Personalized Online Analytical Processing in Big Data Context Using User Profile and Search Context

2017 ◽  
Vol 8 (4) ◽  
pp. 67-80 ◽  
Author(s):  
Menaceur Sadek ◽  
Makhlouf Derdour ◽  
Bouramoul Abdelkrim
Keyword(s):  
Big Data ◽  
Large Data ◽  
User Profile ◽  
Relevant Information ◽  
Multidimensional Analysis ◽  
Online Analytical Processing ◽  
Data Sets ◽  
Process Data ◽  
Analytical Processing ◽  
Data Context

This article is part of the field of analysis and personalization of large data sets (Big Data). This aspect of analysis and customization has become a major issue that has generated a lot of questions in recent years. Indeed, it is difficult for inexperienced or casual users to extract relevant information in a Big Data context, for volume, the velocity and the variability of data make it difficult for the user to capture, manage and process data by methods and traditional tools. In this article, the authors propose a new approach for personalizing OLAP analysis in a Big Data context by using context and user profile. The proposed approach is based on five complementary layers namely: Extern layer, layer for the formulation of the contexts defined in the system, profiling and querying layer and layer for the construction of personalized OLAP cubes and a final one for multidimensional analysis cubes. The conducted experiment has shown that taking context and user profile into account improves the results of online analytical processing in the context of Big Data.

Online Analytical Processing and Data-Cube Technologies

2008 ◽  
pp. 627-635
Author(s):  
Lixin Fu ◽  
Wen-Chen Hu
Keyword(s):  
Decision Support ◽  
Large Data ◽  
Data Cube ◽  
Online Analytical Processing ◽  
Data Sets ◽  
Data Warehouses ◽  
Strategic Decisions ◽  
Executive Information Systems ◽  
Executive Management ◽  
Analytical Processing

Since the late ’80s and early ’90s, database technologies have evolved to a new level of applications: online analytical processing (OLAP), where executive management can make quick and effective strategic decisions based on knowledge in terms of queries against large amounts of stored data. Some OLAP systems are also regarded as decision support systems (DSSs) or executive information systems (EIS). The traditional, well-established online transactional processing (OLTP) systems such as relational database management systems (RDBMS) mainly deal with mission-critical daily transactions. Typically, there are a large number of short, simple queries such as lookups, insertions, and deletions. The main focus is transaction throughput, consistency, concurrency, and failure recovery issues. OLAP systems, on the other hand, are mainly analytical and informational. OLAP systems are usually closely coupled with data warehouses, which can contain very large data sets that may include historical data as well as data integrated from different departments and geographical locations. So the sizes of data warehouses are usually significantly larger than common OLTP systems. In addition, the workloads of OLAP are quite different from those of traditional transaction systems: The queries are unpredictable and much more complicated. For example, an OLAP query could be, “For each type of car and each manufacturer, list market share change in terms of car sales between the first quarter of 2005 and the first quarter of 2006.” The purpose of these queries is not for the daily operational maintenance of data; instead, it is for deeper knowledge from data used for decision support.

scholarly journals ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 146 ◽  
Author(s):  
Guanming Wu ◽  
Eric Dawson ◽  
Adrian Duong ◽  
Robin Haw ◽  
Lincoln Stein
Keyword(s):  
Experimental Data ◽  
Data Analysis ◽  
Graphical Models ◽  
High Throughput ◽  
Interaction Network ◽  
Large Data ◽  
Relevant Information ◽  
Data Sets ◽  
Data Types ◽  
Biological Studies

High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databases. This app provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use this app to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models. We believe our app will give researchers substantial power to analyze intrinsically noisy high-throughput experimental data to find biologically relevant information.

scholarly journals Bought and Sold: Exploring the Effects of Big Data on User Agency and Commodification

2021 ◽  
Author(s):  
Kristia M. Pavlakos
Keyword(s):  
Social Sciences ◽  
Big Data ◽  
Data Privacy ◽  
Large Data ◽  
Data Sets ◽  
Privacy Regulation ◽  
Scholarly Literature ◽  
User Interests ◽  
The Social ◽  
The Relationship

Big Data1is a phenomenon that has been increasingly studied in the academy in recent years, especially in technological and scientific contexts. However, it is still a relatively new field of academic study; because it has been previously considered in mainly technological contexts, more attention needs to be drawn to the contributions made in Big Data scholarship in the social sciences by scholars like Omar Tene and Jules Polonetsky, Bart Custers, Kate Crawford, Nick Couldry, and Jose van Dijk. The purpose of this Major Research Paper is to gain insight into the issues surrounding privacy and user rights, roles, and commodification in relation to Big Data in a social sciences context. The term “Big Data” describes the collection, aggregation, and analysis of large data sets. While corporations are usually responsible for the analysis and dissemination of the data, most of this data is user generated, and there must be considerations regarding the user’s rights and roles. In this paper, I raise three main issues that shape the discussion: how users can be more active agents in data ownership, how consent measures can be made to actively reflect user interests instead of focusing on benefitting corporations, and how user agency can be preserved. Through an analysis of social sciences scholarly literature on Big Data, privacy, and user commodification, I wish to determine how these concepts are being discussed, where there have been advancements in privacy regulation and the prevention of user commodification, and where there is a need to improve these measures. In doing this, I hope to discover a way to better facilitate the relationship between data collectors and analysts, and user-generators. 1 While there is no definitive resolution as to whether or not to capitalize the term “Big Data”, in capitalizing it I chose to conform with such authors as boyd and Crawford (2012), Couldry and Turow (2014), and Dalton and Thatcher (2015), who do so in the scholarly literature.

A Detailed Study on Classification Algorithms in Big Data

2020 ◽  
pp. 30-46
Author(s):  
Saranya N. ◽  
Saravana Selvam
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Linear Regression ◽  
Comprehensive Evaluation ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Classification Methods ◽  
Computing Science ◽  
Data Collections

After an era of managing data collection difficulties, these days the issue has turned into the problem of how to process these vast amounts of information. Scientists, as well as researchers, think that today, probably the most essential topic in computing science is Big Data. Big Data is used to clarify the huge volume of data that could exist in any structure. This makes it difficult for standard controlling approaches for mining the best possible data through such large data sets. Classification in Big Data is a procedure of summing up data sets dependent on various examples. There are distinctive classification frameworks which help us to classify data collections. A few methods that discussed in the chapter are Multi-Layer Perception Linear Regression, C4.5, CART, J48, SVM, ID3, Random Forest, and KNN. The target of this chapter is to provide a comprehensive evaluation of classification methods that are in effect commonly utilized.

Uncertainty-Based Clustering Algorithms for Large Data Sets

2018 ◽  
pp. 1-33 ◽  
Author(s):  
B. K. Tripathy ◽  
Hari Seetha ◽  
M. N. Murty
Keyword(s):  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Mining Machine ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
New Algorithms

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.

Electronic Records Management - An Old Solution to a New Problem

Big Data ◽  
2016 ◽  
pp. 2249-2274
Author(s):  
Chinh Nguyen ◽  
Rosemary Stockdale ◽  
Helana Scheepers ◽  
Jason Sargent
Keyword(s):  
Big Data ◽  
Rapid Development ◽  
Large Data ◽  
Large Data Sets ◽  
Electronic Records ◽  
Future Research ◽  
Records Management ◽  
Data Sets ◽  
Interactive Nature ◽  
Electronic Records Management

The rapid development of technology and interactive nature of Government 2.0 (Gov 2.0) is generating large data sets for Government, resulting in a struggle to control, manage, and extract the right information. Therefore, research into these large data sets (termed Big Data) has become necessary. Governments are now spending significant finances on storing and processing vast amounts of information because of the huge proliferation and complexity of Big Data and a lack of effective records management. On the other hand, there is a method called Electronic Records Management (ERM), for controlling and governing the important data of an organisation. This paper investigates the challenges identified from reviewing the literature for Gov 2.0, Big Data, and ERM in order to develop a better understanding of the application of ERM to Big Data to extract useable information in the context of Gov 2.0. The paper suggests that a key building block in providing useable information to stakeholders could potentially be ERM with its well established governance policies. A framework is constructed to illustrate how ERM can play a role in the context of Gov 2.0. Future research is necessary to address the specific constraints and expectations placed on governments in terms of data retention and use.

An Investigation Into the Efficacy of Deep Learning Tools for Big Data Analysis in Health Care

2020 ◽  
pp. 1826-1838
Author(s):  
Rojalina Priyadarshini ◽  
Rabindra K. Barik ◽  
Chhabi Panigrahi ◽  
Harishchandra Dubey ◽  
Brojo Kishore Mishra
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Large Data ◽  
Big Data Analysis ◽  
Optimization Techniques ◽  
Data Sets ◽  
Learning Tools ◽  
Healthcare Applications ◽  
Proper Training ◽  
Future Prediction

This article describes how machine learning (ML) algorithms are very useful for analysis of data and finding some meaningful information out of them, which could be used in various other applications. In the last few years, an explosive growth has been seen in the dimension and structure of data. There are several difficulties faced by conventional ML algorithms while dealing with such highly voluminous and unstructured big data. The modern ML tools are designed and used to deal with all sorts of complexities of data. Deep learning (DL) is one of the modern ML tools which are commonly used to find the hidden structure and cohesion among these large data sets by giving proper training in parallel platforms with intelligent optimization techniques to further analyze and interpret the data for future prediction and classification. This article focuses on the use of DL tools and software which are used in past couple of years in various areas and especially in the area of healthcare applications.

Research Challenges in Big Data Analytics

2017 ◽  
pp. 83-99
Author(s):  
Sivamathi Chokkalingam ◽  
Vijayarani S.
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
New Technologies ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Sets ◽  
Data Types ◽  
Customer Preferences ◽  
Research Challenges

The term Big Data refers to large-scale information management and analysis technologies that exceed the capability of traditional data processing technologies. Big Data is differentiated from traditional technologies in three ways: volume, velocity and variety of data. Big data analytics is the process of analyzing large data sets which contains a variety of data types to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. Since Big Data is new emerging field, there is a need for development of new technologies and algorithms for handling big data. The main objective of this paper is to provide knowledge about various research challenges of Big Data analytics. A brief overview of various types of Big Data analytics is discussed in this paper. For each analytics, the paper describes process steps and tools. A banking application is given for each analytics. Some of research challenges and possible solutions for those challenges of big data analytics are also discussed.

Applying the K-Means Algorithm in Big Raw Data Sets with Hadoop and MapReduce

2016 ◽  
pp. 1220-1243
Author(s):  
Ilias K. Savvas ◽  
Georgia N. Sofianidou ◽  
M-Tahar Kechadi
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
File System ◽  
Large Data ◽  
Large Data Sets ◽  
Distributed File System ◽  
Data Sets ◽  
Raw Data ◽  
Hadoop Distributed File System ◽  
Access To Data

Big data refers to data sets whose size is beyond the capabilities of most current hardware and software technologies. The Apache Hadoop software library is a framework for distributed processing of large data sets, while HDFS is a distributed file system that provides high-throughput access to data-driven applications, and MapReduce is software framework for distributed computing of large data sets. Huge collections of raw data require fast and accurate mining processes in order to extract useful knowledge. One of the most popular techniques of data mining is the K-means clustering algorithm. In this study, the authors develop a distributed version of the K-means algorithm using the MapReduce framework on the Hadoop Distributed File System. The theoretical and experimental results of the technique prove its efficiency; thus, HDFS and MapReduce can apply to big data with very promising results.

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