data query
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2021 ◽  
Vol 10 (12) ◽  
pp. 832
Author(s):  
Xiangfu Meng ◽  
Lin Zhu ◽  
Qing Li ◽  
Xiaoyan Zhang

Resource Description Framework (RDF), as a standard metadata description framework proposed by the World Wide Web Consortium (W3C), is suitable for modeling and querying Web data. With the growing importance of RDF data in Web data management, there is an increasing need for modeling and querying RDF data. Previous approaches mainly focus on querying RDF. However, a large amount of RDF data have spatial and temporal features. Therefore, it is important to study spatiotemporal RDF data query approaches. In this paper, firstly, we formally define spatiotemporal RDF data, and construct a spatiotemporal RDF model st-RDF that is used to represent and manipulate spatiotemporal RDF data. Secondly, we present a spatiotemporal RDF query algorithm stQuery based on subgraph matching. This algorithm can quickly determine whether the query result is empty for queries whose temporal or spatial range exceeds a specific range by adopting a preliminary query filtering mechanism in the query process. Thirdly, we propose a sorting strategy that calculates the matching order of query nodes to speed up the subgraph matching. Finally, we conduct experiments in terms of effect and query efficiency. The experimental results show the performance advantages of our approach.


Author(s):  
Vafa Veliyeva Vafa Veliyeva

Rosenberq SM,et al.Breast 2015 [Managmen of breast cancerin very young women] Ribnikar D,et al. Curr Treat Options Oncol 2015 [Breast cancer under age 40:a different approach] Henry NL, Shah PD Heider I, Freer PE, Jaqsi R. Chapter 88.[ Cancer of the breast.]Elsevier 2020 National Cancer Inctitute. Physician Data Query (PDQ).[ Breast Cancer Treatment] 2019


2021 ◽  
pp. jim-2021-001986
Author(s):  
Kavitha Subramoney ◽  
Omar Elsheikh ◽  
Saira Butt ◽  
Daniel Romano ◽  
Lindsey Reese ◽  
...  

Hospitalized patients with COVID-19 must have a safe discharge plan to prevent readmissions. We assessed patients with COVID-19 admitted to hospitals belonging to a single health system between April 2020 and June 2020. Demographics, vitals and laboratory data were obtained by electronic data query and discharge processes were reviewed by manual abstraction. Over the study period, 94 out of 912 (10.3%) patients were readmitted within 14 days of discharge. Readmitted patients were older and spent more time in the intensive care unit (p<0.01). Statistical differences were noted in discharge-day heart rates, temperatures, platelet counts, and neutrophil and lymphocyte percentages between the readmitted and non-readmitted groups. Readmitted patients were less likely to be discharged home and to receive complete discharge instructions or home oxygen (p<0.01). Age, duration of intensive care unit stay, disposition destinations other than home, incomplete discharge planning and no arrangement for home oxygen may be associated with 14-day readmissions in patients with COVID-19. Certain clinical parameters on discharge day, while statistically different, may not reach clinically discriminant thresholds. Structured discharge processes may improve outcomes.


When searching for a movie, users often remember only the incidents happened in the movie instead of the actors or directors of that movie. However, these searches are not supported in our current movie information systems as data query is usually based on keywords. This research proposes a solution to search and query movies based on the content of the movie or the incidents happened in the movie. In our research, we have analysed and designed some movie representation models suitable for context-based movie searching. We also propose a quadruple-based representation model to resolve the disadvantages in current semantic web’s triple-based model. Our system is capable of processing user requests precisely and has proven to have advantages over current movie information systems.


Semantic Web ◽  
2021 ◽  
pp. 1-17
Author(s):  
Lucia Siciliani ◽  
Pierpaolo Basile ◽  
Pasquale Lops ◽  
Giovanni Semeraro

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users’ questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user’s question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art.


Author(s):  
Lise Kim ◽  
Esma Yahia ◽  
Frédéric Segonds ◽  
Philippe Véron ◽  
Victor Fau

2021 ◽  
pp. 475-484
Author(s):  
Aarti Chugh ◽  
Vivek Kumar Sharma ◽  
Manjot Kaur Bhatia ◽  
Charu Jain

2021 ◽  
Vol 2 (3) ◽  
pp. 1-28
Author(s):  
Jie Song ◽  
Qiang He ◽  
Feifei Chen ◽  
Ye Yuan ◽  
Ge Yu

In big data query processing, there is a trade-off between query accuracy and query efficiency, for example, sampling query approaches trade-off query completeness for efficiency. In this article, we argue that query performance can be significantly improved by slightly losing the possibility of query completeness, that is, the chance that a query is complete. To quantify the possibility, we define a new concept, Probability of query Completeness (hereinafter referred to as PC). For example, If a query is executed 100 times, PC = 0.95 guarantees that there are no more than 5 incomplete results among 100 results. Leveraging the probabilistic data placement and scanning, we trade off PC for query performance. In the article, we propose PoBery (POssibly-complete Big data quERY), a method that supports neither complete queries nor incomplete queries, but possibly-complete queries. The experimental results conducted on HiBench prove that PoBery can significantly accelerate queries while ensuring the PC. Specifically, it is guaranteed that the percentage of complete queries is larger than the given PC confidence. Through comparison with state-of-the-art key-value stores, we show that while Drill-based PoBery performs as fast as Drill on complete queries, it is 1.7 ×, 1.1 ×, and 1.5 × faster on average than Drill, Impala, and Hive, respectively, on possibly-complete queries.


2021 ◽  
Vol 20 ◽  
pp. 139-145
Author(s):  
Ray-I Chang ◽  
Yu-Hsien Chu ◽  
Chia-Hui Wang ◽  
Niang-Ying Huang

Wireless Sensor Networks (WSNs) contain many sensor nodes which are placed in chosen spatial area to temporally monitor the environmental changes. As the sensor data is big, it should be well organized and stored in cloud servers to support efficient data query. In this paper, we first adopt the streamed sensor data as "data cubes" to enhance data compression by video-like lossless compression (VLLC). With layered tree structure of WSNs, compression can be done on the aggregation nodes of edge computing. Then, an algorithm is designed to well organize and store these VLLC data cubes into cloud servers to support cost-effect big data query with parallel processing. Our experiments are tested by real-world sensor data. Results show that our method can save 94% construction time and 79% storage space to achieve the same retrieval time in data query when compared with a well-known database MySQL


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