Association rule mining is a key issue in data mining.
However, the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes.
In this paper, we introduce a new measure w-support, which does not require pre-assigned weights.
It takes the quality of transactions into consideration using link-based models.
A fast mining algorithm is given, and a large amount of experimental results are presented.
The weights are completely derived from the internal structure of the database based on the
Assumption that good transactions consist of good items.
An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product’s required maintenance. Here, a new approach, called Estimation of Defects based on Defect Decay Model (ED3M) is presented that computes an estimate the defects in an ongoing testing process. ED3M is based on estimation theory. Unlike many existing approaches, the technique presented here does not depend on historical data from previous projects or any assumptions about the requirements and/or testers’ productivity. It is a completely automated approach that relies only on the data collected during an ongoing testing process. This is a key advantage of the ED3M approach as it makes it widely applicable in different testing environments. Here, the ED3M approach has been evaluated using five data sets from large industrial projects and two data sets from the literature. In addition, a performance analysis has been conducted using simulated data sets to explore its behavior using different models for the input data. The results are very promising; they indicate the ED3M approach provides accurate estimates with as fast or better convergence time in comparison to well-known alternative techniques, while only using defect data as the input.
Intermittently connected mobile networks are wireless networks where most of the time there does not exist a complete path from the source to the destination. There are many real networks that follow this model, for example, wildlife tracking sensor networks, military networks, vehicular ad hoc networks, etc. In this context, conventional routing schemes fail, because they try to establish complete end-to-end paths, before any data is sent. To deal with such networks researchers have suggested to use flooding-based routing schemes. While flooding-based schemes have a high probability of delivery, they waste a lot of energy and suffer from severe contention which can significantly degrade their performance. Furthermore, proposed efforts to reduce the over head of flooding-based schemes have often been plagued by large delays. With this in mind, we introduce a new family of routing schemes that “spray” a few message copies into the network, and then route each copy independently towards the destination. We show that, if carefully designed, spray routing not only performs significantly fewer transmissions per message, but also has lower average delivery delays than existing schemes; furthermore, it is highly scalable and retains good performance under a large range of scenarios. Finally, we use our theoretical framework proposed in our 2004paper to analyze the performance of spray routing. We also use this theory to show how to choose the number of copies to be sprayed and how to optimally distribute these copies to relays.
Data stream management systems need to adaptively control their resources, since stream characteristics and query workload may vary over time. In this paper, we investigate an approach to adaptive resource management for continuous sliding window queries that adjusts window sizes and time granularities to keep resource usage within bounds. These two novel techniques differ from standard load shedding approaches based on sampling, as they ensure exact query answers for given user-defined quality of service specifications, even under query reoptimization. In order to quantify the effects of both techniques on the various operations in a query plan, we develop an appropriate cost model for estimating operator resource allocation in terms of memory usage and processing costs. A thorough experimental study not only validates the accuracy of our cost model but also demonstrates the efficacy and scalability of the proposed techniques
