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this type of privacy requires communicating the whole database, or n bits (where n is the number of bits in the database). Our schemes use the replication to gain substantial saving. In particular, we have ffl A two database scheme with communication complexity of O(n1=3).ffl

the performance of these methods against workstation traces. The primary result is that by adjusting the clock speed at a fine grain, substantial CPU energy can be saved with a limited impact on performance.

Businesses require accurate forecasts of time series data that is not continuous. Often, time series data is intermittent (discontinuous or interrupted). Intermittent time series data points are mostly zero (the base value), with occasional departures from the base value. Intermittent time series are common in business and economic data. For example, at progressively lower levels of data disaggregation (larger frequency, smaller geography, or both), the time series data is often intermittent. The most commonly used forecasting techniques are continuous time series methods such as exponential smoothing methods (ESM). Continuous methods are meant to forecast the future values with respect to future time periods. Because the most likely future value is zero (the base value), these models are inadequate when compared to the naïve model of simply zero (the base value). In contrast, intermittent demand methods (IDM) forecast the future average demand per period, which is more appropriate, especially for many inventory control systems. Additionally, IDM is useful for forecasting time series data that is hierarchical (for example, when the upper levels of aggregation are continuous and the lower levels of disaggregation are intermittent). This paper exposes the inadequacy of continuous time series methods when compared to IDM for forecasting future average demand per period for intermittent time series. This paper demonstrates a technique and system of large-scale automatic forecasting of intermittent demand series. This paper explains how SAS Forecast Server is used as this system.

the data to wired access points when in proximity. This leads to substantial power savings at the sensors as they only have to transmit over a short range. Detailed performance analysis is presented based on a simple model of the system incorporating key system variables such as number of MULEs, sensors

, and then prunes entire partitions as soon as it is determined that they cannot contain outliers. This results in substantial savings in computation. We present the results of an extensive experimental study on real-life and synthetic data sets. The results from a real-life NBA database highlight and reveal

by two recursive filters moving in opposite directions. In addition to the noise truncature immunity which results, the recursive nature of the filtering operations leads, with sequential machines, to a substantial saving in computational effort (five multiplications and five additions for one pixel

Over the last three decades Taiwan and South Korea have seen high sustained rates of growth in output and income. While high savings rates and substantial investments in new capital equipment are clearly an important part of their success, it is impossible to ignore the role of the export market

This paper analyzes the impact of automatic enrollment on 401(k) savings behavior. We have two key findings. First, 401(k) participation is significantly higher under automatic enrollment. Second, a substantial fraction of 401(k) participants hired under automatic enrollment retain both the default

to generate substantial life-cycle savings. This paper suggests the importance of and need for sub-stantially greater research and data collection on intergenerational transfers. Life-cycle models of savings that emphasize savings for retirement as the dominant form of capital accumulation should give way