Rough Set-based Intelligent Agent Grid Data Management

AbstractData Management of Astronomy Data is often a laborious task and it is even more challenging for the extraordinary amounts of data expected from the world’s largest radio telescope, Square Kilometre Array. There are overt issues in transferring the voluminous data and the traditional data transfer methods are fragile especially for the data transfer between two continents. To address this, a new data transfer framework is proposed and the data transfer is achieved using two steps: international and local transfers. The efficiency of different end-to-end data transfer tools used in is evaluated on different dataset sizes. Further, a comparative study of two IRIS grid data transfer methods is made to understand each methods’ advantages and disadvantages. This study can be used as a reference for the development of future SKA’s data transfer operations.

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An Intelligent Agent Based Intrusion Detection System Using Fuzzy Rough Set Based Outlier Detection

International Journal of Electronics Signals and Systems ◽

10.47893/ijess.2011.1018 ◽

2011 ◽

pp. 91-94

Author(s):

N. Jaisankar ◽

M Ganapathy ◽

A Kannan ◽

K Anand

Keyword(s):

Intrusion Detection ◽

False Alarm ◽

False Alarm Rate ◽

Outlier Detection ◽

Rough Set ◽

Intelligent Agent ◽

Experimental Result ◽

Fuzzy Rough Set ◽

Data Set ◽

Agent Based

Since existing Intrusion Detection Systems (IDS) including misuse detection and anomoly detection are generally incapable of detecting new type of attacks. However, all these systems are capable of detecting intruders with high false alarm rate. It is an urgent need to develop IDS with very high Detection rate and with low False alarm rate. To satisfy this need we propose a new intelligent agent based IDS using Fuzzy Rough Set based outlier detection and Fuzzy Rough set based SVM. In this proposed model we intorduced two different inteligent agents namely feature selection agent to select the required feature set using fuzzy rough sets and decision making agent manager for making final decision. Moreover, we have introduced fuzzy rough set based outlier detection algorithm to detect outliers. We have also adopted Fuzzy Rough based SVM in our system to classify and detect anomalies efficiently. Finally, we have used KDD Cup 99 data set for our experiment, the experimental result show that the proposed intelligent agent based model improves the overall accuracy and reduces the false alarm rate.

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Smart Grid Data Management

Communication Networks for Smart Grids - Computer Communications and Networks ◽

10.1007/978-1-4471-6302-2_10 ◽

2013 ◽

pp. 265-284 ◽

Cited By ~ 3

Author(s):

Kenneth C. Budka ◽

Jayant G. Deshpande ◽

Marina Thottan

Keyword(s):

Smart Grid ◽

Data Management ◽

Grid Data

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Certificateless Provable Data Possession Scheme for Cloud-Based Smart Grid Data Management Systems

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2017.2761806 ◽

2018 ◽

Vol 14 (3) ◽

pp. 1232-1241 ◽

Cited By ~ 26

Author(s):

Debiao He ◽

Neeraj Kumar ◽

Sherali Zeadally ◽

Huaqun Wang

Keyword(s):

Smart Grid ◽

Data Management ◽

Management Systems ◽

Grid Data ◽

Data Management Systems ◽

Provable Data Possession

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Dynamic Data Citation Service—Subset Tool for Operational Data Management

Data ◽

10.3390/data4030115 ◽

2019 ◽

Vol 4 (3) ◽

pp. 115 ◽

Cited By ~ 1

Author(s):

Schubert ◽

Seyerl ◽

Sack

Keyword(s):

Data Management ◽

Data Cube ◽

Data Repository ◽

Data Provenance ◽

Grid Data ◽

Dynamic Data ◽

Model Calculations ◽

Data Services ◽

Data Citation ◽

Management Concepts

In earth observation and climatological sciences, data and their data services grow on a daily basis in a large spatial extent due to the high coverage rate of satellite sensors, model calculations, but also by continuous meteorological in situ observations. In order to reuse such data, especially data fragments as well as their data services in a collaborative and reproducible manner by citing the origin source, data analysts, e.g., researchers or impact modelers, need a possibility to identify the exact version, precise time information, parameter, and names of the dataset used. A manual process would make the citation of data fragments as a subset of an entire dataset rather complex and imprecise to obtain. Data in climate research are in most cases multidimensional, structured grid data that can change partially over time. The citation of such evolving content requires the approach of “dynamic data citation”. The applied approach is based on associating queries with persistent identifiers. These queries contain the subsetting parameters, e.g., the spatial coordinates of the desired study area or the time frame with a start and end date, which are automatically included in the metadata of the newly generated subset and thus represent the information about the data history, the data provenance, which has to be established in data repository ecosystems. The Research Data Alliance Data Citation Working Group (RDA Data Citation WG) summarized the scientific status quo as well as the state of the art from existing citation and data management concepts and developed the scalable dynamic data citation methodology of evolving data. The Data Centre at the Climate Change Centre Austria (CCCA) has implemented the given recommendations and offers since 2017 an operational service on dynamic data citation on climate scenario data. With the consciousness that the objective of this topic brings a lot of dependencies on bibliographic citation research which is still under discussion, the CCCA service on Dynamic Data Citation focused on the climate domain specific issues, like characteristics of data, formats, software environment, and usage behavior. The current effort beyond spreading made experiences will be the scalability of the implementation, e.g., towards the potential of an Open Data Cube solution.

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