Active RFID tags provide potentially useful information on LOS at a low cost and with minimal impact on the work environment. Machine learning techniques may be employed to handle the variable nature of RFID's radio signals. Work on mapping LOS data to activities will involve integration with other sensors and task analysis techniques.
The computation of multidimensional aggregates is a common operation in OLAP applications. The major bottleneck is the large volume of data that needs to be processed which leads to prohibitively expensive query execution times. On the other hand, data analysts are primarily concerned with discerning trends in the data and thus a system that provides approximate answers in a timely fashion would suit their requirements better. In this article we present the prime factor scheme, a novel method for compressing data in a warehouse. Our data compression method is based on aggregating data on each dimension of the data warehouse. We used both real world and synthetic data to compare our scheme against the Haar wavelet and our experiments on range-sum queries show that it outperforms the latter scheme with respect to both decoding time and error rate, while maintaining comparable compression ratios. One encouraging feature is the stability of the error rate when compared to the Haar wavelet. Although wavelets have been shown to be effective at compressing data, the approximate answers they provide varies widely, even for identical types of queries on nearly identical values in distinct parts of the data. This problem has been attributed to the thresholding technique used to reduce the size of the encoded data and is an integral part of the wavelet compression scheme. In contrast the prime factor scheme does not rely on thresholding but keeps a smaller version of every data element from the original data and is thus able to achieve a much higher degree of error stability which is important from a Data Analysts point of view.
The computation of multidimensional aggregates is a common operation in OLAP applications. The major bottleneck is the large volume of data that needs to be processed which leads to prohibitively expensive query execution times. On the other hand, data analysts are primarily concerned with discerning trends in the data and thus a system that provides approximate answers in a timely fashion would suit their requirements better. In this article we present the prime factor scheme, a novel method for compressing data in a warehouse. Our data compression method is based on aggregating data on each dimension of the data warehouse. We used both real world and synthetic data to compare our scheme against the Haar wavelet and our experiments on range-sum queries show that it outperforms the latter scheme with respect to both decoding time and error rate, while maintaining comparable compression ratios. One encouraging feature is the stability of the error rate when compared to the Haar wavelet. Although wavelets have been shown to be effective at compressing data, the approximate answers they provide varies widely, even for identical types of queries on nearly identical values in distinct parts of the data. This problem has been attributed to the thresholding technique used to reduce the size of the encoded data and is an integral part of the wavelet compression scheme. In contrast the prime factor scheme does not rely on thresholding but keeps a smaller version of every data element from the original data and is thus able to achieve a much higher degree of error stability which is important from a Data Analysts point of view.
Hospitals are traditionally slow to adopt new information systems (IS). However, health care funders and regulators are demanding greater use of IS as part of the solution to chronic problems with patient safety and access to medical records. One technology offering benefits in these areas is Radio Frequency Identification (RFID). Pilot systems have demonstrated the feasibility of a wide range of hospital applications, but few have been fully implemented. This chapter investigates the factors that have restricted the adoption of RFID technology in hospitals. It draws on related work on the adoption of IS generally, published case studies of RFID pilots, and interviews with clinicians, IS staff and RFID vendors operating in New Zealand (NZ) hospitals. The chapter concludes with an analysis of the key differences between RFID and other IS, and which RFID applications have the greatest chance of successful implementation in hospitals.
Wi-Fi (also known as IEEE 802.11b) networks are gaining widespread popularity as wireless local area networks (WLANs) due to their simplicity in operation, robustness, low cost, and user mobility offered by the technology. It is a viable technology for wireless local area networking applications in both business and home environments. This chapter reports on a survey of large New Zealand organizations focusing on the level of Wi-Fi deployment, reasons for non-deployment, the scope of deployment, investment in deployment, problems encountered, and future plans. Our findings show that most organizations have at least considered the technology, though a much smaller proportion has deployed it on any significant scale. A follow up review of the latest published case studies and surveys suggests that while Wi-Fi networks are consolidating, interest is growing in wider area wireless networks.
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