Business analysis as an effective data processing tool

2020 ◽  
pp. 64-70
Author(s):  
Ponomarenko ◽  
Teleus
Keyword(s):  
Data Processing ◽  
Business Intelligence ◽  
Large Data ◽  
Comprehensive Analysis ◽  
Large Data Sets ◽  
Data Sets ◽  
Business Analytics ◽  
Software Products ◽  
Data Processing Tool ◽  
Analysis System

The subject of the research is the approach to the possibility of using business intelligence for integrated data processing and analysis in order to optimize the company’s activities. The purpose of writing this article is to study the concept of the BI-systems peculiarities use as one of the advanced approaches to the pro- cessing and analysis of large data sets that are continuously accumulated from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the pos- sibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty consists the features of using the business analytics model in modern conditions to optimize the activities of companies through the use of complex information, which in many cases is unstructured, are identified. The main directions of working with big data are disclosed, starting from the stage of collection and storage in specialized repositories, and ending with a comprehensive analysis of information. The main advantages of using dashboards in the process of demonstrating research results are given. A comprehensive analysis of software products in the business intelligence market has been carried out. Conclusions. The use of business intelligence allows companies to optimize their activities by making effective management decisions. The availability of a large number of BI tools al- lows company to adapt the analysis system in accordance with available data and existing needs of the company. Software solutions make it possible to build dash- boards with the settings of the selected system of indicators.

THE APPLICATION OF THE MS EXCEL PROGRAM AND THE INFORMALIZED BUSINESS INTELLIGENCE ANALYTICS PLATFORMS IN THE MANAGEMENT OF THE ENTERPRISES

2017 ◽  
Vol 5 (1) ◽  
pp. 0-0
Author(s):  
Dariusz Prokopowicz ◽  
Jan Grzegorek
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Business Intelligence ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Business Analytics ◽  
Business Decision ◽  
Decision Making Processes ◽  
Analytical Formulas

Rapid progress is being made in the field of IT applications in the analysis of the economic and financial situation of enterprises and in the processes supporting management of organizations. In terms of the fastest growing areas of information and communication technology, which are the prerequisites for the progress of online electronic banking, it is necessary to disseminate the standards of financial operations have been carried out. The cloud as well as the use of large data sets in the so-called. Big Data platforms. The current Big Data technology solutions are not just large databases, data warehouses allow for multifaceted analysis of huge volumes of quantitative data for periodic managerial reporting. Business decision-making processes should be based on the analysis of reliable and up-to-date market and business data. The information necessary for the decision-making processes has been collected, stored, ordered and pre-summed up in the form of Business Intelligence analytics reports in corporations. Business Intelligence analyzes give managers the ability to analyze the large data sets in real time, which significantly contributes to improving business management efficiency. At present, business analytics use either the advanced analytical formulas of Ms Excel or computerized platforms that include ready-made Business Intelligence reporting formulas.

Multidimensional Business Benchmarking Analysis on Data Warehouses

2017 ◽  
Vol 13 (1) ◽  
pp. 51-75 ◽  
Author(s):  
Akiko Campbell ◽  
Xiangbo Mao ◽  
Jian Pei ◽  
Abdullah Al-Barakati
Keyword(s):  
Business Intelligence ◽  
State Of The Art ◽  
Technical Problem ◽  
Large Data ◽  
Data Cube ◽  
Large Data Sets ◽  
Weather Data ◽  
Data Sets ◽  
Business Analytics ◽  
Data Set

Benchmarking analysis has been used extensively in industry for business analytics. Surprisingly, how to conduct benchmarking analysis efficiently over large data sets remains a technical problem untouched. In this paper, the authors formulate benchmark queries in the context of data warehousing and business intelligence, and develop a series of algorithms to answer benchmark queries efficiently. Their methods employ several interesting ideas and the state-of-the-art data cube computation techniques to reduce the number of aggregate cells that need to be computed and indexed. An empirical study using the TPC-H data sets and the Weather data set demonstrates the efficiency and scalability of their methods.

Multi-Variable, High Order, Performance Models (2005C)

2005 ◽  
Author(s):  
David Japikse ◽  
Oleg Dubitsky ◽  
Kerry N. Oliphant ◽  
Robert J. Pelton ◽  
Daniel Maynes ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Data ◽  
High Order ◽  
Large Data Sets ◽  
Data Sets ◽  
Performance Models ◽  
Statistical Accuracy ◽  
Evaluation Methodologies ◽  
New Models ◽  
The Impact

In the course of developing advanced data processing and advanced performance models, as presented in companion papers, a number of basic scientific and mathematical questions arose. This paper deals with questions such as uniqueness, convergence, statistical accuracy, training, and evaluation methodologies. The process of bringing together large data sets and utilizing them, with outside data supplementation, is considered in detail. After these questions are focused carefully, emphasis is placed on how the new models, based on highly refined data processing, can best be used in the design world. The impact of this work on designs of the future is discussed. It is expected that this methodology will assist designers to move beyond contemporary design practices.

SGTools: a suite of tools for processing and analyzing large data sets from in situ X-ray scattering experiments

2022 ◽  
Vol 55 (1) ◽  
Author(s):  
Nie Zhao ◽  
Chunming Yang ◽  
Fenggang Bian ◽  
Daoyou Guo ◽  
Xiaoping Ouyang
Keyword(s):  
Data Processing ◽  
Small Angle ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
X Ray ◽  
Intensity Mapping ◽  
X Ray Scattering ◽  
Ray Scattering

In situ synchrotron small-angle X-ray scattering (SAXS) is a powerful tool for studying dynamic processes during material preparation and application. The processing and analysis of large data sets generated from in situ X-ray scattering experiments are often tedious and time consuming. However, data processing software for in situ experiments is relatively rare, especially for grazing-incidence small-angle X-ray scattering (GISAXS). This article presents an open-source software suite (SGTools) to perform data processing and analysis for SAXS and GISAXS experiments. The processing modules in this software include (i) raw data calibration and background correction; (ii) data reduction by multiple methods; (iii) animation generation and intensity mapping for in situ X-ray scattering experiments; and (iv) further data analysis for the sample with an order degree and interface correlation. This article provides the main features and framework of SGTools. The workflow of the software is also elucidated to allow users to develop new features. Three examples are demonstrated to illustrate the use of SGTools for dealing with SAXS and GISAXS data. Finally, the limitations and future features of the software are also discussed.

scholarly journals Analysis of 46,046 SARS-CoV-2 whole-genomes leveraging principal component analysis (PCA)

2020 ◽  
Author(s):  
Christiane Scherer ◽  
James Grover ◽  
Darby Kammeraad ◽  
Gabe Rudy ◽  
Andreas Scherer
Keyword(s):  
Principal Component Analysis ◽  
Population Genetics ◽  
Phylogenetic Analysis ◽  
Principal Component ◽  
Large Data ◽  
Component Analysis ◽  
Comprehensive Analysis ◽  
Large Data Sets ◽  
Data Sets ◽  
Whole Genomes

AbstractSince the beginning of the global SARS-CoV-2 pandemic, there have been a number of efforts to understand the mutations and clusters of genetic lines of the SARS-CoV-2 virus. Until now, phylogenetic analysis methods have been used for this purpose. Here we show that Principal Component Analysis (PCA), which is widely used in population genetics, can not only help us to understand existing findings about the mutation processes of the virus, but can also provide even deeper insights into these processes while being less sensitive to sequencing gaps. Here we describe a comprehensive analysis of a 46,046 SARS-CoV-2 genome sequence dataset downloaded from the GISAID database in June of this year.SummaryPCA provides deep insights into the analysis of large data sets of SARS-CoV-2 genomes, revealing virus lineages that have thus far been unnoticed.

Performance tests on merge sort and recursive merge sort for big data processing

2017 ◽  
Vol 1 (21) ◽  
pp. 19-35 ◽  
Author(s):  
Zbigniew Marszałek
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Performance Tests ◽  
Merge Sort ◽  
The Stability ◽  
Search For Information ◽  
Sort Algorithm

Merge sort algorithm is widely used in databases to organize and search for information. In the work the author describes some newly proposed not recursive version of the merge sort algorithm for large data sets. Tests of the algorithm confirm the effectiveness of the method and the stability of the proposed version.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

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