scholarly journals A Comparison of recommendation algorithms based on use of linked data and cloud

2018 ◽  
Vol 7 (2.6) ◽  
pp. 126
Author(s):  
Ankita Ranjan ◽  
Vinay M
Keyword(s):  
Big Data ◽  
Data Acquisition ◽  
Linked Data ◽  
User Involvement ◽  
Huge Amount ◽  
Amount Of Information ◽  
Recommendation Algorithms ◽  
Cloud Server

Recommendation generation is a critical need in today's time. With the advent of big data and the increasing number of users, generation of most suitable recommendation is essential. There are many issues already associated with recommendations such as data acquisition, scalability, etc.. Moreover, the users today look to get best recommendations at the minimum effort on their side. Thus it becomes difficult to manage such huge amount of information, extract the needed data and present it to the user with least user involvement. In this research, we surveyed some recommendation algorithms and analyze their applications on an open cloud server which uses linked data to generate automated recommendations.

scholarly journals Geolocation for research purposes: geotags as a data source

Inter ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 65-80
Author(s):  
Anna V. Strelnikova ◽  
Anastasia A. Burova
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Applied Research ◽  
Huge Amount ◽  
Ability To Work ◽  
Online Data ◽  
Research Opportunities ◽  
Amount Of Information ◽  
Or Groups ◽  
Data Source

The worldwide spread of social networks provides new opportunities to collect data for various applied research. The geotagging function, which is present in most social networks, is extremely useful in the case of spatial research. For urban researchers, this is more than just an opportunity to collect a huge amount of information about the behavior of people in space, but also to analyze the space itself. However, this type of data has both significant advantages and limitations. The article includes highlighting the most significant characteristics that urban researchers need to consider working with social networks: their volume (the ability to work with them as big data), non-reactivity (they are reproduced by the users themselves) and additional interpretations (they allow us to understand the meanings of certain elements of space for individual individuals or groups). The authors also illustrate the research opportunities provided by online data in combination with geotagging with various empirical examples.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

Impact of Big Data over Telecom Industry

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Muhammad Waqar Khan ◽  
Muhammad Asghar Khan ◽  
Muhammad Alam ◽  
Wajahat Ali
Keyword(s):  
Big Data ◽  
Data Science ◽  
Cell Phones ◽  
Smart Phones ◽  
World Population ◽  
Huge Amount ◽  
Scale Down ◽  
Telecom Industry ◽  
Telecom Sector ◽  
Theoretical Computing

<p>During past few years, data is growing exponentially attracting researchers to work a popular term, the Big Data. Big Data is observed in various fields, such as information technology, telecommunication, theoretical computing, mathematics, data mining and data warehousing. Data science is frequently referred with Big Data as it uses methods to scale down the Big Data. Currently<br />more than 3.2 billion of the world population is connected to internet out of which 46% are connected via smart phones. Over 5.5 billion people are using cell phones. As technology is rapidly shifting from ordinary cell phones towards smart phones, therefore proportion of using internet is also growing. There<br />is a forecast that by 2020 around 7 billion people at the globe will be using internet out of which 52% will be using their smart phones to connect. In year 2050 that figure will be touching 95% of world population. Every device connect to internet generates data. As majority of the devices are using smart phones to<br />generate this data by using applications such as Instagram, WhatsApp, Apple, Google, Google+, Twitter, Flickr etc., therefore this huge amount of data is becoming a big threat for telecom sector. This paper is giving a comparison of amount of Big Data generated by telecom industry. Based on the collected data<br />we use forecasting tools to predict the amount of Big Data will be generated in future and also identify threats that telecom industry will be facing from that huge amount of Big Data.</p>

scholarly journals CoPart: a context-based partitioning technique for big data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sara Migliorini ◽  
Alberto Belussi ◽  
Elisa Quintarelli ◽  
Damiano Carra
Keyword(s):  
Big Data ◽  
Quality Criteria ◽  
Query Execution ◽  
Data Correlation ◽  
Huge Amount ◽  
Programming Paradigm ◽  
Computation Node ◽  
Contextual Dimension ◽  
Partitioning Technique ◽  
The Way

AbstractThe MapReduce programming paradigm is frequently used in order to process and analyse a huge amount of data. This paradigm relies on the ability to apply the same operation in parallel on independent chunks of data. The consequence is that the overall performances greatly depend on the way data are partitioned among the various computation nodes. The default partitioning technique, provided by systems like Hadoop or Spark, basically performs a random subdivision of the input records, without considering the nature and correlation between them. Even if such approach can be appropriate in the simplest case where all the input records have to be always analyzed, it becomes a limit for sophisticated analyses, in which correlations between records can be exploited to preliminarily prune unnecessary computations. In this paper we design a context-based multi-dimensional partitioning technique, called CoPart, which takes care of data correlation in order to determine how records are subdivided between splits (i.e., units of work assigned to a computation node). More specifically, it considers not only the correlation of data w.r.t. contextual attributes, but also the distribution of each contextual dimension in the dataset. We experimentally compare our approach with existing ones, considering both quality criteria and the query execution times.

scholarly journals A Weakly Supervised Academic Search Model Based on Knowledge-Enhanced Feature Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mingying Xu ◽  
Junping Du ◽  
Feifei Kou ◽  
Meiyu Liang ◽  
Xin Xu ◽  
...  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Feature Representation ◽  
Search Model ◽  
Model Based ◽  
Proposed Model ◽  
Cloud Server ◽  
Weakly Supervised ◽  
Internet Of Things Technology ◽  
Academic Search

Internet of Things search has great potential applications with the rapid development of Internet of Things technology. Combining Internet of Things technology and academic search to build academic search framework based on Internet of Things is an effective solution to realize massive academic resource search. Recently, the academic big data has been characterized by a large number of types and spanning many fields. The traditional web search technology is no longer suitable for the search environment of academic big data. Thus, this paper designs academic search framework based on Internet of Things Technology. In order to alleviate the pressure of the cloud server processing massive academic big data, the edge server is introduced to clean and remove the redundancy of the data to form a clean data for further analysis and processing by the cloud server. Edge computing network effectively makes up for the deficiency of cloud computing in the conditions of distributed and high concurrent access, reduces long-distance data transmission, and improves the quality of network user experience. For Academic Search, this paper proposes a novel weakly supervised academic search model based on knowledge-enhanced feature representation. The proposed model can relieve high cost of acquisition of manually labeled data by obtaining a lot of pseudolabeled data and consider word-level interactive matching and sentence-level semantic matching for more accurate matching in the process of academic search. The experimental result on academic datasets demonstrate that the performance of the proposed model is much better than that of the existing methods.

scholarly journals Design of Link Evaluation Method to Improve Reliability based on Linked Open Big Data and Natural Language Processing

2018 ◽  
Vol 7 (3.33) ◽  
pp. 168
Author(s):  
Yonglak SHON ◽  
Jaeyoung PARK ◽  
Jangmook KANG ◽  
Sangwon LEE
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Language Processing ◽  
Confidence Level ◽  
Linked Data ◽  
Evaluation Method ◽  
Data Sets ◽  
Knowledge Based ◽  
Global Data ◽  
Improve Reliability

The LOD data sets consist of RDF Triples based on the Ontology, a specification of existing facts, and by linking them to previously disclosed knowledge based on linked data principles. These structured LOD clouds form a large global data network, which provides a more accurate foundation for users to deliver the desired information. However, it is difficult to identify that, if the presence of the same object is identified differently across several LOD data sets, they are inherently identical. This is because objects with different URIs in the LOD datasets must be different and they must be closely examined for similarities in order to judge them as identical. The aim of this study is that the prosed model, RILE, evaluates similarity by comparing object values of existing specified predicates. After performing experiments with our model, we could check the improvement of the confidence level of the connection by extracting the link value.  

scholarly journals Content profiling and translation scenarios

2016 ◽  
Vol 3 (1) ◽  
pp. 18-37 ◽  
Author(s):  
Sheila Castilho ◽  
Sharon O'Brien
Keyword(s):  
Strategic Management ◽  
End Users ◽  
Huge Amount ◽  
Amount Of Information ◽  
Different Content

Today’s companies are overwhelmed with the need to create a huge amount of content, faster, customized, and for numerous media platforms, in order to support their products. Struggling with managing this amount of information, companies have now realised that the strategic management of multilingual enterprise content has become essential. Strategic management involves profiling content, its uses, its end readers and deciding what should be translated, into which languages, using which translation processes and technology. Profiling enterprise content is necessary in order to maximize the quality of the content and its translation at minimum effort and cost by reducing complexity. By targeting the audience, content could be categorized according to the expectation of the end-users, and so, different translation scenarios can be applied to different content types. This article will discuss the challenges of profiling content within the enterprise, as well as translation scenarios focusing on the decisions that push content in one or another direction.

scholarly journals Applications of Big Data in the Digital India: Opportunities and Challenges

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
Vinay Kumar ◽  
Arpana Chaturvedi
Keyword(s):  
Big Data ◽  
Social Networking ◽  
Exponential Growth ◽  
Social Networking Sites ◽  
Unstructured Data ◽  
Threat Perception ◽  
Data Repository ◽  
Huge Amount ◽  
Real Challenge ◽  
Area Of Application

<div><p><em>With the advent of Social Networking Sites (SNS), volumes of data are generated daily. Most of these data are multimedia type and unstructured with exponential growth. This exponential growth of variety, volume and complexity of structured and unstructured data leads to the concept of big data. Managing big data and harnessing its benefits is a real challenge. With increase in access to big data repository for various applications, security and access control is another aspect that needs to be considered while managing big data. We have discussed area of application of big data, opportunities it provides and challenges that we face in the managing such huge amount of data for various applications. Issues related to security against different threat perception of big data are also discussed. </em></p></div>

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