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We're back with How I Launched This: A SaaS story! This week, Stephanie Wong (@stephr_wong) talks with Alvin Richards about Redis Labs, a company optimizing the Redis open source in-memory database system to build better managed tools for enterprise clients.Alvin begins the show describing how his love of solving complex development problems and great people skills have put him in a unique position to act as intermediary between engineers and clients, gaining insights into real-world problems and how to solve them. Looking to the future, Alvin's team also anticipates client needs, creating database products that will continue to help clients as their projects evolve.Later in the show, Alvin describes how the Redis system built in the cloud was reworked to also provide on-prem offerings. We learn how Redis Labs was able to fill a gap in the market by offering a database product that both developers and clients could understand, adapt, and use. Alvin introduces us to other Redis Labs products, including Redis for Enterprise which allows tiering between memory forms, in-memory caching, scaling, and more for a flexible database experience.We wrap up the show with a discussion of what it's like coordinating the development of such a large open source project and why Redis Labs supports open source. Alvin offers advice to other companies, stressing the importance of building solutions with both the creator and client in mind and educating clients and developers to use the software effectively. We talk about the future of open source in SaaS companies and how important it will be for scaling SaaS technology. Alvin concludes by encouraging everyone to ultimately find joy in what they do.Episode Links:RedisRedis LabsDockerMemorystoreMongoDBElasticRedis University

We're back with How I Launched This: A SaaS story! This week, Stephanie Wong (@stephr_wong) talks with Alvin Richards about Redis Labs, a company optimizing the Redis open source in-memory database system to build better managed tools for enterprise clients.Alvin begins the show describing how his love of solving complex development problems and great people skills have put him in a unique position to act as intermediary between engineers and clients, gaining insights into real-world problems and how to solve them. Looking to the future, Alvin's team also anticipates client needs, creating database products that will continue to help clients as their projects evolve.Later in the show, Alvin describes how the Redis system built in the cloud was reworked to also provide on-prem offerings. We learn how Redis Labs was able to fill a gap in the market by offering a database product that both developers and clients could understand, adapt, and use. Alvin introduces us to other Redis Labs products, including Redis for Enterprise which allows tiering between memory forms, in-memory caching, scaling, and more for a flexible database experience.We wrap up the show with a discussion of what it's like coordinating the development of such a large open source project and why Redis Labs supports open source. Alvin offers advice to other companies, stressing the importance of building solutions with both the creator and client in mind and educating clients and developers to use the software effectively. We talk about the future of open source in SaaS companies and how important it will be for scaling SaaS technology. Alvin concludes by encouraging everyone to ultimately find joy in what they do.Episode Links:RedisRedis LabsDockerMemorystoreMongoDBElasticRedis University

We're back with How I Launched This: A SaaS story! This week, Stephanie Wong (@stephr_wong) talks with Alvin Richards about Redis Labs, a company optimizing the Redis open source in-memory database system to build better managed tools for enterprise clients.Alvin begins the show describing how his love of solving complex development problems and great people skills have put him in a unique position to act as intermediary between engineers and clients, gaining insights into real-world problems and how to solve them. Looking to the future, Alvin's team also anticipates client needs, creating database products that will continue to help clients as their projects evolve.Later in the show, Alvin describes how the Redis system built in the cloud was reworked to also provide on-prem offerings. We learn how Redis Labs was able to fill a gap in the market by offering a database product that both developers and clients could understand, adapt, and use. Alvin introduces us to other Redis Labs products, including Redis for Enterprise which allows tiering between memory forms, in-memory caching, scaling, and more for a flexible database experience.We wrap up the show with a discussion of what it's like coordinating the development of such a large open source project and why Redis Labs supports open source. Alvin offers advice to other companies, stressing the importance of building solutions with both the creator and client in mind and educating clients and developers to use the software effectively. We talk about the future of open source in SaaS companies and how important it will be for scaling SaaS technology. Alvin concludes by encouraging everyone to ultimately find joy in what they do.Episode Links:RedisRedis LabsDockerMemorystoreMongoDBElasticRedis University

We're back with How I Launched This: A SaaS story! This week, Stephanie Wong (@stephr_wong) talks with Alvin Richards about Redis Labs, a company optimizing the Redis open source in-memory database system to build better managed tools for enterprise clients.Alvin begins the show describing how his love of solving complex development problems and great people skills have put him in a unique position to act as intermediary between engineers and clients, gaining insights into real-world problems and how to solve them. Looking to the future, Alvin's team also anticipates client needs, creating database products that will continue to help clients as their projects evolve.Later in the show, Alvin describes how the Redis system built in the cloud was reworked to also provide on-prem offerings. We learn how Redis Labs was able to fill a gap in the market by offering a database product that both developers and clients could understand, adapt, and use. Alvin introduces us to other Redis Labs products, including Redis for Enterprise which allows tiering between memory forms, in-memory caching, scaling, and more for a flexible database experience.We wrap up the show with a discussion of what it's like coordinating the development of such a large open source project and why Redis Labs supports open source. Alvin offers advice to other companies, stressing the importance of building solutions with both the creator and client in mind and educating clients and developers to use the software effectively. We talk about the future of open source in SaaS companies and how important it will be for scaling SaaS technology. Alvin concludes by encouraging everyone to ultimately find joy in what they do.Episode Links:RedisRedis LabsDockerMemorystoreMongoDBElasticRedis University

We're back with How I Launched This: A SaaS story! This week, Stephanie Wong (@stephr_wong) talks with Alvin Richards about Redis Labs, a company optimizing the Redis open source in-memory database system to build better managed tools for enterprise clients.Alvin begins the show describing how his love of solving complex development problems and great people skills have put him in a unique position to act as intermediary between engineers and clients, gaining insights into real-world problems and how to solve them. Looking to the future, Alvin's team also anticipates client needs, creating database products that will continue to help clients as their projects evolve.Later in the show, Alvin describes how the Redis system built in the cloud was reworked to also provide on-prem offerings. We learn how Redis Labs was able to fill a gap in the market by offering a database product that both developers and clients could understand, adapt, and use. Alvin introduces us to other Redis Labs products, including Redis for Enterprise which allows tiering between memory forms, in-memory caching, scaling, and more for a flexible database experience.We wrap up the show with a discussion of what it's like coordinating the development of such a large open source project and why Redis Labs supports open source. Alvin offers advice to other companies, stressing the importance of building solutions with both the creator and client in mind and educating clients and developers to use the software effectively. We talk about the future of open source in SaaS companies and how important it will be for scaling SaaS technology. Alvin concludes by encouraging everyone to ultimately find joy in what they do.Episode Links:RedisRedis LabsDockerMemorystoreMongoDBElasticRedis University

Thesis advisor, David K. Hsiao

Thesis advisor, David K. Hsiao

The study of temporal database systems is relatively new in the field of computer science. Two developments have led to the present interest. The advances of the storage technology for large amounts of data and applications' requirements for time-dependent data have prompted our study of temporal databases. This thesis conducts a survey of the major research areas concerning temporal databases. Temporal data, taxonomies of temporal data models, temporal data languages, and temporal database systems are presented. It is argued here that future database systems should handle the temporal domain by an integrated temporal database system. By understanding the present technology and the need of temporal database systems, our research in the area of real-time temporal database systems can begin. It is the purpose of this thesis to provide the background information and research references of temporal database systems as a first step towards the real-time database system research. Real-time database systems are time-constrained and temporally constituted. Solutions in temporal database systems can contribute to the design of real-time military applications using temporal database computers.

An object model and the corresponding instance diagram embody all the concepts-object identity, structure, behavior, interaction, inheritance, and encapsulation-of the object-oriented paradigm. In an object-oriented data model, an entity is represented as an instance (object) of a class that has a set of properties and operations (methods) applied to the objects. A class, and hence an object, may inherit properties and methods from related classes. Objects and classes are dynamic and can be created at any time. With an object model, database implementation may begin. As with many other areas in computer science, true understanding of a novel concept comes only from hands-on work. In the case of object-oriented database systems, this means constructing simple object models and writing programs to implement and process them. This tutorial gives the fundamentals to tackle these tasks.

An object model and the corresponding instance diagram embody all the concepts-object identity, structure, behavior, interaction, inheritance, and encapsulation-of the object-oriented paradigm. In an object-oriented data model, an entity is represented as an instance (object) of a class that has a set of properties and operations (methods) applied to the objects. A class, and hence an object, may inherit properties and methods from related classes. Objects and classes are dynamic and can be created at any time. With an object model, database implementation may begin. As with many other areas in computer science, true understanding of a novel concept comes only from hands-on work. In the case of object-oriented database systems, this means constructing simple object models and writing programs to implement and process them. This tutorial gives the fundamentals to tackle these tasks.

A shared information system is a series of computer systems interconnected by some kind of communication network. There are data repositories residing on each computer. These data repositories must somehow be integrated. The purpose for using distributed and multi-database systems is to allow users to view collections of data repositories as if they were a single entity. Multidatabase systems, better known as heterogeneous multidatabase systems, are characterized by dissimilar data models, concurrency and optimization strategies and access methods. Unlike homogenous systems, the data models that compose the global database can be based on different types of data models. It is not necessary that all participant databases use the same data model. Federated distributed database systems are a special case of multidatabase systems. They are completely autonomous and do not rely on the global data dictionary to process distributed queries. Processing distributed query requests in federated databases is very difficult since there are multiple independent databases with their own rules for query optimization, deadlock detection, and concurrency. Expert systems can play a role in this type of environment by supplying a knowledge base that contains rules for data object conversion, rules for resolving naming conflicts, and rules for exchanging data.

A user of a database management system has an intuitive idea of a transaction as a sequence of database commands that he or she submits. The user expects this sequence of commands to be executed in the order of submission, without interference from other database commands submitted by other users. Techniques for doing this while concurrently supporting multiple database users are well known for conventional (i.e., not multilevel) database systems [2]. Transaction management theory for conventional database systems is not only mature, but useful in practice. The corresponding theory for multilevel secure database systems is still developing but some progress has been made [3,5,6,7,8,9,10].

This book is for the beginners students of database. It include all new and old methods of database.

The Department of Defense (DOD) has an extensive family of models used to simulate the mission level interaction of weapon systems. Interoperability and reuse of the underlying data files used to create simulation scenarios pose great challenges in this regard. Unlike traditional data integration methods common to federated database research, the emerging field of agent-oriented information systems (AOIS) views data as the central focus of an application while also providing an overall architectural framework for application development. We develop an AOIS solution relevant to this problem domain by combining object-oriented data modeling (OMT), a persistent programming language using a commercial object oriented database (ObjectStore ), and an agent- oriented analysis and design methodology (MaSE). Requirements from a contractor-led effort at the Air Force Research Laboratory (AFRL) known as CERTCORT are the basis for analysis and design of our system. We implement prototypical information-layer applications to conceptually demonstrate the reusability and integration of scenarios across simulation models.

The DSTO UAS Database contains geometric, aerodynamic, and performance data for nearly nine hundred semi-autonomous and remotely piloted unmanned aircraft systems (UAS) utilising fixed-wing unmanned aerial vehicles (UAVs). The Database has been created using information from a variety of public sources and is intended to serve as a repository and as a source of data for analysis of the various systems. Here, the characteristics of fixed-wing UAVs are examined as functions of their weight. Where appropriate, fixedwing UAVs are compared with manned aircraft and birds to provide the reader with an overview of the contents of the Database and to indicate some of its possible uses. Other demonstrated applications include historical analyses. The Database has recently been converted from its original Microsoft Excel spreadsheet format to a structured database format to make it more widely available to and searchable by potential users. Periodic maintenance, further validation of the data, and on-going additions are planned.

This article is from Source Code for Biology and Medicine , volume 9 . Abstract Background: Advances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging. Methods: To overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase. Results and conclusion: In this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.

This article is from Source Code for Biology and Medicine , volume 9 . Abstract Background: Advances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging. Methods: To overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase. Results and conclusion: In this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.

This article is from Source Code for Biology and Medicine , volume 9 . Abstract Background: Advances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging. Methods: To overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase. Results and conclusion: In this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.

Association for Computing Machinery: Transactions on Database Systems 1978: Volume 3 , Issue CONTENTS. Digitized from IA1534630-02 . Next issue: sim_acm-transactions-on-database-systems_1978-03_3_1 .

Association for Computing Machinery: Transactions on Database Systems 1982: Volume 7 , Issue CONTENTS. Digitized from IA1534630-02 . Previous issue: sim_acm-transactions-on-database-systems_1981-12_6_4 . Next issue: sim_acm-transactions-on-database-systems_1982-03_7_1 .

Association for Computing Machinery: Transactions on Database Systems 1994: Volume 19 , Issue CONTENTS. Digitized from IA1534630-02 . Previous issue: sim_acm-transactions-on-database-systems_1993-12_18_4 . Next issue: sim_acm-transactions-on-database-systems_1994-03_19_1 .

Association for Computing Machinery: Transactions on Database Systems 1994: Volume 19 , Issue CONTENTS. Digitized from IA1534630-02 . Previous issue: sim_acm-transactions-on-database-systems_1993-12_18_4 . Next issue: sim_acm-transactions-on-database-systems_1994-03_19_1 .

Association for Computing Machinery: Transactions on Database Systems 1994: Volume 19 , Issue CONTENTS. Digitized from IA1534630-02 . Previous issue: sim_acm-transactions-on-database-systems_1993-12_18_4 . Next issue: sim_acm-transactions-on-database-systems_1994-03_19_1 .

Association for Computing Machinery: Transactions on Database Systems 1994: Volume 19 , Issue CONTENTS. Digitized from IA1534630-02 . Previous issue: sim_acm-transactions-on-database-systems_1993-12_18_4 . Next issue: sim_acm-transactions-on-database-systems_1994-03_19_1 .

Association for Computing Machinery: Transactions on Database Systems 1994: Volume 19 , Issue CONTENTS. Digitized from IA1534630-02 . Previous issue: sim_acm-transactions-on-database-systems_1993-12_18_4 . Next issue: sim_acm-transactions-on-database-systems_1994-03_19_1 .

Andy Pavlo (https://www.cs.cmu.edu/~pavlo/) Slides: https://15721.courses.cs.cmu.edu/spring2024/slides/07-compilation.pdf Notes: https://15721.courses.cs.cmu.edu/spring2024/notes/07-compilation.pdf 15-721 Advanced Database Systems (Spring 2024) Carnegie Mellon University https://15721.courses.cs.cmu.edu/spring2024/

Andy Pavlo (https://www.cs.cmu.edu/~pavlo/) Slides: https://15721.courses.cs.cmu.edu/spring2024/slides/07-compilation.pdf Notes: https://15721.courses.cs.cmu.edu/spring2024/notes/07-compilation.pdf 15-721 Advanced Database Systems (Spring 2024) Carnegie Mellon University https://15721.courses.cs.cmu.edu/spring2024/

This document is an white paper about how to connect reverse engineering and programing skills to extract data from a proprietary implementation of a database system to build EML-Tools for data format conversion into raw data. This article shows how to access data of a source software system without any interface for data conversion. We discuss how raw data can be transfered into structural format by using XML or any other custom designed software solution. For demonstration purposes only, we will use a CRM system called Harmony(r) by Harmony(r) Software AG, the programing language Python and methods of computer security, which are used to get quick access to the raw data. All trademarks are property of their owners, as Harmony(r) is of Harmony Software AG.

Check the channel for more related content

Andy Pavlo (https://www.cs.cmu.edu/~pavlo/) Slides: https://15721.courses.cs.cmu.edu/spring2024/slides/21-yellowbrick.pdf Notes: https://15721.courses.cs.cmu.edu/spring2024/notes/21-yellowbrick.pdf 15-721 Advanced Database Systems (Spring 2024) Carnegie Mellon University https://15721.courses.cs.cmu.edu/spring2024/

Andy Pavlo (https://www.cs.cmu.edu/~pavlo/) Slides: https://15721.courses.cs.cmu.edu/spring2024/slides/03-data2.pdf Notes: https://15721.courses.cs.cmu.edu/spring2024/notes/03-data2.pdf 15-721 Advanced Database Systems (Spring 2024) Carnegie Mellon University https://15721.courses.cs.cmu.edu/spring2024/

In today's world, Web-Based Distance Education Systems have a great importance. Web-based Distance Education Systems are usually known as Learning Management Systems (LMS). In this article, a database design, which was developed to create an educational institution as a Learning Management System, is described. In this sense, developed Learning Management System consists of basis of Virtual Education Institutions. In this study, a fully relational database design has been realized in compliance with SCORM standards and got ready to be used as Virtual Education Institutions. This system can be used for any required education institute and it can be run within the same interface. In LMS that will be generated, a faculty or institute can be defined and academic and all administrative processes of the defined institute can be managed with the designed system. Proposed database design has been used in a LMS of Afyon Kocatepe University. In this system, many processes like indexing, uploading, downloading, production and editing of web based learning materials can also be performed easily and safely. (Contains 17 figures.)

Title from cover

Andy Pavlo (https://www.cs.cmu.edu/~pavlo/) Slides: https://15721.courses.cs.cmu.edu/spring2024/slides/18-databricks.pdf Notes: https://15721.courses.cs.cmu.edu/spring2024/notes/18-databricks.pdf 15-721 Advanced Database Systems (Spring 2024) Carnegie Mellon University https://15721.courses.cs.cmu.edu/spring2024/

The key elements of research performed during the year long effort of this project are: Investigate the effects of heterogeneity in distributed real time systems; Study the requirements to TRAC towards building a heterogeneous database system; Study the effects of performance modeling on distributed database performance; and Experiment with an ORACLE based heterogeneous system.

Andy Pavlo (https://www.cs.cmu.edu/~pavlo/) Slides: https://15721.courses.cs.cmu.edu/spring2024/slides/01-modernolap.pdf Notes: https://15721.courses.cs.cmu.edu/spring2024/notes/01-modernolap.pdf 15-721 Advanced Database Systems (Spring 2024) Carnegie Mellon University https://15721.courses.cs.cmu.edu/spring2024/

Andy Pavlo (https://www.cs.cmu.edu/~pavlo/) Slides: https://15721.courses.cs.cmu.edu/spring2024/slides/01-modernolap.pdf Notes: https://15721.courses.cs.cmu.edu/spring2024/notes/01-modernolap.pdf 15-721 Advanced Database Systems (Spring 2024) Carnegie Mellon University https://15721.courses.cs.cmu.edu/spring2024/

Computer systems have enjoyed an exponential growth in processor speed for the last 20 years, while DRAM main memory speed improves only moderately. Today a cache miss to main memory takes hundreds of processor cycles, and this gap between processor and DRAM speeds is widening exponentially. Recent database performance studies have pointed out that about 50% or more of the execution time of database systems in main memory is often wasted due to cache misses. On the other hand, several decades of database studies have invented many I/O techniques to better tolerate the disk-to-memory gap. Increasingly larger main memories in server systems can hold more and more hot data in databases. Therefore, the memory-to-cache gap is becoming the major performance problem for database systems. In this talk, I rethink database system designs to exploit modern computer architecture features. In particular, I exploit the CPU cache hierarchy and software prefetch instructions supported by most modern processors for relational database systems. Cache prefetching can load data into the cache before use, thus reducing the impact of cache misses. I focus on important structures and algorithms with intriguing, non-contiguous memory access patterns: the B+-Tree index, and the hash join algorithm. I will present novel prefetching techniques for B+-trees and hash joins. I will report on the results of extensive performance studies, demonstrating the dramatic performance improvements of the prefetching techniques over traditional algorithms and other cache friendly schemes. ©2005 Microsoft Corporation. All rights reserved.

Association for Computing Machinery: Transactions on Database Systems 1989: Volume 14 , Issue CONTENTS. Digitized from IA1534630-02 . Previous issue: sim_acm-transactions-on-database-systems_1988-12_13_4 . Next issue: sim_acm-transactions-on-database-systems_1989-03_14_1 .

Association for Computing Machinery: Transactions on Database Systems 1993: Volume 18 , Issue CONTENTS. Digitized from IA1534630-02 . Previous issue: sim_acm-transactions-on-database-systems_1992-12_17_4 . Next issue: sim_acm-transactions-on-database-systems_1993-03_18_1 .

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Studies in [7, 8, 9] concluded that for a variety of reasons, optimistic concurrency control appears well-suited to real-time database systems. Especially, they showed that in a real-time database system that discards tardy transactions, optimistic concurrency control outperforms locking. In this paper, we show that the optimistic algorithms used in those studies incur restarts unnecessary to ensure data consistency. We present a new optimistic concurrency control algorithm that can avoid such unnecessary restarts by adjusting serialization order dynamically, and demonstrate that the new algorithm outperforms the previous ones over a wide range of system workload. It appears that this algorithm is a promising candidate for basic concurrency control mechanism for real-time database systems.

The purpose of this paper is twofold. First, we investigate the impact of concurrency control on transaction execution cost and system throughout in centralized and distributed data base systems (DBS) based on slow and fast (local) networks. Second, we show that in terms of transaction execution cost and DBS throughput, there are some applications for which any distributed DBS can be more effective than any centralized DBS and vice versa. We also argue that for other applications the decision in favour of distributed or centralized DBS should be based on the comparison of specific DBS systems. (Author)

This thesis contains program documentation for the Air Force Institute of Technology School of Systems and Logistics Graduate Programs Office Database Management System (DBMS) application. Chapter I, Database Dictionaries, contains a description of data in the DBMS database. Chapter II, Screen Format Files, contains a listing of DBMS screen display program code. Chapter III, Report Specifications, contains Concentric Data Systems R&R Relational Report Writer report specifications. Chapter IV, Program Documentation, contains Ashton-Tate dBASE III Plus program code written for this application. Application development adn the DBMS User's Manual are documented in Volume (1): Development and User's Manual.

This thesis contains program documentation for the Air Force Institute of Technology School of Systems and Logistics Graduate Programs Office Database Management System (DBMS) application. Chapter I, Database Dictionaries, contains a description of data in the DBMS database. Chapter II, Screen Format Files, contains a listing of DBMS screen display program code. Chapter III, Report Specifications, contains Concentric Data Systems R&R Relational Report Writer report specifications. Chapter IV, Program Documentation, contains Ashton-Tate dBASE III Plus program code written for this application. Application development adn the DBMS User's Manual are documented in Volume (1): Development and User's Manual.

This thesis contains program documentation for the Air Force Institute of Technology School of Systems and Logistics Graduate Programs Office Database Management System (DBMS) application. Chapter I, Database Dictionaries, contains a description of data in the DBMS database. Chapter II, Screen Format Files, contains a listing of DBMS screen display program code. Chapter III, Report Specifications, contains Concentric Data Systems R&R Relational Report Writer report specifications. Chapter IV, Program Documentation, contains Ashton-Tate dBASE III Plus program code written for this application. Application development adn the DBMS User's Manual are documented in Volume (1): Development and User's Manual.

This thesis contains program documentation for the Air Force Institute of Technology School of Systems and Logistics Graduate Programs Office Database Management System (DBMS) application. Chapter I, Database Dictionaries, contains a description of data in the DBMS database. Chapter II, Screen Format Files, contains a listing of DBMS screen display program code. Chapter III, Report Specifications, contains Concentric Data Systems R&R Relational Report Writer report specifications. Chapter IV, Program Documentation, contains Ashton-Tate dBASE III Plus program code written for this application. Application development adn the DBMS User's Manual are documented in Volume (1): Development and User's Manual.

This thesis contains program documentation for the Air Force Institute of Technology School of Systems and Logistics Graduate Programs Office Database Management System (DBMS) application. Chapter I, Database Dictionaries, contains a description of data in the DBMS database. Chapter II, Screen Format Files, contains a listing of DBMS screen display program code. Chapter III, Report Specifications, contains Concentric Data Systems R&R Relational Report Writer report specifications. Chapter IV, Program Documentation, contains Ashton-Tate dBASE III Plus program code written for this application. Application development adn the DBMS User's Manual are documented in Volume (1): Development and User's Manual.

This thesis contains program documentation for the Air Force Institute of Technology School of Systems and Logistics Graduate Programs Office Database Management System (DBMS) application. Chapter I, Database Dictionaries, contains a description of data in the DBMS database. Chapter II, Screen Format Files, contains a listing of DBMS screen display program code. Chapter III, Report Specifications, contains Concentric Data Systems R&R Relational Report Writer report specifications. Chapter IV, Program Documentation, contains Ashton-Tate dBASE III Plus program code written for this application. Application development adn the DBMS User's Manual are documented in Volume (1): Development and User's Manual.

This thesis contains program documentation for the Air Force Institute of Technology School of Systems and Logistics Graduate Programs Office Database Management System (DBMS) application. Chapter I, Database Dictionaries, contains a description of data in the DBMS database. Chapter II, Screen Format Files, contains a listing of DBMS screen display program code. Chapter III, Report Specifications, contains Concentric Data Systems R&R Relational Report Writer report specifications. Chapter IV, Program Documentation, contains Ashton-Tate dBASE III Plus program code written for this application. Application development adn the DBMS User's Manual are documented in Volume (1): Development and User's Manual.