#GETORGCHART AJAX FOREIGN KEY FULL#
From the experimental results of these studies, the results got from the sampled data are close to or even exceed the results of the full amount of data. Hence this article focuses on researching sampling for data profiling tasks in big data context and investigates the application of sampling in different categories of data profiling. However, data profiling is computationally expensive, especially for large data sets.
#GETORGCHART AJAX FOREIGN KEY SERIES#
Data profiling is the activity that finds metadata of data set and has many use cases, e.g., performing data profiling tasks on relational data, graph data, and time series data for anomaly detection and data repair. Sampling technology has been widely used in big data context. Sampling methods can effectively reduce the amount of data and help speed up data processing. The large amount of data is highly demanding hardware resources and time consuming. For some traditional data mining algorithms, machine learning algorithms and data profiling tasks, it is very difficult to handle such a large amount of data. On the other hand, the 5V characteristic of big data, especially Volume which means large amount of data, brings challenges to storage and processing. On the one hand, we can analyze and mine big data to discover hidden information and get more potential value. Big data brings us new opportunities and challenges. Therefore, sampling technology plays an important role in the era of big data, and we also have reason to believe that sampling technology will become an indispensable step in big data processing in the future.ĭue to the development of internet technology and computer science, data is exploding at an exponential rate. Therefore, this paper focuses on researching sampling and profiling in big data context and investigates the application of sampling in different categories of data profiling tasks. Hence, sampling technology has been widely studied and used in big data context, e.g., methods for determining sample size, combining sampling with big data processing frameworks. We compare the performance of HoPF with two baseline approaches that both assume the existence of primary keys.ĭue to the development of internet technology and computer science, data is exploding at an exponential rate. The results show that our method is able to retrieve on average 88% of all primary keys, and 91% of all foreign keys. We evaluate precision and recall on three benchmarks and two real-world datasets. Several pruning rules are employed to speed up the procedure. Using score functions, our approach is able to effectively extract the true PKs and FKs from the vast sets of valid UCCs and INDs. PKs and FKs are subsets of the sets of unique column combinations (UCCs) and inclusion dependencies (INDs), respectively, for which efficient discovery algorithms are known. We study the problem of discovering primary keys and foreign keys automatically and propose an algorithm to detect both, namely Holistic Primary Key and Foreign Key Detection (HoPF). Detecting them manually is time-consuming and even infeasible in large-scale datasets. However, in many cases, these constraints are unknown or not documented. Primary keys (PKs) and foreign keys (FKs) are important elements of relational schemata in various applications, such as query optimization and data integration.
0 kommentar(er)
