An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Applications of wireless sensor networks for urban areas

The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.

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Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges

A framework for collaborative computing and multi-sensor data fusion in body sensor networks

Day-ahead electricity price forecasting via the application of artificial neural network based models

A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting

Online discovery of Heart Rate Variability patterns in mobile healthcare services

Environmental monitoring is required to understand the effects of various kinds of phenomena such as a flood, a typhoon, or a forest fire. To detect the environmental conditions in remote places, monitoring applications employ the sensor networks to detect conditions, context models to understand phenomena, and computing technology to process the large volumes of data. In this paper, we present an air pollution monitoring system to provide alarm messages about potentially dangerous areas with sensor data analysis. We design the data analysis steps to understand the detected air pollution regions and levels. The analyzed data is used to track the pollution and to give an alarm. This implemented monitoring system is used to mitigate the damages caused by air pollution.

Design of Sensor Data Processing Steps in an Air Pollution Monitoring System

Mining temporal frequent patterns in transaction databases, time-series databases, and many other kinds of databases have been widely studied in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and long patterns. In this paper, we propose an efficient temporal frequent pattern mining method using the TFP-tree (Temporal Frequent Pattern tree). This approach has three advantages: (i) one can scan the transaction only once for reducing significantly the I/O cost; (ii) one can store all transactions in leaf nodes but only save the star calendar patterns in the internal nodes. So we can save a large amount of memory. Moreover, we divide the transactions into many partitions by maximum size domain which significantly saves the memory; (iii) we efficiently discover each star calendar pattern in internal node using the frequent calendar patterns of leaf node. Thus we can reduce significantly the computational time. Our performance study shows that the TFP-tree is efficient and scalable for mining, and is about an order of magnitude faster than the classical frequent pattern mining algorithms.

Discovery of temporal frequent patterns using TFP-Tree

Environmental monitoring applications are designed for supplying derived and often integrated information by tracking and analyzing phenomena. To determine the condition of a target place, they employ a geosensor network to get the heterogeneous sensor data. To effectively handle a large volume of sensor data, applications need a data abstraction model, which supports the summarized data representation by encapsulating raw data. For faster data processing to answer a user’s queries with representative attributes of an abstracted model, we propose such a data abstraction model, the Layered Slopes in Grid for Sensor Data Abstraction (LSGSA), which is based on the SGSA. In a single grid-based layer for each sensor type, collected data is represented by slope directional vectors in two layered slopes, such as height and surface. To answer a user query in a central monitoring server, LSGSA is used to reduce the time needed to extract event features from raw sensor data as a preprocessing step for interpreting the observed data. The extracted features are used to understand the current data trends and the progress of a detected phenomenon without accessing raw sensor data.

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Yongmi Lee

Papers

A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting

Online discovery of Heart Rate Variability patterns in mobile healthcare services

Design of Sensor Data Processing Steps in an Air Pollution Monitoring System

Discovery of temporal frequent patterns using TFP-Tree

Geosensor data representation using layered slope grids.