Advances in wireless networks and positioning technologies (e.g., CPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering ana...
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ISBN:
(纸本)9783540717027
Advances in wireless networks and positioning technologies (e.g., CPS) have enabled new data management applications that monitor moving objects. In such new applications, realtime data analysis such as clustering analysis is becoming one of the most important requirements. In this paper, we present the problem of clustering moving objects in spatial networks and propose a unified framework to address this problem. Due to the innate feature of continuously changing positions of moving objects, the clustering results dynamically change. By exploiting the unique features of road networks, our framework first introduces a notion of cluster block (CB) as the underlying clustering unit. We then divide the clustering process into the continuous maintenance of CBs and periodical construction of clusters with different criteria based on CBs. The algorithms for efficiently maintaining and organizing the CBs to construct clusters are proposed. Extensive experimental results show that our clustering framework achieves high efficiency for clustering moving objects in real road networks.
Feature selection is a powerful tool of dimension reduction from datasets. In the last decade, more and more researchers have paid attentions on feature selection. Further, some researchers begin to focus on feature s...
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Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based ...
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Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based (i.e., SSD-based) re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes (e.g., LRU and LFU) significantly shorten SSDs' lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular (LOP) data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs' lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.
It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations a...
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Owing to rapid progress in field of nanotechnology, various mathematical model has been proposed regarding the flow of nanofluids. Based on boosted thermal properties, multidisciplinary applications of such materials ...
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The wandering of older persons with Covid is one of the several behavioral difficulties that they experience, and it is the source of the most anxiety for their caregivers. Using a novel mobile phone-based safety assi...
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Recent years have seen an increasing interest in clustering data comprising multiple domains or modalities, such as categorical, numerical and transactional, etc. This kind of data is sometimes found within the contex...
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In many applications, it is useful to detect the evolving patterns in a data stream, and be able to capture them accurately (e.g. detecting the purchasing trends of customers over time on an ecommerce website). Data s...
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In many applications, it is useful to detect the evolving patterns in a data stream, and be able to capture them accurately (e.g. detecting the purchasing trends of customers over time on an ecommerce website). Data stream mining is challenging because of harsh constraints due to the continuous arrival of huge amounts of data that prevent unlimited storage and processing in memory, and the lack of control over the data arrival pattern. In this paper, we present a new approach to discover the evolving dense clusters in a dynamic data stream by incrementally updating the cluster parameters using a method based on robust statistics. Our approach exhibits robustness toward an unknown number of outliers, with no assumptions about the number of clusters. Moreover, it can adapt to the evolution of the clusters in the input data stream.
The purpose of this chapter is to provide an introduction to the field of machine learning techniques for intelligent agents based on the contributions in the workshop of ’Machine Learning and Intelligent Ag...
ISBN:
(纸本)3540424903
The purpose of this chapter is to provide an introduction to the field of machine learning techniques for intelligent agents based on the contributions in the workshop of ’Machine Learning and Intelligent Agents’ [20], which was held in conjunction with the Advanced Course on Artificial Intelligence (ACAI ’99) on Machine Learning & Applications, at Chania, Greece.
Current Sundanese stemmers either ignore reduplication words or define rules to handle only affixes. There is a significant amount of reduplication words in the Sundanese language. Because of that, it is impossible to...
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