scholarly journals Coping with big: Does big data lead to ‘bigger’ innovation?

2016 ◽  
Vol 4 (1) ◽  
pp. 1-3
Author(s):  
Marko Torkkeli ◽  
Anne-Laure Mention ◽  
João José Pinto Ferreira
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Historical Data ◽  
Critical Issue ◽  
Time Data ◽  
The Future ◽  
Long Time ◽  
Spring Issue ◽  
First Time ◽  
Minority Report

This Spring Issue will discuss about big data and multiple aspects of its usability and applicability. Many of us have seen blockbuster movies Back to the future (premiere in 1985), The Terminator (1984) or Minority report (2002). The unifying element of the above mentioned movies is that manuscripts are introducing a superior competitive advantage factor. The protagonists create an advantage by having either real-time data (sometimes from the future) or all relevant (big and historical) data with enormous computing capacity over competitors. A bit after first two of those movies premiered, NASA scientists Cox and Ellsworth (1997) published an article where term ‘big data’ appeared first time (Press, 2014). Intelligence needs to be topped up in a way to create advantage. Data has been there for a long time, in all forms and sizes. It is applied in almost single every business sector and it is getting faster in sense of usability. The data storage capacity has been exponentially increasing over time, but the usability of this wealth of data remains a critical issue.(...)

scholarly journals Context Aware Quick Sensor Service (CQSS) to Remote Patients

2019 ◽  
Vol 8 (9) ◽  
pp. 768-774
Keyword(s):  
Data Storage ◽  
Processing Speed ◽  
Historical Data ◽  
Threshold Value ◽  
Context Aware ◽  
Cyclic Patterns ◽  
Long Time ◽  
The Times ◽  
Remote Health

Using sensors in healthcare can greatly improve the quality of life, especially for elderly patients. The data from the sensors of the patients is constantly monitored for abnormalities at a server. Whenever this data crosses a threshold value, the information is notified to the corresponding doctor. The doctor can then take the necessary action. However an inspection of historical data has shown that some conditions of patients have cyclic patterns and the medications are often repeated. The proposed system is designed to assist the doctor in diagnosis by retrieving those patterns. We have compared the times taken for receiving responses from the two different systems and a significant amount of improvement was noticed. We have introduced a Dynamic Context Aware Technique (DCAT) which can improve the quality of 24 hour monitoring patient. This paper presents the design and implementation of a system based on DCAT using SAMSUNG GEAR S (Heart rate monitor sensor.The backend remote centralized computation and data storage can decreases the workload of the remote health care provider by avoiding of sending the identical and similar cases data to the doctors. This improves the processing speed and also gives solutions in case of the unavailability of doctors in some cases. Experimental results based on real datasets show that our system is highly efficient and scalable to a long time monitoring patients.

Design of Computer Multimedia Intelligent Platform Using Big Data Analysis

2022 ◽  
Author(s):  
Zhenna Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Multimedia Information ◽  
Real Time ◽  
Data Storage ◽  
Big Data Analysis ◽  
Intelligent Vehicle ◽  
Time Data ◽  
Vehicle Operation ◽  
Big Data Technology

This exploration aims to transfer, process and store multimedia information timely, accurately and comprehensively through computer comprehensive technology processing, and organically combine various elements under the background of big data analysis, so as to form a complete intelligent platform design for multimedia information processing and application. In this exploration, the intelligent vehicle monitoring system is taken as an example. Data acquisition, data transmission, real-time data processing, data storage and data application are realized through the real-time data stream processing framework of [Formula: see text] of big data technology. Data interaction is realized through Spring, Spring MVC, VUE front-end framework, and Ajax asynchronous communication local update technology. Data storage is achieved through Red is cache database, and intelligent vehicle operation supervision system is achieved through multimedia information technology processing. Its purpose is to manage the vehicle information, real-time monitor the running state of the vehicle and give an alarm when there are some problems. The basic functions of vehicle operation monitoring and management system based on big data analysis are realized. The research on the design of vehicle operation monitoring and management system based on big data analysis shows that big data technology can be applied to the design of computer multimedia intelligent platform, and provides a reference case for the development of computer multimedia intelligent platform based on big data analysis.

scholarly journals Comparative study of NoSQL databases for big data storage

2018 ◽  
Vol 7 (2.6) ◽  
pp. 83
Author(s):  
Gourav Bathla ◽  
Rinkle Rani ◽  
Himanshu Aggarwal
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Relational Databases ◽  
Large Scale ◽  
Technology Selection ◽  
Business Organizations ◽  
Nosql Databases ◽  
Real Solutions ◽  
Adequate Knowledge ◽  
Long Time

Big data is a collection of large scale of structured, semi-structured and unstructured data. It is generated due to Social networks, Business organizations, interaction and views of social connected users. It is used for important decision making in business and research organizations. Storage which is efficient to process this large scale of data to extract important information in less response time is the need of current competitive time. Relational databases which have ruled the storage technology for such a long time seems not suitable for mixed types of data. Data can not be represented just in the form of rows and columns in tables. NoSQL (Not only SQL) is complementary to SQL technology which can provide various formats for storage that can be easily compatible with high velocity,large volume and different variety of data. NoSQL databases are categorized in four techniques- Column oriented, Key Value based, Graph based and Document oriented databases. There are approximately 120 real solutions existing for these categories; most commonly used solutions are elaborated in Introduction section. Several research works have been carried out to analyze these NoSQL technology solutions. These studies have not mentioned the situations in which a particular data storage technique is to be chosen. In this study and analysis, we have tried our best to provide answer on technology selection based on specific requirement to the reader. In previous research, comparisons amongNoSQL data storage techniques have been described by using real examples like MongoDB, Neo4J etc. Our observation is that if users have adequate knowledge of NoSQL categories and their comparison, then it is easy for them to choose best suitable category and then real solutions can be selected from this category.

scholarly journals Long-Time Data Storage: Relevant Time Scales

Challenges ◽  
2011 ◽  
Vol 2 (1) ◽  
pp. 19-36 ◽  
Author(s):  
Miko C. Elwenspoek
Keyword(s):  
Data Storage ◽  
Time Scales ◽  
Time Data ◽  
Long Time

scholarly journals A Benchmark for Suitability of Alluxio over Spark

2020 ◽  
Vol 10 (1) ◽  
pp. 245-250
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
Storage Systems ◽  
Distributed Storage ◽  
Storage System ◽  
Large Data ◽  
Time Data ◽  
Big Data Applications ◽  
Access To Data

Big data applications play an important role in real time data processing. Apache Spark is a data processing framework with in-memory data engine that quickly processes large data sets. It can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools. Spark’s in-memory processing cannot share data between the applications and hence, the RAM memory will be insufficient for storing petabytes of data. Alluxio is a virtual distributed storage system that leverages memory for data storage and provides faster access to data in different storage systems. Alluxio helps to speed up data intensive Spark applications, with various storage systems. In this work, the performance of applications on Spark as well as Spark running over Alluxio have been studied with respect to several storage formats such as Parquet, ORC, CSV, and JSON; and four types of queries from Star Schema Benchmark (SSB). A benchmark is evolved to suggest the suitability of Spark Alluxio combination for big data applications. It is found that Alluxio is suitable for applications that use databases of size more than 2.6 GB storing data in JSON and CSV formats. Spark is found suitable for applications that use storage formats such as parquet and ORC with database sizes less than 2.6GB.

scholarly journals TELECOM BIG DATA FOR URBAN TRANSPORT ANALYSIS – A CASE STUDY OF SPLIT-DALMATIA COUNTY IN CROATIA

2017 ◽  
Vol XLII-4/W3 ◽  
pp. 5-10 ◽  
Author(s):  
M. Baučić ◽  
N. Jajac ◽  
M. Bućan
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Privacy ◽  
New Technologies ◽  
Big Data Analytics ◽  
Urban Transport ◽  
Time Data ◽  
Customer Data ◽  
Gis Applications ◽  
International Transport

Today, big data has become widely available and the new technologies are being developed for big data storage architecture and big data analytics. An ongoing challenge is how to incorporate big data into GIS applications supporting the various domains. International Transport Forum explains how the arrival of big data and real-time data, together with new data processing algorithms lead to new insights and operational improvements of transport. Based on the telecom customer data, the Study of Tourist Movement and Traffic in Split-Dalmatia County in Croatia is carried out as a part of the “IPA Adriatic CBC//N.0086/INTERMODAL” project. This paper briefly explains the big data used in the study and the results of the study. Furthermore, this paper investigates the main considerations when using telecom customer big data: data privacy and data quality. The paper concludes with GIS visualisation and proposes the further use of big data used in the study.

A Data-Driven Analysis of the Paradigm Shift From Permanent to Contractual Recruitment

2021 ◽  
Vol 11 (2) ◽  
pp. 1-16
Author(s):  
Shyla ◽  
Vishal Bhatnagar ◽  
Raju Ranjan ◽  
Arushi Jain
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Paradigm Shift ◽  
High Volume ◽  
Large Datasets ◽  
Streaming Data ◽  
Sensor Data ◽  
Time Data ◽  
Contract Workers ◽  
Programming Framework

Big data is the high-volume, high-variety data which involves data storage, data management, and data analysis that presents a wide view of business possibility for real-time data, sensor data, and streaming data over the web. Big data relies on technology, analysis, and mythology where technology deals with computation power, accuracy, linking, and large datasets; analysis is to find patterns by analyzing large datasets to discover hidden information; and mythology is the wrong beliefs that large datasets give insight knowledge of data that is not obtained by small datasets. In this paper, the authors analyzed the major benefits the organization see from employing contract workers using map reduce programming framework.

scholarly journals Telco Data Analytics using Open-Source Data Pipeline: Detailed Architecture and Technology Stack

2021 ◽  
Vol 9 (10) ◽  
pp. 1717-1725
Author(s):  
Abirami T
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Load Balancing ◽  
Open Source ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Time Data ◽  
Data Pipeline ◽  
Real Time Data

Abstract: Open-source technology has influenced data analytics at each step from data storage to data analysis, and visualization. Open source for telco big data analytics enables sharp insights by enhancing problem discoverability and solution feasibility. This research paper talks about different technology stacks using open source for telco big data analytics that are used to deploy various tools including data collection, data storage, data processing, data analysis, and data visualization. This open source pipeline micro-services architecture built with modular technology stack and orchestrated by Kubernetes, can ingest data from multiple sources, process real-time data and provide business and network intelligence. Major idea of using open source technology in our architecture is to reduce cost and manage easily. Kubernetes is an industry adopted open source container orchestrator that offers fault-tolerance, application scaling, and load-balancing. The results can be displayed on the intuitive open source dashboard like Grafana for telecom operators. Our architecture is flexible and can be easily customized based on the telecommunication industry needs. Using the proposed architecture, the telecommunication sectors can get quick decision making with nearly 30% lower CapEX which is made possible using COTS hardware. Index Terms: Big data analytics, Data pipeline architecture, Open Source technologies, Real-time data processing, Faulttolerance, Load-balancing, Kubernetes, BDA, Open source dashboard

scholarly journals First Cavernicolous Population of Crumomyia Parentela (Séguy, 1963) and a Review of Cave-Dwelling Fauna of Sphaeroceridae (Diptera) in the Czech Republic

2014 ◽  
Vol 63 (2) ◽  
Author(s):  
Jindřich Roháček
Keyword(s):  
Czech Republic ◽  
The Czech Republic ◽  
The Future ◽  
Long Time ◽  
The Alps ◽  
Glacial Relict ◽  
First Time ◽  
Cave Habitat

Abstract Crumomyia parentela (Séguy, 1963) (Sphaeroceridae) is recorded from the Czech Republic for the first time, based on specimens collected in the cave Cyrilka in the Moravskoslezské Beskydy Mts. They are affiliated to subspecies C. p. alpicola (Roháček, 1980) but because of more reduced eyes and shorter wings than have other specimens known from the Alps and Carpathians this population is concluded to have survived as a glacial relict in the cave habitat for a long time. The cavernicolous fauna of Sphaeroceridae in the Czech Republic is surveyed and its members (15 species) are classified according to their affiliation to the cave milieu. No trogloxenous or troglobiont species were found; most species (12) are hemitroglophilous and only 3 are troglophilous, viz. Crumomyia p. alpicola, Herniosina bequaerti (Villeneuve, 1917) and Terrilimosina racovitzai (Bezzi, 1911). It is presupposed that some additional hemitroglophilous, possibly one more troglophilous but no troglobiont species could be found in cave systems in the Czech Republic in the future.

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