Large Real-time Data Research Articles

An Improved Stream Processing Access

Migration of Legacy applications into modern Cloud, IOT architecture are challenging tasks and many researchers are showing interest to build modern Real time cloud and IOT based applications like smart cities, Video mining, Health care, Industrial event monitoring and many more for modern human life. Such applications should require efficient online data streaming techniques to process large amount of unstructured online data streams instead of offline. Modern customer centric applications with different verticals are looking for distributed and horizontal data streaming approaches. Many real time streaming approaches are emerging to utilize or process large real-time data by replacing legacy centralized scenarios which are causing more memory utilization, delay and fault tolerance. In this paper we present common models and architectures for real time utilization of cloud and IoT based application stream processing. Utilization of the real-time data of IoT/Cloud applications are possible with collective streaming techniques of network, data processing. In this paper we are focusing on improving stream processing techniques, limitations and future research directions for real-time stream processing

Sistem Informasi Strategi Pemasaran Produk dengan Metode Market Basket Analysis dan Sales Forecasting: Swalayan Kota Makassar

One of the large real time data storage sectors is in the marketing sector in the form of sales transactions, prices, and availability of goods stored in databases in certain supermarkets.The amount of supermarkets with the digital cash register system allow storing sales transaction data. Therefore this research aims to designed an application that contains two statistical analysis methods, namely Market Basket Analysis and Sales Forecasting using shiny dashboard package in Market Basket Analysis Menu shows various features that can facilitate businessactors in optimizing product layout in a store shelves i.e. summary products, rules table, scatter plot, graph, matrix and grouphed, and Parallel coordinats. While the sales forecasting menu shows various features i.e. a description of the number of stock items, the number of items sold, capital, profit, the highest frequency of goods sold, and the value of parameter predictions for the next periods.

A Joint Transmission Power Control and Duty-Cycle Approach for Smart Healthcare System

Emerging revolution in the healthcare has caught the attention of both the industry and academia due to the rapid proliferation in the wearable devices and innovative techniques. In the mean-time, body sensor networks (BSNs) have become the potential candidate in transforming the entire landscape of the medical world. However, large battery lifetime and less power drain are very vital for these resource-constrained sensor devices while collecting the bio signals. Hence, minimizing their charge and energy depletions are still very challenging tasks. It is examined through large real-time data sets that due to the dynamic nature of the wireless channel, the traditional predictive transmission power control (TPC) and a constant transmission power techniques are no more supportive and potential candidates for BSNs. Thus, this paper first proposes a novel joint TPC and duty-cycle adaptation-based framework for pervasive healthcare. Second, an adaptive energy-efficient transmission power control algorithm is developed by adapting the temporal variation in the on-body wireless channel amid static (i.e., standing and walking at a constant speed) and dynamic (i.e., running) body postures. Third, a feedback control-based duty-cycle algorithm is proposed for adjusting the execution period of tasks (i.e., sensing and transmission). Fourth, system-level battery and energy harvesting models are proposed for body sensor nodes by examining the energy depletion of sensing and transmission tasks. It is validated through Monte Carlo experimental analysis that the proposed algorithm saves more energy of 11.5% with reasonable packet loss ratio by adjusting both the transmission power and the duty cycle unlike the conventional constant TPC and PTPC methods.

Comparative Study on Swarm Search Feature Selection for Big Data Stream Mining

In the modern world there is huge development in the field of networking technology which handles huge data at a time. This data can be structured, semi structured or unstructured. To perform efficient mining of valuable information from such type of data the big data technology is gaining importance nowadays. Data mining application is been used in public and private sectors of industry because of its advantage over conventional networking technology to analyze large real time data. Data mining mainly relies on 3 V’s namely, Volume, Varity and Velocity of processing data. Volume refers to the huge amount of data it collects, Velocity refers to the speed at which it process the data and Variety defines that multi-dimensional data which can be numbers, dates, strings, geospatial data, 3D data, audio files, video files, social files, etc. These data which is stored in big data will be from different source at different rate and of different type; hence it will not be synchronized. This is one of the biggest challenges in working with big data. Second challenge is related to mining the valuable and relevant information from such data adhering to 3rd V i.e. Velocity. Speed is highly important as it is associated with cost of processing. On the other hand, mining through the high dimensional data the search space from which an optimal feature subset is determined and it is enhanced in size, guiding to a difficult stipulate in computation. With respect to handle the troubles, the research work is generally based on the high-dimensionality and streaming structure of data feeds in big data, a new inconsequential feature selection methodology that can be used to identify the feature selection methods in the big data. Some of the research work illustrates the different kinds of optimization methods for data stream mining would lead to tremendous changes in big data. This research work is focused on discussing various research methods that focus on finding the efficient feature selection methods which is used to avoid main challenges and produce optimal solutions. The previous methods are described with their advantages and disadvantages, consequently that the additional research works can be focused more. The tentative experiments were on the entire research works in Mat lab simulation surroundings and it is differentiated with everyone to identify the good methodologies beneath the different performance measures.

Windows High-Speed Drawing Technology Research

The display speed of image and large real-time data processing is a huge challenge for realtime system. This paper completed a thorough research on existing drawing technology on the platform of windows; analyzed adaptive characteristics of using the general high-speed drawing techniques for high speed drawing and its merits and demerits. Finally, through a lot of experiments and simulations of high speed drawing process after optimization and combination, tested their drawing performance and efficiency in order to select an appropriate drawing method to develop a high-speed graphics engine for large real-time data.

반도체 공정의 생산성 향상을 위한 실시간 대용량 데이터의 효율적인 저장 기법

반도체 산업이 발전함에 따라 생산 효율을 높이기 위해 무인 자동 생산 공정이 요구되고 있다. 이러한 무인자동화 생산 관리 시스템은 생산성 향상을 위해 생산 공정에서 발생하는 대량의 실시간 데이터 분석 및 관리를 필요로 한다. 따라서 실시간으로 발생하는 대용량 데이터를 저장하기 위한 저장 관리 시스템이 요구된다. 기존의 저장 관리 시스템으로 오라클, MY-SQL, MS-SQL 등의 디스크 기반 DBMS가 있다. 하지만 기존의 디스크 기반 DBMS는 반도체 장비로부터 실시간으로 발생하는 대용량 데이터 처리에 한계가 있다. 본 논문에서는 대용량 데이터를 저비용으로 실시간 저장하기 위해 블록 단위 삽입 트랜잭션을 이용한 압축-합병 저장 기법을 제안한다. 제안 기법은 블록 단위 트랜잭션을 이용하여 실시간 데이터를 빠르게 저장하며 데이터를 압축하고 압축된 데이터를 합병하여 저장하기 때문에 보다 적은 디스크 공간을 사용하여 저장할 수 있다. 따라서 반도체 공정에서 빠르게 발생하는 대용량 데이터를 기존 DBMS보다 빠르게 저장이 가능하고 저장 공간 비용을 감소시킨다. 【Automatic semiconductor manufacturing systems are demanded to improve the efficiency of the semiconductor production process. These systems include the functionalities such as the analysis and management schemes for very large real-time data in order to enhance the productivities. So, it requires the efficient storage management system to store very large real-time data. Traditional database management systems(e.g. Oracle, MY-SQL, MS-SQL) are based on disk. However, previous DBMS's have the limitation on the low storing performance. In this paper, we propose a compress-merge storing method of very large real-time data using insert transaction of a block unit. The proposed method shows better processing performances compare to conventional DBMS's. Also compress-merge method makes it possible that it can store large real-time data on low storage cost. Therefore, the proposed method can be applied to an efficient storage management system in the semiconductor production process.】

Distributed real time database systems: background and literature review

Today's real-time systems (RTS) are characterized by managing large volumes of dispersed data making real-time distributed data processing a reality. Large business houses need to do distributed processing for many reasons, and they often must do it in order to stay competitive. So, efficient database management algorithms and protocols for accessing and manipulating data are required to satisfy timing constraints of supported applications. Therefore, new research in distributed real-time database systems (DRTDBS) is needed to investigate possible ways of applying database systems technology to real-time systems. This paper first discusses the performance issues that are important to DRTDBS, and then surveys the research that has been done so far on the issues like priority assignment policy, commit protocols and optimizing the use of memory in non-replicated/replicated environment pertaining to distributed real time transaction processing. In fact, this study provides a foundation for addressing performance issues important for the management of very large real time data and pointer to other publications in journals and conference proceedings for further investigation of unanswered research questions.