Big Data over Networks最新文献

Big data processing for smart grid security 智能电网安全的大数据处理

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.009

Lanchao Liu, Zhu Han, H. Poor, Shuguang Cui

引用次数: 3

Optimization algorithms for big data with application in wireless networks 大数据优化算法及其在无线网络中的应用

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.004

Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun, Z. Luo

{"title":"Optimization algorithms for big data with application in wireless networks","authors":"Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun, Z. Luo","doi":"10.1017/CBO9781316162750.004","DOIUrl":"https://doi.org/10.1017/CBO9781316162750.004","url":null,"abstract":"This chapter proposes the use of modern first-order large-scale optimization techniques to manage a cloudbased densely deployed next-generation wireless network. In the first part of the chapter we survey a few popular first-order methods for large-scale optimization, including the block coordinate descent (BCD) method, the block successive upper-bound minimization (BSUM) method and the alternating direction method of multipliers (ADMM). In the second part of the chapter, we show that many difficult problems in managing large wireless networks can be solved efficiently and in a parallel manner, by modern first-order optimization methods. Extensive numerical results are provided to demonstrate the benefit of the proposed approach. Disciplines Signal Processing | Systems and Communications | Systems Engineering Comments This is a chapter published as Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun and Zhi-Quan Luo \"Optimization Algorithms for Big Data with Application in Wireless Networks,\" in Big Data over Networks, ed. Shuguang Cui, Alfred O. Hero III, Zhi-quan Luo, and Jose M. F. Moura (Cambridge: Cambridge University Press, 2016), pp. 66-100. Posted with permission. This book chapter is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/imse_pubs/171 I 3 Optimization algorithms for big data with application in wireless networks Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun, and Zhi-Quan Luo This chapter proposes the use of modern first-order large-scale optimization techniques to manage a cloud-based densely deployed next-generation wireless network. In the first part of the chapter we survey a few popular first-order methods for large-scale optimization, including the block coordinate descent (BCD) method, the block successive upper-bound minimization (BSUM) method and the alternating direction method of multipliers (ADMM). In the second part of the chapter, we show that many difficult problems in managing large wireless networks can be solved efficiently and in a parallel manner, by modern first-order optimization methods. Extensive numerical results are provided to demonstrate the benefit of the proposed approach.","PeriodicalId":415319,"journal":{"name":"Big Data over Networks","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127884554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Inference of gene networks associated with the host response to infectious disease 与宿主对传染病反应相关的基因网络推断

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.014

Zhe-Hong Gan, Xin Yuan, Ricardo Henao, E. Tsalik, L. Carin

引用次数: 2

Large-scale correlation mining for biomolecular network discovery 生物分子网络发现的大规模关联挖掘

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.016

A. Hero, B. Rajaratnam

{"title":"Large-scale correlation mining for biomolecular network discovery","authors":"A. Hero, B. Rajaratnam","doi":"10.1017/CBO9781316162750.016","DOIUrl":"https://doi.org/10.1017/CBO9781316162750.016","url":null,"abstract":"Continuing advances in high-throughput mRNA probing, gene sequencing and microscopic imaging technology is producing a wealth of biomarker data on many different living organisms and conditions. Scientists hope that increasing amounts of relevant data will eventually lead to better understanding of the network of interactions between the thousands of molecules that regulate these organisms. Thus progress in understanding the biological science has become increasingly dependent on progress in understanding the data science. Data mining tools have been of particular relevance since they can sometimes be used to effectively separate the “wheat” from the “chaff”, winnowing the massive amount of data down to a few important data dimensions. Correlation mining is a data mining tool that is particularly useful for probing statistical correlations between biomarkers and recovering properties of their correlation networks. However, since the number of correlations between biomarkers is quadratically larger than the number biomarkers, the scalability of correlation mining in the big data setting becomes an issue. Furthermore, there are phase transitions that govern the correlation mining discoveries that must be understood in order for these discoveries to be reliable and of high confidence. This is especially important to understand at big data scales where the number of samples is fixed and the number of biomarkers becomes unbounded, a sampling regime referred to as the ”purely-high dimensional setting.” In this chapter, we will discuss some of the main advances and challenges in correlation mining in the context of large scale biomolecular networks with a focus on medicine. A new correlation mining application will be introduced: discovery of correlation sign flips between edges in a pair of correlation or partial correlation networks. The pair of networks could respectively correspond to a disease (or treatment) group and a control group. This paper is to appear as a chapter in the book Big Data over Networks from Cambridge University Press (ISBN: 9781107099005). 4 Large scale correlation mining for biomolecular network discovery","PeriodicalId":415319,"journal":{"name":"Big Data over Networks","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129715502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Big data aware wireless communication: challenges and opportunities 大数据感知无线通信:挑战与机遇

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.008

Suzhi Bi, Rui Zhang, Z. Ding, Shuguang Cui

{"title":"Big data aware wireless communication: challenges and opportunities","authors":"Suzhi Bi, Rui Zhang, Z. Ding, Shuguang Cui","doi":"10.1017/CBO9781316162750.008","DOIUrl":"https://doi.org/10.1017/CBO9781316162750.008","url":null,"abstract":"The fast-growing wireless data service is pushing our communication network's processing power to its limit. The ever-increasing data traffic poses imminent challenges to all aspects of the wireless system design, such as spectrum efficiency, computing capabilities, and backhaul link capacity, etc. At the same time, the massive amount of mobile data traffic may also lead to potential system performance gain that is otherwise not achievable with conventional wireless signal processing models. In this chapter, we investigate the challenges and opportunities in the design of scalable wireless systems to embrace such a “big data” era.We review state-of-the-art techniques in wireless big data processing and study the potential implementations of key technologies in the future wireless systems. We show that proper wireless system designs could harness, and in fact take advantages of the mobile big data traffic. Introduction After decades of rapid growth in data services, modern society has entered the so-called “ big data ” era , where the mobile network is a major contributor. As of the year 2013, the global penetration of mobile subscribers had reached 92%, producing staggeringly 6800 PetaBytes (6.8 × 10 18 ) of mobile data worldwide [1]. The surge of mobile data traffic in recent years is mainly attributed to the popularity of smartphones, mobile tablets, and other smart mobile devices. Mobile broadband applications such as web surfing, social networking, and online videos are now ubiquitously accessible by these mobile devices, without limitations from time and location. The recent survey shows that the number of smartphone users currently accounts for merely 25%–30% of the entire mobile subscribers. However, the figure will double in the next three years and continue to grow given the considerable room in the smartphone market for further uptake.With a compound annual growth rate of 45%, it is expected that the mobile data traffic will increase by ten times from 2013 to 2019. In addition to the vast amount of wireless data, wireless signal processing often amplifies the system big data effect in the pursuit of higher performance gain. To combat the fading channel, diversity schemes, especially the MIMO antenna technologies, are extensively used in both mobile terminals (MTs) and base stations (BSs). Numerous schemes with co-located and distributed antennas have been proposed over the years to increase the data rate or extend the cellular coverage.","PeriodicalId":415319,"journal":{"name":"Big Data over Networks","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128829533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A unified distributed algorithm for non-cooperative games 非合作博弈的统一分布式算法

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.005

J. Pang, Meisam Razaviyayn

{"title":"A unified distributed algorithm for non-cooperative games","authors":"J. Pang, Meisam Razaviyayn","doi":"10.1017/CBO9781316162750.005","DOIUrl":"https://doi.org/10.1017/CBO9781316162750.005","url":null,"abstract":"This chapter presents a unified framework for the design and analysis of distributed algorithms for computing first-order stationary solutions of non-cooperative games with non-differentiable player objective functions. These games are closely associated with multi-agent optimization wherein a large number of selfish players compete noncooperatively to optimize their individual objectives under various constraints. Unlike centralized algorithms that require a certain system mechanism to coordinate the players’ actions, distributed algorithms have the advantage that the players, either individually or in subgroups, can each make their best responses without full information of their rivals’ actions. These distributed algorithms by nature are particularly suited for solving hugesize games where the large number of players in the game makes the coordination of the players almost impossible. The distributed algorithms are distinguished by several features: parallel versus sequential implementations, scheduled versus randomized player selections, synchronized versus asynchronous transfer of information, and individual versus multiple player updates. Covering many variations of distributed algorithms, the unified algorithm employs convex surrogate functions to handle nonsmooth nonconvex functions and a (possibly multi-valued) choice function to dictate the players’ turns to update their strategies. There are two general approaches to establish the convergence of such algorithms: contraction versus potential based, each requiring different properties of the players’ objective functions. We present the details of the convergence analysis based on these two approaches and discuss randomized extensions of the algorithms that require less coordination and hence are more suitable for big data problems. Introduction Introduced by John von Neumann [1], modern-day game theory has developed into a very fruitful research discipline with applications in many fields. There are two major classifications of a game, cooperative versus non-cooperative. This chapter pertains to one aspect of non-cooperative games for potential applications to big data, namely, the computation of a “solution” to such a game by a distributed algorithm. In a (basic) non-cooperative game, there are finitely many selfish players/agents each optimizing a rival-dependent objective by choosing feasible strategies satisfying certain private constraints. Providing a solution concept to such a game, a Nash equilibrium (NE) [2, 3] is by definition a tuple of strategies, one for each player, such that no player will be better off by unilaterally deviating from his/her equilibrium strategy while the rivals keep executing their equilibrium strategies.","PeriodicalId":415319,"journal":{"name":"Big Data over Networks","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125944075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Sparsity-aware distributed learning 稀疏感知分布式学习

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.003

S. Chouvardas, Y. Kopsinis, S. Theodoridis

引用次数: 4

Distributed big data storage in optical wireless networks 光纤无线网络中的分布式大数据存储

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.007

Chen Gong, Zhengyuan Xu, Xiaodong Wang

引用次数: 0

Inference of gene regulatory networks: validation and uncertainty 基因调控网络的推断:验证和不确定性

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.013

Xiaoning Qian, Byung-Jun Yoon, E. Dougherty

{"title":"Inference of gene regulatory networks: validation and uncertainty","authors":"Xiaoning Qian, Byung-Jun Yoon, E. Dougherty","doi":"10.1017/CBO9781316162750.013","DOIUrl":"https://doi.org/10.1017/CBO9781316162750.013","url":null,"abstract":"A fundamental problem of biology is to construct gene regulatory networks that characterize the operational interaction among genes. The term “gene” is used generically because such networks could involve gene products. Numerous inference algorithms have been proposed. The validity, or accuracy, of such algorithms is of central concern. Given data generated by a ground-truth network, how well does a model network inferred from the data match the data-generating network? This chapter discusses a general paradigm for inference validation based on defining a distance between networks and judging validity according to the distance between the original network and the inferred network. Such a distance will typically be based on some network characteristics, such as connectivity, rule structure, or steady-state distribution. It can also be based on some objective for which the model network is being employed, such as deriving an intervention strategy to apply to the original network with the aim of correcting aberrant behavior. Rather than assuming that a single network is inferred, one can take the perspective that the inference procedure leads to an “uncertainty class” of networks, to which belongs the ground-truth network. In this case, we define a measure of uncertainty in terms of the cost that uncertainty imposes on the objective, for which the model network is to be employed, the example discussed in the current chapter involving intervention in the yeast cell cycle network.","PeriodicalId":415319,"journal":{"name":"Big Data over Networks","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131171328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Big data analytics systems 大数据分析系统

Big Data over Networks Pub Date : 1900-01-01 DOI: 10.1017/CBO9781316162750.006

G. Ananthanarayanan, Ishai Menache

引用次数: 2