Distributed Data Mining for Sensor Networks (English)

Description: September 24, 2004

In conjunction with Knowledge Discovery in Data Streams workshop.

Advances in computing and communication over wired and wireless networks have resulted in many pervasive distributed computing environments. Sensor networks define one such class of distributed environments that offer many interesting possibilities in many data and labor intensive application domains. Monitoring forest fires, tracking moving targets, identifying unusual behaviors from vehicle data systems are some examples. Most of these sensor networks deal with distributed and constrained computing, storage, power, and communication-bandwidth resources. Mining in such environments naturally calls for proper utilization of these distributed resources. Moreover, in many ubiquitous applications privacy of the data is indeed an issue. For example, consider the problem of monitoring driving behavior in a commercial fleet using vehicle sensor networks. We must protect the privacy of the good drivers; but report the bad drivers in a fleet. A growing number of these applications deal with distributed data streams that require quick analysis and a quick response. Most off-the-shelf data mining systems are designed to work as a monolithic centralized application primarily from static data. They normally down-load the relevant data to a centralized location and then perform the data mining operations. This centralized approach may not always work well in many of the distributed, ubiquitous, and possibly privacysensitive data mining applications over sensor networks. Data centralization may cause massive drainage of power, increase response time, and make the overall architecture not very scalable. The field of Distributed Data Mining (DDM) offers an alternate choice. It pays careful attention to the distributed resources of data, computing, communication, and human factors in order to use them in a near optimal fashion. This tutorial will offer an introduction to the emerging field of Distributed Data Mining, specifically in the context of sensor networks. The attendees will be exposed to the following aspects of this field:
  1. An overview of DDM and sensor networks
  2. An overview of the existing DDM algorithms
  3. More detailed discussion of some important DDM algorithms that are appropriate for resource constrained sensor network applications.
  4. An overview of the systems research issues in DDM
  5. Detailed case study of an existing sensor network data management and mining system; hands on demonstration
  6. Future directions
  7. Pointers to more advanced material and resources
Lecturer: Kargupta, Hillol
Language: English
URL:
Matrial: T3.pdf (3303 KB)
Date: 2004
Address: ECML/PKDD2004