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Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users’ search history (or query logs ), are often insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study the use of entities found in queries to provide recommendations. Specifically, we extract entities from a query, and use these entities to explore new ones by consulting an information source. The discovered entities are then used to suggest new queries to the user. In this article, we examine two information sources: (1) a knowledge base (or KB), such as YAGO and Freebase; and (2) a click log, which contains the URLs accessed by a query user. We study how to use these sources to find new entities useful for query recommendation. We further study a hybrid framework that integrates different query recommendation methods effectively. As shown in the experiments, our proposed approaches provide better recommendations than existing solutions for long-tail queries. In addition, our query recommendation process takes less than 100ms to complete. Thus, our solution is suitable for providing online query recommendation services for search engines.

Exploratory Data Analysis (EDA) is a crucial step in any data science project. However, existing Python libraries fall short in supporting data scientists to complete common EDA tasks for statistical modeling. Their API design is either too low level, which is optimized for plotting rather than EDA, or too high level, which is hard to specify more fine-grained EDA tasks. In response, we propose DataPrep.EDA, a novel task-centric EDA system in Python. Dat-aPrep.EDA allows data scientists to declaratively specify a wide range of EDA tasks in different granularity with a single function call. We identify a number of challenges to implement Dat-aPrep.EDA, and propose effective solutions to improve the scalability, usability, customizability of the system. In particular, we discuss some lessons learned from using Dask to build the data processing pipelines for EDA tasks and describe our approaches to accelerate the pipelines. We conduct extensive experiments to compare DataPrep.EDA with Pandas-profiling, the state-of-the-art EDA system in Python. The experiments show that DataPrep.EDA significantly outperforms Pandas-profiling in terms of both speed and user experience. DataPrep.EDA is open-sourced as an EDA component of DataPrep: https://github.com/sfu-db/dataprep.

With the development of information technology, health management and big data have risen and developed in recent years. Big data need proper analysis and shape in order to extract meaningful information from it. This paper analyzes the application status and prospects of big data in the field of health management. The results show that the most widely used big data in health management are intelligent wearable devices. Big data applications in football players’ mental health monitoring systems and chronic disease health management systems also have a good prospect. The intelligent wearable device is applied to several aspects of sports work: teaching and sports training, real-time monitoring of football players’ physical exercise process, collecting football players’ heart rate, calorie consumption, exercise steps, and track, blood pressure, blood oxygen, and other physical exercise data; through monitoring the heart rate, we can get the intensity and duration of football players’ physical exercise in school; through the calculation, we can also get the football players’ time energy consumption and understand the overall situation of football players’ physical exercise in school; through step counting and track monitoring, we can master the number of steps and track of football players; by monitoring the changes of blood oxygen and blood pressure of football players, we need to build a third-party residents’ health information storage and analysis system and further realize the marketization of residents’ health big data. The experimental results of the proposed study show the effectiveness of the proposed work.

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