CS 525s - Sensor and Stream Data Management - Fall 2006

Professor

Elke A. Rundensteiner, Fuller Labs 238, mailto:rundenst (at) cs.wpi.edu

Course homepage

Location at http://web.cs.wpi.edu/~cs525/f06s-EAR/

Class Lectures

Fuller Labs 311, Thursdays at 6:00pm - 8:50pm

Office Hours:

Thursdays right after class (8:50pm onwards)
Mondays FL 238 at 12:00-1:00 noon (check : changes announced here)

If you have any questions that cannot wait, please stop by my office or send electronic mail. In the later case, I'll do my best to answer your email within a 24 hour time window.

Mailing List for Course

Note that a email mailing list for this course has been set up. Details to be announced in class.

Topic of Course

In this course we will study the emerging research area of sensor and stream data management. This area has recently been receiving a significant attention within and also beyond the database community. This novel technology in part is driven by ubiquitous computing, i.e., by computing through small devices, sensors, cell phones, PDAs, and the like 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 great promise without developing methods for guaranteeing real-time access to relevant data. This is one area where data management meets ubiquitous computing technology. Further, relevant data is typically pushed into the computing infrastructure for further processing, then termed 'data streams'. Data streams are prevalent: they are rapid and unbounded streams of data such as sensor readings, but also telephone call records, financial tickers, web usage logs, network packet traces, and so on. With streams everywhere, a new area, called Stream Data Management (SDM), 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 online 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.

Text Book

The topic is too new and evolving so rapidly for there to exist a comprehensive and up-to-date text book that would contain all the material we will study in this course. Thus instead we will be utilizing a variety of publications taken directly from the primary literature. These manuscripts will be linked into our schedule indexed by the day on which the material is to be studied in our course.

In general, there will be a core collection of readings in selected topics in sensor and stream data management around which our discussions will be centered. These topics may include adaptive query processing, query optimization, in-sensor query processing, plan migration, distributed computing, on-line aggregation and mining, shedding and approximation, constraint-aware computing, and others.

Recommended Background

You should have a basic familiarity with relational databases, equivalent of a beginning course in databases. Instead, if you have used a database system before in practice, you may also have sufficient basic understanding required for the course. If you are in doubt, please talk to the instructor.

Objectives

This course will provide you with an overview of a selected subset of advanced topics in database systems, in particular, in this offering of the course we will focus on selected topics in sensor and stream data manageemtn. The goal is to expose you to the current active areas in databases of interest to both academia and industry by reading book chapters as well as papers from the recent literature and discussing them in class. You will also get a more in-depth look into some set of technologies by conducting a course project, and by preparing tutorials on several database topics and then presenting them to the students in the course.

Specific objectives include:

• Gain knowledge in fundamental database concepts, algorithms and techniques (which are present in most specializations of the database field).
• 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.
• Learn to identify and independently learn material relevant to understand topics new to the student.
• Practice skills to communicate your ideas to an audience in a presentation or a scientific discussion.

Learning Outcomes

Upon completion of this course, students should be able to:
• Explain the differences and similarities between traditional database systems and stream engines.
• Describe the different components, including their underlying principles and algorithms, of a stream engine.
• Design and develop a stream- or sensor-related database project.
• Demonstrate skills to critically analyze technical literature and assess technological advances in databases.
• Communicate their ideas effectively in the form of a presentation to a technical audience.
• Overall, able to take first "baby steps" in database research, and perhaps computer science research in general.

end of syllabus