Welcome, fellow code warriors of Edmonton! You’ve made it through the gauntlet of undergraduate algorithms courses, survived the infamous “Systems Programming with C” class that haunts your dreams, and somehow convinced a professor to supervise your Master’s thesis. But now, as you stare at that blank document titled “Dissertation_Final_FINAL_v2_actually_final.docx,” you’re experiencing what we in the tech world call a critical system failure.
Don’t panic. This isn’t a kernel panic—it’s just dissertation anxiety, and unlike segmentation faults, it’s completely fixable.
Picture this: It’s -30°C outside, your heating bill is higher than your student loan debt, and you’re debugging both your neural network implementation AND your motivation to continue living. Sound familiar? You’re not alone in this frozen wasteland of academic despair.
Edmonton’s computer science community knows the struggle. Between the University of Alberta’s rigorous standards, NAIT’s practical approach, and MacEwan’s growing tech programs, Master’s students across the city are united in one universal experience: dissertation-induced existential crisis.
But here’s the thing—just like any complex system, your dissertation challenges can be solved with the right approach, proper debugging techniques, and maybe a little professional help along the way.
Think of your Master’s dissertation as the ultimate coding challenge—except instead of optimizing for time complexity, you’re optimizing for academic rigor, and instead of submitting to LeetCode, you’re submitting to a committee of professors who’ve been caffeinated since the Clinton administration.
A Computer Science Master’s dissertation is essentially your opportunity to prove that you can:
Let’s break down the typical structure of a Computer Science Master’s dissertation, because understanding the architecture is the first step to successful implementation:
Your title page is like your application’s landing page—it needs to make a good first impression without crashing. The abstract is your API documentation: concise, informative, and hopefully comprehensible to humans.
Pro Tip: Always write the rest of the paper first and leave the abstract until last. To summarize a function, you have to understand how it operates; that’s like unit testing before you report on it.
This is where you sell your project. Why does this problem matter? Why should anyone care about your specific approach to optimizing blockchain consensus algorithms or improving recommendation systems? Think of it as your elevator pitch, except the elevator is stuck between floors and you have 15 pages to convince the fire department that your research is worth the rescue effort.
The literature review is like examining the commit history of your field—you need to understand what’s been tried before, what worked, what failed spectacularly, and where the current gaps exist. This isn’t just academic busy work; it’s competitive analysis for your intellectual product.
Edmonton-Specific Note: University of Alberta’s library system is your friend here. Their digital collections and interlibrary loan services can help you access papers that aren’t freely available. Plus, the Cameron Library has those cozy study nooks perfect for existential contemplation.
Here’s where you get to be the systems architect of your own research. Whether you’re building machine learning models, developing new algorithms, or conducting user studies, this section is your technical documentation. Be specific enough that another graduate student could theoretically reproduce your work (though we both know they won’t, because nobody reads methodology sections that carefully).
This is your code in production. What happened when you actually ran your experiments? Did your deep learning model achieve state-of-the-art results, or did it become sentient and start ordering pizza? Both outcomes are valid research contributions, but you need to document them properly.
Every good project ends with a retrospective. What worked? What didn’t? If you could start over, what would you do differently? (Besides choosing a different research topic that doesn’t require you to understand quantum computing.)
Let’s address the elephant in the server room: you’re probably a better programmer than you are a writer. This isn’t a personal failing—it’s a feature, not a bug. Most Computer Science programs focus on technical skills, not academic writing, which is why you can implement a distributed hash table but struggle to write a coherent paragraph about it.
Here are the most common writing challenges Edmonton CS students face:
You understand your research intimately—every algorithm, every optimization, every edge case. But explaining it to your committee (some of whom might not specialize in your specific subfield) is like explaining recursion to your parents: theoretically possible, but requiring infinite patience.
“My research isn’t original enough.” “Someone probably already solved this.” “I’m just combining existing techniques.” “My results aren’t revolutionary.”
Stop right there. Most groundbreaking research in computer science is incremental improvement, not paradigm shifts. The iPhone wasn’t the first smartphone; it was just a better implementation of existing concepts. Your dissertation doesn’t need to cure cancer or achieve artificial general intelligence—it just needs to make a meaningful contribution to the field.
You have 47 different datasets, 12 different evaluation metrics, and 847 different ways to present your results. How do you choose? This is where the dissertation process can feel like being stuck in an infinite loop without a break condition.
Let’s be honest—Edmonton winters are not conducive to creativity. Between November and March, your vitamin D levels are lower than your GPA after that algorithms midterm, and your motivation follows suit. The seasonal affective disorder is real, and it affects your writing productivity just as much as your general will to live.
Sometimes, the best debugging strategy is pair programming. When it comes to dissertation writing, this might mean working with professional academic writing services. In the same way that you depend on established tools for coding, don’t hesitate to use helpful tips from experts when writing your thesis.
Professional dissertation help isn’t about academic dishonesty—it’s about collaborative development. These services can provide:
Research Guidance: Like running your technical decisions by a senior developer before starting to code.
Structural Support: Divide your ideas and pieces of research so that they follow a logical storyline.
Language and Style Editing: Ensuring your document is easy to understand, stays consistent and has an academic feel.
Formatting and Citations: Because APA style is harder to master than assembly language.
Technical Writing Assistance: Helping translate complex technical concepts into accessible academic language.
Similar to choosing a development partner, make sure that any academic writing service is dependable, knowledgeable and follows ethical guidelines.
StudyCreek is especially strong in Computer Science dissertations. Their background allows them to spot and treat the challenges of shifting complex computer science ideas into easy-to-understand academic writing.
DissertationHive offers dissertation help, starting at the research phase and concluding in final formatting. They employ experts who have earned graduate degrees in Computer Science and allied subjects.
StudyCorgi includes help for technical subjects. Service users can choose between having a tutor to themselves or working on writing assignments with others.
EssayPro links students with writers trained in meeting the needs of technical writing at the graduate level.
EssayShark offers auction-style bidding systems, enabling you to find writers who are experts in the field and who fit your budget range.
Edusson supports students with custom help from writers who possess advanced degrees in their areas.
Let’s address this directly: working with professional writing services is not cheating, provided you use them ethically. Think of it like using Stack Overflow—you’re not copying and pasting solutions wholesale, but you’re leveraging community expertise to improve your own work.
Ethical use includes:
What crosses the line:
Edmonton’s tech scene is booming, and this creates unique opportunities for CS graduate students. Your dissertation research might directly contribute to local industry needs, making your work more immediately relevant and impactful.
UofA’s Computing Science Department is internationally recognized, which means high standards but also excellent resources. All kinds of research by the department’s groups, from artificial intelligence to quantum computing, open up many chances for collaborators.
Local Advantage: As Amii, the Alberta Machine Intelligence Institute, is located in Edmonton, students can work together with leading AI companies on joint projects.
These institutions bring practical, industry-focused perspectives to computer science education. If you’re pursuing your Master’s at one of these schools, your dissertation might have a more applied focus, which can actually make the writing process more straightforward—you’re solving real problems with measurable outcomes.
Edmonton’s growing tech sector means your research has potential real-world applications. Companies like BioWare, Intuit Canada, and various startups in the area might be interested in your work, providing additional motivation and potential career opportunities post-graduation.
Let’s be practical about the seasonal challenges:
October-December: Use the crisp fall weather for outdoor thinking walks and initial research. This is prime time for literature reviews and early writing.
January-March: Embrace the hibernation mode. This is actually perfect for focused writing sessions. Set up a cozy workspace, invest in a good lamp for light therapy, and use the forced indoor time productively.
April-May: Leverage the spring energy surge for revisions and final pushes. Edmonton’s late spring can provide the motivation boost you need for final editing and defense preparation.
Let’s get specific about the types of research Edmonton CS students typically pursue and the unique writing challenges each presents:
Common Topics: Neural network architectures, computer vision applications, natural language processing, reinforcement learning Writing Challenges: Explaining mathematical concepts clearly, presenting experimental results effectively, discussing limitations and biases Edmonton Angle: With Amii’s presence, AI research here often has strong industry connections
Common Topics: Distributed systems, database optimization, software architecture, performance analysis Writing Challenges: Balancing technical depth with accessibility, presenting benchmarking results, discussing scalability implications Local Relevance: Edmonton’s growing fintech and energy tech sectors provide real-world application contexts
Common Topics: User experience design, accessibility, mobile interfaces, virtual/augmented reality Writing Challenges: Integrating quantitative and qualitative data, presenting user study results, discussing design decisions Edmonton Opportunities: The city’s diverse population provides rich user study opportunities
Common Topics: Cryptographic protocols, network security, privacy-preserving algorithms, blockchain applications Writing Challenges: Explaining security concepts to non-experts, presenting threat models clearly, discussing ethical implications Industry Connection: Alberta’s oil and gas industry creates demand for industrial cybersecurity research
Common Topics: Big data processing, predictive modeling, data visualization, statistical analysis Writing Challenges: Presenting statistical results clearly, discussing data quality and limitations, explaining methodology choices Local Application: Healthcare data analysis opportunities through Alberta Health Services connections
Let’s treat writing your dissertation as if you are making software which is a familiar approach to CS students.
You should have your requirements clearly in mind before writing the first line of code – or the first sentence here.
Just as you wouldn’t start coding without a system architecture, don’t start writing without a detailed outline:
Now comes the coding—I mean, writing. Apply software engineering principles:
In software development, we have the concept of technical debt—shortcuts and compromises that make future development more difficult. In dissertation writing, there’s “academic debt”—the accumulated stress, confusion, and procrastination that makes progress increasingly difficult.
The Infinite Recursion Problem: You keep rewriting the same sections without making progress. Like a function that calls itself without a base case, you’re stuck in an endless loop of revisions.
The Null Pointer Exception: Your motivation has crashed and burned. You can’t even open your dissertation document without feeling existential dread.
The Memory Leak: You’re spending increasing amounts of time on the dissertation but seeing decreasing returns. Your mental resources are being consumed without productive output.
The Deadlock: You keep needing inspiration before you can write, but inspiration won’t appear until you write something. Neither process can proceed.
The Buffer Overflow: You have too much research, too many ideas, and no clear way to organize them into a coherent argument.
Professional academic writing services can help you recover from academic debt just like refactoring helps manage technical debt:
Code Review Equivalent: Having experienced academics review your work and provide specific, actionable feedback.
Pair Programming Equivalent: Working collaboratively with writing experts to develop and refine your ideas.
Architecture Consultation: Getting help with the overall structure and organization of your dissertation.
Performance Optimization: Improving the clarity and impact of your writing.
Let’s talk numbers, because as CS students, we appreciate quantitative analysis:
Time Investment Without Help:
Time Investment With Professional Support:
While harder to quantify, the mental health benefits of professional support are significant:
For Edmonton students specifically, professional support can be especially valuable during the winter months when seasonal depression compounds academic stress.
A well-written dissertation can:
Let’s examine some successful approaches from Edmonton CS graduates:
Student: Sarah M., University of Alberta, 2023 Topic: Machine Learning Applications in Oil Sands Monitoring Strategy: Partnered with Suncor Energy for real-world data and application context Writing Approach: Used professional editing services to translate technical findings into policy recommendations Outcome: Published in two conferences, hired by Google Canada
Key Lesson: Real-world applications make your research more compelling and easier to write about.
Student: Ahmed K., NAIT, 2022 Topic: Microservices Architecture for Educational Technology Strategy: Developed actual software tools and documented the process Writing Approach: Treated dissertation like comprehensive software documentation Outcome: Created successful EdTech startup, dissertation served as technical whitepaper
Key Lesson: Good technical documentation skills translate directly to academic writing.
Student: Maria L., MacEwan University, 2023 Topic: Accessibility in Web Design Strategy: Worked with local disability advocacy groups for user research Writing Approach: Used professional writing services to help structure mixed-methods research Outcome: Research adopted by City of Edmonton for website redesign
Key Lesson: Community engagement provides rich material and clear impact narrative.
Here are actionable strategies specific to the Edmonton CS community:
University of Alberta Libraries: Extensive digital collections and research support services. The librarians actually know how to help with computer science research—use them!
Edmonton Public Library: Free access to academic databases and quiet study spaces throughout the city. The Stanley Milner Library downtown has excellent technical collections.
Startup Edmonton: Networking opportunities that can provide real-world context for your research.
Edmonton.rb, Edmonton Python, and other tech meetups: Connect with local developers who might be interested in your research.
Fall (September-November): Prime research and writing time. Use the comfortable weather for campus visits and library work.
Winter (December-February): Embrace the hibernation period for focused writing. Set up a comfortable home office and establish consistent daily routines.
Spring (March-May): Energy returns with the sunlight. Perfect time for revisions, defense preparation, and job searching.
Summer (June-August): If you’re still writing, take advantage of reduced campus activity for quiet research time. Many Edmonton tech companies offer internships that could provide practical application for your research.
University Research Groups: Join or audit seminars in your research area. The social aspect helps combat isolation, and exposure to other research broadens your perspective.
Conference Attendance: CASCON (Canadian Conference on Computer Science) often has sessions relevant to Edmonton researchers. Use conference attendance as motivation to complete chapter drafts.
Industry Connections: Edmonton’s tech sector is still small enough that networking is personal and approachable. Attend local tech events and explain your research—you might find unexpected applications and supporters.
Let’s address the psychological challenges specifically faced by CS students writing dissertations:
Computer science culture often emphasizes individual brilliance and breakthrough innovations. This creates unrealistic expectations for dissertation research, leading to imposter syndrome when your work feels incremental rather than revolutionary.
Reality Check: Most important CS research is incremental improvement, not paradigm shifts. TCP/IP wasn’t a single breakthrough—it was the result of decades of incremental network protocol improvements.
Coping Strategy: Focus on the specific contribution of your work, not its perceived magnitude. Every optimization matters, every bug fix has value, every small improvement builds toward larger progress.
CS students are trained to write code that compiles and runs correctly. Academic writing doesn’t have a compiler to tell you when you’re “done”—it’s more like iterative software development with subjective acceptance criteria.
Mindset Shift: Consider your dissertation as the first release, instead of the completed one. The functionality should not fail, but it isn’t necessary for it to be perfect.
Practical Application: Assign yourself specific due dates for every part and follow those times. “Good enough to submit for feedback” is better than “perfect but never finished.”
Dissertation writing is inherently solitary, which contrasts sharply with the collaborative nature of most software development. Edmonton winters compound this isolation.
Solution Strategies:
In software development, scope creep kills projects. In dissertation writing, it creates never-ending documents that never get submitted.
Scope Management Techniques:
As CS students, we should leverage technology to make the writing process more efficient:
LaTeX vs. Word: For CS dissertations, LaTeX is generally superior for mathematical notation, algorithm presentation, and citation management. However, if your advisor prefers Word comments and track changes, the collaboration benefits might outweigh LaTeX’s technical advantages.
Reference Management: Zotero or Mendeley for citation management. Both integrate well with LaTeX and Word, and both can automatically pull citation information from academic databases.
Version Control: Git for your dissertation files. Create branches for major revisions, tag significant milestones, and never lose work again.
Writing Environment:
The Pomodoro Technique for Writing: Keep writing for 25 minutes and then take a 5-minute break. This mirrors the natural rhythm of debugging sessions.
Test-Driven Writing: Write your thesis statement or section conclusion first, then write content to support it. This is like writing unit tests before implementing functionality.
Continuous Integration for Academia: Set up automated backups, regular advisor updates, and consistent formatting checks.
Jupyter Notebooks: Perfect for documenting experiments and creating reproducible research. Your dissertation can reference specific notebook cells for detailed technical information.
Version Control for Data: Git LFS or DVC for managing large datasets and experimental results.
Visualization Tools: matplotlib, ggplot2, or D3.js for creating publication-quality figures.
Your dissertation defense is like a code review with your most critical stakeholders. Preparation is essential:
Committee Composition: Your committee likely includes experts in your specific area plus broader computer science faculty. Some members might not be familiar with your exact research domain.
Presentation Strategy: Like designing a user interface, you need to accommodate different levels of technical expertise without oversimplifying or condescending.
Technical Depth Questions:
Broader Impact Questions:
Future Work Questions:
Technical Slides: Use pseudocode rather than actual code for algorithm explanations. Include complexity analysis and performance metrics.
Results Presentation: Focus on the most compelling findings, not comprehensive data dumps. Use visualization effectively.
Demo Strategy: Live demos are risky—prepare recorded video demonstrations as backup.
Time Management: Practice with a timer. Most defenses allow 20-30 minutes for presentation, followed by questions.
Your dissertation experience develops skills directly applicable to your professional career:
The ability to explain complex technical concepts clearly is invaluable in:
Managing a multi-year independent research project demonstrates:
Dissertation research develops critical thinking skills for:
Your CS Master’s degree positions you well for Edmonton’s growing tech sector:
Established Companies:
Growing Startups:
Government and Public Sector:
Understanding Edmonton’s tech landscape can help frame your dissertation’s relevance and impact:
Energy Technology: Alberta’s traditional strength in oil and gas creates opportunities for CS research in:
Financial Technology: Edmonton’s growing fintech sector needs research in:
Healthcare Technology: Alberta Health Services and local health tech companies drive demand for:
Agricultural Technology: Alberta’s agricultural sector benefits from:
Alberta Machine Intelligence Institute (Amii): World-class AI research with industry partnerships.
TEC Edmonton: University of Alberta’s technology transfer office, helping commercialize research.
Startup Edmonton: Ecosystem support for technology entrepreneurship.
NAIT Applied Research: Practical, industry-focused research opportunities.
Writing a Computer Science Master’s dissertation in Edmonton doesn’t have to be a solo debugging session that lasts forever. Like any complex system, success comes from understanding the requirements, designing a solid architecture, implementing incrementally, and knowing when to seek help from the community.
Whether you’re implementing machine learning algorithms in the dead of winter or developing distributed systems while the Oilers are making their playoff run, remember that your dissertation is ultimately about contributing something valuable to the field of computer science. It doesn’t need to be revolutionary—it just needs to be yours, completed, and defensible.
Professional writing services like StudyCreek, DissertationHive, StudyCorgi, EssayPro, EssayShark, and Edusson can provide the collaborative support you need to debug your writing process and optimize your academic performance. Think of them as your technical writing stack—tools and expertise that help you focus on what you do best: innovative computer science research.
The growth of Edmonton’s tech community is thanks in part to your work. If you go to a small business, a large technology firm or continue to investigate the topic, the abilities you learn through your dissertation process will always help you in your job.
Here’s the trick: pause, have another cup of Joe or tea (shh, we won’t judge) and always remember: one commit is all you need to start any successful software project. Your dissertation is just a really important repository that you’re building one commit at a time.
Now stop reading blog posts and go write that thesis. Your future self will thank you, and Edmonton’s tech sector is waiting for your contribution.
Information
Paper Format: Number of pages: Type of work: Type of paper: Sources needed MLA 1 Double
spaced Writing from scratch Essay No specific sources required Subject Business
Topic PATENT CASE STUDY QUESTIONS
Academic Level: Bachelor
Paper details
1 page double spaced
Roger Schlafly applied for a patent for two prime numbers. (A prime number cannot be evenly
divided by any number other than itself and 1—2, 3, 5, 7, 11, 13, for example.) Schlafly’s
numbers are a bit longer—one is 150 digits, the other is 300. His numbers, when used together,
can help perform the type of mathematical operation necessary for exchanging coded messages
by computer. Should the PTO issue this patent?
Name:
Tutor:
Date:
Roger Schlafly Patent
Roger Schlafly prospered in exploiting something that no mathematician has ever
achieved. Essentially, the scholar untested a number. The inexplicable incident is the newest turn
in the legend of conveying software copyrights that irritated U.S. Trademark and Patent for about
twenty years (Ludwig, 2016). Schlafly describes that if one surveyed asking about the patent of
prime numbers, the answer most individuals would give is that it’s ridiculous. Roger has not only
patented any number, but also ensures that his figures have a length of approximately 150 digits.
This implies that the figures have a feature that makes them conceivable to yield the definite
shortcut while executing integrated division. An enhancement in division is good start for
individuals applying the Hellman primary civic cryptography method. This method uses
recurring integrated divisions as a device for decrypting and encrypting secret programs
(Golovanov, pg 57). Cryptographic are formulated by hundreds of characters long, meaning any
slightest advancement it’s time-saving.
Even though this patent assists in speeding up few calculations in mathematics, patents
decelerate evolvement of emerging software. These patents are escalating, nearly 4500 were
approved in 1994, and 5400 were anticipated to be granted in 1995. In the year 1972, the U.S.
Supreme reigned that the computer systems should not be patented. In 1978 the lower court
reinterpreted, the higher court’s decision of banning patenting algorithms (Golovanov, pg 87).
Regrettably, the governing never demarcated the meaning of algorithms in mathematics.
Roger's patent appears apt reliable with new Patent Organization strategy. Patent that is
titled "Partial Integrated Reduction Technique" terms algorithm as getting prime numbers of
having this precise feature. Most presentations of patent stop there. Roger’s proceeds, appealing
the prime figures of having the feature. One has 500 characters long; the other has 1,024.
However, the numerals gratify the main desires of patentability (Ludwig, pg 74). It is innovative,
since there is no record of being used before, and they are valuable since they can be used for cryptography. The PTO should issue this patent since it is solely available for any idea and the
algorithms are beneficially applied.
Work Cited
Golovanov, Sergey Y., and Alexey V. Monastyrsky. "System and methods for detection of
fraudulent online transactions." U.S. Patent No. 9,363,286. 7 Jun. 2016.
Ludwig, Adrian L., Curtis Gerald Condra IV, and Nicholas Neil Kralevich IV. "Configuration
file updater." U.S. Patent No. 9,275,006. 1 Mar. 2016.
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