Wide-area sensor infrastructures, remote sensors, RFIDs, phasor measurements, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. With the recent proliferation of smart-phones and similar GPS enabled mobile devices with several onboard sensors, collection of sensor data is no longer limited to scientific communities, but has reached general public. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including adaptability to national or homeland security, critical infrastructures monitoring, smart grids, disaster preparedness and management, greenhouse emissions and climate change, and transportation. 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 human-induced tactical decisions or strategic policy based on decision sciences and decision support systems.
The challenges for the knowledge discovery community are expected to be immense. On the one hand are dynamic data streams or events that require real-time analysis methodologies and systems, while on the other hand are static data that require high end computing for generating offline predictive insights, which in turn can facilitate real-time analysis. The online and real-time knowledge discovery imply immediate opportunities as well as intriguing short- and long-term challenges for practitioners and researchers in knowledge discovery. The opportunities would be to develop new data mining approaches and adapt traditional and emerging knowledge discovery methodologies to the requirements of the emerging problems. In addition, emerging societal problems require knowledge discovery solutions that are designed to investigate anomalies, rare events, hotspots, changes, extremes and nonlinear processes, and departures from the normal.
According to the data mining and domain experts present at the NSF-sponsored Next Generation Data Mining Summit (NGDM '09) held in October 2009, "finding the next generation of solutions to these challenges is critical to sustain our world and civilization." The SensorKDD series of workshops, held in conjunction with the prestigious ACM SIGKDD International Conference of Knowledge Discovery and Data Mining from 2007-2010, have aimed at bringing researchers, from different academic and applied communities, together to address these challenges and moving toward the development of the next generation data mining solutions require to address these challenges. The proposed 5th International Workshop on Knowledge Discovery from Sensor Data (SensorKDD-2011) is the next step in this successful series of workshops with the objective of providing a platform for researchers to present and discuss their research in the area of knowledge discovery from sensor data.
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Better drilling through sensor analytics: a case study in live operational intelligence
- Chetan Gupta,
- Krishnamurthy Viswanathan,
- Lakshminarayan Choudur,
- Ming Hao,
- Umeshwar Dayal,
- Ravigopal Vennelakanti,
- Paul Helm,
- Anil Dev,
- Sunil Manjunath,
- Sastry Dhulipala,
- Sangamesh Bellad
In this paper, we present our Live Operational Intelligence (LOI) framework for developing, deploying, and executing applications that mine and analyze large amounts of data collected from multiple data sources to help operations staff take more ...
Gas sensor drift mitigation using classifier ensembles
Sensor drift remains to be one of the challenging problems in chemical sensing. To address this problem we collected an extensive data set for six different volatile organic compounds over a period of three years under tightly-controlled operating ...
Design considerations for the WISDM smart phone-based sensor mining architecture
Smart phones comprise a large and rapidly growing market. These devices provide unprecedented opportunities for sensor mining since they include a large variety of sensors, including an: acceleration sensor (accelerometer), location sensor (GPS), ...
Pattern recognition and classification for multivariate time series
Nowadays we are faced with fast growing and permanently evolving data, including social networks and sensor data recorded from smart phones or vehicles. Temporally evolving data brings a lot of new challenges to the data mining and machine learning ...
Supervised and semi-supervised online boosting tree for industrial machine vision application
Machine learning techniques are being used extensively for knowledge discovery and data mining in industrial inspection applications. Traditionally, all of the training samples are required to be presented for training a classifier at a batch mode. ...
Precise anytime clustering of noisy sensor data with logarithmic complexity
Clustering of streaming sensor data aims at providing online summaries of the observed stream. This task is mostly done under limited processing and storage resources. This makes the sensed stream speed (data per time) a sensitive restriction when ...
Identifying user traits by mining smart phone accelerometer data
Smart phones are quite sophisticated and increasingly incorporate diverse and powerful sensors. One such sensor is the tri-axial accelerometer, which measures acceleration in all three spatial dimensions. The accelerometer was initially included for ...
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Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data