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"big data sets"
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How data happened : a history from the age of reason to the age of algorithms
\"From facial recognition--capable of checking people into flights or identifying undocumented residents--to automated decision systems that inform who gets loans and who receives bail, each of us moves through a world determined by data-empowered algorithms. But these technologies didn't just appear: they are part of a history that goes back centuries, from the census enshrined in the US Constitution to the birth of eugenics in Victorian Britain to the development of Google search. Expanding on the popular course they created at Columbia University, Chris Wiggins and Matthew L. Jones illuminate the ways in which data has long been used as a tool and a weapon in arguing for what is true, as well as a means of rearranging or defending power. They explore how data was created and curated, as well as how new mathematical and computational techniques developed to contend with that data serve to shape people, ideas, society, military operations, and economies. Although technology and mathematics are at its heart, the story of data ultimately concerns an unstable game among states, corporations, and people. How were new technical and scientific capabilities developed; who supported, advanced, or funded these capabilities or transitions; and how did they change who could do what, from what, and to whom? Wiggins and Jones focus on these questions as they trace data's historical arc, and look to the future. By understanding the trajectory of data--where it has been and where it might yet go--Wiggins and Jones argue that we can understand how to bend it to ends that we collectively choose, with intentionality and purpose.\"-- Publisher marketing.
Book
Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions
Big data has potential to unlock novel groundbreaking opportunities in power grid that enhances a multitude of technical, social, and economic gains. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data. In particular, computational complexity, data security, and operational integration of big data into power system planning and operational frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. In this context, suitable big data analytics combined with visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state‐of‐the‐art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into power system planning and operational frameworks. Detailed information for utilities looking to apply big data analytics and insights on how utilities can enhance revenue streams and bring disruptive innovation are discussed. General guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.
Journal Article
The prediction of crystal densities of a big data set using 1D and 2D structure features
A large data set of over 30 thousand organic compounds containing carbon, nitrogen, oxygen, fluorine, and hydrogen was collected, and the density of each compound was predicted by 1D descriptors derived from its molecular formula and 2D descriptors derived from its constitutional structural features. The 2D structural features are composed of Benson’s groups, corrected groups, and 2D structural features of the whole molecular structures. All the descriptors were extracted by an in-house program in Java with a function to ensure that each atom (or bond) of molecules is represented by Benson’s groups once for atom-based (or bond-based) descriptors. Partial least square (PLS) and random forest (RF) methods were used separately to build models to predict the density. Further, the variable selection of descriptors was performed by variable importance of RF. For partial least square, the combination of the models constructed by descriptors based on the atoms and the bonds achieved the best results in this paper: for the cross-validation of the training set, the Pearson correlation coefficient (
R
) = 0.9270, mean absolute error (
MAE
) = 0.0270 g·cm
−3
, and root mean squared error (
RMSE
) = 0.0426 g·cm
−3
; for the prediction of the test set,
R
= 0.9454,
MAE
= 0.0263 g·cm
−3
, and
RMSE
= 0.0375 g·cm
−3
.
Journal Article
Cultural communication in double-layer coupling social network based on association rules in big data
In the big data set around the world, cultural communication in a single-layer social network cannot handle data in a two-tier social network. Combined with the characteristics of cultural communication, a two-layer coupled social network model for predicting cultural information dissemination is proposed. According to the law of big data set in the process of cultural communication, the ethical review and theoretical construction of cultural network communication was carried out. Combining the online two-layer coupling network and the CRF word segmentation algorithm, the known link relationship is obtained, and the feature attributes of the network are extracted. This paper mainly completed the construction of online social network link prediction. Starting from the topological structure of two-layer coupled social network, based on the association rules to analyze the data generated by information dissemination between two-layer coupled network nodes, a two-layer coupled network information propagation law model is proposed. The prediction and construction of the cultural information communication link in the online social network is completed, the process of data opening and sharing is accelerated, and the privacy and copyright issues in the process of cultural communication are solved. The influence of immune node and node self-healing time on the cultural information dissemination process of two-layer coupled social network is discussed. By comparing the information dissemination in social networks and the information dissemination in two-layer coupled social networks, the control of certain lyric cultures can be effectively prevented. The experimental results show that it has reference value.
Journal Article
Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions
Big data has a potential to unlock novel groundbreaking opportunities in the power grid sector that enhances a multitude of technical, social, and economic gains. The currently untapped potential of applying the science of big data for better planning and operation of the power grid is a very challenging task and needs significant efforts all-around. As power grid technologies evolve in conjunction with measurement and communication technologies, this results in unprecedented amount of heterogeneous big data sets from diverse sources. In particular, computational complexity, data security, and operational integration of big data into utility decision frameworks are the key challenges to transform the heterogeneous large dataset into actionable outcomes. Moreover, due to the complex nature of power grids along with the need to balance power in real time, seamless integration of big data into utility operations is very critical. In this context, big data analytics combined with grid visualization can lead to better situational awareness and predictive decisions. This paper presents a comprehensive state-of-the-art review of big data analytics and its applications in power grids, and also identifies challenges and opportunities from utility, industry, and research perspectives. The paper analyzes research gaps and presents insights on future research directions to integrate big data analytics into electric utility decision framework. Detailed information for utilities looking to apply big data analytics and details insights on how utilities can enhance revenue streams and bring disruptive innovation in the industry are discussed. More importantly, general guidelines for utilities to make the right investment in the adoption of big data analytics by unveiling interdependencies among critical infrastructures and operations are also provided.
Journal Article
Improving traffic flow forecasting with relevance vector machine and a randomized controlled statistical testing
High-accuracy traffic flow forecasting is vital to the development of intelligent city transportation systems. Recently, traffic flow forecasting models based on the kernel method have been widely applied due to their great generalization capability. The aim of this article is twofold: A novel kernel learning method, relevance vector machine, is employed to short-term traffic flow forecasting so as to capture the inner correlation between sequential traffic flow data, it is a type of nonlinear model which is accurate and using only a small number of relevant basis functions automatically selected. So that it can find concise data representations which are adequate for the learning task retaining as much information as possible. On the other hand, the sample size for learning has a significant impact on forecasting accuracy. How to balancing the relationship between the sample size and the forecasting accuracy is an important research topic. A randomized controlled statistical testing is layout to evaluating the impacts of sample size of the new proposed traffic flow forecasting model. The experimental results show that the new model achieves similar or better forecasting and generalization performance compared to some old ones; besides, it is less sensitive to the size of learning sample.
Journal Article
Multi-Manned Assembly Line Balancing: Workforce Synchronization for Big Data Sets through Simulated Annealing
The assembly of large and complex products such as cars, trucks, and white goods typically involves a huge amount of production resources such as workers, pieces of equipment, and layout areas. In this context, multi-manned workstations commonly characterize these assembly lines. The simultaneous operators’ activity in the same assembly station suggests considering compatibility/incompatibility between the different mounting positions, equipment sharing, and worker cooperation. The management of all these aspects significantly increases the balancing problem complexity due to the determination of the start/end times of each task. This paper proposes a new mixed-integer programming model to simultaneously optimize the line efficiency, the line length, and the workload smoothness. A customized procedure based on a simulated annealing algorithm is developed to effectively solve this problem. The aforementioned procedure is applied to the balancing of the real assembly line of European sports car manufacturers distinguished by 665 tasks and numerous synchronization constraints. The experimental results present remarkable performances obtained by the proposed procedure both in terms of solution quality and computation time. The proposed approach is the practical reference for efficient multi-manned assembly line design, task assignment, equipment allocation, and mounting position management in the considered industrial fields.
Journal Article
Big Data and Real-Time Web
This chapter contains sections titled:
What is Big Data and Where Did it Come From?
Some Key Examples of Big Data
Say Hello to the Data Geeks
“No SQL” and Some of its Flavours
Book Chapter
On Data Mining
This chapter presents a general overview of data mining with the intent of motivating readers to explore the topics in data mining further, and gain expertise in this very important area. It discusses some of the steps that occur in the process of data reduction. Data reduction involves data complexity considerations, data representations of the data mining model, and inference considerations. The data mining techniques are used for extracting patterns and values from a big data set. The data visualization techniques'such as a bubble chart, parallel coordinate plots, tree maps, geographic information system charts, data dashboards, key performance indicator, and others play a vital role in the data mining process. The chapter discusses some aspects of data preparation for data mining. Classification and Regression Trees is primarily a binary splitting tree which provides an appealing tree‐like graphical display that enables a straightforward interpretation of data.
Book Chapter