2009 Proceeding
Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including adaptability to climate change, electric grid monitoring, disaster preparedness and management, national or homeland security, and the management of critical infrastructures. The raw data from sensors need to be efficiently managed and transformed to usable information through data fusion, which in turn must be converted to predictive insights via knowledge discovery, ultimately facilitating automated or humaninduced tactical decisions or strategic policy based on decision sciences and decision support systems.
Proceeding Downloads
A data modeling approach to climate change attribution
Climate modeling and analysis of climate change have largely been based on forward simulation with physical models. We propose here a data centric approach to climate study based solely on the actual observed data. This novel approach utilizes a variety ...
Space missions & sensor networking: challenging scenarios
Sensor networking is a paradigm getting familiar in space missions and services. The talk will provide a panoramic view of examples of challenging missions related to earth environment and to space exploration, where sensor knowledge discovery ...
How optimized environmental sensing helps address information overload on the web
In this talk, we tackle a fundamental problem that arises when using sensors to monitor the ecological condition of rivers and lakes, the network of pipes that bring water to our taps, or the activities of an elderly individual when sitting on a chair: ...
Handling outliers and concept drift in online mass flow prediction in CFB boilers
In this paper we consider an application of data mining technology to the analysis of time series data from a pilot circulating fluidized bed (CFB) reactor. We focus on the problem of the online mass prediction in CFB boilers. We present a framework ...
An exploration of climate data using complex networks
To discover patterns in historical data, climate scientists have applied various clustering methods with the goal of identifying regions that share some common climatological behavior. However, past approaches are limited by the fact that they either ...
A comparison of SNOTEL and AMSR-E snow water equivalent datasets in western U.S. watersheds
It is a consensus among earth scientists that climate change will result in an increased frequency of extreme events (e.g., precipitation, snow). Streamflow forecasts and flood/drought analyses, given this high variability in the climatic driver (...
EDISKCO: energy efficient distributed in-sensor-network k-center clustering with outliers
Clustering is an established data mining technique for grouping objects based on similarity. For sensor networks one aims at grouping sensor measurements in groups of similar measurements. As sensor networks have limited resources in terms of available ...
Phenological event detection from multitemporal image data
Monitoring biomass over large geographic regions for seasonal changes in vegetation and crop phenology is important for many applications. In this paper we a present a novel clustering based change detection method using MODIS NDVI time series data. We ...
Mining in a mobile environment
Distributed PRocessing in Mobile Environments (DPRiME) is a framework for processing large data sets across an ad-hoc network. Developed to address the shortcomings of Google's MapReduce outside of a fully-connected network, DPRiME separates nodes on ...
On the identification of intra-seasonal changes in the Indian summer monsoon
Intra-seasonal changes in the Indian summer monsoon are generally characterized by its active and break (A&B) states. Existing methods for identifying the A&B states using rainfall data rely on subjective thresholds, ignore temporal dependence in the ...
Reduction of ground-based sensor sites for spatio-temporal analysis of aerosols
In many remote sensing applications it is important to use multiple sensors to be able to understand the major spatio-temporal distribution patterns of an observed phenomenon. A particular remote sensing application addressed in this study is estimation ...
OcVFDT: one-class very fast decision tree for one-class classification of data streams
Current research on data stream classification mainly focuses on supervised learning, in which a fully labeled data stream is needed for training. However, fully labeled data streams are expensive to obtain, which make the supervised learning approach ...
A frequent pattern based framework for event detection in sensor network stream data
In this paper, we presented a frequent pattern based framework for event detection in stream data, it consists of frequent pattern discovery, frequent pattern selection and modeling three phases: In the first phase, a MNOE (Mining Non-Overlapping ...
Supervised clustering via principal component analysis in a retrieval application
In regression problems where the number of predictors exceeds the number of observations and the correlation between the predictors is high, a dimensionality reduction or a variable selection approach is demanded. In this paper we deal with a real ...
A novel measure for validating clustering results applied to road traffic
The clustering validation and clustering interpretation are the two last steps of clustering process. The validation step permits to evaluate the goodness of clustering results using some measures. Valid results are then generally interpreted and used ...
SkyTree: scalable skyline computation for sensor data
Skyline queries have gained attention for supporting multi-criteria analysis of large-scale datasets. While a lot of skyline algorithms have been proposed, most of the algorithms build upon pre-computed index structures that cannot generally be ...
Clustering of power quality event data collected via monitoring systems installed on the electricity network
In this paper, a k-means-based clustering method applied to power quality event data is described. The data are collected by the power quality (PQ) monitors, which are developed through the National PQ Project and installed on the electricity network. ...
Change detection in rainfall and temperature patterns over India
The changes in rainfall and temperature patterns over India were detected using Mann-Kendall trend test, Bayesian change point analysis, and a hidden Markov model. A regionalization method was developed to identify homogeneous regions that experience ...
Anomaly detection and spatio-temporal analysis of global climate system
Knowledge discovery from temporal, spatial and spatio-temporal data is pivotal for understanding and predicting the behavior of Earth's ecosystem model. An important influence leaving its impact on the ecosystem is the global climate system. In this ...
Cited By
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Mittal V and Kashyap I An Overview of Real World Applications with Concept Drifting Data Streams, SSRN Electronic Journal, 10.2139/ssrn.3170324
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Huang Q, Cox R, Shaurette M and Wang J (2012). Intelligent Building Hazard Detection Using Wireless Sensor Network and Machine Learning Techniques International Conference on Computing in Civil Engineering, 10.1061/9780784412343.0061, 9780784412343, (485-492), Online publication date: 11-Jun-2012.
- Chandola V, Omitaomu O, Ganguly A, Vatsavai R, Chawla N, Gama J and Gaber M (2011). Knowledge discovery from sensor data (SensorKDD), ACM SIGKDD Explorations Newsletter, 12:2, (50-53), Online publication date: 31-Mar-2011.
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Omitaomu O, Vatsavai R, Ganguly A, Chawla N, Gama J and Gaber M (2010). Knowledge discovery from sensor data (SensorKDD), ACM SIGKDD Explorations Newsletter, 10.1145/1809400.1809417, 11:2, (84-87), Online publication date: 27-May-2010.
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Yuan J, Chen J, Sciusco P, Kolluru V, Saraf S, John R and Ochirbat B (2022). Land Use Hotspots of the Two Largest Landlocked Countries: Kazakhstan and Mongolia, Remote Sensing, 10.3390/rs14081805, 14:8, (1805)
- Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Recommendations
Knowledge discovery from sensor data (SensorKDD)
Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to ...