Catálogo de publicaciones - libros

Data stream values are often associated with multiple aspects . For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc.) as well as location. Aside from time stamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, tensor streams, and an effective algorithmic framework, window-based tensor analysis (WTA). Two variations of WTA, independent-window tensor analysis (IW) and moving-window tensor analysis (MW), are presented and evaluated extensively on real data sets. Finally, we illustrate one important application, Multi-Aspect Correlation Analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.

Evolving threat situations in a post-9/11 world demand faster and more reliable decisions to thwart the adversary. One critical path to enhanced threat recognition is through online knowledge discovery based on dynamic, heterogeneous data available from strategically placed wide-area sensor networks. The knowledge discovery process needs to coordinate adaptive predictive analysis with real-time analysis and decision support systems. The ability to detect precursors and signatures of rare events and change from massive and disparate data in real time may require a paradigm shift in the science of knowledge discovery. This chapter describes a case study in the area of transportation security to describe both the key challenges, as well as the possible solutions, in this high-priority area. A suite of knowledge discovery tools developed for the purpose is described along with a discussion on future requirements.

The current advances in sensors and sensor infrastructures offer new opportunities for monitoring the operations and conditions of man-made and natural environments. The ability to generate insights or new knowledge from sensor data is critical for many high-priority scientific applications especially weather, climate, and associated natural hazards. One example is sensor-based early warning systems for geophysical extremes such as tsunamis or extreme rainfall, which can help preempt disaster damage. Indeed, the loss of life during the 2004 Indian Ocean tsunami may have been significantly reduced, if not totally prevented, had sensor-based early warning systems been in place. One other example is high-resolution risk-mapping of insights obtained through a combination of historical and real-time sensor data, with physics-based computer simulations. Weather, climate and associated natural hazards have established history of using sensor data, such as data from DOPPLER radars. Recent advances in sensor technology and computational strengths have created a need for new approaches to analyzing data associated with weather, climate, and associated natural hazards. Knowledge discovery offers tools for extracting new, useful and hidden insights from data repositories. However, knowledge discovery techniques need to be geared towards scalable and efficient implementations of predictive insights, online or fast real-time analysis of incremental information, and solution processes for strategic and tactical decisions. Predictive insights regarding weather, climate and associated natural hazards may require models of rare, anomalous and extreme events, nonlinear phenomena, and change analysis, in particular from massive volumes of dynamic data streams. On the other hand, historical data may also be noisy and incomplete, thus robust tools need to be developed for these situations. This chapter describes some of the research challenges of knowledge discovery from sensor data for weather, climate and associated natural hazard applications and summarizes our approach towards addressing these challenges.

The LEGO MINDSTORMS NXT ( http://mindstorms.lego.com/ .)— armed with its embedded ARM7 and ATmega48 microcontrollers (MCUs), Bluetooth radio, four input ports, three output ports, and dozens of sensors—is proposed as an educational platform for TinyOS. ( http://www.tinyos.net .) The purpose of this chapter is to assess NXT for use in wireless sensor network education. To this end, the following items are evaluated: NXT hardware/software, LEGO MINDSTORMS “ecosystem”, and educational elements. We outline how this platform can be used for educational purposes due to the wide selection of available and affordable sensors. For hardware developers, the ease of creating new sensors will be hard to resist. Also, in the context of education, TinyOS can be compared to other embedded operating systems based on the same hardware. This chapter argues that this comparability facilitate across-community adoption and awareness of TinyOS. Finally, we present the first TinyOS project on NXT, hosted both at TinyOS 2.x contrib and SourceForge under the nxtmote name.