Cleveland State University

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1 Cleveland State University CIS 612 Modern Database Processing & Big Data (3-0-3) Fall 2015 Section 50 Class Nbr Tues, Thu 4:30 5:45 PM Prerequisites: CIS 505 and CIS 530. CIS 611 Preferred. Instructor: Dr. Sunnie S. Chung Office Location: BU0112 Phone: Webpage: Office Time: Mon, Wed 2:00 3:30 PM and 5:45 6:45 PM (or by appointment) Class Location: BU 0112 Section 50 Tue & Thu 4:30-5:45 PM Catalog Description: Detailed study of modern database processing and non-relational database systems for big data processing. First, the course presents practical approaches to advanced database processing features and its applications. The course advances with study of semi-structured/non-structured database processing, JSON, XML data processing, XPATH, and XQuery. The course continues study of building distributed database and web service systems with web data processing for data analytics. The course extends study with architectures and features of Massively Parallel Processing (MPP) systems for big data processing Parallel Data Warehouse (PDW) with OLAP, Map Reduce with Hadoop, and Cloud Computing. It continues an exploration of applications and big data processing systems of NoSQLand NewSQ such as Google s Big Table, Hive, HBase, PigLatin, Mongo DB, VoltDB. Finally, the course will explore the most significant industry research trend for big data processing and data analytics. Key Concepts: Modern Database Programming, Web Data Processing, Semi-structured database processing, JASON, XML data processing, XPath, XQuery, Unstructured data processing, web data processing, Massively Parallel Processing (MPP) systems, Map Reduce with Hadoop, Cloud Computing, Parallel Data Warehouse (PDW), OLAP Cube, Big Data Processing, Google s Big Table, Hive, HBase, PigLatin, ACID, MongoDB, VoltDB, NoSQL, NewSQL, and Cloud Computing for Data Analytics. Expected Outcomes: Upon successful completion of the course, the student will be able to create modern database applications to process non-traditional data such as web data, semi-structured/unstructured data, - JASON, XML data, or plain text data. The student will be able to design data warehouse, create OLAP Cubes and write analytical data mining queries. More importantly, the student will obtain comprehensive knowledge and analytical skills to build an infrastructure for a big data processing system for data analytics and its applications by understandings the problems of big data processing and its well known approaches in the modern database systems. The student will further extend knowledge with the architectures and the features of modern parallel computing systems for big data processing PDW, Map Reduce and Hadoop, and Cloud Computing. The student will obtain hands on experiences in major NoSQL/NewSQL systems to create Map Reduce applications for big data processing systems and further exposed to the significant current database industry research trends for big data processing. List of Required Materials: Any RDBMS: Oracle Database 11g or higher - This is available at Microsoft SQL Server 2012, Microsoft Visual Studio 2012 or any higher Microsoft SQL Server Business Intelligence Data Analytic Tool 2012 OLAP Server and SQL Server Data Tool They are available at the Microsoft Academic Alliance program: Adventure Works 2012 Data Warehouse Database for SQL Server 2012 Instruction will be given in class

2 Applied Analytics Using SAS Enterprise Miner Several NoSQL/NewSQL Open Source Systems Installation/Set Up details will be given in class Text Book: 1. Lecture Notes will be available on the class webpage 2. "Fundamentals of Database Systems". Elmasri / Navathe. Ed. Addison/Wesley Pub Co. 6th Edition, (2011). 3. List of Selected Database Industry R&D Papers on Big Data Processing and Data Analytics will be given in class Supplement Text Book: 1. Data Mining with Microsoft SQL Server BI Data Tool (2008 or 2012), Jamie MacLennan, Bogdan Crivat, Publisher: Wiley; 1 Edition ISBN-10: ISBN-13: Edition: 1 2. Database Management Systems 3rd Edition by Raghu Ramakrishnan and Johannes Gehrke. Ed. McGraw-Hill. Available at Official Calendar Please consult the university web page at: Final exam: Tues Dec 8 4:00-6:00 PM Grading: The course grade is based on a student's overall performance through the entire Semester. The final grade is distributed among the following components: 1. Exams (Midterm & Final) 40% (15% Midterm, 25% Final) 2. Computer Labs 30% (about 3-4 Assignments) 3. 1 Project on Big data processing: 2 person group project (20%) 4. Research Topic Presentation : 10% A 94% + A: Outstanding (student's performance is genuinely excellent) A- 90% - 93% B+ 87% - 89% B 80% - 86% B: Very Good (student's performance is clearly commendable but not necessarily outstanding) B- 70% - 79% C <70% C: Good (student's performance meets every course requirement and is acceptable; not distinguished) D F < 60% D: Below Average (student's performance fails to meet course objectives and standards) F: Failure (student's performance is unacceptable) Examination Policy: Students are allowed to bring to the tests a summary page (standard letter size) with their own notes. During the exams: (1) the use of books, cell phones, calculators, or any electronic devices is prohibited, and (2) students must not share any materials. Make-Up Exam Policy: No makeup exams will be given unless notified and agreed to in advance. Requests will be considered only in case of exceptional demonstrated need. Homework Policy: The students are expected to attend all classes. The students are responsible for collecting the notes, handouts and any other course material distributed during the class period. All

3 assignments must be individually and independently completed and must represent the effort of the student turning in the assignment. Should two or more students turn in substantially the same solution or output, in the judgment of the instructor, the solution will be considered group effort. All involved in group effort homework will receive a zero grade for that assignment. A student turning in a group effort assignment more than once will automatically receive an F grade for the course. Late Assignment: All lab assignments are due at the beginning of class on the date specified. Laboratory Assignments handed in after the class has begun will be accepted with a 25% grade penalty for up to a week and then not accepted at all. All laboratory assignments must be completed. Failure to do so will lower your course grade one additional letter grade. Student Conduct: Students are expected to do their own work. Academic misconduct, student misconduct, cheating and plagiarism will not be tolerated. Violations will be subject to disciplinary action as specified in the CSU Student Conduct Code. A copy can be obtained on the web page at: or by contacting Valerie Hinton Hannah, Judicial Affairs Officer in the Department of Student Life (MC 106 [email protected] ). For more information consult the following web page CSU Judicial Affairs available at Course Schedule: The schedule of topics and their order of coverage is given below. The schedule and topics to be covered may vary depending upon the progress made. Week of Topic Reading 1,2 Review: Database Foundations DBMS Architecture, Overview of Query Processing and Execution Steps Complex Queries, Transaction, Advanced Topics in Views Big Data Elmasri. Chp. 8,10,11 Lecture Notes 2, 3, 4 Modern Database Programming: Database Triggers Embedded SQL, Dynamic SQL, PHP Stored Procedure, User Defined Function (UDF), User Defined Type (UDT), User Defined Aggregate (UDA), Table Function, CLR, LINQ,.NET Database Programmability and Extensibility in Microsoft SQL Server by José A. Blakeley, et al (Microsoft Corporation) in the proceedings of SIGMOD , 5, 6 Modern Databases: Enhanced Data Models for Advanced Applications Semi Structured and Unstructured Databases: XML Data Processing: - XML Schema, Syntax/Semantics, Protocol - XPath - XQuery Data Transformation from Semi Structure to Relation JSON Data Processing Lecture Notes, Elmasri, 10, 11 Listed Papers Lecture Notes, Elmasri 12, 19, 20, 21 Listed papers. Introduction to Information Retrieval and Web Data Processing Data Models for Unstructured Data Processing Bigtable: A Distributed Storage System for Structured Data, by

4 Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Google, Inc. in the Proceedings of OSDI Overview of Query Processing Techniques and Operators: Join, Projection, Selection, Group By, Aggregations 8 Data Warehouse and OLAP (Tentative for the limited time) - Decision Support Technology - On Line Analytical Processing - Star Schema - OLAP Aggregation Operators: Data Cube, Roll Up, Drill Down - Building BI Data Analytics using DW and OLAP - MDX, DMX Ramakrishnan 11, 12 Lecture Notes. Selected Papers J. Han Chap 1,2,3. Listed Papers, Lecture Notes An Overview of Data Warehousing and OLAP Technology by Surajit Chaudhuri (Microsoft) and Umeshwar Dayal (HP Labs), in the proceedings of IEEE 1995 Data Cube: A Relational Aggregation Operator Generalizing Group By, Cross Tab, and SubTotals by Jim Gray (Microsoft), et al, in the proceedings of IEEE Data Mining for Data Analytics (Tentative for the limited time) - Concept & Architecture - Support and Confidence - Association Rules - Data Mining Algorithms: o Associate Rule Mining o APRIORI Algorithm and Optimization o Frequent Pattern Tree o Decision Tree - Lift and Other Correlation Measures J Han Chap 6. Listed Papers, Lecture Notes 9, 10, 11, 12, 13, 14 - Integrating association rule mining with relational database systems: Alternatives and implications by S. Sarawagi, et al (IBM Almaden Research Center) in the proceedings of SIGMOD'98 Big Data Processing and Parallel Computing: Introduction of Big Data Massively Parallel Processing Systems: Google Map Reduce Tutorial Apache Hadoop File System Pig Latin on Apache Hadoop by Yahoo and Apache Data Warehouse HIVE with Hadoop by Facebook HBase Key Value Stores Map Reduce Join Algorithms Parallel Data Warehouse with OLAP Query Processing: Microsoft Extended PDW with Map Reduce and Hadoop : Oracle, Teradata Columnar Databases : SAP HANA Databases Extended PDW with Columnar Data Processing :Teradata, Microsoft MongoDB Cassandra VoltDB NoSQL vs NewSQL ACID Listed papers. Lecture Notes

5 - MapReduce: Simplified Data Processing on Large Clusters by Jeffrey Dean (Google) and Sanjay Ghemawat (Google) in the proceedings of OSDI Apathy Hadoop in White Papers by Apache, Yahoo - Pig Latin: A Not-So-Foreign Language for Data Processing, Christopher Olston, et al. (Yahoo! Research) in the proceedings of SIGMOD Data Warehousing and Analytics Infrastructure at Facebook. by Ashish Thusoo, et al. (Facebook) in the proceedings of SIGMOD Petabyte Scale Databases and Storage Systems Deployed at Facebook, Dhruba Borthakur, et al. in the proceedings of SIGMOD Fast Data in the Era of Big Data: Twitter s Real-Time Related Query Suggestion Architecture, Gilad Mishne, Jeff Dalton, Zhenghua Li, Aneesh Sharma, Jimmy Lin (Twitter, Inc), SIGMOD The Big Data Ecosystem at LinkedIn - Roshan Sumbaly, Jay Kreps, and Sam Shah (LinkedIn), SIGMOD Avatara: OLAP for Webscale Analytics Products Lili Wu Roshan Sumbaly Chris Riccomini Gordon Koo Hyung Jin Kim Jay, Kreps Sam Shah (LinkedIn), SIGMOD More to be given in class Cloud Computing - SQL Azure as a Self-Managing Database Service: Lessons Learned and Challenges Ahead by Kunal Mukerjee, et al (Microsoft) in the proceedings of IEEE Computer Society Technical Committee on Data Engineering , 16 Presentation of Significant Database Industry Research Papers on Big Data Processing: List of Selected Papers will be given in class. Selected Papers Technical Topics: Select one and prepare a 20 min talk on the subject. 1. Semistructured/Unstructured Data Processing using Structured Data Model 2. Data Warehousing and Analytics Infrastructure at Facebook 3. Parallel Computing for Big Data Processing: Google Map Reduce with Apache Hadoop Parallel Data Warehouse (PDW) 4. MapReduce: Simplified Data Processing on Large Clusters by Google 5. Lammal, Ralf. Google's MapReduce Programming Model Revisited. 6. Open Source Apache Hadoop 7. Pig Latin, Hbase, Hive 8. Map Reduce Join Algorithmes, 9. Data Partition Techniques 10. SQL vs NoSQL 11. Processing MR/Hadoop with PDW : Oracle, Teradata 12. Columnar Databases : SAP Hana 13. Information Retrieval: How Google Search Engine Works 14. Cloud Computing : Microsoft AZURE, Google Cloud. More New Topics to come here.

6 NOTE: The instructor reserves the right to retain, for pedagogical reasons, either the original or a copy of your work submitted either individually or as a group project for this class. Students' names will be deleted from any retained items.