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The predictability of data values is studied at a fundamental level. Two basic predictor models are defined: Computational predictors perform an operation on previous values to yield predicted next value values. Examples we study ...
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The predictability of data values is studied at a fundamental level. Two basic predictor models are defined: Computational predictors perform an operation on previous values to yield predicted next value values. Examples we study are stride value prediction and last value prediction; Contest-Based predictors match recent value history (context) with previous value history and predict values based entirely on previously observed patterns. To understand the potential of value prediction we perform simulations with unbounded prediction tables that are immediately updated using correct data values.
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In the location-aware services, past mobile device cache invalidation-replacement practises used are ineffective if the client travel route varies rapidly. In addition, in terms of storage expense, previous cache invalidation-repl...
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In the location-aware services, past mobile device cache invalidation-replacement practises used are ineffective if the client travel route varies rapidly. In addition, in terms of storage expense, previous cache invalidation-replacement policies indicate high storage overhead. These limitations of past policies are inspiration for this research work. The paper describes the models to solve the aforementioned challenges using two different approaches separately for predicting the future path for the user movement. In the first approach, the most prevalent sequential pattern mining and clustering (SPMC) technique is used to pre-process the user's movement trajectory and find out the pattern that appears frequently. In the second approach, frequent patterns are forwarded into the mobility Markov chain and matrix (MMCM) algorithm leading to a reduction in the size of candidate sets and, therefore, efficiency enhancement of mining sequence patterns. Analytical results show significant caching performance improvement compared to previous caching policies.
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Earlier mobile client cache invalidation-replacement policies used in the location-based information system are not appropriate if the path for the client movement is changing rapidly. Further, previous cache invalidation-replacem...
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Earlier mobile client cache invalidation-replacement policies used in the location-based information system are not appropriate if the path for the client movement is changing rapidly. Further, previous cache invalidation-replacement policies show high server overhead in terms of processing costs. Therefore, the objective of this work is to solve the aforementioned challenges by developing a novel effective approach for predicting the future path for the user movement by the use of mobility Markov chain and matrix created to estimate the future movement path (FMP) used in the revised spatio-temporal cost estimation of a data item in cache replacement for contribution to the cache hit ratio improvement. The user's predicted future movement path is further used in optimal sub-polygon selection for reducing the storage overhead in cache invalidation valid scope representation. Client-server queuing model is used for simulation of CEMP-IR in location-based services (LBS). Analytical results show significant caching performance improvement compared to previous policies such as Manhattan, FAR, PPRRP, SPMC-CRP and CEFAB for LBS.
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In this paper, a new binary arithmetic coding strategy with adaptive-weight context classification is introduced to solve the context dilution and context quantization problems for bitplane coding. In our method, the weight, obtai...
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In this paper, a new binary arithmetic coding strategy with adaptive-weight context classification is introduced to solve the context dilution and context quantization problems for bitplane coding. In our method, the weight, obtained using a regressive-prediction algorithm, represents the degree of importance of the current coefficient/block in the wavelet transform domain. Regarding the weights as contexts, the coder reduces the context number by classifying the weights using the Lloyd-Max algorithm, such that high-order is approximated as low-order context arithmetic coding. The experimental results show that our method effectively improves the arithmetic coding performance and outperforms the compression performances of SPECK, SPIHT and JPEG2000.
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It is difficult to predict water quality in a reservoir because of the complex physical, chemical, and biological processes involved. In contrast to the well-known numeric models and artificial neural network models, Linguistic Mo...
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It is difficult to predict water quality in a reservoir because of the complex physical, chemical, and biological processes involved. In contrast to the well-known numeric models and artificial neural network models, Linguistic Models (LM) with context-based fuzzy clustering can offer reliable predictions of water quality. The main characteristics of LM are that it is user-centric and that it inherently dwells upon collections of highly interpretable and user-oriented entities, such as information granules. In this paper, we propose a model for evaluating water quality and then evaluate the effectiveness of the proposed method by performing comparisons on water quality data sets from a reservoir. Finally, we found that the proposed method not only has the better prediction performance than other models, but also can offer reliable intervals for uncertainty evaluation about the water quality.
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Purpose - Location-prediction enables the next generation of location-based applications.The purpose of this paper is to provide a historical summary of research in personal location-prediction. Location-prediction began as a ...
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Purpose - Location-prediction enables the next generation of location-based applications.The purpose of this paper is to provide a historical summary of research in personal location-prediction. Location-prediction began as a tool for network management, predicting the load on particular cellular towers or WiFi access points. With the increasing popularity of mobile devices, location-prediction turned personal, predicting individuals' next locations given their current locations. Design/methodology/approach - This paper includes an overview of prediction techniques and reviews several location-prediction projects comparing the raw location data, feature extraction, choice of prediction algorithms and their results. Findings - A new trend has emerged, that of employing additional context to improve or expand predictions. Incorporating temporal information enables location-predictions farther out into the future. Appending place types or place names can improve predictions or develop prediction applications that could be used in any locale. Finally, the authors explore research into diverse types of context, such as people's personal contacts or health activities. Originality/value - This overview provides a broad background for future research in prediction.
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Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring...
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Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.
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Addressing the transmission and storage problems of aurora images that Arctic Yellow River Station of China faces, the present paper proposes a two-stage lossless compression algorithm based on weighted motion compensation and con...
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Addressing the transmission and storage problems of aurora images that Arctic Yellow River Station of China faces, the present paper proposes a two-stage lossless compression algorithm based on weighted motion compensation and context-based modeling. As no specialized compression algorithms for aurora images are available, proposing a new algorithm that offers a satisfactory lossless compression performance for aurora images is necessary. The proposed algorithm utilizes the weighted motion compensation to obtain the motion vector based on the unusual movement characteristics of aurora images. Subsequently, the context-based model is combined with the motion vector. Experimental results indicated that the proposed algorithm outperformed the state-of-the-art lossless compression methods.
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In this paper, we describe the development of an internet-based system and a novel mobile home based device for the management of medication. We extend these concepts through the descriptions of an enhanced service with the use of...
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In this paper, we describe the development of an internet-based system and a novel mobile home based device for the management of medication. We extend these concepts through the descriptions of an enhanced service with the use of mobile phone technology and home based digital TV services.
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The accurate determination of user interest in terms of geographic information is essential to numerous mobile applications, such as recommender systems and mobile advertising. User interest is greatly influenced by the usage cont...
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The accurate determination of user interest in terms of geographic information is essential to numerous mobile applications, such as recommender systems and mobile advertising. User interest is greatly influenced by the usage context and varies across individuals; therefore, a user interest model should incorporate these individual needs and propensities. In this paper, we present an approach to model user interest in a contextualized and personalized manner based on location-based social networks. Multinomial logistic regression is employed to quantify the relationship between user interest and usage context at both the aggregate and individual levels. The proposed approach is tested in a real-world application using Foursquare check-ins issued between February and June 2014 in the three major cities of Chicago, Los Angeles and New York. Results demonstrate the capability of the contextualization process for capturing contextual influences on user interest, and that such influences can be observed at a fine-grained scale at the individual level through the personalization process. The proposed approach therefore enables contextualized and personalized estimation of user interest, thereby contributing useful information to follow-up mobile applications. (C) 2016 Elsevier Ltd. All rights reserved.
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