We present a dynamic index structure for join sampling. Built for an (equi-) join Q---let IN be
the total number of tuples in the input relations of Q---the structure uses~ O (IN) space …

Modern applications are increasingly generating and persisting data across geo-distributed
data centers or edge clusters rather than a single cloud. This paradigm introduces …

Approximate query processing (AQP) is one of the key techniques to cope with big data
querying problem on account that it obtains approximate answers efficiently. To address non …

Knowledge graphs (KGs) have gained prominence for representing real-world facts, with
queries of KGs being crucial for their application. Aggregate queries, as one of the most …

For exploratory data analysis, it is often desirable to know what answers you are likely to get
before actually obtaining those answers. This can potentially be achieved by designing …

For exploratory data analysis, it is often desirable to know what answers you are likely to get
before actually obtaining those answers. This can potentially be achieved by designing …

For interactive data exploration, approximate query processing (AQP) is a useful approach
that usually uses samples to provide a timely response for queries by trading query …

This paper presents a hybrid prediction and search approach (HPS) for building
visualization systems of big data. The basic idea is training a regression model to predict a …

Regular expressions with capture variables, also known as “regex-formulas,” extract
relations of spans (intervals identified by their start and end indices) from text. In turn, the …

In plenty of data analysis tasks, a basic and time-consuming process is to produce a large
number of solutions and feed them into downstream processing. Various enumeration …