scholarly journals Multi-Agent based MapReduce Model for Efficient Utilization of System Resources

2018 ◽  
Vol 11 (2) ◽  
pp. 504 ◽  
Author(s):  
Heena Kousar ◽  
B.R. Prasad Babu
Keyword(s):  
Social Media ◽  
Real Time ◽  
Historical Data ◽  
Sensor Data ◽  
Forecasting Model ◽  
Agent Based ◽  
Online Processing ◽  
Inflow Forecasting ◽  
System Resource ◽  
State Of Art

<p>Recently with increased adoption of big data, Internet of Things and sensor technology by various organization for provisioning smart intelligent services for various application uses. Data processing on real-time social media and sensor data is been a key area of research in recent times and these data are massive and continuous. Smart application using sensor and social media data can be classified into three class: 1) online processing of streaming data; 2) online processing of historical data; and 3) hybrid processing of both. The existing model are designed considering stream or batch processing. For provisioning real-time processing MapReduce framework using Hadoop framework is considered by state-of-art technique for data inflow forecasting. However, the Hadoop based forecasting model are not efficient in fully utilizing system resource. Agent based MapReduce forecasting model is adopted by state-of-art technique to utilize system efficiently. However, they incurs high computation overhead, thus increase cost of computing cost. To overcome this work present an agent based Data Inflow Forecasting (DIF) model for both stream and non-stream (historical) data by using Multivariate Gaussian Mixture (MGM) model. This work present an Agent based MapReduce (AMR) framework to process data in real-time and utilize system resource efficiently. To provide scalability for processing social media and sensor data DIF-AMR model adopts cloud computing architecture. Experiment are conducted to evaluate performance of DIF-AMR of over existing model shows significant performance improvement in terms of computation time.</p>

scholarly journals Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model

2021 ◽  
Vol 13 (13) ◽  
pp. 7000
Author(s):  
Ulfia A. Lenfers ◽  
Nima Ahmady-Moghaddam ◽  
Daniel Glake ◽  
Florian Ocker ◽  
Daniel Osterholz ◽  
...  
Keyword(s):  
Real Time ◽  
Real World ◽  
City Planning ◽  
Sensor Data ◽  
City Management ◽  
Proof Of Concept ◽  
Agent Based Model ◽  
Time Data ◽  
Agent Based ◽  
Real Time Data

The current trend towards living in big cities contributes to an increased demand for efficient and sustainable space and resource allocation in urban environments. This leads to enormous pressure for resource minimization in city planning. One pillar of efficient city management is a smart intermodal traffic system. Planning and organizing the various kinds of modes of transport in a complex and dynamically adaptive system such as a city is inherently challenging. By deliberately simplifying reality, models can help decision-makers shape the traffic systems of tomorrow. Meanwhile, Smart City initiatives are investing in sensors to observe and manage many kinds of urban resources, making up a part of the Internet of Things (IoT) that produces massive amounts of data relevant for urban planning and monitoring. We use these new data sources of smart cities by integrating real-time data of IoT sensors in an ongoing simulation. In this sense, the model is a digital twin of its real-world counterpart, being augmented with real-world data. To our knowledge, this is a novel instance of real-time correction during simulation of an agent-based model. The process of creating a valid mapping between model components and real-world objects posed several challenges and offered valuable insights, particularly when studying the interaction between humans and their environment. As a proof-of-concept for our implementation, we designed a showcase with bike rental stations in Hamburg-Harburg, a southern district of Hamburg, Germany. Our objective was to investigate the concept of real-time data correction in agent-based modeling, which we consider to hold great potential for improving the predictive capabilities of models. In particular, we hope that the chosen proof-of-concept informs the ongoing politically supported trends in mobility—away from individual and private transport and towards—in Hamburg.

scholarly journals An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

2021 ◽  
Vol 13 (7) ◽  
pp. 175
Author(s):  
Latifah Almuqren ◽  
Fatma S. Alrayes ◽  
Alexandra I. Cristea
Keyword(s):  
Social Media ◽  
Customer Satisfaction ◽  
Real Time ◽  
Prediction Models ◽  
Historical Data ◽  
Arabic Language ◽  
Telecommunication Service ◽  
Customer Data ◽  
Customer Churn ◽  
Twitter Mining

With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company.

scholarly journals Pemantauan Kualitas Udara Terintegrasi dengan Semantic Web Of Thing

2020 ◽  
Vol 4 (2) ◽  
pp. 153
Author(s):  
M. Udin Harun Al Rasyid ◽  
Rengga Asmara ◽  
Hendi Yanuar Setianto
Keyword(s):  
Social Media ◽  
Semantic Web ◽  
Real Time ◽  
Linked Data ◽  
Structured Data ◽  
Sensor Data ◽  
Sensor Web ◽  
Semantic Sensor Web ◽  
Living Organisms ◽  
Set Up

Abstrak: Udara merupakan salah satu sumber daya alam yang paling penting bagi keberadaan makhluk hidup di bumi ini. Semua organisme hidup membutuhkan kualitas udara yang baik bebas dari gas berbahaya untuk melanjutkan hidup mereka. Beberapa organisasi telah membuat sistem monitoring dengan struktur data yang berbeda tanpa adanya standar penyamaan. Di sisi lain, manusia masih membutuhkan waktu untuk menafsirkan data-data sensor untuk mendapatkan informasi. Linked Data merupakan metode untuk merepresentasikan dan menghubungkan data terstruktur pada web. Data terstruktur tersebut diintegrasikan dengan Semantic Sensor Web (SSW) yang dipublikasikan pada beberapa format sehingga mudah dibaca mesin dan dapat dihubungkan ke data terstruktur lainnya. Kemudian, untuk menyajikan data yang aktual, sistem monitoring didesain untuk menerima data secara terus-menerus, diquery secara real-time dan dibagikan melalui sosial media.   Kata kunci: Linked Data, Pemantauan Kualitas Udara, Semantic Web, Sosial Media.   Abstract: Air is one of the most essential natural resources for the existence and survival of the entire life on this planet. all living organisms need good quality of air which is free of harmful gases to continue their life. Some organizations have set up monitoring systems with different data structures without an equalization standard. On the other hand, humans still need time to interpret sensor data to get information. Linked Data is a method for representing and connecting structured data on the web. The structured data is integrated with the Semantic Sensor Web (SSW) which is published in several formats so that it is easy to read and can be connected to other structured data. Then, to present the actual data, the monitoring system is designed to receive data continuously, queried in real time and shared through social media   Keywords: Air Quality Monitoring, Linked Data, Semantic Web, Social Media

scholarly journals Real-Time Human-In-The-Loop Simulation with Mobile Agents, Chat Bots, and Crowd Sensing for Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4356 ◽  
Author(s):  
Stefan Bosse ◽  
Uwe Engel
Keyword(s):  
Social Media ◽  
Social Interaction ◽  
Real Time ◽  
Virtual Worlds ◽  
Real World ◽  
Smart City ◽  
Mobile Agents ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Crowd Sensing

Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods.

Techniques of Viral Marketing

2019 ◽  
Vol 118 (6) ◽  
pp. 97-99
Author(s):  
Arockia Jeyasheela A ◽  
Dr.S. Chandramohan
Keyword(s):  
Social Media ◽  
Real Time ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
Consumer Perception ◽  
Key Word ◽  
The Right ◽  
A Company

This study is discussed about the viral marketing. It is a one of the key success of marketing. This paper gave the techniques of viral marketing. It can be delivered word of mouth. It can be created by both the representatives of a company and consumer (individuals or communities). The right viral message with go to right consumer to the right time. Viral marketing is easy to attract the consumer. It is most important advertising to consumer. It involves consumer perception, organization contribution, blogs, SMO (Social Media Optimize), SEO (Social Engine Optimize). Principles of viral marketing are social profile gathering, Proximity Market, Real time Key word density.

System Resource Management for Distributed Real-Time Systems.

1995 ◽  
Author(s):  
Michael Davis ◽  
Elin L. Klaseen ◽  
Louis C. Schreier ◽  
Alan R. Downing ◽  
Jon Peha
Keyword(s):  
Resource Management ◽  
Real Time ◽  
Real Time Systems ◽  
System Resource ◽  
Time Systems

scholarly journals Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter

2021 ◽  
Author(s):  
Kathrin Eismann
Keyword(s):  
Social Media ◽  
Agent Based ◽  
Social Media Networks ◽  
Agent Based Simulations

AbstractSocial media networks (SMN) such as Facebook and Twitter are infamous for facilitating the spread of potentially false rumors. Although it has been argued that SMN enable their users to identify and challenge false rumors through collective efforts to make sense of unverified information—a process typically referred to as self-correction—evidence suggests that users frequently fail to distinguish among rumors before they have been resolved. How users evaluate the veracity of a rumor can depend on the appraisals of others who participate in a conversation. Affordances such as the searchability of SMN, which enables users to learn about a rumor through dedicated search and query features rather than relying on interactions with their relational connections, might therefore affect the veracity judgments at which they arrive. This paper uses agent-based simulations to illustrate that searchability can hinder actors seeking to evaluate the trustworthiness of a rumor’s source and hence impede self-correction. The findings indicate that exchanges between related users can increase the likelihood that trustworthy agents transmit rumor messages, which can promote the propagation of useful information and corrective posts.

Social Listening to Create Bespoke Customer Experiences: Best Practices for Hospitality Operators

2021 ◽  
pp. 193896552199308
Author(s):  
Kathryn A. LaTour ◽  
Ana Brant
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Real Time ◽  
Brand Image ◽  
Customer Experience ◽  
Current Experience ◽  
Social Listening

Most hospitality operators use social media in their communications as a means to communicate brand image and provide information to customers. Our focus is on a two-way exchange whereby a customer’s social posting is reacted to in real-time by the provider to enhance the customer’s current experience. Using social media in this way is new, and the provider needs to carefully balance privacy and personalization. We describe the process by which the Dorchester Collection Customer Experience (CX) Team approached its social listening program and share lessons to identify best practices for hospitality operators wanting to delight their customers through insights gained from social listening.

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