Web Engineering for Workflow-based Applications: Models, Systems and Methodologies

This dissertation presents novel solutions for the construction of Workflow-based Web applications: The Web Engineering DSL Framework, a stakeholder-oriented Web Engineering methodology based on Domain-Specific Languages; the Workflow DSL for the efficient engineering of Web-based Workflows with strong stakeholder involvement; the Dialog DSL for the usability-oriented development of advanced Web-based dialogs; the Web Engineering Reuse Sphere enabling holistic, stakeholder-oriented reuse

Why is the snowflake schema a good data warehouse design?

Database design for data warehouses is based on the notion of the snowflake schema and its important special case, the star schema. The snowflake schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a snowflake schema in terms of an acyclic database schema whose join tree satisfies certain structural properties. We then define a normal form for snowflake schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that snowflake schemas in this normal form are independent as well as separable when the relation schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the snowflake schema and show that the redundancy of the primary key of the fact table is zero

An Investigation into the distuibution of portable documents in a prepress environment

Portable document files (PDF\u27s) are used in many types of environments; such as, office, world wide web, and desktop publishing to name a few. PDF technology allows electronic documents to become truly portable. PDF files maintain their original look and feel across various computer systems and platforms. PDF use in a prepress environment was tested by creating various electronic mechanicals with numerous file formats and having these PDF files imaged across the country on various high resolution imagesetting devices. It has been determined that PDF files can be used in a prepress environment at this point in time in some situations

An image delta compression tool: IDelta

The purpose of this thesis is to present a modified version of the algorithm used in the open source differencing tool zdelta, entitled iDelta . This algorithm will manage file data and will be built specifically to difference images in the Photoshop file format

Content Editing, Site Developing and Web Designing Guide for a Drupal 7 Based, Large Touchscreen Site

In 2011, the School of Information and Library Science at the University of North Carolina at Chapel Hill installed a 46-inch touchscreen computer in the Manning Hall lobby. After discussing the system's role with stakeholders, a Drupal-based website was developed with the following system goals: showcase the contributed work and accomplishments of current library and information science students, inspire unique and creative student work for the touchscreen, demonstrate educational opportunities to prospective students, provide an introduction for SILS newcomers and when idle, display event announcements or community interest topics. This document is for content editors, site developers, system administrators, and web designers who will maintain and expand the site. The manual is divided into eight sections: Introduction, Overview of Site Navigation and Site Sections, Adding and Editing Content, Basic Site Maintenance and Administration, Overview of Site Technology, Reference for Site Builders/Developers, Reference for Site Designers and Future Work.Master of Science in Information Scienc

Camera Component for the ESTCube-2 Mission Control System

ESTCube-2 on teine Eesti Tudengisatelliidi Programmi satelliidi projekt. Sellepeamiseks uurimisülesandeks on plasma piduri tehnoloogia testimine maa orbiidil. Projekti riistvara ja tarkvara arendavad üliõpilased. Arendamisel on tarkvara nimega missiooni juhtimissüsteem, mis aitaks satelliiti pärast orbiidile saatmist jälgida ja juhtida. Süsteem koosneb mitmest osast ja rakendustest, mis on mikroteenuste arhitektuuri sees ühendatud. Käesoleva lõputöö autor kirjeldab väljakutseid, mis on seotud satelliidi piltide kuvamisega kaasaegses veebirakenduses, analüüsib võimalikke lahendusi ja annab ülevaate tarkvaraprototüübi arenduse protsessist.ESTCube-2 is the second satellite project of the Estonian Student Satellite Programme. Its main scientific mission is to test the plasma break technology on the low Earth orbit. The hardware and the software for the project are developed by students. Computer software called Mission Control System is being developed to monitor and control the satellite after launch. The system consists of multiple components and applications connected in a microservice architecture. This paper focuses on the research and development process of the Mission Control System camera component. The author describes challenges related to displaying the satellite images in a modern web application, analyses possible solutions and provides an overview of the software prototype implementation

Addressing Memory Bottlenecks for Emerging Applications

There has been a recent emergence of applications from the domain of machine learning, data mining, numerical analysis and image processing. These applications are becoming the primary algorithms driving many important user-facing applications and becoming pervasive in our daily lives. Due to their increasing usage in both mobile and datacenter workloads, it is necessary to understand the software and hardware demands of these applications, and design techniques to match their growing needs. This dissertation studies the performance bottlenecks that arise when we try to improve the performance of these applications on current hardware systems. We observe that most of these applications are data-intensive, i.e., they operate on a large amount of data. Consequently, these applications put significant pressure on the memory. Interestingly, we notice that this pressure is not just limited to one memory structure. Instead, different applications stress different levels of the memory hierarchy. For example, training Deep Neural Networks (DNN), an emerging machine learning approach, is currently limited by the size of the GPU main memory. On the other spectrum, improving DNN inference on CPUs is bottlenecked by Physical Register File (PRF) bandwidth. Concretely, this dissertation tackles four such memory bottlenecks for these emerging applications across the memory hierarchy (off-chip memory, on-chip memory and physical register file), presenting hardware and software techniques to address these bottlenecks and improve the performance of the emerging applications. For on-chip memory, we present two scenarios where emerging applications perform at a sub-optimal performance. First, many applications have a large number of marginal bits that do not contribute to the application accuracy, wasting unnecessary space and transfer costs. We present ACME, an asymmetric compute-memory paradigm, that removes marginal bits from the memory hierarchy while performing the computation in full precision. Second, we tackle the contention in shared caches for these emerging applications that arise in datacenters where multiple applications can share the same cache capacity. We present ShapeShifter, a runtime system that continuously monitors the runtime environment, detects changes in the cache availability and dynamically recompiles the application on the fly to efficiently utilize the cache capacity. For physical register file, we observe that DNN inference on CPUs is primarily limited by the PRF bandwidth. Increasing the number of compute units in CPU requires increasing the read ports in the PRF. In this case, PRF quickly reaches a point where latency could no longer be met. To solve this problem, we present LEDL, locality extensions for deep learning on CPUs, that entails a rearchitected FMA and PRF design tailored for the heavy data reuse inherent in DNN inference. Finally, a significant challenge facing both the researchers and industry practitioners is that as the DNNs grow deeper and larger, the DNN training is limited by the size of the GPU main memory, restricting the size of the networks which GPUs can train. To tackle this challenge, we first identify the primary contributors to this heavy memory footprint, finding that the feature maps (intermediate layer outputs) are the heaviest contributors in training as opposed to the weights in inference. Then, we present Gist, a runtime system, that uses three efficient data encoding techniques to reduce the footprint of DNN training.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146016/1/anijain_1.pd