The Internet and DSS – Massive, Real-Time Data Availability Is Changing the DSS Landscape

AbstrakJaringan internet yang saat ini di gunakan untuk penyimpanan data atau halaman informasi pada website menjadi rentan terhadap serangan, untuk meninkatkan keamanan website dan jaringannya, di butuhkan honeypot yang mampu menangkap serangan yang di lakukan pada jaringan lokal dan internet. Untuk memudahkan administrator mengatasi serangan digunakanlah pengelompokan serangan dengan metode K-Means untuk mengambil ip penyerang. Pembagian kelompok pada titik cluster akan menghasilkan output ip penyerang.serangan di ambil sercara realtime dari log yang di miliki honeypot dengan memanfaatkan MHN.Abstract The number of internet networks used for data storage or information pages on the website is vulnerable to attacks, to secure the security of their websites and networks, requiring honeypots that are capable of capturing attacks on local networks and the internet. To make it easier for administrators to tackle attacks in the use of attacking groupings with the K-Means method to retrieve the attacker ip. Group divisions at the cluster point will generate the ip output of the attacker. The strike is taken as realtime from the logs that have honeypot by utilizing the MHN.

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Remote Monitoring Technologies: Real-time Data Displayed on the Internet

10.13031/2013.37276 ◽

2011 ◽

Author(s):

John Nowatzki ◽

Ganesh Bora

Keyword(s):

Real Time ◽

Remote Monitoring ◽

The Internet ◽

Time Data ◽

Real Time Data ◽

Monitoring Technologies

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Performance analysis for similarity data fusion model for enabling time series indexing in internet of things applications

PeerJ Computer Science ◽

10.7717/peerj-cs.500 ◽

2021 ◽

Vol 7 ◽

pp. e500

Author(s):

Mina Younan ◽

Essam H. Houssein ◽

Mohamed Elhoseny ◽

Abd El-mageid Ali

Keyword(s):

Time Series ◽

Performance Analysis ◽

Internet Of Things ◽

Real Time ◽

The Internet ◽

Fusion Model ◽

Time Data ◽

Iot Applications ◽

Real Time Data ◽

Smart Things

The Internet of Things (IoT) has penetrating all things and objects around us giving them the ability to interact with the Internet, i.e., things become Smart Things (SThs). As a result, SThs produce massive real-time data (i.e., big IoT data). Smartness of IoT applications bases mainly on services such as automatic control, events handling, and decision making. Consumers of the IoT services are not only human users, but also SThs. Consequently, the potential of IoT applications relies on supporting services such as searching, retrieving, mining, analyzing, and sharing real-time data. For enhancing search service in the IoT, our previous work presents a promising solution, called Cluster Representative (ClRe), for indexing similar SThs in IoT applications. ClRe algorithms could reduce similar indexing by O(K − 1), where K is number of Time Series (TS) in a cluster. Multiple extensions for ClRe algorithms were presented in another work for enhancing accuracy of indexed data. In this theme, this paper studies performance analysis of ClRe algorithms, proposes two novel execution methods: (a) Linear execution (LE) and (b) Pair-merge execution (PME), and studies sorting impact on TS execution for enhancing similarity rate for some ClRe extensions. The proposed execution methods are evaluated with real examples and proved using Szeged-weather dataset on ClRe 3.0 and its extensions; where they produce representatives with higher similarities compared to the other extensions. Evaluation results indicate that PME could improve performance of ClRe 3.0 by = 20.5%, ClRe 3.1 by = 17.7%, and ClRe 3.2 by = 6.4% in average.

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Real-time data analytics in healthcare using the Internet of Things

Real-Time Data Analytics for Large Scale Sensor Data ◽

10.1016/b978-0-12-818014-3.00002-4 ◽

2020 ◽

pp. 37-50

Author(s):

Satya Narayan Sahu ◽

Maheswata Moharana ◽

Purna Chandra Prusti ◽

Shanta Chakrabarty ◽

Fahmida Khan ◽

...

Keyword(s):

Internet Of Things ◽

Real Time ◽

Data Analytics ◽

The Internet ◽

Time Data ◽

Real Time Data ◽

The Internet Of Things

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Real-Time GIS for Health Disaster Response in the Largest Archipelagic Country

Prehospital and Disaster Medicine ◽

10.1017/s1049023x19003686 ◽

2019 ◽

Vol 34 (s1) ◽

pp. s162-s162

Author(s):

Ahmad Alim

Keyword(s):

Real Time ◽

Disaster Response ◽

White Paper ◽

Response Monitoring ◽

The Internet ◽

Time Data ◽

Internet Connection ◽

Green Approach ◽

Real Time Data ◽

Response Team

Introduction:Besides being located on the Pacific Ring of Fire, Indonesia is the largest archipelago country in the world. Some parts of the country are not very accessible. It raises difficulties in controlling and monitoring a disaster response mission remotely in real-time. Muhammadiyah, the Indonesian non-governmental organization (NGO) that has been responding to disaster since 1919, used Geographic Information Systems (GIS) for Health Disaster Response (HDR) in the Lombok Earthquake 2018, in cooperation with ESRI Indonesia, as one alternative to disaster response controlling and monitoring.Aim:To show the benefit of using real-time GIS for HDR in an archipelago country.Methods:While responding to the disaster in Lombok, the Muhammadiyah Health Disaster Response Team was collecting data of patient, medication, problem, need, location, and resource with computers and smartphones, inputting the data that was forwarded to the ArcGIS platform. The Health Disaster Response Team coordinator and Muhammadiyah Board monitored and analyzed the health response through the GIS dashboard in Yogyakarta, 652km far from Lombok Island.Results:Using real-time GIS has been useful for disaster response. It was efficient by cutting flight and other transport costs, connected by the internet, and communicative by graphic and map dashboard. It was a green approach since it was paperless, and analysis-friendly by real-time data compilation and computation.Discussion:One of the big gaps in disaster response monitoring seems to be real-time data. Especially in an archipelago country, it is costly, time-consuming, and resource consuming. Daily big data may be frustrating and can become “white paper syndrome.” One of the good approaches to that is GIS Web services although it must be realized that the internet connection in a rural area can be another challenge. It can be solved by in-gadget data memory that can be delivered while the internet connection is available.

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Internet Map Services and Weather Data

Handbook of Research on Innovations in Database Technologies and Applications ◽

10.4018/978-1-60566-242-8.ch033 ◽

2009 ◽

pp. 300-306

Author(s):

Maurie Caitlin Kelly ◽

Bernd J. Haupt ◽

Ryan E. Baxter

Keyword(s):

Real Time ◽

Driving Forces ◽

Weather Data ◽

The Internet ◽

Time Data ◽

Geospatial Information ◽

Emerging Trends ◽

Real Time Data ◽

The Face ◽

Future Direction

Internet map services (IMSs) are redefining the ways in which people interact with geospatial information system (GIS) data. The driving forces behind this trend are the pervasiveness of GIS software and the emerging popularity of mobile devices and navigation systems utilizing GPS (Global Positioning System), as well as the ever-increasing availability of geospatial data on the Internet. These forces are also influencing the increasing need for temporal or real-time data. One trend that has become particularly promising in addressing this need is the development of IMS. IMS is changing the face of data access and creating an environment in which users can view, download, and query geospatial and real-time data into their own desktop software programs via the Internet. In this section, the authors will provide a brief description of the evolution and system architecture of an IMS, identify some common challenges related to implementing an IMS, and provide an example of how IMSs have been developed using real-time weather data from the National Digital Forecast Database (NDFD). Finally, the authors will briefly touch on some emerging trends in IMS, as well as discuss the future direction of IMS and their role in providing access to real-time data.

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