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From imitation to innovation,from following to leading,is not only a major change in the development of science and technology in China at this stage,but also a major strategic demand for national development.In recent years,relevant scholars at home and abroad have carried out the research of science and technology development trend analysis and hot spot tracking,but due to the lack of systematic big data collection and governance system,the scope of data analysis and mining is often limited to the single data sample of science and technology literature.Aiming at the goal of forward-looking prediction of science and technology development,this paper comprehensively analyzes the massive heterogeneous data that affect the development process of science and technology,such as all kinds of scientific and technological literature,scholar dynamics,forum hot spots and social comments.By building a data-driven big data governance system,this paper solves the data remediation problems in the process of detection and discovery,accurate collection,cleaning and aggregation,fusion processing,model construction,prediction and calculation.At the same time,on the basis of big data remediation,LDA model is used to achieve technology trend prediction and ana-lysis.The research results provide technical support for the system to solve the problem of hidden information discovery and relationship reasoning in massive scientific and technological big data.

With the development of technologies such as Internet of things, cloud computing and artificial intelligence, big data becomes a research hotspot and is applied in many fields. The booming aviation field has natural big data soil, which has been paid more and more attention. In recent years, scholars have begun to study aviation oriented big data technology. Meanwhile airlines have also begun to use aviation big data to provide services for them, and promote it to the height of development strategy. Research and practice have shown that aviation big data can not only help to reduce the company??s operating costs, but also improve the quality of customer experience. In this paper, the definition of aviation big data is firstly given from the perspective of data and system, and the corresponding organization structures are described systematically. Secondly, the key technologies of aviation big data are elaborated in detail from five aspects: collection, storage management, preprocessing, analysis and virtual simulation and visualization, and some main models and algorithms are compared and analyzed. This paper describes the typical application scenarios of aviation big data from many aspects. Finally, the problems existing in aviation big data and the future research directions are analyzed in an in-depth way, so as to provide useful references for related research and applications.

Big Data optimizes the process of information processing, expands the scale of information processing, and improves the application effect of big data and computer information technology. However, big data has also triggered data leakage, network viruses and other security risks. In the context of big data, in order to give full play to the application advantages of computer information security technology, it is necessary to give full play to all kinds of technology to avoid the problems such as system failures, external attacks and so on, then ensure that the computer system in a safe and stable operating environment. This paper analyzes the problems of computer information security technology under the background of big data, and puts forward the corresponding application strategies based on the concrete superiority technology.

Materials descriptors with multivariate, multiphase, and multiscale of a complex system have been treated as the remarkable materials genome, addressing the composition–processing–structure–property–performance (CPSPP) relationships during the development of advanced materials. With the aid of high‐performance computations, big data, and artificial intelligence technologies, it is still a challenge to derive an explainable machine learning (ML) model to reveal the underlying CPSPP relationship, especially, under the extreme conditions. This work supports a smart strategy to derive an explainable model of composition–property–performance relationships via a hybrid data‐driven and knowledge‐enabled model, and designing superhard high‐entropy diboride ceramics (HEBs) with a cost‐effective approach. Five dominate features and optimal model were screened out from 149 features and nine algorithms by ML and validated in first‐principles calculations. From Shapley additive explanations (SHAP) and electronic bottom layer, the predicted hardness increases with the improved mean electronegativity and electron work function (EWF) and decreases with growing average d valence electrons of composition. The 14 undeveloped potential superhard HEBs candidates via ML are further validated by first‐principles calculations. Moreover, this EWF‐ML model not only has better capability to distinguish the differences of solutes in same group of periodic table but is also a more effective method for material design than that of valence electron concentration. [ABSTRACT FROM AUTHOR]

Big data technology has triggered a boom in research and applications around the world. The construction industry has ushered in a new technological change in this context. Researchers have conducted in-depth research on the intersection of big data and architecture, but lack quantitative analysis and comprehensive evaluation of the research results. This article draws a series of knowledge maps with the help of the CiteSpace software using the relevant literature in the Web of Science database between 2007 and 2022 as data samples to comprehensively grasp the research development at the intersection of big data and the construction industry. The knowledge base, research hotspots, and domain evolution trends in the intersection of big data and the construction industry are analyzed quantitatively and aided by qualitative analysis through visualization, respectively. The results show that Chinese and American scholars have published more relevant papers in international journals, and some well-known universities in both countries constitute the main group of research institutions. The research hotspots are BIM, data mining, building energy saving, smart cities, and disaster prevention and damage prevention. In the future, the research on the integration and application of the construction industry with emerging technologies, such as big data, BIM, and cloud computing will be connected more closely. This study provides a preliminary overall picture of the research of big data in the field of construction by sorting out and analyzing the existing results. [ABSTRACT FROM AUTHOR]

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other "omics" approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These "omics" approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with "omics" tools can allow rapid gene identification and eventually accelerate crop-improvement programs. [ABSTRACT FROM AUTHOR]

With the rapid development of modern medical information technology, hospitals are accumulating huge amounts of clinical data while providing medical services to patients, and in the era of big data, how to mine valuable information from the huge amount of clinical data so as to make new contributions to future disease diagnosis and medical research. In order to solve this problem, more and more scholars have introduced data mining techniques into the medical field in recent years, and mining and analysing medical data is a hot topic at present. If spinal TB is detected and treated early, not only can spinal deformities be prevented and treated but also the course of treatment can be shortened, the financial burden on the patient can be reduced, spinal function can be maintained, and eradication can be achieved without the need for surgical intervention. Early detection of spinal tuberculosis is the key to preventing and treating it. Therefore, in this paper, we use meta-analysis and data mining techniques to process and analyse the medical data of spinal tuberculosis disease, its main inflammatory factors expression characteristics, and the causes of patient recurrence. [ABSTRACT FROM AUTHOR]

From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers' attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the Affection-Behavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled Latent Dirichlet Allocation (LDA), Long Short-Term Memory (LSTM), and Snow Natural Language Processing (NLP), based on social media comments text dataset. Similar to the questionnaires published results, this article verified that 61% of consumers expressed negative sentiment toward BDPD in general. Differently, on a finer scale, this study further measured the negative sentiments that differ significantly among different topics. The measurement results show that the topics "Regular Customers Priced High" (69%) and "Usage Intention" (67%) occupy the top two places of negative sentiment among consumers, and the topic "Precision Marketing" (42%) is at the bottom. Moreover, semantic recognition results that 49% of consumers' comments involve multiple topics, indicating that consumers have a pretty clear cognition of the complex status of the BDPD. Importantly, this study found some topics that had not been focused on in previous studies, such as more than 8% of consumers calling for government and legal departments to regulate BDPD behavior, which indicates that quite enough consumers are losing confidence in the self-discipline of the platform enterprises. Another interesting result is that consumers who pursue solutions to the BDPD belong to two mutually exclusive groups: government protection and self-protection. The significance of this study is that it reminds the e-commerce platforms to pay attention to the potential harm for consumers' psychology while bringing additional profits through the BDPD. Otherwise, the negative consumer attitudes may cause damage to brand image, business reputation, and the sustainable development of the platforms themselves. It also provides the government supervision departments an advanced analysis method reference for more effective administration to protect social fairness. [ABSTRACT FROM AUTHOR]

Big Data platforms recently employ resource management systems, such as YARN, Mesos, and Google Borg, to provision computational resources. These systems adopt containerization to share the computing resources in a multi-tenant setting with low performance overhead and interference. However, it may be observed that tenants often interfere with each other on the underlying Big Data File Systems (BDFS), e.g., Hadoop File System, which have been widely deployed as a persistent layer in current data centers. A solution with systematic generality is to containerize BDFS itself to isolate and allocate its IO sources to multiple tenants. To this end, we conduct analysis on the ineffectiveness of proportionally sharing BDFS IO resource via containerization. This ineffectiveness is due to the scheduler of containerization in “pseudo-starvation” status, in which most of IO requests are backlogged in BDFS rather than in containerization scheduler. Without enough backlogged IO requests, existing schedulers might have to maximize device utilization rather than enforce proportional sharing policy. To resolve this ineffectiveness issue, we develop a cross-layer system called BDFS-Container, which containerizes BDFS at the Linux block IO level. Central to BDFS-Container, we propose and design a proactive IOPS throttling-based mechanism named IOPS Regulator, which achieves a trade-off between maximizing IO utilization and accurately proportional IO sharing. The evaluation results show that our method can improve proportionally sharing BDFS IO resources by 74.4 percent on average. [ABSTRACT FROM AUTHOR]

Existing multitask Takagi–Sugeno–Kang (TSK) fuzzy modeling methods always produce high complex fuzzy models with numerous redundant rules and consequent parameters. To this end, we propose a novel multitask TSK fuzzy modeling method called mtSparseTSK, which learns a compact set of fuzzy rules and shared consequent parameters across tasks in a unified procedure. Specifically, we consider the fuzzy rule reduction and consequent parameter selection across tasks by devising novel group sparsity regularizations in the learning criterion of the model. We also integrate the intertask relations in the proposed TSK model for multitask learning. We fully utilize the block structure in the TSK fuzzy models in formulating a joint block sparse optimization problem and develop a procedure for alternating direction method of multipliers (ADMMs) to find the optimal solution of the problem. Experiments on the synthetic and real-world datasets demonstrate the distinctive performance of the proposed methods over the existing ones on multitask fuzzy system modeling. [ABSTRACT FROM AUTHOR]

In the big data era, destination images have played an increasingly important role in tourism development. However, seldom tourism research has utilised big data analytics to examine destination images from travel blogs. Therefore, this study proposes and evaluates a big data analytical approach using latent Dirichlet allocation to extract attributes of online destination images from 140,286 travel blogs about 20 cities in China. Results reveal 14 dimensions with 54 attributes of destination images of the studied cities. Interesting findings are discovered between online destination images and tourism cities. This study also summarises the implications for tourism research and practice. [ABSTRACT FROM AUTHOR]

Execution of analytic frameworks on sample data sets is the current trend in response to increasing data size and demand for real-time analysis. Additionally, high-performance, energy-efficient Solid-State Drive (SSD) arrays are the primary storage subsystem for parallel data analysis systems. To exploit the benefits of SSD arrays when executing sample data set analytics, several key areas must be considered. First, due to logical to physical address translation, random data choice in data sampling jobs can cause unbalanced workloads among SSDs in the array. Second, after the data choice, existing task schedulers in data analysis frameworks can introduce non-negligible resource contentions resulting from the suboptimal Input/Output (I/O). The performance of SSDs is unpredictable because of their varying maintenance costs at runtime, which renders them hard to be managed by the scheduler. With the trend towards sample set data analytics and the use of SSDs, it is increasingly important to ensure balanced workloads and minimize resource contention. Without addressing these areas, sample-set data analytics on SSDs will continue to suffer from performance inefficiencies. In this paper, we propose ApproxSSD to perform on-disk layout-aware data sampling on SSD arrays. This proposed framework leverages data selection and task scheduling to improve the performance of many applications. ApproxSSD decouples I/O from the computation in task execution. This avoids potential I/O contentions and suboptimal workload balances. We have developed an open-source prototype system of ApproxSSD in Scala at Github. Our evaluation shows that ApproxSSD can achieve up to 2.7 times speed up at 10 percent sampling ratio under an example sampling workload when compared to Spark, while simultaneously maintaining high output accuracy. [ABSTRACT FROM AUTHOR]

As a result of increasing data volume and velocity, Big Data science at exascale has shifted towards the in-situ paradigm, where large scale simulations run concurrently alongside data analytics. With in-situ, data generated from simulations can be processed while still in memory, thereby avoiding the slow storage bottleneck. However, running simulations and analytics together on shared resources will likely result in substantial contention if left unmanaged, as demonstrated in this work, leading to much reduced efficiency of simulations and analytics. Recently, virtualization technologies such as Linux containers have been widely applied to data centers and physical clusters to provide highly efficient and elastic resource provisioning for consolidated workloads including scientific simulations and data analytics. In this paper, we investigate to facilitate network traffic manipulation and reduce mutual interference on the network for in-situ applications in virtual clusters. In order to dynamically allocate the network bandwidth when it is needed, we adopt SARIMA-based techniques to analyze and predict MPI traffic issued from simulations. Although this can be an effective technique, the naïve usage of network virtualization can lead to performance degradation for bursty asynchronous transmissions within an MPI job. We analyze and resolve this performance degradation in virtual clusters. [ABSTRACT FROM AUTHOR]

Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from '-omics' technologies. Created from a biologist's perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org. [ABSTRACT FROM AUTHOR]

Abstract In the era of Big Data, data assets have become a strategic resource which cannot be overlooked by both society and enterprises. However, data is not equal to the assets. This paper first introduces the necessary conditions of data assetization and discriminates the concepts of data governance, data management and data asset management. Then it focuses on the unique connotation and characteristics of grid data assets. With reference to the mainstream theory of data management, the framework for the grid data asset management is set up in the combination of the characteristics of data assets, business needs and the actual situation in the power supply enterprises. Finally, this paper puts forward higher system requirements and technical requirements for China's power supply enterprises to conduct data asset management. [ABSTRACT FROM AUTHOR]

Abstract With the continuous development of information technology, data assets are becoming more and more important to enterprises. But at the same time, because of the large volume and complexity, it is difficult for many enterprises, especially the traditional large enterprises, to arrange and analyze the data assets well in order to recognize the value of the data assets. Using the data generated from business operation of a Power Grid Corp, a qualitative analysis of the characteristics of data assets in the electric-power industry has been made. At the same time, using the method of complex network, taking the reference and business connection between data as weight, the importance of different business data has been analyzed based on PageRank algorithm. The analysis results can provide effective basis for the Power Grid Corp to manage its data assets, evaluate the value of data and make good use of the data asset. [ABSTRACT FROM AUTHOR]

The distributed file system, HDFS, is widely deployed as the bedrock for many parallel big data analysis. However, when running multiple parallel applications over the shared file system, the data requests from different processes/executors will unfortunately be served in a surprisingly imbalanced fashion on the distributed storage servers. These imbalanced access patterns among storage nodes are caused because a). unlike conventional parallel file system using striping policies to evenly distribute data among storage nodes, data-intensive file system such as HDFS store each data unit, referred to as chunk file, with several copies based on a relative random policy, which can result in an uneven data distribution among storage nodes; b). based on the data retrieval policy in HDFS, the more data a storage node contains, the higher probability the storage node could be selected to serve the data. Therefore, on the nodes serving multiple chunk files, the data requests from different processes/executors will compete for shared resources such as  hard disk head and network bandwidth, resulting in a degraded I/O performance. In this paper, we first conduct a complete analysis on how remote and imbalanced read/write patterns occur and how they are affected by the size of the cluster. We then propose novel methods, referred to as Opass, to optimize parallel data reads, as well as to reduce the imbalance of parallel writes on distributed file systems. Our proposed methods can benefit parallel data-intensive analysis with various parallel data access strategies. Opass adopts new matching-based algorithms to match processes to data so as to compute the maximum degree of data locality and balanced data access. Furthermore, to reduce the imbalance of parallel writes, Opass employs a heatmap for monitoring the I/O statuses of storage nodes and performs HM-LRU policy to select a local optimal storage node for serving write requests. Experiments are conducted on PRObE’s Marmot 128-node cluster testbed and the results from both benchmark and well-known parallel applications show the performance benefits and scalability of Opass. [ABSTRACT FROM PUBLISHER]

Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation. [ABSTRACT FROM PUBLISHER]

During the last few decades, Data-intensive File Systems (DiFS), such as Google File System (GFS) and Hadoop Distributed File System (HDFS) have become the key storage architectures for big data processing. These storage systems usually divide files into fixed-sized blocks (or chunks). Each block is replicated (usually three-way) and distributed pseudo-randomly across the cluster. The master node (namenode) uses a huge table to record the locations of each block and its replicas. However, with the increasing size of the data, the block location table and its corresponding maintenance could occupy more than half of the memory space and 30% of processing capacity in master node, which severely limit the scalability and performance of master node. We argue that the physical data distribution and maintenance should be separated out from the metadata management and performed by each storage node autonomously. In this paper, we propose Deister, a novel block management scheme that is built on an invertible deterministic declustering distribution method called Intersected Shifted Declustering (ISD). Deister is amendable to current research on scaling the namespace management in master node. In Deister, the huge table for maintaining the block locations in the master node is eliminated and the maintenance of the block-node mapping is performed autonomously on each data node. Results show that as compared with the HDFS default configuration, Deister is able to achieve identical performance with a saving of about half of the RAM space and 30% of processing capacity in master node and is expected to scale to double the size of current single namenode HDFS cluster, pushing the scalability bottleneck of master node back to namespace management. [ABSTRACT FROM AUTHOR]

Reliability is a critical metric in the design and development of replication-based big data storage systems such as Hadoop File System (HDFS). In the system with thousands of machines and storage devices, even in-frequent failures become likely. In Google File System, the annual disk failure rate is 2.88%, which means that you were expected to see 8760 disk failures in a year. Unfortunately, given an increasing number of node failures, how often a cluster starts losing data when being scaled out is not well investigated. Moreover, there is no systemic method that can be used to quantify the reliability for multi-way replication based data placement methods, which has been widely used in enterprise large-scale storage systems to improve the I/O parallelism. In this paper, we develop a new reliability model by incorporating the probability of replica loss to investigate the system reliability of multi-way declustering data layouts and analyze their potential parallel recovery possibilities. Our comprehensive simulation results on Matlab and SHARPE show that the shifted declustering data layout outperforms the random declustering layout in a multi-way replication scale-out architecture, in terms of data loss probability and system reliability by up to 63% and 85%, respectively. Our study on both 5-year and 10-year system reliability equipped with various recovery bandwidth settings shows that the shifted declustering layout surpasses the two baseline approaches in both cases by consuming up to 79% and 87% less recovery bandwidth for copyset, as well as 4.8% and 10.2% less recovery bandwidth for random layout. [ABSTRACT FROM AUTHOR]

Training feedforward neural networks (FNNs) is one of the most critical issues in FNNs studies. However, most FNNs training methods cannot be directly applied for very large datasets because they have high computational and space complexity. In order to tackle this problem, the CCMEB (Center-Constrained Minimum Enclosing Ball) problem in hidden feature space of FNN is discussed and a novel learning algorithm called HFSR-GCVM (hidden-feature-space regression using generalized core vector machine) is developed accordingly. In HFSR-GCVM, a novel learning criterion using L2-norm penalty-based ε -insensitive function is formulated and the parameters in the hidden nodes are generated randomly independent of the training sets. Moreover, the learning of parameters in its output layer is proved equivalent to a special CCMEB problem in FNN hidden feature space. As most CCMEB approximation based machine learning algorithms, the proposed HFSR-GCVM training algorithm has the following merits: The maximal training time of the HFSR-GCVM training is linear with the size of training datasets and the maximal space consumption is independent of the size of training datasets. The experiments on regression tasks confirm the above conclusions. [ABSTRACT FROM AUTHOR]

With development of IOT(Internet of Things), more and more wearable systems have been used to strengthen existing applications. Therefore, for the problems that we can’t monitor abnormal conditions of heart rate as well as carrying out scientific and efficient training plans based on knowledge from variation of them. A ZigBee and big data analysis based pulse monitoring system has been proposed. The system is composed of multiple ZigBee based pulse monitoring sensors, customized gateways and back-end system. Individuals’ pulse information are collected by the sensors and passed to back-end system to support big data analysis of the training conditions. To guarantee collecting efficient pulse signal, we have researched photo electricity based dynamic and continuous heart rate monitoring methods as well as comprehensive anti-jamming methods. Finally, by using according big data analysis methods we have built up the training model by the standards such as different age, different mood and so on. Results shows the system can be used to improve the physical training level; accumulate the training data of the individuals and support more efficient and scientific training plans. [ABSTRACT FROM AUTHOR]

The explosive growth in Big Data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution using exhaustive comparison is infeasible due to the prohibitive computational complexity and memory requirement. In response, approximate nearest neighbor (ANN) search based on hashing techniques has become popular due to its promising performance in both efficiency and accuracy. Prior randomized hashing methods, e.g., locality-sensitive hashing (LSH), explore data-independent hash functions with random projections or permutations. Although having elegant theoretic guarantees on the search quality in certain metric spaces, performance of randomized hashing has been shown insufficient in many real-world applications. As a remedy, new approaches incorporating data-driven learning methods in development of advanced hash functions have emerged. Such learning-to-hash methods exploit information such as data distributions or class labels when optimizing the hash codes or functions. Importantly, the learned hash codes are able to preserve the proximity of neighboring data in the original feature spaces in the hash code spaces. The goal of this paper is to provide readers with systematic understanding of insights, pros, and cons of the emerging techniques. We provide a comprehensive survey of the learning-to-hash framework and representative techniques of various types, including unsupervised, semisupervised, and supervised. In addition, we also summarize recent hashing approaches utilizing the deep learning models. Finally, we discuss the future direction and trends of research in this area. [ABSTRACT FROM PUBLISHER]

Abstract: The China sustainable development is the simultaneously three key facets, including urbanism, industrialization, and information. Building a Smart Low-carbon City is an energy strategy for the sustainable development of Chinese cities. One technology way for this strategy is the City Intelligent Energy Network (CIEN), supporting a green, ecological development of the Chinese cities. CIEN starts from the redistribute of city land-use, taking small gas turbine as the major power, combining with the new energy mode, such as solar energy, wind energy and low level energy (geothermal, ice cold storage, water cold storage). CIEN could supply relatively large network and grid with electricity, heat, and cold, realizing the efficient, integrate and gradable use of the energy network technology. As CIEN is on its starting point, so this kind of technology still need the combination work with the lab research and applicable field test. Considering the city unit scale, the transportation mode, energy resources, and the traditional mode to power grid and other factors, the key point is to balance the economical use and the resource capability and adaptability. This paper tries to use the 2007 statistical data including city economy, construction, population, and different energy parameter, to establish the comprehensive model. Combining the model results, the CIEN adaptability will be discussed, and meaningful policies will be proposed, with the focusing of how to build a Smart Low-carbon city. [Copyright &y& Elsevier]