Real-Time Data Visualization Using Business Intelligence Techniques in Small and Medium Enterprises for Making a Faster Decision on Sales Data

2022 ◽  
pp. 189-198
Author(s):  
Owes Quruny Shubho ◽  
Zerin Nasrin Tumpa ◽  
Walid Ibna Rakib Dipto ◽  
Md Rasel Alam
Keyword(s):  
Real Time ◽  
Data Visualization ◽  
Business Intelligence ◽  
Small And Medium Enterprises ◽  
Time Data ◽  
Sales Data ◽  
Real Time Data ◽  
Medium Enterprises

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.

Augmented Reality Based Real-Time Data Visualization Concept for Ultra-Efficient Car Prototype

2017 ◽  
Author(s):  
Peter Wozniak ◽  
Oliver Vauderwange ◽  
Nicolas Javahiraly ◽  
Dan Curticapean
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Data Visualization ◽  
Time Data ◽  
Real Time Data

scholarly journals Parallelisation of a Cache-Based Stream-Relation Join for a Near-Real-Time Data Warehouse

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1299
Author(s):  
M. Asif Naeem ◽  
Habib Khan ◽  
Saad Aslam ◽  
Noreen Jamil
Keyword(s):  
Real Time ◽  
Data Warehouse ◽  
Cost Model ◽  
Parallel Execution ◽  
Stream Data ◽  
Time Data ◽  
Sales Data ◽  
Master Data ◽  
Real Time Data ◽  
Two Phases

Near real-time data warehousing is an important area of research, as business organisations want to analyse their businesses sales with minimal latency. Therefore, sales data generated by data sources need to reflect immediately in the data warehouse. This requires near-real-time transformation of the stream of sales data with a disk-based relation called master data in the staging area. For this purpose, a stream-relation join is required. The main problem in stream-relation joins is the different nature of inputs; stream data is fast and bursty, whereas the disk-based relation is slow due to high disk I/O cost. To resolve this problem, a famous algorithm CACHEJOIN (cache join) was published in the literature. The algorithm has two phases, the disk-probing phase and the stream-probing phase. These two phases execute sequentially; that means stream tuples wait unnecessarily due to the sequential execution of both phases. This limits the algorithm to exploiting CPU resources optimally. In this paper, we address this issue by presenting a robust algorithm called PCSRJ (parallelised cache-based stream relation join). The new algorithm enables the execution of both disk-probing and stream-probing phases of CACHEJOIN in parallel. The algorithm distributes the disk-based relation on two separate nodes and enables parallel execution of CACHEJOIN on each node. The algorithm also implements a strategy of splitting the stream data on each node depending on the relevant part of the relation. We developed a cost model for PCSRJ and validated it empirically. We compared the service rates of both algorithms using a synthetic dataset. Our experiments showed that PCSRJ significantly outperforms CACHEJOIN.

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%).

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