Fall 2011

Silvia Nittel

Course Description

In this course we will study the research area of stream data management, with a special interest in sensor data streams. This novel technology partly is driven by computing through small devices, sensors, smart phones, and other  that are embedded in the environment. Similarly, many applications ranging from scientific, energy, weather, environment, and health services in the future will depend on such technologies. Performing computations on such an infrastructure cannot achieve its promise without developing methods for guaranteeing real-time access and processing of relevant data. A novel paradigm of generic data management is required. The relevant data is typically pushed into the computing infrastructure for further processing, and is termed 'data streams'. Data streams are prevalent: they are rapid and unbounded streams of data such as sensor readings, but also phone call records, stock market updates, web usage logs, etc.. With streams everywhere, a research area, called Data Stream Management (DSM), has emerged aiming to produce generic software technology similar to that of Database Management Systems for streaming data. We will study this novel technology targeting to meet the need of real-time monitoring applications, in which continuous queries operate in near real-time over data streams. In summary, our objective will be to learn about these novel advances in the field, and also to gain an understanding of the fundamental similarities as well as differences between data stream processing and traditional data management.

Topics covered:

• Introduction to data stream management
• Prototype data stream systems
• Data model and query language concepts
• Stream query processing
• Stream query optimization techniques
• Adaptive stream query processing techniques

Credits:           3  
Prerequisite:   Graduate standing, SIE 555, programming experience in Java, C++, or C

Course Texts 

Reading material, powerpoint slides of lecture material and assignments will be available via Umaine/Blackboard.

Software: 

Course Goals and Objectives

• Introduce students to central concepts of data stream management concepts, applications, and programming
• Develop an understanding of data stream processing and optimization
• Learn in particular new advances in the subject areas of sensor and stream data management systems.
• Develop skills needed to critically analyze technical literature.
• Get practice designing a research or application project related to stream or sensor data management.
• Practice skills to communicate your ideas to an audience in a presentation or a scientific discussion.

Faculty Information

Dr. Silvia Nittel
Spatial Informatics,  
School of Computing and Information Science
334 Boardman Hall
University of Maine
nittel@spatial.maine.edu

 
Office Hours:
Office hours for this course will be announced at the beginning of the semester. Alternatively, contact me by email to arrange a time to meet.

Grading, Class Policies and Course Expectations

As a graduate level course, you are expected to exhibit high quality work that demonstrates sound understanding of the concepts and their complexity. Earning an “A” represents oral and written work that is of exceptionally high quality and demonstrates superb understanding of the course material. A “B” grade represents oral and written work that is of good quality and demonstrates a sound understanding of course material. A “C” grade represents a minimally adequate completion of assignments and participation demonstrating a limited understanding of course material.

Grading criteria:

Assignments and class participation– 70%

            Final Project – 30%

Academic honesty

Academic honesty is expected.  Plagiarism is unacceptable in this course and will result in a failing grade.

Students with disabilities:

If you have a disability for which you may be requesting an accommodation, please contact Ann Smith, Coordinator of Services for Students with Disabilities (Onward Building, 581-2319), as early as possible in the term.