Clustering is one of the main task of data mining, where groups of similar objects are discovered and grouping of similar data as well as outliers detection are performed. Processing of huge datasets requires scalable models of computations and distributed computing environments, therefore efficient parallel clustering methods are required for this purpose. Usually for parallel data analytics the MapReduce processing model is used. But growing computer power of heterogeneous platforms based on graphic processors and FPGA accelerators causes that CUDA and OpenCL models may be interesting alternative to MapReduce. This paper presents comparative analysis of effectiveness of applying MapReduce and CUDA/OpenCL processing models for clustering. We compare different methods of clustering in terms of their possibilities of parallelization using both models of computation. The conclusions indicate directions for further work in this area.
The paper aims to present the concepts of an innovative, integrated visitor support system for distributed entertainment parks, based on Internet of Things (IoT) technology and Big Data analysis. The proposed system will include logistical functions to streamline the customer service process in the centers, offering a profiled tourist product based on unique natural or thematic value. Basing on the modular structure, implementation and integration of the modern IT network, tracking and monitoring technologies, Fog Computing, decision support system and Big Data concepts, it is planned to create a flexible, scalable, reliable, fault tolerant and high security product, corresponding to the expectations of the potential recipients.
Proc. SPIE. 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018
KEYWORDS: Mobile devices, Internet, Analytics, Data modeling, Visualization, Sensors, Computer simulations, Sensor networks, Data acquisition, Smart sensors, Systems modeling, Instrument modeling, Data fusion, Data analysis
Major modern theme parks, consisting of tens or even close to one hundred of tourist attractions, are growing continually more complex. The operation of such parks is becoming increasingly more difficult which makes developing their management systems a very challenging endeavor. More so when many different smart objects like cameras, mobile terminals and various sensors (e.g. RFIDs) are deployed throughout the environment which must be integrated into the system. Moreover, in order for the system to be conformable with the concept of Internet of Things and with the idea of Smart City, it should make an intelligent use of a distributed sensor network and provide smart capabilities, i.e. improving the process of tourists management within a smart territory, e.g. automatic congestion avoidance. Implementing such algorithms which involve Big Data analytics can be a very demanding task, especially when the system is required to be scalable to support a huge number of smart devices. Therefore the ability to accurately simulate theme park and test many different scenarios (e.g. distinct hardware configurations, varying positions of attractions, contrasting tourists’ actions) becomes imperative. In this paper we describe a method for simulating IoT-based theme parks. Presented methodology integrates several models: tourists behavior model, tourist attractions model, theme park model and simulation model. Our goal is to create a computer simulation which is able to efficiently model smart theme park.
The article presents comparative analysis of solutions utilized in real time location systems (RTLS). Particular focus is paid to feasibility of implementing the described systems for the purpose of theme park management. Selected aspects from systems such as: RFID, Infrared, Bluetooth, Wi-Fi, UWB or optical systems are considered. The discussion aims to address the question, which real time location system posses the widest capabilities in the context of their applications in servicing traffic for the tourism industry.