Titlebook: Data Quality and Trust in Big Data; 5th International Wo Hakim Hacid,Quan Z. Sheng,Rui Zhou Conference proceedings 2019 Springer Nature Swi

antenna

Leonardo Augusto de Andrade BarbosaT, we first propose some factors to capture the costs and benefits of a relationship. Then, based on these factors, we propose a trust metric called .; at that point, we propose a trust prediction method, based on Matrix Factorization and apply the context of trust in a mathematical model. Finally,

originality

Studies in the History of Law and Justicee the recommendation quality. For instance, users who used to watch . movie, may be less likely to receive . movie, leading to redundant type of items and decreasing user’s satisfaction. In this paper, we aim to exploit user’s personality type and incorporate it as a primary and enduring domain-inde

低三下四之人

A Novel Data Quality Metric for Minimality, others, the basis for artificial intelligence applications. While the majority of research into data quality refers to the data values of an information system, less research is concerned with schema quality. However, a poorly designed schema negatively impacts the quality of the data, for example,

Desert

Automated Schema Quality Measurement in Large-Scale Information Systems,f the stored data, e.g., it may lead to inconsistencies and anomalies at the data-level. Even if the initial schema had an ideal design, changes during the life cycle can negatively affect the schema quality and have to be tackled. Especially in Big Data environments there are two major challenges:

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使習慣于

配置

,CNR: Cross-network Recommendation Embedding User’s Personality, of the most challenging problems in these systems is the data sparsity problem, i.e., lack of sufficient amount of available users’ interactions data. Recently, cross-network recommender systems with the idea of integrating users’ activities from multiple domain were presented as a successful solut

HALL

Hormones

GIDDY

Data-Intensive Computing Acceleration with Python in Xilinx FPGA, techniques, such as CNN/RNN, over massive chunks of data objects. These services require novel devices with configurable high throughput in I/O (i.e., data-based model training), and uniquely large computation capability (i.e., large number of convolutional operations). In this paper, we present ou