scholarly journals Methods for secure cloud processing of big data

2021 ◽  
Vol 22 (3) ◽  
pp. 1650
Author(s):  
Yerzhan N. Seitkulov ◽  
Seilkhan N. Boranbayev ◽  
Gulden B. Ulyukova ◽  
Banu B. Yergaliyeva ◽  
Dina Satybaldina
Keyword(s):  
Big Data ◽  
Research Work ◽  
Economic Problem ◽  
Large Set ◽  
Client Server ◽  
Cloud Processing ◽  
Secure Outsourcing ◽  
New Methods ◽  
Secure Protocol ◽  
Active Attacks

We study new methods of secure cloud processing of big data when solving applied computationally-complex problems with secret parameters. This is one of the topical issues of secure client-server communication. As part of our research work, we model the client-server interactions: we give specific definitions of such concepts as “solvable by the protocol”, “secure protocol”, “correct protocol”, as well as actualize the well-known concepts-“active attacks” and “passive attacks”. First, we will outline the theory and methods of secure outsourcing for various abstract equations with secret parameters, and then present the results of using these methods in solving applied problems with secret parameters, arising from the modeling of economic processes. Many economic tasks involve processing a large set of economic indicators. Therefore, we are considering a typical economic problem that can only be solved on very powerful computers.

scholarly journals Opportunities and Challenges of Feature Selection Methods for High Dimensional Data: A Review

2021 ◽  
Vol 26 (1) ◽  
pp. 67-77
Author(s):  
Siva Sankari Subbiah ◽  
Jayakumar Chinnappan
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Large Scale ◽  
High Dimensional Data ◽  
Research Work ◽  
Basic Feature ◽  
High Dimensional ◽  
Selection Methods ◽  
Fast Development ◽  
Improved Accuracy

Now a day, all the organizations collecting huge volume of data without knowing its usefulness. The fast development of Internet helps the organizations to capture data in many different formats through Internet of Things (IoT), social media and from other disparate sources. The dimension of the dataset increases day by day at an extraordinary rate resulting in large scale dataset with high dimensionality. The present paper reviews the opportunities and challenges of feature selection for processing the high dimensional data with reduced complexity and improved accuracy. In the modern big data world the feature selection has a significance in reducing the dimensionality and overfitting of the learning process. Many feature selection methods have been proposed by researchers for obtaining more relevant features especially from the big datasets that helps to provide accurate learning results without degradation in performance. This paper discusses the importance of feature selection, basic feature selection approaches, centralized and distributed big data processing using Hadoop and Spark, challenges of feature selection and provides the summary of the related research work done by various researchers. As a result, the big data analysis with the feature selection improves the accuracy of the learning.

scholarly journals KLASIFIKASI TEKS SOSIAL MEDIA TWITTER MENGGUNAKAN SUPPORT VECTOR MACHINE (Studi Kasus Penusukan Wiranto)

2019 ◽  
Vol 2 (2) ◽  
pp. 43
Author(s):  
Lalu Mutawalli ◽  
Mohammad Taufan Asri Zaen ◽  
Wire Bagye
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Big Data ◽  
Mass Communication ◽  
Confusion Matrix ◽  
Classification Model ◽  
Support Vector ◽  
Large Set ◽  
Appearance Time ◽  
Data Production

In the era of technological disruption of mass communication, social media became a reference in absorbing public opinion. The digitalization of data is very rapidly produced by social media users because it is an attempt to represent the feelings of the audience. Data production in question is the user posts the status and comments on social media. Data production by the public in social media raises a very large set of data or can be referred to as big data. Big data is a collection of data sets in very large numbers, complex, has a relatively fast appearance time, so that makes it difficult to handle. Analysis of big data with data mining methods to get knowledge patterns in it. This study analyzes the sentiments of netizens on Twitter social media on Mr. Wiranto stabbing case. The results of the sentiment analysis showed 41% gave positive comments, 29% commented neutrally, and 29% commented negatively on events. Besides, modeling of the data is carried out using a support vector machine algorithm to create a system capable of classifying positive, neutral, and negative connotations. The classification model that has been made is then tested using the confusion matrix technique with each result is a precision value of 83%, a recall value of 80%, and finally, as much as 80% obtained in testing the accuracy.

scholarly journals Research on Performance Evaluation of Diversified Government Quality Work and Analysis on Application Practice in Nei Mongol

2021 ◽  
Vol 96 ◽  
pp. 01011
Author(s):  
Lei Feng ◽  
Juxiu Huang ◽  
Jingxing Liao
Keyword(s):  
Big Data ◽  
Research Work ◽  
Evaluation Process ◽  
Government Performance ◽  
Relevant Research ◽  
Quality Work ◽  
The Public ◽  
Government Quality ◽  
Local Quality ◽  
Big Data Technology

The evaluation of public satisfaction with government quality work is an evaluation form to evaluate government performance from the perspective of the public. The evaluation process is open and transparent, and the results are relatively objective and fair. Taking the application practice in Nei Mongol as an example, in this paper, an index framework is designed and constructed, 12 leagues and cities in the whole region are covered by the investigation, and the actual effect of local quality work is explored and analyzed in combination with big data technology so as to provide enlightenment and reference for relevant research work in the quality field.

The Library Big Data Research

2018 ◽  
pp. 892-905
Author(s):  
Shaochun Xu ◽  
Wencai Du ◽  
Chunning Wang ◽  
Dapeng Liu
Keyword(s):  
Big Data ◽  
Research Work ◽  
The Public ◽  
Research Institutes ◽  
Library Research ◽  
The People ◽  
Intellectual Assets ◽  
The Status ◽  
Visualization Tools ◽  
Stored Information

Libraries are widely used by government, universities, research institutes, and the public since they are storing and managing intellectual assets. The library information directly stored in libraries and about the people interaction with libraries can be transformed into accessible data which then will be used by researchers to help library better serve users. Librarians need to understand how to transform, analyze, and present data in order to facilitate such knowledge creation. For example, the challenges they face include how to make big datasets more useful, visible and accessible. Fortunately, with new and powerful analytics of big data, such as information visualization tools, researchers/users can look at data in new ways and mine it for information they intend to have. Moreover, interaction of users and stored information has been taken into librarian's consideration to improve library service quality. In this work, the authors discuss the characteristics of datasets in library and argue against a popular confusion that data involved in library research is not big enough, conduct a review for the research work on library big data and then summarize the applications and research directions in this field. The status of big data research in library in China is discussed. The challenges associated with it are also discussed and explored.

Intelligent Big Data Analytics

2017 ◽  
pp. 50-72 ◽  
Author(s):  
Dheeraj Malhotra ◽  
Neha Verma ◽  
Om Prakash Rishi ◽  
Jatinder Singh
Keyword(s):  
Big Data ◽  
System Design ◽  
Research Work ◽  
Big Data Analytics ◽  
Map Reduce ◽  
Time Intervals ◽  
Online Commerce ◽  
Online Purchase ◽  
Online Retailers ◽  
Novel Approaches

With the explosive increase in regular E Commerce users, online commerce companies must have more customer friendly websites to satisfy the personalized requirements of online customer to progress their market share over competition; Different individuals have different purchase requirements at different time intervals and hence novel approaches are often required to be deployed by online retailers in order to identify the latest purchase requirements of customer. This research work proposes a novel MR apriori algorithm and system design of a tool called IMSS-SE, which can be used to blend benefits of Apriori-based Map Reduce framework with Intelligent technologies for B2C E-commerce in order to assist the online user to easily search and rank various E Commerce websites which can satisfy his personalized online purchase requirement. An extensive experimental evaluation shows that proposed system can better satisfy the personalized search requirements of E Commerce users than generic search engines.

scholarly journals Vaex: big data exploration in the era of Gaia

2018 ◽  
Vol 618 ◽  
pp. A13 ◽  
Author(s):  
Maarten A. Breddels ◽  
Jovan Veljanoski
Keyword(s):  
Big Data ◽  
Volume Rendering ◽  
Data Exploration ◽  
Streaming Algorithms ◽  
Desktop Computer ◽  
Client Server ◽  
Key Points ◽  
Computational Part ◽  
Boolean Expressions

We present a new Python library, called vaex, intended to handle extremely large tabular datasets such as astronomical catalogues like the Gaia catalogue, N-body simulations, or other datasets which can be structured in rows and columns. Fast computations of statistics on regular N-dimensional grids allows analysis and visualization in the order of a billion rows per second, for a high-end desktop computer. We use streaming algorithms, memory mapped files, and a zero memory copy policy to allow exploration of datasets larger than memory, for example out-of-core algorithms. Vaex allows arbitrary (mathematical) transformations using normal Python expressions and (a subset of) numpy functions which are “lazily” evaluated and computed when needed in small chunks, which avoids wasting of memory. Boolean expressions (which are also lazily evaluated) can be used to explore subsets of the data, which we call selections. Vaex uses a similar DataFrame API as Pandas, a very popular library, which helps migration from Pandas. Visualization is one of the key points of vaex, and is done using binned statistics in 1d (e.g. histogram), in 2d (e.g. 2d histograms with colourmapping) and 3d (using volume rendering). Vaex is split in in several packages: vaex-core for the computational part, vaex-viz for visualization mostly based on matplotlib, vaex-jupyter for visualization in the Jupyter notebook/lab based in IPyWidgets, vaex-server for the (optional) client-server communication, vaex-ui for the Qt based interface, vaex-hdf5 for HDF5 based memory mapped storage, vaex-astro for astronomy related selections, transformations, and memory mapped (column based) FITS storage.

scholarly journals The Personality Panorama: Conceptualizing Personality through Big Behavioural Data

2020 ◽  
Vol 34 (5) ◽  
pp. 599-612 ◽  
Author(s):  
Ryan L. Boyd ◽  
Paola Pasca ◽  
Kevin Lanning
Keyword(s):  
Big Data ◽  
New Technologies ◽  
Historical Thinking ◽  
Self Report ◽  
Ethical Considerations ◽  
Personality Psychology ◽  
The Public ◽  
New Methods ◽  
Behavioural Assessment ◽  
The Face

Personality psychology has long been grounded in data typologies, particularly in the delineation of behavioural, life outcome, informant–report, and self–report sources of data from one another. Such data typologies are becoming obsolete in the face of new methods, technologies, and data philosophies. In this article, we discuss personality psychology's historical thinking about data, modern data theory's place in personality psychology, and several qualities of big data that urge a rethinking of personality itself. We call for a move away from self–report questionnaires and a reprioritization of the study of behaviour within personality science. With big data and behavioural assessment, we have the potential to witness the confluence of situated, seamlessly interacting psychological processes, forming an inclusive, dynamic, multiangle view of personality. However, big behavioural data come hand in hand with important ethical considerations, and our emerging ability to create a ‘personality panopticon’ requires careful and thoughtful navigation. For our research to improve and thrive in partnership with new technologies, we must not only wield our new tools thoughtfully, but humanely. Through discourse and collaboration with other disciplines and the general public, we can foster mutual growth and ensure that humanity's burgeoning technological capabilities serve, rather than control, the public interest. © 2020 European Association of Personality Psychology

On: “Gravity vertical gradient measurements for the detection of small geologic and anthropogenic forms” by Z. J. Fajklewicz (GEOPHYSICS, October 1976, p. 1016–1030)

Geophysics ◽  
1977 ◽  
Vol 42 (4) ◽  
pp. 872-873
Author(s):  
Stephen Thyssen‐Bornemisza
Keyword(s):  
Research Work ◽  
Vertical Gradient ◽  
Gravity Gradient ◽  
Wind Gust ◽  
Near Surface ◽  
New Methods ◽  
Vertical Gravity Gradient ◽  
Surface Variation ◽  
Gradient Measurements ◽  
Vertical Gravity

In his paper, Fajklewicz discusses the improvement of vertical gravity gradient measurements arising from a very stable tower apparently not affected by wind gust vibration and climatic changes. Further, the lower plate where the gravity meter is resting can be changed in position to avoid possible disturbances from surface and near‐surface variation, and new methods for correcting and interpreting observed gradients over the vertical interval of about 3 m are presented. Some 1000 field stations were observed, including research work and industrial application.

Data “big” and “small” – Examples from the Australian lexical database

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Claire Bowern
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Qualitative Data ◽  
Language Change ◽  
First Century ◽  
Lexical Database ◽  
Traditional Methods ◽  
New Methods ◽  
Statistical Approaches ◽  
Insight Into

AbstractThe twenty-first Century has been billed the era of “big data”, and linguists are participating in this trend. We are seeing an increased reliance on statistical and quantitative arguments in most fields of linguistics, including the oldest parts of the field, such as the study of language change. The increased use of statistical methods changes the types of questions we can ask of our data, as well as how we evaluate the answers. But this all has the prerequisite of certain types of data, coded in certain ways. We cannot make powerful statistical arguments from the qualitative data that historical linguists are used to working with. In this paper I survey a few types of work based on a lexical database of Pama-Nyungan languages, the largest family in Aboriginal Australia. I highlight the flexibility with which large-scale databases can be deployed, especially when combined with traditional methods. “Big” data may require new methods, but the combination of statistical approaches and traditional methods is necessary for us to gain new insight into old problems.

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