On Optimizing the k-Ward Micro-aggregation Technique for Secure Statistical Databases

2006 ◽  
pp. 324-335 ◽  
Author(s):  
Ebaa Fayyoumi ◽  
B. John Oommen
Keyword(s):  
Statistical Databases ◽  
Aggregation Technique

scholarly journals Proportional lumpability and proportional bisimilarity

2021 ◽  
Author(s):  
Andrea Marin ◽  
Carla Piazza ◽  
Sabina Rossi
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Upper And Lower Bounds ◽  
General Definition ◽  
Performance Indices ◽  
State Aggregation ◽  
Structural Regularity ◽  
Original Definition ◽  
Aggregation Technique ◽  
Definition Of

AbstractIn this paper, we deal with the lumpability approach to cope with the state space explosion problem inherent to the computation of the stationary performance indices of large stochastic models. The lumpability method is based on a state aggregation technique and applies to Markov chains exhibiting some structural regularity. Moreover, it allows one to efficiently compute the exact values of the stationary performance indices when the model is actually lumpable. The notion of quasi-lumpability is based on the idea that a Markov chain can be altered by relatively small perturbations of the transition rates in such a way that the new resulting Markov chain is lumpable. In this case, only upper and lower bounds on the performance indices can be derived. Here, we introduce a novel notion of quasi-lumpability, named proportional lumpability, which extends the original definition of lumpability but, differently from the general definition of quasi-lumpability, it allows one to derive exact stationary performance indices for the original process. We then introduce the notion of proportional bisimilarity for the terms of the performance process algebra PEPA. Proportional bisimilarity induces a proportional lumpability on the underlying continuous-time Markov chains. Finally, we prove some compositionality results and show the applicability of our theory through examples.

Query evaluability in statistical databases

1990 ◽  
Vol 2 (4) ◽  
pp. 425-430 ◽  
Author(s):  
F.M. Malvestuto ◽  
M. Moscarini
Keyword(s):  
Statistical Databases

Security of statistical databases

1981 ◽  
Vol 6 (1) ◽  
pp. 95-112 ◽  
Author(s):  
Jan Schlörer
Keyword(s):  
Statistical Databases

scholarly journals A Genetic Algorithm Approach for Group Recommender System Based on Partial Rankings

2018 ◽  
Vol 29 (1) ◽  
pp. 653-663 ◽  
Author(s):  
Ritu Meena ◽  
Kamal K. Bharadwaj
Keyword(s):  
Genetic Algorithm ◽  
Recommender Systems ◽  
Rank Aggregation ◽  
Partial Ranking ◽  
Novel Approach ◽  
Kendall Tau Distance ◽  
Ultimate Objective ◽  
Aggregation Technique ◽  
The Individual ◽  
Group Recommender

Abstract Many recommender systems frequently make suggestions for group consumable items to the individual users. There has been much work done in group recommender systems (GRSs) with full ranking, but partial ranking (PR) where items are partially ranked still remains a challenge. The ultimate objective of this work is to propose rank aggregation technique for effectively handling the PR problem. Additionally, in real applications, most of the studies have focused on PR without ties (PRWOT). However, the rankings may have ties where some items are placed in the same position, but where some items are partially ranked to be aggregated may not be permutations. In this work, in order to handle problem of PR in GRS for PRWOT and PR with ties (PRWT), we propose a novel approach to GRS based on genetic algorithm (GA) where for PRWOT Spearman foot rule distance and for PRWT Kendall tau distance with bucket order are used as fitness functions. Experimental results are presented that clearly demonstrate that our proposed GRS based on GA for PRWOT (GRS-GA-PRWOT) and PRWT (GRS-GA-PRWT) outperforms well-known baseline GRS techniques.

A New Type of Cluster-Ion Source for Thin Film Deposition

1990 ◽  
Vol 206 ◽  
Author(s):  
Hellmut Haberland ◽  
Martin Karrais ◽  
Martin Mall
Keyword(s):  
Electron Impact ◽  
Plane Surface ◽  
Thin Film Deposition ◽  
Ion Source ◽  
Film Deposition ◽  
Cluster Ions ◽  
Cluster Ion ◽  
Impact Ionisation ◽  
Aggregation Technique ◽  
High Degree

ABSTRACTAtoms are gas discharge sputtered from a solid target. They are condensed to form clusters using the gas aggregation technique. An intense beam of clusters of all solid materials can be obtained. Up to 80 % of the clusters can be ionised without using additional electron impact ionisation. Total deposition rates vary between 1 and 1000 Å per second depending on cluster diameter, which can be varied between 3 and 500 nm. Thin films of Al, Cu, and Mo have been produced so far. For non accelerated beams a weakly adhering mostly coulored deposit is obtained. Accelerating the cluster ions this changes to a strongly adhering film, having a shiny metallic appearance, and a very sharp and plane surface as seen in an electron microscope. The advantages compared to Kyoto ICB-method are: easy control of the cluster size, no electron impact ionisation, high degree of ionisation, and sputtering is used instead of thermal evaporation, which allows the use of high melting point materials.

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