Recursive estimation algorithms for power controls of wireless communication networks

Power control problems for wireless communication networks are investigated in direct-sequence codedivision multiple-access （DS/CDMA） channels. It is shown that the underlying problem can be formulated as a constrained optimization problem in a stochastic framework. For effective solutions to this optimization problem in real time, recursive algorithms of stochastic approximation type are developed that can solve the problem with unknown system components. Under broad conditions, convergence of the algorithms is established by using weak convergence methods.

A Hadoop Performance Prediction Model Based on Random Forest

MapReduce is a programming model for processing large data sets, and Hadoop is the most popular open-source implementation of MapReduce. To achieve high performance, up to 190 Hadoop configuration parameters must be manually tunned. This is not only time-consuming but also error-pron. In this paper, we propose a new performance model based on random forest, a recently devel- oped machine-learning algorithm. The model, called RFMS, is used to predict the performance of a Hadoop system according to the system＇ s configuration parameters. RFMS is created from 2000 distinct fine-grained performance observations with different Hadoop configurations. We test RFMS against the measured performance of representative workloads from the Hadoop Micro-benchmark suite. The results show that the prediction accuracy of RFMS achieves 95% on average and up to 99%. This new, highly accurate prediction model can be used to automatically optimize the performance of Hadoop systems.

Big Data: Where Dreams Take Flight

From academia to industry, big data has become a buzzword in information technology. The US Federal Government is paying much attention to the big-data revolution. In 2012, fourteen US government departments allocated funds to 87 big-data projects [1].

A System for Detecting Refueling Behavior along Freight Trajectories and Recommending Refueling Alternatives

Smart refueling can reduce costs and lower the possibility of an emergency. Refueling intelligence can only be obtained by mining historical refueling behaviors from big data, however, without devices, such as fuel tank cursors, and cooperation from drivers, these behaviors are hard to detect. Thus, detecting refueling behaviors from big dala derived from easy-to-approach trajectories is one of/he most efficient retrieve evidences for research of refueling behaviors. In this paper, we describe a complete procecdure for detecting refoeling behavior in big data derived from freight trajectories. This procedure involves the inte- gration of spatial data mining and machine-learning techniques. The key pall of the methodology is a pattern detector that extends the naive Bayes classifier. By draw＇ing on the spatial and temporal characteristics of freight trajectories, refileling behaviors can be identified with high accuracy. Fu,lher, we present a refueling prediction and recommendation system to show how our refueling detector can be used practically in big data. Our experimetlts on real trajeclories show that our refueling detector is accurate, and the system performs well.

Mobile Cloud Computing and Applications

In 2010, cloud computing gained momentum. Cloud computing is a model for real-time, on-demand, pay-for-use network access to a shared pool of configurable computing and storage resources. It has matured from a promising business concept to a working reality in both the private and public IT sectors. The U.S. government, for example, has requested all its agencies to evaluate cloud computing alternatives as part of their budget submissions for new IT investment.

MIX-RS:A Multi-Indexing System Based on HDFS for Remote Sensing Data Storage

A large volume of Remote Sensing(RS)data has been generated with the deployment of satellite technologies.The data facilitate research in ecological monitoring,land management and desertification,etc.The characteristics of RS data(e.g.,enormous volume,large single-file size,and demanding requirement of fault tolerance)make the Hadoop Distributed File System(HDFS)an ideal choice for RS data storage as it is efficient,scalable,and equipped with a data replication mechanism for failure resilience.To use RS data,one of the most important techniques is geospatial indexing.However,the large data volume makes it time-consuming to efficiently construct and leverage.Considering that most modern geospatial data centres are equipped with HDFS-based big data processing infrastructures,deploying multiple geospatial indices becomes natural to optimise the efficacy.Moreover,because of the reliability introduced by high-quality hardware and the infrequently modified property of the RS data,the use of multi-indexing will not cause large overhead.Therefore,we design a framework called Multi-IndeXing-RS(MIX-RS)that unifies the multi-indexing mechanism on top of the HDFS with data replication enabled for both fault tolerance and geospatial indexing efficiency.Given the fault tolerance provided by the HDFS,RS data are structurally stored inside for faster geospatial indexing.Additionally,multi-indexing enhances efficiency.The proposed technique naturally sits on top of the HDFS to form a holistic framework without incurring severe overhead or sophisticated system implementation efforts.The MIX-RS framework is implemented and evaluated using real remote sensing data provided by the Chinese Academy of Sciences,demonstrating excellent geospatial indexing performance.

Multiple Routes Recommendation System on Massive Taxi Trajectories

This paper presents a cloud-based multiple-route recommendation system, xGo, that enables smartphone users to choose suitable routes based on knowledge discovered in real taxi trajectories. In modern cities, GPS-equipped taxicabs report their locations regularly, which generates a huge volume of trajectory data every day. The optimized routes can be learned by mining these massive repositories of spatio-temporal information. We propose a system that can store and manage GPS log files in a cloud-based platform, probe traffic conditions, take advantage of taxi driver route-selection intelligence, and recommend an optimal path or multiple candidates to meet customized requirements. Specifically, we leverage a Hadoop-based distributed route clustering algorithm to distinguish different routes and predict traffic conditions through the latent traffic rhythm. We evaluate our system using a real-world dataset（〉100 GB） generated by about 20 000 taxis over a 2-month period in Shenzhen, China. Our experiments reveal that our service can provide appropriate routes in real time and estimate traffic conditions accurately.

Construction and Implementation of Big Data in Healthcare in Yichang City,Hubei Province

Environmental pollution,aging,emerging infectious diseases,and unhealthy lifestyles are affecting human health and resulting in serious social and economic burdens.The government-led healthcare big data platform in Yichang has continuously worked towards exploring the use of comprehensive health-related information through top-level design and scientific planning.The platform is based on the following principle:“Openness,inclusiveness,and win-win cooperation.”So far,by relying on one-to-one verification,comparison,correlation,and correction with the source.

Economic Burden of Malignant Tumors——Yichang City,Hubei Province,China,2019

Summary What is already known about this topic?Malignant tumors are common chronic noncommunicable disease and have caused serious health hazards to residents and heavy economic burden of disease to the society.What is added by this report?This is the first report on the economic burden of multiple types of malignant tumors in Yichang City.In 2019,the direct medical burden of lung cancer in Yichang was the highest,reaching 561.67 million CNY,and the indirect economic burden of lung cancer in Yichang was higher than that of other malignant tumors,costing 326.49 million CNY.

The Effects of Diabetes and Hypertension on the Severity of COVID-19- Yichang, Hubei Province, 2020

Summary What is already known on this topic?COVID-19 has become a serious public health issue.A higher proportion of severe patients were senior patients with underlying diseases such as diabetes and hypertension and had a lack of statistical evidence so far.What is added by this report?When severe illness was compared with non-severe illness,senior patients were at a greater risk(4.71)than young and middle-aged patients,as well as the odds ratio was about 2.99 patients with diabetes compared to patients without diabetes and hypertension.COVID-19-infectious senior patients with diabetes were inclined to suffer severe illness.