scholarly journals Real-Time Analysis of Online Sources for Supporting Business Intelligence Illustrated with Bitcoin Investments and IoT Smart-Meter Sensors in Smart Cities

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1101 ◽  
Author(s):  
Iván García-Magariño ◽  
Moustafa M. Nasralla ◽  
Shah Nazir
Keyword(s):  
Data Management ◽  
Real Time ◽  
Business Intelligence ◽  
Smart Cities ◽  
Online Information ◽  
Smart Meter ◽  
Time Data ◽  
Real Time Analysis ◽  
Real Time Data ◽  
Historic Data

Real-time data management analytics involve capturing data in real-time and, at the same time, processing data in a light way to provide an effective real-time support. Real-time data management analytics are key for supporting decisions of business intelligence. The proposed approach covers all these phases by (a) monitoring online information from websites with Selenium-based software and incrementally conforming a database, and (b) incrementally updating summarized information to support real-time decisions. We have illustrated this approach for the investor–company field with the particular fields of Bitcoin cryptocurrency and Internet-of-Things (IoT) smart-meter sensors in smart cities. The results of 40 simulations on historic data showed that one of the proposed investor strategies achieved 7.96% of profits on average in less than two weeks. However, these simulations and other simulations of up to 69 days showed that the benefits were highly variable in these two sets of simulations (respective standard deviations were 24.6% and 19.2%).

scholarly journals Management of Emergency Services in Lombardy during COVID-19 epidemic using Business Intelligence

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
G M Sechi ◽  
M Migliori ◽  
G Dassi ◽  
A Pagliosa ◽  
R Bonora ◽  
...  
Keyword(s):  
Emergency Medical Services ◽  
Real Time ◽  
Business Intelligence ◽  
First Aid ◽  
Time Data ◽  
Lombardy Region ◽  
Daily Number ◽  
Intelligence System ◽  
Emergency Medical ◽  
Real Time Data

Abstract Background In Italy on the 20th of February, the first Italian patient was tested positive for Coronavirus Disease 2019 (COVID-19) in the Lombardy region. The Regional Emergency Medical Services (EMS) Trust (Azienda Regionale Emergenza Urgenza, AREU) of the Lombardy region decided to apply a Business Intelligence (BI) System to take timely decisions on the management of EMS and to monitor the spread of the disease in the region in order to better respond to the outbreak. Methods Since the beginning of the COVID-19 outbreak, AREU developed a BI System to track the daily number of first aid requests received from 1.1.2. (Public Safety Answering Point 1). BI evaluates the number of requests that have been classified as respiratory and/or infectious episodes during the telephone dispatch interview. Moreover, BI analyses the pattern of the epidemic, identifying the numerical trend of episodes in each municipality (increasing, stable, decreasing). Currently, AREU is still implementing the BI as the epidemic is still ongoing. Results In the Lombardy region on the 20th of February the number of the first aid requests for respiratory and/or infectious episodes were 314. This figure increased sharply during the month of February and March reaching its peak on the 16th of March with 1537 episodes. In the area around Bergamo, this number experienced a greater rise compared to the rest of the Lombardy territory, going from 74 episodes on the 20th of February to 694 on the 13th of March. Therefore, AREU decided to reallocate in the territory the resources (ambulances and human resources) based on the real-time data elaborated by the BI system. Conclusions The BI System has been of paramount importance in taking timely decisions on the management of EMS during the COVID-19 outbreak in the Lombardy region. Indeed, BI can be usefully applied to promptly identify the trend of the COVID-19 epidemic and, consequently, make informed decisions to improve the response to the outbreak. Key messages The Emergency Medical Services Trust of the Lombardy region applied a Business Intelligence System to promptly respond to the outbreak of COVID-19 and reallocate the resources based on real-time data. AREU used a Business Intelligence System to track the daily number of first aid requests that have been classified as respiratory and/or infectious episodes during the telephone dispatch interview.

scholarly journals Kajian Pendekatan Binary Log dalam Change Data Capture

2017 ◽  
pp. 11
Author(s):  
Muhammad Febrian Rachmadhan Amri ◽  
I Made Sukarsa ◽  
I Ketut Adi Purnawan
Keyword(s):  
Data Management ◽  
Real Time ◽  
Data Warehouse ◽  
Data Capture ◽  
Problem Solution ◽  
Time Data ◽  
Quality Of Data ◽  
Data Manipulation ◽  
Change Data Capture ◽  
Real Time Data

The online business era causes the form of transactions to occur so quickly that the information stored in the data warehouse becomes invalid. Companies are required to have a strong system, which is a system that is real time in order to be able to perform data loading into the media repository that resides on different hosts in the near-real time. Data Warehouse is used as a media repository of data that has the nature of subject-oriented, integrated, time-variant, and is fixed. Data Warehouse can be built into real time management with the advantages possessed and utilize Change Data Capture. Change Data Capture (CDC) is a technique that can be used as problem solution to build real time data warehousing (RTDW). The binary log approach in change data capture is made to record any data manipulation activity that occurs at the OLTP level and is managed back before being stored into the Data Warehouse (loading process). This can improve the quality of data management so that the creation of the right information, because the information available is always updated. Testing shows that Binary Log approach in Change Data Capture (BinlogCDC) is able to generate real time data management, valid current information, dynamic communication between systems, and data management without losing any information from data manipulation.

scholarly journals Real-time data analysis model at LHC and connections to other experiments and fields

2020 ◽  
Vol 245 ◽  
pp. 11005
Author(s):  
Arantza Oyanguren
Keyword(s):  
Real Time ◽  
Hadron Collider ◽  
Proton Collision ◽  
Analysis Model ◽  
Time Analysis ◽  
Time Data ◽  
Proton Proton ◽  
Real Time Analysis ◽  
Real Time Data ◽  
Proton Proton Collision

With the upcoming increase of proton-proton collision rates at the Large Hadron Collider (LHC) experiments, and the corresponding increase of data volumes, real-time analysis becomes a key ingredient to be able to analyse and select the interesting data within the available computing resources. In this talk I will review the main features of the techniques followed by the ATLAS, CMS and LHCb experiments. Similar challenges have to be faced in other fields, such as astronomy and cosmology, and I will comment about them.

scholarly journals Mining Iot Data for Next Generation Smart Cities

2019 ◽  
Vol 8 (6) ◽  
pp. 259-262
Keyword(s):  
Real Time ◽  
Crucial Role ◽  
Data Analytics ◽  
Data Science ◽  
Smart Cities ◽  
Classification Algorithms ◽  
Time Data ◽  
New Era ◽  
Real Time Data ◽  
Iot Devices

Convergence of Cloud, IoT, Networking devices and Data science has ignited a new era of smart cities concept all around us. The backbone of any smart city is the underlying infrastructure involving thousands of IoT devices connected together to work in real time. Data Analytics can play a crucial role in gaining valuable insights into the volumes of data generated by these devices. The objective of this paper is to apply some most commonly used classification algorithms to a real time dataset and compare their performance on IoT data. The performance summary of the algorithms under test is also tabulated

scholarly journals Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2994 ◽  
Author(s):  
Bhagya Silva ◽  
Murad Khan ◽  
Changsu Jung ◽  
Jihun Seo ◽  
Diyan Muhammad ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

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