Titlebook: Data Management Technologies and Applications; 10th International C Alfredo Cuzzocrea,Oleg Gusikhin,Christoph Quix Conference proceedings 2

Racket

書目名稱Data Management Technologies and Applications影響因子(影響力)




書目名稱Data Management Technologies and Applications影響因子(影響力)學(xué)科排名




書目名稱Data Management Technologies and Applications網(wǎng)絡(luò)公開度




書目名稱Data Management Technologies and Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Data Management Technologies and Applications被引頻次




書目名稱Data Management Technologies and Applications被引頻次學(xué)科排名




書目名稱Data Management Technologies and Applications年度引用




書目名稱Data Management Technologies and Applications年度引用學(xué)科排名




書目名稱Data Management Technologies and Applications讀者反饋




書目名稱Data Management Technologies and Applications讀者反饋學(xué)科排名

Myosin

,Towards Comparable Ratings: Quantifying Evaluative Phrases in?Physician Reviews,f. We apply the best performing transformer model, XLM-RoBERTa, to a large physician review dataset and correlate the results with existing metadata. As a result, we can show different correlations between the sentiment polarity of certain aspect classes (e.g., friendliness, practice equipment) and

Exposition

,SubTempora: A Hybrid Approach for?Optimising Subgraph Searching,ge indexing sizes..In this paper, we study the problem of subgraph searching in a transactional graph database. We present a new compact representation and faster algorithm to reduce the search space by using (1) a compact data structure for indexing the subgraph patterns, and (2) state-of-the-art c

Incise

聰明

,Combining Image and?Text Matching for?Product Classification in?Retail,s based on the Global Product Classification (GPC) standard. Our experiments show that the combination of text-based and image-based classification leads to better results and is a promising approach to reduce the manual effort for product classification in retail.

phlegm

,Automatic Sentiment Labelling of?Multimodal Data,abels to the ‘Combined-Text-Features’. We test whether classifier models, using these ‘Combined-Text-Features’ with the Afinn labelling, can provide comparable results as when using other multimodal features and other labelling (human labelling). CNN, BiLSTM, and BERT models are used for the experim

phlegm

,From Cracked Accounts to?Fake IDs: User Profiling on?German Telegram Black Market Channels,oducts as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-

Genetics

Data Mining and Machine Learning to Predict the Sulphur Content in the Hot Metal of a Coke-Fired Bl can negatively affect the final quality of the product and increase energy consumption. In this sense, data mining, machine learning and the use of artificial neural networks are competitive alternatives to contribute to solving the new challenges of the steel industry. The database used for the nu

Mawkish

arbovirus

Platelet Counting and Function Testingf. We apply the best performing transformer model, XLM-RoBERTa, to a large physician review dataset and correlate the results with existing metadata. As a result, we can show different correlations between the sentiment polarity of certain aspect classes (e.g., friendliness, practice equipment) and