CSC 555 Mining Big Data
Summary
This is a graduate course in large scale data mining applications. Specific topics to be covered include:
* Fundamentals of distributed file systems and MapReduce (MR) technology
* Advantages of an MR-based system compared to a relational database
* Tuning MR algorithm performance and tools for mining massive data sets
* Hadoop-based tools for clustering, similarity search, classification and data warehousing
Texts
Mining of Massive Datasets, by Anand Rajaraman and Jeffrey D. Ullman, Cambridge University Press. Free download at http://i.stanford.edu/~ullman/mmds.html
Hadoop: The Definitive Guide, by Tom White, O'Reilly Media, 4th edition 2015. ISBN-13: 978-1491901632
Grading
There will be homework assignments given most weeks; assignments (with associated readings) will be posted on the course web site and will be due one week after the day they are posted, unless otherwise noted. Details of the submission process will be discussed in class; it is your responsibility to verify that your submitted files are readable and submitted in the correct locations. Late assignments will be accepted up to three days late with a 10% penalty for each day or fraction of a day that the assignment is late; these penalties will be assessed uniformly and in full to all assignments submitted at any point beyond the posted due date and time (including those submitted or re-submitted later the same day). The homework assignments will be worth a total of 50% of the course grade. There will be no exams in this class; instead, students will work on a take-home exam (submitted via D2L) to apply the concepts covered in class. The take-home will be worth 50% of the grade and will consist of two parts: Part 1 due at midterm mark and Part 2 due on the day of the scheduled final exam.
Prerequisites
(CSC 453 or DSC 450) and (DSC 441 or CSC 480) or (MAT 491 and MAT 449)
Regarding Email Communication
Please begin the subject line of any email to me with "CSC 555", so that I can easily identify your messages. I will reply to email messages within one business day after the day I receive them; therefore questions that are only received by me on an assignment's due date (or late the night before) are not guaranteed replies before the assignment is due. Please plan accordingly and begin the assignments early enough to ask questions and receive answers. If you are having problems, send me a detailed description of the problems you are having; I will try to guide you in locating and solving your problems yourself, rather than simply solve your problems for you.
Regarding Academic Integrity
You are expected to be familiar with and to adhere to DePaul's Academic Integrity Policy, which is available on-line at http://academicintegrity.depaul.edu/AcademicIntegrityPolicy.pdf. Violations of the Academic Integrity Policy will be dealt with decisively; penalties may range up to an automatic F in the course and possible expulsion.
Plagiarism includes, but is not limited to: Turning in another person's work as your own (including hiring someone else to complete an assignment for you); Starting with another person's work and modifying it to turn in as your own; Cutting and pasting, or otherwise copying, sections of another person's work into your assignment; Allowing another person (such as a tutor) to write part of your assignment; and so on. (Obviously, any examples that I post qualify as "another person's work".) Supplying such assistance to another student is considered an equivalent violation of the policy. You may feel free to discuss the assignments with other students at a general level. However, when it comes to actually completing your assignment, you must work independently. Your assignments must be entirely your own individual work. If you have any questions or doubts about what plagiarism entails, you should consult me.
Week 1: Relational Databases, Hadoop
- Relational database review
- Cloud computing
- Introduction to Hadoop (HDFS, MapReduce fundamentals)
Week 2: Hadoop, Distributed Computing
- Hadoop (vs R-DBMS) trade-offs
- Performance issues, parallel computing, intro to hashing
- Implementing relational operators in Hadoop
Week 3: Virtual Instances and Performance Tuning
- Cryptography (public & private keys)
- Generalizing MapReduce
- Hive
Week 4: Hadoop Ecosystem
- Hadoop Examples, Hadoop 2.0
- NoSQL data-store systems
- Advanced Hive features
Week 5: Multi-node clusters
- Multi-node cluster setup
- Firewall configuration
- Pig
- Hadoop streaming
Week 6: Hadoop and Mahout
- Hadoop Streaming (with python)
- Mahout tools
- Compression approaches
Week 7: Link Analysis and Clustering
- Hadoop cluster configuration
- NoSQL data engines and HBase
- Link analysis
Week 8
- Document analysis
- Streaming database engines (Storm)
- In-Memory database enginse (Spark)
Week 9: Mining Data Streams
- Recommender systems
- More on streaming & in-memory data engines
- Mining Data Streams
Week 10: Advertising on the Web
- Advertising on the web
- Mining Social-Network Graphs
This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.
Evaluations are a way for students to provide valuable feedback regarding their instructor and the course. Detailed feedback will enable the instructor to continuously tailor teaching methods and course
content to meet the learning goals of the course and the academic needs of the students. They are a requirement of the course and are key to continue to provide you with the highest quality of teaching. The
evaluations are anonymous; the instructor and administration do not track who entered what responses. A program is used to check if the student completed the evaluations, but the evaluation is completely
separate from the student’s identity. Since 100% participation is our goal, students are sent periodic reminders over three weeks. Students do not receive reminders once they complete the evaluation.
Students complete the evaluation online in CampusConnect.
This course will be subject to the university's academic integrity policy. More information can be found at http://academicintegrity.depaul.edu/ If you
have any questions be sure to consult with your professor.
All students are expected to abide by the University's Academic Integrity Policy which prohibits cheating and other misconduct in student coursework. Publicly sharing or posting online any prior or current materials from this course (including exam questions or answers), is considered to be providing unauthorized assistance prohibited by the policy. Both students who share/post and students who access or use such materials are considered to be cheating under the Policy and will be subject to sanctions for violations of Academic Integrity.
All students are required to manage their class schedules each term in accordance with the deadlines for enrolling and withdrawing as indicated in the University Academic Calendar. Information on enrollment, withdrawal, grading and incompletes can be found at http://www.cdm.depaul.edu/Current%20Students/Pages/PoliciesandProcedures.aspx.
Students who feel they may need an accommodation based on the impact of a disability should contact the instructor privately to discuss their specific needs. All discussions will remain confidential.
To ensure that you receive the most appropriate accommodation based on your needs, contact the instructor as early as possible in the quarter (preferably within the first week of class), and make sure that
you have contacted the Center for Students with Disabilities (CSD) at:
Lewis Center 1420, 25 East Jackson Blvd.
Phone number: (312)362-8002
Fax: (312)362-6544
TTY: (773)325.7296