Top Research Papers on Data Analytics

This panel will try to explore detailed analysis on big data analytics, a term used to identify the datasets that whose size is beyond the ability of typical database software tools to store, manage and analyze.

The need for financial analysts to continually adapt to the rapidly evolving technological landscape to effectively shape corporate strategy is stressed, emphasizing the growing importance of real-time data analysis and automation in business optimization.

The purpose of this research is to build a crime analysis model using supervised techniques to predict the arrest status of serious crimes in Chicago based on specific indicators, such as timeframe, location in terms of district, community, and beat, and crime type among others.

Multilevel analyses have become increasingly common in psychological research, although unfortunately, many researchers’ understanding of multilevel analysis has lagged behind this increased interest and use. Many researchers have heard of and are curious about multilevel modeling (MLM), but they are unfamiliar with it, perhaps so unfamiliar that they do not know where to start. This unfamiliarity is probably due in part to the fact that many graduate programs in psychology do not offer (or have not offered) courses in multilevel analysis. This chapter is an attempt to meet this need by famili...

This dissertation is the rise of “big data” and the use of analytics to mine that data, which is basically used to analyze the large amount of data.

Using a commercial reservoir properties database, three data analytic models are created and tested to predict ultimate oil and gas recovery efficiencies, using the following methods borrowed from data analytics: linear regression, linear regression with feature selection, and Bayesian network.

This chapter provides a review of existing work to study the impact of big data on the analysis of agriculture and creates many chances in the field of agriculture towards smart farming by using hardware, software.

This paper concentrates on implementation and application of analytics in health care domain by developing and implementing a rule based algorithm to predict the presence of the disease.

This paper presents a location analytics algorithm that takes this influential demographic information into account and analyzes the spots-in combination with geographical data-to recommend locations based on these two factors.

The purpose of this paper to design a customized Java based application which enables analysts to capture the traffic at the bottleneck under the mean field communication environment where a large number of devices are communicating with each other.

Using data analytics and data mining will analyze the factors for churn rate, which will determine the various factors impacting the customers churn rate in telecom industry.

A new visual analytics tool for analysing microarray data with several thousands of attributes that provides an interface that allows the user to explore the variables of these data groups and to extract more information from all the attributes.

This example of the new text mining feature in JMP starts with a multi-step text preparation using techniques like stemming and tokenizing, which is pivotal for the subsequent analysis phase, exploring data clusters and semantics.

This project conducted crime analysis and investigation using the Chicago crime dataset to gain more information and insights on why, when and how criminal activities are carried out and identifies the relationship of socioeconomic indicators like poverty rate and unemployment to crime.

The basics of website analytics and the potential benefits derived from analyzing that data are introduced.

The study investigates the cognitive mechanisms through which data analysts’ commitment to BDA implementation is shaped and affected and draws on the premises of the transactional theory of stress and the literature on the commitment to organizational change.

The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.

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This research paper has addressed about Hadoop tool that how it analyses Big Data and how Big Data Security Analytics is applied to analyze the various threats and securing the business data more accurately.

An overview of two open source big data analytic tools is provided followed by a simple demonstration of application of these tools on transport dataset.

This chapter specifies a brief study of big data techniques to analyze these types of data, and includes a wide study of Hadoops characteristics, Hadoop architecture, advantages ofbig data and big data eco system.

This review paper is discussing about technique how Big Data is analyzed by using the technique of Hadoop and why the Big Data Security Analytics is important to mitigate the security threats to secure the enterprise data more efficiently.

This report discusses work at the crossroads of thermodynamics and data analysis, and demonstrates that statistical mechanical free energy is a multinomial log likelihood.

Network servers are the most popular location for common breaches, such as hacking and information technology (IT) incidents, unauthorized access, theft, loss, and improper disposal.

In implementing the strategy-based structure science review, this review applies objects, institutions, and strategies to the project here and gives professional expertise to recommend rules on how governments and associations can plan underground cybercrime assaults in all businesses.

A tool featuring different exploratory approaches analyze and visualize spatiotemporal data to build and confirm hypotheses and helps the user to find patterns, anomalies and groups in a data set that can not be processed manually.

This project collected Arabic tweets about a certain event that happened in 2018 and showed that the Voting classifier with SVM in Python gives the best performance, in terms of accuracy, precision, and recall, as compared with the other algorithms.

This paper analyzes three different classification methods: classification trees, k-nearest neighbors, and artificial neural networks to determine if there are any performance gaps between the methods.

Aim of this paper is to provide a framework that can analyse the performance of the students and provide a scalable MapReduce-based analytics model using Spark framework that was experimented with a first semester course.

The final report of the Bachelor project at TOPdesk in which the students built an application for gathering usage data from the Topdesk software is presented.

This chapter outlines the key principles of machine learning and predictive analytics and explains the new fundamentals of big data and the evolving technology.

This paper provides a discussion on why the traditional data mining tools cannot be used for big data mining and how the big data is different from data mining.

The objective is to identify an approach using big data analytics framework that can enable scientific research community to process large scale data and support them in identifying clinically significant targets.

The challenges associated with handling big data, including its volume, variety, and velocity, are discussed, and how data engineering and cleaning techniques are crucial for preparing data for analysis.

This research proposes a data analytics model for marketing decision making, which allows reducing the gap between competitors, regardless of their ownership structure.

The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.

The transactional theory of stress is used to elaborate on the role of implementation of BDA in such seemingly contradictory results and proposes that depending on their psychological capital, data analysts may perceive such technological change either as inspirational or impeding.

Big data analytics have the potential to identify efficiencies that can be made in a wide range of sectors, and to lead to innovative new products and services, greater competitiveness and economic growth.

Through the Internet, the organizations managers had access to massive amounts of data and these data are presented in different formats, namely, structured, semi-structured and unstructured. These variety of data is essentially generated from social networks

This course introduces the student to concepts of big data management, database management, data mining techniques and the underlying statistics that support big data analytics and uses the programming language R as the primary tool for analysis.

The DPDA (data preparation for data analytics) project group unites industry users, system vendors and the research community under a common vision of developing a universal, standardized and adaptable tool for process-driven data preparation.

This paper outlines the vision for the next generation data analytics processing systems, revolving around the concept of data-less data analytics, which is based on the premise that current data analytics systems are not fit for purpose.

This paper gives an expansive review of composing on Big Data and Predictive Analytics and gives unpretentious components of urgent thoughts in this rising field.

This survey aims to provide an insightful overview of state-of-the-art development in the Lie group machine learning, to comprehensively understand the state of the state, identify the most appropriate tools for particular applications, and identify directions for future research.

Durch die Digitalisierung im Kontext von Industrie 4.0 steigen die Datenmengen in produzierenden Unternehmen an. Data-Analytics-Methoden erlauben es, automatisiert Muster in diesen Daten zu erkennen, die Rückschlüsse auf die Leistungsfähigkeit des Produktionssystems geben. Der Beitrag beschreibt, wie sich Data-Analytics-Methoden nutzen lassen, um Einflussfaktoren auf die Termintreue zu identifizieren. Für die Evaluation werden die Produktionsdaten eines mittelständischen Prototypenfertigers analysiert.   With digitization in the context of industry 4.0 the data volumes in manufacturing compa...

In the present invention, an analyst starts a quantitative analysis assistance program, and specifies data files A and B obtained by measuring the same sample under different measurement conditions and performing quantitative computations.

This book provides a data-centric view of onlinesocial networks; a topic which has been missing from much of the literature and provides unprecedented opportunities for datatics in the context of social networks.

There is a silent revolution taking place in the world of analytics, and leaders in analytics continue to provide analytical innovations to help organize manage and extract optimum value from these new streams of data.

The analysis data analytics method includes a step for generating simulation data arrived at by adding data variation to the analysis result data within a range that does not affect identification.

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