Attribute quality management for dynamic identity and access management

Kunz, M.; Puchta, Alexander; Groll, Sebastian; Fuchs, Ludwig; Pernul, Günther

doi:10.1016/j.jisa.2018.11.004

Cited by 10 publications

(12 citation statements)

References 37 publications

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“…Suppose there are N evaluation objects, M evaluation indicators, the evaluation value of the indicators is expressed as m i ij (i ∈ N, J ∈ M), and the standardized indicator value is written as m i ij . It can be concluded from the relevant literature that for benefit attributes, the larger the indicator value, the better; while for cost attributes, the smaller the indicator value, the better [62,63]. In this paper, the indicators in the state layer and the response layer are benefit attributes, while the indicators in the pressure layer are cost attributes.…”

Section: Evaluation Stepsmentioning

confidence: 90%

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

Sun

Yuan

et al. 2019

Sustainability

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In order to evaluate the atmospheric environment sustainability in the provinces of Northeast China, this paper has constructed a comprehensive evaluation model based on the rough set and entropy weight methods. This paper first constructs a Pressure-State-Response (PSR) model with a pressure layer, state layer and response layer, as well as an atmospheric environment evaluation system consisting of 17 indicators. Then, this paper obtains the weight of different indicators by using the rough set method and conducts equal-width discrete analysis and clustering analysis by using SPSS software. This paper has found that different discrete methods will end up with different reduction sets and multiple indicators sharing the same weight. Therefore, this paper has further introduced the entropy weight method based on the weight solution determined by rough sets and solved the attribute reduction sets of different layers by using the Rosetta software. Finally, this paper has further proved the rationality of this evaluation model for atmospheric environment sustainability by comparing the results with those of the entropy weight method alone and those of the rough set method alone. The results show that the sustainability level of the atmospheric environment in Northeast China provinces has first improved, and then worsened, with the atmospheric environment sustainability level reaching the highest level of 0.9275 in 2014, while dropping to the lowest level of 0.6027 in 2017. Therefore, future efforts should focus on reducing the pressure layer and expanding the response layer. Based on analysis of the above evaluation results, this paper has further offered recommendations and solutions for the improvement of atmospheric environment sustainability in the three provinces of Northeast China.

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Section: Evaluation Stepsmentioning

confidence: 90%

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

Sun

Yuan

et al. 2019

Sustainability

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“…xO=O C}i}m C} Q}Ot TDQM [15] Kunz et al:2019 (Total Data Quality Management) [16] Jeusfeld et al 1998 x=O Q=@v= C}i}m |U=vW VwQ DWQ [17] DiT ria et al:2018 (T he Data W arehouse Quality Methodology)…”

mentioning

confidence: 99%

“…C=aq]= C}i}m |Q}oxR=Ov= IQM [15] Kunz et al 2019 [24] falorisi et al:2003 ISTAT VwQ ISTAT [25] Su and Jin2004 Q@ |vD@t |Q}oxR=Ov= VwQ AMEQ [15] Kunz et al:2019…”

mentioning

confidence: 99%

“…pwYLt C=aq]= C}i}m |@=} RQ= w C}r=ai [26] Scannapieco et al:2004 |=ysDU}U QO C}i}m DaQuinCISData [15] Kunz et al:2019…”

mentioning

confidence: 99%

“…|vw=aD C=aq]= [27] De Amicis and Batini2004 |@=} RQ= |= Q@ |WwQ QAFD [15] [31] [2] u= Q=mty QO xO=O C}i}m x=o}=H |@=} RQ= Determinig the use of data quality metadata (DQM) for 2016 w Towt u=tR=U |D=}L |=y|Q}os}tYD decision making purposes and its impact on [7] with weighted metrics [3] u= Q=mty…”

mentioning

confidence: 99%

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اولویت‌بندی شاخص‌های کیفیت داده‌های پژوهشی بر پایه‌ی روش‌های تصمیم‌گیری با معیارهای چندگانه

فر¹,

ارشادی²,

سبحانی³

2020

مهندسی صنایع و مدیریت

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OvR=OQB|t |tra C=k}kLD |xa=W= w pQDvm '|Qw;`tH x@ xm Ov=|D=aq]= |=yxv=t=U Q=OQwNQ@ |@ wr]t C}i}m K]U R= =D CU= sRq`w[wt u}= C}ty= x@ xHwD =@ w |=ysDU}U C}vt= |rY= RmQtD w OvDUy |U=U= |D=aq]= |}=Q=O l} =yxO=O "OvW=@ xm |}=yxO=O=Qi w =yxO=O C}i}m xRwQt= wQu}= R= [1] "CU=yxO=O x@ xDU@=w '|D=aq]= C}i}m w OvQ=Po|t |D}ty= R=L Q}F -=D Q=mw?Um u; Ov}=Qi QO OvwW|t xDiQo Q=m x@ =yu=tR=U QO =yxO=O "Ovm|t =i}= =yu=tR=U R= |Q=}U@ QO = Q |tyt Q=}U@ Vkv xO=O [2] "OvDUy |D} Q}Ot |Q}os}tYD w Q=mw?Um Ov}=Qi R= C}=tL |= Q@ |D=}L`@vt R}v C}i}m uOQ@ q=@ |= Q@ "CU= xOW KQ]t OQmQ=m |oDU}=W u=wva =@ xO=O C}i}m q=@ |= Q@ wQu}= R= w CU= xOW KQ]t |vwo =vwo |=yVwQ u; |Q}oxR=Ov= w =yxO=O CiQW}B =@ xRwQt= [3] "Ov=xDi=} xaUwD =yVwQ u}= =yu=tR=U QO =yxO=O C}i}m uOQ@ V}=Ri= =@ ?}DQD u}O@ "s}DUy xH=wt =yxO=O R= |O=}R sHL =@ C=aq]= |Qw=: vi w xOW QDxO}J}B =yv; C} Q}Ot Q}N= |=yp=U QO xvt=O w xR=Ov= Q_v R= =yxO=O sHL |O=a@= |= Q=O p}@twD= l} , qFt |m} R}i CqwYLt Ovv=ty |D=aq]= pwYLt l} XNWt = Q u; C}i}m w OyO|t =t x@ = Q |D=aq]= 'u; C}i}m =@ \@DQt xm CU= '|Qw=: vi uwJ |tyt |D=}L w |rY= |= RH= R= |D=aq]= |=yxv=t=U C}i}m "Ovm|t C=tON u}vJty "Ov=xOW p}mWD C=tON w C=aq]= |x= Q= QO Q}oQO O= Qi= w =yxO=O 'Ovm|t Ci=} QO |O=YDk= w |a=tDH= '|ovyQi hrDNt |=yxRwL QO QW@ xm |awvDt |D=aq]= |=yxv=t=U s=tD QO Qo}O |wU R= "CU= |D=aq]= |=yxv=t=U Q@ |vD@t R= |m} Qo}Om} =@ =yQ=Ri=sQv uDN=U xJQ=Bm} |Q=wWO w Q= Ri=sQv Q=DN=U |oO}J}B |Q=wWO =@ = Q |D=aq]= |=yxv=t=U |wU R= C=tON |x= Q= xm CU= |U=U= CqmWt |=yVN@ u} QD|U=U= R= |m} |D=k}kLD |D=aq]= |=yxv=t=U "CU= xDN=U wQx@wQ

show abstract

Contributing to Current Challenges in Identity and Access Management with Visual Analytics

Puchta¹,

Böhm

Pernul

2019

Data and Applications Security and Privacy XXXIII

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Enterprises have embraced identity and access management (IAM) systems as central point to manage digital identities and to grant or remove access to information. However, as IAM systems continue to grow, technical and organizational challenges arise. Domain experts have an incomparable amount of knowledge about an organization's specific settings and issues. Thus, especially for organizational IAM challenges to be solved, leveraging the knowledge of internal and external experts is a promising path. Applying Visual Analytics (VA) as an interactive tool set to utilize the expert knowledge can help to solve upcoming challenges. Within this work, the central IAM challenges with need for expert integration are identified by conducting a literature review of academic publications and analyzing the practitioners' point of view. Based on this, we propose an architecture for combining IAM and VA. A prototypical implementation of this architecture showcases the increased understanding and ways of solving the identified IAM challenges.

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Attribute quality management for dynamic identity and access management

Cited by 10 publications

References 37 publications

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

Study on Development Sustainability of Atmospheric Environment in Northeast China by Rough Set and Entropy Weight Method

اولویت‌بندی شاخص‌های کیفیت داده‌های پژوهشی بر پایه‌ی روش‌های تصمیم‌گیری با معیارهای چندگانه

Contributing to Current Challenges in Identity and Access Management with Visual Analytics

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