A Comparison of Heuristics for Scheduling Spatial Clusters to Reduce I/O Cost in Spatial Join Processing

In spatial join processing, a common method to minimize the I/O cost is to partition the spatial objects into clusters, and then to schedule the processing of the clusters in the spatial join processing such that the number of times the same objects to be fetched into memory can be minimized. A key issue of this clustering-and-scheduling approach is how to produce a better sequence of clusters to guide the cluster scheduling thus to reduce the total I/O cost of spatial join processing. This paper describes three cluster sequencing heuristics. An extensive comparison among them has been conducted, and simulation results have shown that, while using the cluster sequences generated to guide the cluster scheduling can significant reduce the I/O cost in fetching spatial objects in spatial join processing, their performance differ

Agent-based Similarity-aware Web Document Pre-fetching

This paper presents an agent-based similarity-aware Web document pre-fetching scheme that is built on the similarity-aware Web caching architecture. A set of agents are employed to carry out certain duties such as document similarity detection, identification of relevant access patterns, document prediction and network traffic monitoring for document pre-fetching. Preliminary simulations have been conducted to evaluate the proposed scheme, and the results have shown that the new pre-fetching scheme outperforms existing Web-document pre-fetching algorithm

Clustering Spatial Data for Join Operations Using Match-based Partition

The spatial join is an operation that combines two sets of spatial data by their spatial relationships. The cost of spatial join could be very high due to the large sizes of spatial objects and the computation-intensive spatial operations. In spatial join processing, a common method to minimize the I/O cost is to partition the spatial objects into clusters and then schedule the processing of the clusters such that the number of times the same objects to be fetched into memory can be minimized. In this paper, we propose a match-based approach to partition a large spatial data set into clusters, which is computed based on the maximal match on the spatial join graph. Simulations have been conducted and the results have shown that, when comparing to existing approaches, our new method can significantly reduce the number of clusters produced in spatial join processin

Effective scheduling algorithm for on-demand XML data broadcasts in wireless environments

The organization of data on wireless channels, which aims to reduce the access time of mobile clients, is a key problem in data broadcasts. Many scheduling algorithms have been designed to organize flat data on air. However, how to effectively schedule semi-structured information such as XML data on wireless channels is still a challenge. In this paper, we firstly propose a novel method to greatly reduce the tuning time by splitting query results into XML snippets and to achieve better access efficiency by combining similar ones. Then we analyze the data broadcast scheduling problem of on-demand XML data broadcasts and define the efficiency of a data item. Based on the definition, a Least Efficient Last (LEL) scheduling algorithm is also devised to effectively organize XML data on wireless channels. Finally, we study the performance of our algorithms through extensive experiments. The results show that our scheduling algorithms can reduce both access time and tuning time signifcantly when compared with existing work

Similarity-aware Web Content Management and Document Pre-fetching

Web caching is intended to reduce network traffic, server load and user-perceived retrieval latency. Web pre-fetching, which can be considered as active caching, builds on regular Web caching, minimizing further a Web user\u27s access delay. To be effective, however, the pre-fetching techniques must be able to predict subsequent Web access with minimum computational overheads. This paper presents a similarity-based mechanism to support similarity-aware Web document pre-fetching between proxy caches and browsing clients. We first define a set of measures to assess similarities between Web documents, and then propose a multi-cache architecture to cache Web documents based on those similarities. A predictor is developed to support the similarity-aware document pre-fetching algorithm. Preliminary experiments have shown that our predictor offers superior performance when compared with some existing prediction algorithms

Clustering of Web Users Using Session-based Similarity Measures

One important research topic in web usage mining is the clustering of web users based on their common properties. Informative knowledge obtained from web user clusters were used for many applications, such as the prefetching of pages between web clients and proxies. This paper presents an approach for measuring similarity of interests among web users from their past access behaviors. The similarity measures are based on the user sessions extracted from the user\u27s access logs. A multi-level scheme for clustering a large number of web users is proposed, as an extension to the method proposed in our previous work (2001). Experiments were conducted and the results obtained show that our clustering method is capable of clustering web users with similar interest

Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm

With recent advances in health systems, the amount of health data is expanding rapidly in various formats. This data originates from many new sources including digital records, mobile devices, and wearable health devices. Big health data offers more opportunities for health data analysis and enhancement of health services via innovative approaches. The objective of this research is to develop a framework to enhance health prediction with the revised fusion node and deep learning paradigms. Fusion node is an information fusion model for constructing prediction systems. Deep learning involves the complex application of machine-learning algorithms, such as Bayesian fusions and neural network, for data extraction and logical inference. Deep learning, combined with information fusion paradigms, can be utilized to provide more comprehensive and reliable predictions from big health data. Based on the proposed framework, an experimental system is developed as an illustration for the framework implementatio

A Data Mining Perspective of the Dual Effect of Rainfall and Temperature on Wheat Yield

This paper presents the final investigation within the series of qualitative and quantitative investigations carried out for the processing and analysis of geographic land-use data in an agricultural context. The geographic data was made up of crop and cereal production land use profiles. These were linked to previously recorded climatic data from fixed weather stations in Australia that was interpolated using ordinary krigeing to fit a grid surface. In this study, the profiles for the stochastic average monthly temperature and rainfall for a selected study area were used to determine their simultaneous effects on crop production at the shire level. The temperature and rainfall were sampled for a selected decade of crop production for the years from 2001 to 2010. The evaluation was carried out using graphical, correlation and data mining regression techniques in order to detect the patterns of crop production in response to the climatic effect across the cropping shires of agricultural region. Data mining classification algorithms within the WEKA software package were used with location as the classifier to make comparisons between predicted and actual wheat yields. The predicted patterns suggested that crop production is affected by the climate variability especially at certain stages of plant growth for some shires

Analysis of channel estimation error of OFDM systems in rayleigh fading

In wireless OFDM applications, since the radio channel is frequency selective and time-varying, a dynamic estimation of channel must be achieved before the demodulation of the transmitted OFDM signals. As an effective approach for solving the channel estimation problems, the pilot-assisted channel estimation technique has received considerable attention in recent years. In this paper, we investigate the channel estimation error in the existing pilot-assisted channel estimation approaches in detail, a new effective channel estimation approach with lower estimation error is proposed as well