scholarly journals Report on the “Advanced Big Data Training School for Life Sciences”, Barcelona 3th-7th September 2018

2019 ◽  
Vol 24 ◽  
pp. e917
Author(s):  
Yen Hoang ◽  
Juliane Pfeil ◽  
Maja Zagorščak ◽  
Axel Y. A. Thieffry ◽  
Eftim Zdravevski ◽  
...  
Keyword(s):  
Big Data ◽  
Computational Biology ◽  
Data Processing ◽  
Life Sciences ◽  
Think Tank ◽  
Training School ◽  
Management Group ◽  
Training Event ◽  
Processing Techniques

The “Advanced Big Data Training School for Life Sciences” took place during September 3-7, 2018, organized by the Data Management Group (DAMA-UPC) at the Technical University of Catalonia (UPC) in Barcelona, Spain. It is the follow-up training school of the first “Big Data Training School for Life Sciences”, held in Uppsala, Sweden, in September 2017, which was defined and structured at the “Think Tank Hackathon”, held in Ljubljana, Slovenia, in February 2018. The aim of this training school was to get participants acquainted with emerging Big Data processing techniques in the field of Computational Biology and Bioinformatics.This article explains in detail the development of the training school, the covered contents and the interaction of the participants within and out of the training event by the student, organizer and lecturer perspective.

scholarly journals Proceedings of the “Think Tank Hackathon’’, Big Data Training School for Life Sciences Follow-up, Ljubljana 6th – 7th February 2018

2018 ◽  
Vol 24 ◽  
pp. e912
Author(s):  
Sabrina K. Schulze ◽  
Živa Ramšak ◽  
Yen Hoang ◽  
Eftim Zdravevski ◽  
Juliane Pfeil ◽  
...  
Keyword(s):  
Big Data ◽  
Life Science ◽  
Life Sciences ◽  
Science Research ◽  
Recent Experience ◽  
Think Tank ◽  
Training School ◽  
Big Data Technologies ◽  
The Cost

On 6th and 7th February 2018, a Think Tank took place in Ljubljana, Slovenia. It was a follow-up of the “Big Data Training School for Life Sciences” held in Uppsala, Sweden, in September 2017. The focus was on identifying topics of interest and optimising the programme for a forthcoming “Advanced” Big Data Training School for Life Science, that we hope is again supported by the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15110). The Think Tank aimed to go into details of several topics that were - to a degree - covered by the former training school. Likewise, discussions embraced the recent experience of the attendees in light of the new knowledge obtained by the first edition of the training school and how it comes from the perspective of their current and upcoming work. The 2018 training school should strive for and further facilitate optimised applications of Big Data technologies in life sciences. The attendees of this hackathon entirely organised this workshop.

scholarly journals An Implementation of Genetic Algorithms in Big Data Processing for Medical Data

2020 ◽  
Vol 9 (3) ◽  
pp. 2215-2217
Keyword(s):  
Genetic Algorithm ◽  
Big Data ◽  
Data Processing ◽  
Medical Data ◽  
Sql Server ◽  
Big Data Processing ◽  
Huge Data ◽  
Cloud Server ◽  
Processing Techniques ◽  
Program Interface

The large amount of real time medical measurement parameters stored in the SQL server needs processing using a specific algorithm. One of the big data processing techniques is available for medical data is Genetic algorithm. The acquired medical parameters are combined together to predict or diagnose the disease using the genetic algorithm. In this paper, the genetic algorithm is used to process the medical measurements data. The medical parameters are posted temporarily in the Representational Structure (REST) Application Program Interface (API) using a gateway protocol MQTT. The genetic algorithm can easily diagnose the disease using the existing stored parameters. The medical parameters of the patient like ECG, Blood pressure and skin temperature are posted frequently in the cloud server for continuous monitoring, and the huge data is also processed using this proposed method.

scholarly journals Report on the “Big Data Training School for Life Sciences”, 18-22 September 2017, Uppsala, Sweden

2018 ◽  
Vol 23 ◽  
pp. e905
Author(s):  
Juliane Pfeil ◽  
Sabrina Kathrin Schulze ◽  
Eftim Zdravevski ◽  
Yen Hoang
Keyword(s):  
Big Data ◽  
Personal Experience ◽  
Life Science ◽  
Life Sciences ◽  
Science Research ◽  
Training School ◽  
Intensive Courses ◽  
Hands On ◽  
Diverse Backgrounds ◽  
The Cost

In September 2017 a "Big Data Training School for Life Sciences" took place in Uppsala, Sweden, jointly organised by EMBnet and the COST Action CHARME (Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research - CA15100). The week programme was divided into hands-on sessions and lectures. In both cases, insights into dealing with big amounts of data were given. This paper describes our personal experience as students’ by providing also some suggestions that we hope can help the organisers as well as other trainers to further increase the efficiency of such intensive courses for students with diverse backgrounds.

scholarly journals Comparison of Data Warehousing and Big Data Principles from an Economic and Technical Standpoint and Their Applicability to Natural Gas Remote Readout Systems

2019 ◽  
Vol 9 (2) ◽  
pp. 45-51
Author(s):  
Vedran Kluk ◽  
Dominika Crnjac Milić ◽  
Zdravko Krpić
Keyword(s):  
Big Data ◽  
Natural Gas ◽  
Data Processing ◽  
New Technologies ◽  
Data Warehousing ◽  
Gas Distribution ◽  
Comparison Results ◽  
Distribution Company ◽  
Gas Consumption ◽  
Processing Techniques

In natural gas remote reading, a large amount of data is collected, posing a problem of storing and processing such data to the companies involved. Two major technologies have recently appeared, becoming a de facto standard in processing large amounts of data, i.e., data warehousing and big data. Each of these technologies provides different data processing techniques. In this paper, serial data processing and parallel data processing are considered in data warehousing and big data, respectively. The paper analyzes the feasibility of implementing new technologies for processing a large amount of data generated by remote reading of natural gas consumption. The research conducted in this paper was made in collaboration with a local natural gas distribution company. A comparison of potential software vendors has shown that Qlik offers the best software package for the requirements provided by the local natural gas distribution company. Comparison results have also shown that other potential vendors also offer software packages of good quality.

scholarly journals An Examination Of Big Data And Blockchain Technology

2019 ◽  
Vol 8 (11) ◽  
pp. 3118-3122
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Blockchain Technology ◽  
Distributed Ledger ◽  
Transaction Process ◽  
Processing Techniques

Blockchain technology is the process of development of bitcoin, the blockchain technology as a distributed ledger of cryptocurrency transactions for digitized, decentralized, trusted and secured manner. The mainstream of blockchain technology is bitcoin, bitcoin concept made with ledger of every single transaction, transactions allows for hashing mechanism to verify the large amounts of data. Big data task requires that large amount of computational space, to generate the terabytes of data for ensuring the successful data processing techniques. The major impact on big data analytics requires more number of data and generated data can be depending upon different sectors from different organizations. This paper presents a state of definition, characteristics, transaction process, and applications, along with discussion of big data analytics are introduced. In blockchain technology covers the flaws of big data in fruitful relationship, with the factors of security, transparency, decentralization and flexibility, so that data to be analyze in different and efficient way for organizations all sizes in data analytics form

Big Data Processing

2020 ◽  
pp. 111-132
Author(s):  
Can Eyupoglu
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Large Scale ◽  
Scale Up ◽  
It Industry ◽  
Governmental Agencies ◽  
Digital World ◽  
Large Scale Data ◽  
International Data ◽  
Processing Techniques

Big data has attracted significant and increasing attention recently and has become a hot topic in the areas of IT industry, finance, business, academia, and scientific research. In the digital world, the amount of generated data has increased. According to the research of International Data Corporation (IDC), 33 zettabytes of data were created in 2018, and it is estimated that the amount of data will scale up more than five times from 2018 to 2025. In addition, the advertising sector, healthcare industry, biomedical companies, private firms, and governmental agencies have to make many investments in the collection, aggregation, and sharing of enormous amounts of data. To process this large-scale data, specific data processing techniques are used rather than conventional methodologies. This chapter deals with the concepts, architectures, technologies, and techniques that process big data.

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