencies between columns to improve the compression rate for column-based database systems, which both reduces main memory consumption and improves query performance. Although a huge variety of algorithms have been proposed for detecting column dependencies in databases, we maintain that increased data volumes and recent developments in hardware architectures demand novel algorithms with much lower runtime overhead and smaller memory footprint. Our novel approach is based on entropy estimations and exploits a combination of sampling and multiple heuristics to render it applicable for a wide range of use cases. We demonstrate the quality of our approach by means of an implementation within the SAP NetWeaver Business Warehouse Accelerator. Our experiments indicate that our approach scales well with the number of columns and produces reliable dependence structure information. This both reduces memory consumption and improves performance for nontrivial queries.