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<h1>Services</h1>
<li><a href="http://dubstep.odin.cse.buffalo.edu">DubStep (CSE4/562)</a></li>
<li><a href="http://dubstep.odin.cse.buffalo.edu">DuBstep (CSE4/562)</a></li>
<li><a href="http://gitlab.odin.cse.buffalo.edu">GitLab (Research)</a></li>
<li><a href="https://github.com/ubodin">GitHub (Software)</a></li>
<li><a href="https://ubodin.slack.com">Slack (Comm)</a></li>

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# Spring 2016 - DRAFT
Data Management Systems (including Relational Databases, Non-Relational Databases, and NoSQL storage systems) form the basis of the Big Data Economy we now live in.  A data management system is responsible for storing data, enabling efficient access to that data, as well as mediating concurrent modifications.  This class approaches the challenges of designing a data management system from a standpoint that is both principled and practical.  The course revolves around a term-long programming assignment, in which you will build a system that answers SQL queries efficiently.  Course lectures will focus on the conceptual basis for this system, and will discuss how the techniques you learn generalize (e.g., to the use of NoSQL systems)
Data Management Systems (including Relational Databases, Non-Relational Databases, and NoSQL storage systems) are the basis for any big data project.  A data management system is responsible for storing data, enabling efficient access to that data, as well as mediating concurrent modifications.  This class teaches data management systems both in terms of the basic principles of their design, and the practical challenges of implementing them. The course is built around a term-long programming assignment, in which you will build a system for answering SQL queries efficiently.  Course lectures will focus on the conceptual basis for this system and how the techniques that you implement in the project generalize (e.g., to the use of NoSQL systems)
In this course, you will learn...
* ... how to efficiently store and retrieve data programatically.
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* __Project Submission__: <a href="http://dubstep.odin.cse.buffalo.edu">http://dubstep.odin.cse.buffalo.edu</a>
* __Project Groups__: 1-4 people
* __Grading__:
* 10% Homework
* 10% Homework (1/week, Lowest 2 grades dropped)
* 40% Exams
* 10% Midterm 1 on __March 1__ (in class)
* 10% Midterm 2 on __April 12__ (in class)
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## Assignment Submission
Homeworks will be collected in recitation on the day they are due, or by email to the instructor and TA(s) __before__ class begins if you are unable to attend class. Late homeworks will __not__ be accepted and will receive a grade of 0. Note that your lowest 2 homework grades will be dropped.
Projects are submitted through the online submission system using GIT. You may submit your assignments to be graded many times as you like. Late submissions for Checkpoint 0 will not be accepted. Submissions for checkpoints 1-3 will receive a 3/15 point penalty per day late. Late penalties are per-submission. Your group's grade is the highest of all grades received by your group for the project, so your grade can never decrease from additional submissions (even if they're late). Don't be afraid to experiment with new ideas once you've gotten a grade you're happy with!
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## Letter Grades
* A: 93 - 100
* A-: 90 - 92
* B+: 87 - 89
* B: 83 - 86
* B-:80 - 82
* C+: 77 -79
* C: 73 76
* C-: 70 -72
* D: 60 69
* F: 0 - 59
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## Academic Integrity
Students may discuss and advise one another on their lab projects, but groups are expected to turn in their own work.  Discussing concepts is permitted.  Referencing another group's code is not.  Cheating on any course deliverable will result in an automatic grade of F in the course.  The University's policy on academic integrity can be reviewed at:
Students may discuss and advise one another on their projects, but groups are expected to turn in their own work.  Discussing concepts is permitted.  Referencing code (e.g., from another group, or from stack overflow or github) is not. When in doubt, ask a TA or the instructor. Violations typically result in an F grade in the course for all students involved.  The Department's policy on academic integrity can be reviewed at:
<center><a href="http://www.cse.buffalo.edu/undergrad/policy_academic.php">UB-CSE's Academic Integrity Policy</a></center>
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## Accessibility Resources
If you have a diagnosed disability (physical, learning, or psychological) that will make it difficult for you to carry out the course work as outlined, or that requires accommodations such as recruiting note-takers, readers, or extended time on exams or assignments, please advise the instructor during the first two weeks of the course so that we may review possible arrangements for reasonable accommodations. In addition, if you have not yet done so, contact:
If you have a diagnosed disability (physical, learning, or psychological) that will make it difficult for you to carry out the course work as outlined, or that requires accommodations such as recruiting note-takers, readers, or extended time on exams or assignments, please advise the instructor and contact the Office of Accessibility Resources during the first two weeks of the course so that we may review possible arrangements for reasonable accommodations. The Office of Accessibility Resources can be reached at:
<center><a href="http://www.student-affairs.buffalo.edu/ods/">The Office of Accessibility Resources</a></center>
<center>(716) 645-2608 | <a href="mailto:accessibility@buffalo.edu">accessibility@buffalo.edu</a></center>
Note that OAR gets extremely busy towards the end of the term. If you anticipate needing special testing accomodations, please make sure to contact them 2-4 weeks in advance.

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* <a href="cse-462/index.html">Spring 2016</a> (Undergraduate Only)
* <a href="cse-562/index.html">Spring 2015</a> (Graduate Only)
* <a href="https://piazza.com/class/hbnssrlgj3o2xl">Spring 2014</a>
* <a href="https://piazza.com/buffalo/spring2013/cse562/home">Spring 2013</a>
* <a href="https://piazza.com/class/hbnssrlgj3o2xl">Spring 2014</a> (Graduate Only)
* <a href="https://piazza.com/buffalo/spring2013/cse562/home">Spring 2013</a> (Graduate Only)
## CSE 662 - Langs & Runtimes for Big Data