scholarly journals Legislative prediction with dual uncertainty minimization from heterogeneous information

2016 ◽  
Vol 10 (2) ◽  
pp. 107-120
Author(s):  
Yu Cheng ◽  
Ankit Agrawal ◽  
Huan Liu ◽  
Alok Choudhary
Keyword(s):  
Heterogeneous Information ◽  
Dual Uncertainty ◽  
Uncertainty Minimization

Modeling and design of role engineering in development of access control for dynamic information systems

2013 ◽  
Vol 61 (3) ◽  
pp. 569-579 ◽  
Author(s):  
A. Poniszewska-Marańda
Keyword(s):  
Information Systems ◽  
Dynamic Information ◽  
Minimal Set ◽  
Distributed Information ◽  
Heterogeneous Information ◽  
System Protection ◽  
Level Model ◽  
Model Independent ◽  
Role Concept ◽  
High Level

Abstract Nowadays, the growth and complexity of functionalities of current information systems, especially dynamic, distributed and heterogeneous information systems, makes the design and creation of such systems a difficult task and at the same time, strategic for businesses. A very important stage of data protection in an information system is the creation of a high level model, independent of the software, satisfying the needs of system protection and security. The process of role engineering, i.e. the identification of roles and setting up in an organization is a complex task. The paper presents the modeling and design stages in the process of role engineering in the aspect of security schema development for information systems, in particular for dynamic, distributed information systems, based on the role concept and the usage concept. Such a schema is created first of all during the design phase of a system. Two actors should cooperate with each other in this creation process, the application developer and the security administrator, to determine the minimal set of user’s roles in agreement with the security constraints that guarantee the global security coherence of the system.

Valuation mapping of hazards of the crisis natural and economic situations

2017 ◽  
Vol 929 (11) ◽  
pp. 40-49
Author(s):  
N.E. Krasnoshtanova ◽  
A.K. Cherkashin
Keyword(s):  
Spatial Data ◽  
Heterogeneous Information ◽  
Secondary Use ◽  
Raster Maps ◽  
Irkutsk Region ◽  
Homogeneous Network ◽  
Surrounding Areas ◽  
Integral Assessment ◽  
Multidimensional Statistics

An innovative technique for the secondary use of cartographic information for creating assessment hazard maps of crisis natural and economic situations and an integral assessment of the sustainability economic development and the quality of live is presented. Valuation mapping was carried for the Slyudyansky district of the Irkutsk region. A database has been created for homogeneous network of plots, which contains heterogeneous information about the nature and socio-economic environment of the district. Spatial data were processed using multidimensional statistics on the base of reliability theory models. An account of the environmental correction for each plots is an important aspect of the proposed technique of assessing and creating through maps. This makes it possible to reduce the evaluation function to an invariant form common to all locations and it is used in through way to create assessment maps for natural and socio-economic objects. As a result, a series of raster maps of through thematic content was made. The map of integral hazard of emergence of economic crisis situation displays the lowest hazard values for the territories of settlements and their surrounding areas, as well as areas along roads and railways. Additionally it allocates undeveloped valley of taiga rivers, advanced for economic use, primarily for recreational purposes.

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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