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1.
The multi-index hashing (MIH) is the state-of-the-art method for indexing binary codes. However, it is based on the dataset codes uniform distribution assumption, and will lower efficiency in dealing with non-uniformly distributed codes. In this paper, we propose a data-oriented multi-index hashing method. We first compute the correlations between bits and learn adaptive projection vector for each binary substring. Then, instead of using substrings as direct indices into hash tables, we project them with corresponding projection vectors to generate new indices. With adaptive projection, the indices in each hash table are nearly uniformly distributed. Besides, we put forward an entropy based measurement to evaluate the distribution of data items in each hash table. Experiments conducted on reference large scale datasets show that compared to the MIH the time performance of our method can be 36.9%~87.4% better . 相似文献
2.
针对圆柱齿轮传统滚齿、铣齿加工效率偏低的问题,提出了展成拉齿加工的工艺方案,从专用刀具、展成切削原理和加工效率计算等方面入手,从理论上分析了该工艺的优越性,例举了双齿条分度机构改装方案,客观论证了圆柱齿轮展成拉齿加工工艺的成熟可靠性。 相似文献
3.
物联网设备已经被广泛应用于各个领域,为保证物联网的安全,排除内部隐患,基于时序特征数据高效索引技术设计物联网感知设备安全自动监测方法。结合时序特征数据高效索引技术提取物联网信息特征,在报文传输过程的基础上,区分不同流量数据之间的差异、恶意攻击软件与感知设备的系统特征,计算样本数据的表征值,得到物联网感知设备的原始信息特征。对数据特征进行分类,计算其数据内的缺失值和错误值,得到特征向量的筛选优化结果,计算训练损失函数,调整实际操作的阈值,保证数据特征分类的准确性。搭建物联网感知设备监测模型,训练判别器,进行物联网的自动监测。分别对数据包、字节以及数据流量进行识别,该监测技术可以准确地区分良性数据与攻击数据,从而保证物联网感知设备的安全。 相似文献
4.
Little work has been reported in the literature to support k-nearest neighbor (k-NN) searches/queries in hybrid data spaces (HDS). An HDS is composed of a combination of continuous and non-ordered discrete dimensions. This combination presents new challenges in data organization and search ordering. In this paper, we present an algorithm for k-NN searches using a multidimensional index structure in hybrid data spaces. We examine the concept of search stages and use the properties of an HDS to derive a new search heuristic that greatly reduces the number of disk accesses in the initial stage of searching. Further, we present a performance model for our algorithm that estimates the cost of performing such searches. Our experimental results demonstrate the effectiveness of our algorithm and the accuracy of our performance estimation model. 相似文献
5.
Some approximate indexing schemes have been recently proposed in metric spaces which sort the objects in the database according to pseudo-scores. It is known that (1) some of them provide a very good trade-off between response time and accuracy, and (2) probability-based pseudo-scores can provide an optimal trade-off in range queries if the probabilities are correctly estimated. Based on these facts, we propose a probabilistic enhancement scheme which can be applied to any pseudo-score based scheme. Our scheme computes probability-based pseudo-scores using pseudo-scores obtained from a pseudo-score based scheme. In order to estimate the probability-based pseudo-scores, we use the object-specific parameters in logistic regression and learn the parameters using MAP (Maximum a Posteriori) estimation and the empirical Bayes method. We also propose a technique which speeds up learning the parameters using pseudo-scores. We applied our scheme to the two state-of-the-art schemes: the standard pivot-based scheme and the permutation-based scheme, and evaluated them using various kinds of datasets from the Metric Space Library. The results showed that our scheme outperformed the conventional schemes, with regard to both the number of distance computations and the CPU time, in all the datasets. 相似文献
6.
《Expert systems with applications》2014,41(2):406-411
Cross impact analysis (CIA) consists of a set of related methodologies that predict the occurrence probability of a specific event and that also predict the conditional probability of a first event given a second event. The conditional probability can be interpreted as the impact of the second event on the first. Most of the CIA methodologies are qualitative that means the occurrence and conditional probabilities are calculated based on estimations of human experts. In recent years, an increased number of quantitative methodologies can be seen that use a large number of data from databases and the internet. Nearly 80% of all data available in the internet are textual information and thus, knowledge structure based approaches on textual information for calculating the conditional probabilities are proposed in literature. In contrast to related methodologies, this work proposes a new quantitative CIA methodology to predict the conditional probability based on the semantic structure of given textual information. Latent semantic indexing is used to identify the hidden semantic patterns standing behind an event and to calculate the impact of the patterns on other semantic textual patterns representing a different event. This enables to calculate the conditional probabilities semantically. A case study shows that this semantic approach can be used to predict the conditional probability of a technology on a different technology. 相似文献
7.
8.
An improved parallel adaptive indexing algorithm on multi-core CPUs is proposed to solve the problems that the parallel adaptive indexing algorithms cannot take full advantage of the CMP's parallel execution resource, and properly process the sequential query pattern. Based on the optimization of the Refined Partition Merge algorithm, our improved parallel adaptive indexing algorithm combines the Parallel Database Cracking method with the Refined Partition Merge algorithm. In our algorithm, when fewer data chunks are in the index, we use the optimized Refined Partition Merge algorithm so as to reduce the probability of conflict between threads, decrease the waiting time, and increase the utilization of the threads, and when more data chunks are in the index, we use the Parallel Database Cracking method so as to take full advantage of the CMP's parallel execution resources. Besides, we propose an optimization for the robustness, which makes our algorithm suitable for two common query patterns. Experiments show that our method can reduce the query time by 25.7%~33.2%, and suit with common query patterns. 相似文献
9.
We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization’s environment and that they appear in different contexts. We use internet information to represent organization’s environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time. 相似文献
10.