Thesis (M. S.)--Architecture, Georgia Institute of Technology, 2007. / Ringholz, David, Committee Chair ; Chung, Wayne, Committee Member ; Johnson, Jennie, Committee Member.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 216-233). Also available via the Internet.

Boston University. University Professors Program Senior theses. / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / 2031-01-02

M.Com. (Business Management) / Please refer to full text to view abstract

The primary purpose of this thesis is to investigate the problems involved in adapting electronic data processing equipment to aid the fashion merchandising departments of retail department stores. In order that this application is placed in its proper perspective, it is also necessary to delve into some of the more fundamental concepts underlying the management planning and control function, to emphasize the relationship of management science methods and electronic data processing equipment to business problems in general, and to survey the operating characteristics of retail stores and the problems they face when introducing electronic data processing equipment. Preceding the discussion of the fashion merchandising application is a chapter on the organization of retail department stores that promotes a better understanding of the institutional framework within which one is working. Both primary and secondary research were undertaken to obtain the required information. The rather sparse literature published on this topic in the retailing field was supplemented by interviews with numerous retail executives of the four major department store chains in the Greater Vancouver area. Relevant literature from other fields of business was also brought into the discussion. This thesis supports the wider use of electronic data processing equipment to solve the increased data handling problems that have been experienced by retail department stores. In addition it is felt that this equipment can play an important role in providing merchandising personnel with information that could lead to improved performance of their functions. However, a greater improvement in performance in fashion merchandising, and in fact all retailing areas, will be realized when there is a more precise definition of the information required and when data handling systems are designed accordingly. / Business, Sauder School of / Graduate

Prof. F. J. Herbst

No description available.

No description available.

This study was undertaken for the purpose of finding the standard requirements and qualifications demanded of employees by the sales, sales service, and executive divisions of the average department stores, and the rewards and benefits enjoyed by the employees for faithful and efficient service rendered to the organization.

This thesis aimed to investigate what Data Stores would fit to be implemented as an Online Feature Store. This is a component in the Machine Learning infrastructure that needs to be able to handle low latency Reads at high throughput with high availability. The thesis evaluated the Data Stores with real feature workloads from Spotify’s Search system. First an investigation was made to find suitable storage systems. NDB and Aerospike were selected because of their state-of-the-art performance together with their suitable functionality. These were then implemented as the Online Feature Store by batch Reading the feature data through a Java program and by using Google Dataflow to input data to the Data Stores. For 1 client NDB achieved about 35% higher batch Read throughput with around 30% lower P99 latency than Aerospike. For 8 clients NDB got 20% higher batch Read throughput, with a varying P99 latency different compared to Aerospike. But in a 8 node setup NDB achieved on average 35% lower latency. Aerospike achieved 50% fasterWrite speeds when writing feature data to the Data Stores. Both Data Stores’ Read performance was found to suffer upon Writing to the data store at the same time as Reading, with the P99 Read latency increasing around 30% for both Data Stores. It was concluded that both Data Stores would work as an Online Feature Store. But NDB achieved better Read performance, which is one of the most important factors for this type of Feature Store. / Den här uppsatsen undersökte vilka datalagringssystem som passar för att implementeras som en Online Feature Store. Detta är en komponent i maskininlärningsinfrastrukturen som måste hantera snabba läsningar med hög genomströmning och hög tillgänglighet. Uppsatsen studerade detta genom att evaluera datalagringssystem med riktig feature data från Spotifys söksystem. En utredning gjordes först för att hitta lovande datalagringssystem för denna uppgift. NDB och Aerospike blev valda på grund av deras topp prestanda och passande funktionalitet. Dessa implementerades sedan som en Online Feature Store genom att batch-läsa feature datan med hjälp av ett Java program samt genom att använda Google Dataflow för att lägga in feature datan i datalagringssystemen. För 1 klient fick NDB runt 35% bättre genomströmning av feature data jämfört med Aerospike för batch läsningar, med ungefär 30% lägre P99 latens. För 8 klienter fick runt 20% högre genomströmning av feature data med en P99 latens som var mer varierande. Men klustren med 8 noder fick NDB i genomsnitt 35% lägre latens. Aerospike var 50% snabbare på att skriva feature datan till datalagringssystemet. Båda systemen led dock av sämre läsprestanda när skrivningar skedde till dem samtidigt. P99 läs-latensen gick då upp runt 30% för båda datalagringssystemen. Sammanfattningsvis funkade båda av de undersökta datalagringssystem som en Online Feature Store. Men NDB hade bättre läsprestanda, vilket är en av de mest viktigaste faktorerna för den här typen av Feature Store.