Applying Big Data Analytics to Monitor Tourist Flow for the Scenic Area Operation Management

Considering the rapid development of the tourist leisure industry and the surge of tourist quantity, insufficient information regarding tourists has placed tremendous pressure on traffic in scenic areas. In this paper, the author uses the Big Data technology and Call Detail Record (CDR) data with the mobile phone real-time location information to monitor the tourist flow and analyse the travel behaviour of tourists in scenic areas. By collecting CDR data and implementing a modelling analysis of the data to simultaneously reflect the distribution of tourist hot spots in Beijing, tourist locations, tourist origins, tourist movements, resident information, and other data, the results provide big data support for alleviating traffic pressure at tourist attractions and tourist routes in the city and rationally allocating traffic resources. The analysis shows that the big data analysis method based on the CDR data of mobile phones can provide real-time information about tourist behaviours in a timely and effective manner. This information can be applied for the operation management of scenic areas and can provide real-time big data support for “smart tourism”.

Download Full-text

Light-Weight, Self-Powered Sensor Based on Triboelectric Nanogenerator for Big Data Analytics in Sports

Electronics ◽

10.3390/electronics10192322 ◽

2021 ◽

Vol 10 (19) ◽

pp. 2322

Author(s):

Xiaofei Ma ◽

Xuan Liu ◽

Xinxing Li ◽

Yunfei Ma

Keyword(s):

Big Data ◽

Real Time ◽

Data Analytics ◽

Mechanical Energy ◽

Rapid Development ◽

Big Data Analytics ◽

Triboelectric Nanogenerator ◽

Light Weight ◽

Table Tennis ◽

Self Powered

With the rapid development of the Internet of Things (IoTs), big data analytics has been widely used in the sport field. In this paper, a light-weight, self-powered sensor based on a triboelectric nanogenerator for big data analytics in sports has been demonstrated. The weight of each sensing unit is ~0.4 g. The friction material consists of polyaniline (PANI) and polytetrafluoroethylene (PTFE). Based on the triboelectric nanogenerator (TENG), the device can convert small amounts of mechanical energy into the electrical signal, which contains information about the hitting position and hitting velocity of table tennis balls. By collecting data from daily table tennis training in real time, the personalized training program can be adjusted. A practical application has been exhibited for collecting table tennis information in real time and, according to these data, coaches can develop personalized training for an amateur to enhance the ability of hand control, which can improve their table tennis skills. This work opens up a new direction in intelligent athletic facilities and big data analytics.

Download Full-text

Big data analytics architecture for real-time traffic control

2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) ◽

10.1109/mtits.2017.8005605 ◽

2017 ◽

Cited By ~ 36

Author(s):

Sasan Amini ◽

Ilias Gerostathopoulos ◽

Christian Prehofer

Keyword(s):

Big Data ◽

Real Time ◽

Traffic Control ◽

Data Analytics ◽

Big Data Analytics ◽

Real Time Traffic

Download Full-text

Cyber-Physical Production Networks, Real-Time Big Data Analytics, and Cognitive Automation in Sustainable Smart Manufacturing

Journal of Self-Governance and Management Economics ◽

10.22381/jsme8220203 ◽

2020 ◽

Vol 8 (2) ◽

pp. 21

Keyword(s):

Big Data ◽

Real Time ◽

Data Analytics ◽

Big Data Analytics ◽

Smart Manufacturing ◽

Production Networks ◽

Cognitive Automation

Download Full-text

Engineering Scalable Distributed Services for Real-Time Big Data Analytics

2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService) ◽

10.1109/bigdataservice.2017.22 ◽

2017 ◽

Author(s):

Sahar Jambi ◽

Kenneth M. Anderson

Keyword(s):

Big Data ◽

Real Time ◽

Data Analytics ◽

Big Data Analytics ◽

Distributed Services

Download Full-text

Modeling Big Data Analytics with a Real-Time Executable Specification Language

Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-8505-5.ch014 ◽

2015 ◽

pp. 289-312

Author(s):

Amir A. Khwaja

Keyword(s):

Big Data ◽

Data Processing ◽

Real Time ◽

Data Analytics ◽

Big Data Analytics ◽

Exception Handling ◽

Specification Language ◽

Big Data Processing ◽

Business Decisions ◽

Concurrent Processes

Big data explosion has already happened and the situation is only going to exacerbate with such a high number of data sources and high-end technology prevalent everywhere, generating data at a frantic pace. One of the most important aspects of big data is being able to capture, process, and analyze data as it is happening in real-time to allow real-time business decisions. Alternate approaches must be investigated especially consisting of highly parallel and real-time computations for big data processing. The chapter presents RealSpec real-time specification language that may be used for the modeling of big data analytics due to the inherent language features needed for real-time big data processing such as concurrent processes, multi-threading, resource modeling, timing constraints, and exception handling. The chapter provides an overview of RealSpec and applies the language to a detailed big data event recognition case study to demonstrate language applicability to big data framework and analytics modeling.

Download Full-text

Modeling Big Data Analytics with a Real-Time Executable Specification Language

Big Data ◽

10.4018/978-1-4666-9840-6.ch021 ◽

2016 ◽

pp. 418-440

Author(s):

Amir A. Khwaja

Keyword(s):

Big Data ◽

Data Processing ◽

Real Time ◽

Data Analytics ◽

Big Data Analytics ◽

Exception Handling ◽

Specification Language ◽

Big Data Processing ◽

Business Decisions ◽

Concurrent Processes

Download Full-text

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

Advances in Computational Intelligence and Robotics - Decision Support Systems and Industrial IoT in Smart Grid, Factories, and Cities ◽

10.4018/978-1-7998-7468-3.ch004 ◽

2021 ◽

pp. 63-90

Author(s):

Suresh P. ◽

Keerthika P. ◽

Sathiyamoorthi V. ◽

Logeswaran K. ◽

Manjula Devi R. ◽

...

Keyword(s):

Big Data ◽

Real Time ◽

Smart City ◽

Traffic Management ◽

Smart Cities ◽

Big Data Analytics ◽

Streaming Data ◽

City Development ◽

Time Data ◽

Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

Download Full-text