scholarly journals Pricing Personal Data Based on Data Provenance

2019 ◽  
Vol 9 (16) ◽  
pp. 3388
Author(s):  
Yuncheng Shen ◽  
Bing Guo ◽  
Yan Shen ◽  
Fan Wu ◽  
Hong Zhang ◽  
...  
Keyword(s):  
Polynomial Time ◽  
Hot Spot ◽  
Personal Data ◽  
Exact Algorithm ◽  
Experimental Results ◽  
Approximate Algorithm ◽  
Data Provenance ◽  
Pricing Model ◽  
Industry Research ◽  
Exponential Complexity

Data have become an important asset. Mining the value contained in personal data, making personal data an exchangeable commodity, has become a hot spot of industry research. Then, how to price personal data reasonably becomes a problem we have to face. Based on previous research on data provenance, this paper proposes a novel minimum provenance pricing method, which is to price the minimum source tuple set that contributes to the query. Our pricing model first sets prices for source tuples according to their importance and then makes query pricing based on data provenance, which considers both the importance of the data itself and the relationships between the data. We design an exact algorithm that can calculate the exact price of a query in exponential complexity. Furthermore, we design an easy approximate algorithm, which can calculate the approximate price of the query in polynomial time. We instantiated our model with a select-joint query and a complex query and extensively evaluated its performances on two practical datasets. The experimental results show that our pricing model is feasible.

scholarly journals Research of Personalized Recommendation Technology Based on Knowledge Graphs

2021 ◽  
Vol 11 (15) ◽  
pp. 7104
Author(s):  
Xu Yang ◽  
Ziyi Huan ◽  
Yisong Zhai ◽  
Ting Lin
Keyword(s):  
Neural Network ◽  
Hot Spot ◽  
Experimental Results ◽  
Graph Representation ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Technology ◽  
Data Set ◽  
Model Based ◽  
Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

scholarly journals Computing Simplicial Depth by Using Importance Sampling Algorithm and Its Application

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fanyu Meng ◽  
Wei Shao ◽  
Yuxia Su
Keyword(s):  
Importance Sampling ◽  
Testing Machine ◽  
Exact Algorithm ◽  
Real Data ◽  
Exact Algorithms ◽  
Approximate Algorithm ◽  
Sampling Algorithm ◽  
Simplicial Depth ◽  
Low Efficiency ◽  
Np Problem

Simplicial depth (SD) plays an important role in discriminant analysis, hypothesis testing, machine learning, and engineering computations. However, the computation of simplicial depth is hugely challenging because the exact algorithm is an NP problem with dimension d and sample size n as input arguments. The approximate algorithm for simplicial depth computation has extremely low efficiency, especially in high-dimensional cases. In this study, we design an importance sampling algorithm for the computation of simplicial depth. As an advanced Monte Carlo method, the proposed algorithm outperforms other approximate and exact algorithms in accuracy and efficiency, as shown by simulated and real data experiments. Furthermore, we illustrate the robustness of simplicial depth in regression analysis through a concrete physical data experiment.

Task-Level Learning from Demonstration and Generation of Action Examples for Hierarchical Control Structure

2014 ◽  
Vol 565 ◽  
pp. 194-197
Author(s):  
Anna Gorbenko
Keyword(s):  
Polynomial Time ◽  
Control Structure ◽  
Hierarchical Control ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Robot Learning ◽  
Experimental Results ◽  
Learning From Demonstration ◽  
Selection Of ◽  
Task Level

We consider the problem of the task-level robot learning from demonstration. In particular, we consider a model that uses the hierarchical control structure. For this model, we propose the problem of selection of action examples. We present a polynomial time algorithm for solution of this problem. Also, we consider some experimental results for task-level learning from demonstration.

AN ALGORITHM FOR COMPUTING TYPICAL TESTORS BASED ON ELIMINATION OF GAPS AND REDUCTION OF COLUMNS

2013 ◽  
Vol 27 (08) ◽  
pp. 1350022 ◽  
Author(s):  
ALEXSEY LIAS-RODRÍGUEZ ◽  
GUILLERMO SANCHEZ-DIAZ
Keyword(s):  
Feature Selection ◽  
Supervised Classification ◽  
State Of The Art ◽  
Experimental Results ◽  
Classification Problems ◽  
Fast Implementation ◽  
The Matrix ◽  
Feature Relevance ◽  
Exponential Complexity

Typical testors are useful tools for feature selection and for determining feature relevance in supervised classification problems. Nowadays, computing all typical testors of a training matrix is very expensive; all reported algorithms have exponential complexity depending on the number of columns in the matrix. In this paper, we introduce the faster algorithm BR (Boolean Recursive), called fast-BR algorithm, that is based on elimination of gaps and reduction of columns. Fast-BR algorithm is designed to generate all typical testors from a training matrix, requiring a reduced number of operations. Experimental results using this fast implementation and the comparison with other state-of-the-art related algorithms that generate typical testors are presented.

Identifying Most Preferential Skyline Product Combinations

2017 ◽  
Vol 31 (11) ◽  
pp. 1759022 ◽  
Author(s):  
Zhibang Yang ◽  
Xu Zhou ◽  
Jin Mei ◽  
Yifu Zeng ◽  
Guoqing Xiao ◽  
...  
Keyword(s):  
Discount Rate ◽  
Purchase Intention ◽  
Exact Algorithm ◽  
Approximate Algorithm ◽  
Department Stores ◽  
Skyline Query ◽  
Efficiency And Effectiveness ◽  
Promotion Strategies ◽  
First Time ◽  
High Dimensional Datasets

Nowadays, department stores and online merchants usually develop some price promotion strategies to attract customers and increase their purchase intention. Therefore, it is significant for customers to pick out attractive products and obtain the maximum discount rate. Admittedly, the skyline query is a most useful tool to find out attractive products. However, it does little to help select the product combinations with the maximum discount rate. Motivated by this, we identify an interesting problem, a most preferential skyline product (MPSP) combination discovering problem, which is NP-hard, for the first time in the literature. This problem aims to report all skyline product combinations having the maximum discount rate. Since the exact algorithm for the MPSP is not scalable to large or high-dimensional datasets, we design an approximate algorithm that guarantees the accuracy of the results. The experiment results demonstrate the efficiency and effectiveness of our proposed algorithms.

scholarly journals A pricing model for Big Personal Data

2016 ◽  
Vol 21 (5) ◽  
pp. 482-490 ◽  
Author(s):  
Yuncheng Shen ◽  
Bing Guo ◽  
Yan Shen ◽  
Xuliang Duan ◽  
Xiangqian Dong ◽  
...  
Keyword(s):  
Personal Data ◽  
Pricing Model

An Exact Algorithm for Finding K-Biclique Vertex Partitions of Bipartites

2013 ◽  
Vol 710 ◽  
pp. 687-691
Author(s):  
Pei Qiang Liu
Keyword(s):  
Data Mining ◽  
Information Security ◽  
Computational Biology ◽  
Exact Algorithm ◽  
Experimental Results ◽  
Sufficient Condition ◽  
Partition Problem ◽  
Fast Speed ◽  
Vertex Partition ◽  
Vertex Partitions

Biclustering has been extensively studied in many fields such as data mining, e-commerce, computational biology, information security, etc. Problems of finding bicliques in bipartite, which are variants of biclustering, have received much attention in recent years due to its importance for biclustering. The k-biclique vertex partition problem proposed by Bein et al. is one of finding bicliques problems in bipartite. Its aim is to find k bicliques (kk) such that each vertex of the bipartite occurs in exactly one member of these bicliques. First, we give a sufficient condition of the k-biclique vertex partition problem. Moreover, we present an exact algorithm for finding k-biclique vertex partitions of a bipartite. Finally, we propose a method to generate simulated datasets used to test the algorithm. Experimental results on simulated datasets show that the algorithm can find k-biclique vertex partitions of a bipartite with relatively fast speed.

Evaporative Thermal Performance of Vapor Chambers Under Nonuniform Heating Conditions

2008 ◽  
Vol 130 (12) ◽  
Author(s):  
Je-Young Chang ◽  
Ravi S. Prasher ◽  
Suzana Prstic ◽  
P. Cheng ◽  
H. B. Ma
Keyword(s):  
Heat Flux ◽  
Thermal Resistance ◽  
Thermal Performance ◽  
Hot Spots ◽  
Hot Spot ◽  
Heat Fluxes ◽  
Experimental Results ◽  
Nonuniform Heating ◽  
Test Results ◽  
Uniform Heating

This paper reports the test results of vapor chambers using copper post heaters and silicon die heaters. Experiments were conducted to understand the effects of nonuniform heating conditions (hot spots) on the evaporative thermal performance of vapor chambers. In contrast to the copper post heater, which provides ideal heating, a silicon chip package was developed to replicate more realistic heat source boundary conditions of microprocessors. The vapor chambers were tested for hot spot heat fluxes as high as 746 W/cm2. The experimental results show that evaporator thermal resistance is not sensitive to nonuniform heat conditions, i.e., it is the same as in the uniform heating case. In addition, a model was developed to predict the effective thickness of a sintered-wick layer saturated with water at the evaporator. The model assumes that the pore sizes in the sintered particle wick layer are distributed nonuniformly. With an increase of heat flux, liquid in the larger size pores are dried out first, followed by drying of smaller size pores. Statistical analysis of the pore size distribution is used to calculate the fraction of the pores that remain saturated with liquid at a given heat flux condition. The model successfully predicts the experimental results of evaporative thermal resistance of vapor chambers for both uniform and nonuniform heat fluxes.

Effect of the Thickness of Dies for the Solidification Balance of Permanent Mould Casting AZ91D Magnesium Alloy

2009 ◽  
Vol 610-613 ◽  
pp. 750-753
Author(s):  
Jian Hua Zhao ◽  
Si Yuan Long ◽  
Hui Xu ◽  
Li Yan
Keyword(s):  
Numerical Simulation ◽  
Magnesium Alloy ◽  
Wall Thickness ◽  
Hot Spot ◽  
Experimental Results ◽  
Single Cycle ◽  
Chilling Effect ◽  
Az91d Magnesium Alloy ◽  
Mould Casting ◽  
Cooling Ability

The hot spot forming tendency during solidification of AZ91D magnesium alloy in permanent mould casting with the dies of different wall thickness via numerical simulation with Anycasting software was studied in the present paper. The experimental results showed that in a single cycle casting the increase in the thickness enhanced the cooling ability of the mould and promoted balanced solidification in a certain degree, while in multi-cycle casting, the thickened die-wall gradually lose its localized chilling effect. In contrast, the die with a decreased wall thickness in a certain range was easier to achieve the desired solidification balance.

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