Progressive query processing enables data scientists to efficiently analyze and explore large datasets. Data scientists can start further analyses earlier if the progressive result can represent the complete results well. Most progressive processing frameworks carefully control which parts of the input to process in order to improve the quality of progressive results. The input control strategies work well when the data are processed uniformly. However, the progressive results will be biased towards the join keys if the processed data are not uniform. A recently proposed input&output framework named QPJ corrects the bias by temporarily hiding some results. The framework dynamically estimates the distribution of the complete result and outputs progressive results with a similar distribution to the estimated complete result. This demo presents QPJVis, which is a progressive query processing system designed to inherently process the progressive queries using the QPJ framework. Additionally, we also implement an input control framework, Prism, in QPJVis so that users can compare the difference between the input&output framework and a purely input framework.
more »
« less
This content will become publicly available on September 19, 2025
QPJVis Demo: Quality-boost Progressive Join Query Processing System
Progressive query processing enables data scientists to efficiently analyze and explore large datasets. Data scientists can start further analyses earlier if the progressive result can represent the complete results well. Most progressive processing frameworks carefully control which parts of the input to process in order to improve the quality of progressive results. The input control strategies work well when the data are processed uniformly. However, the progressive results will be biased towards the join keys if the processed data are not uniform. A recently proposed input&output framework named QPJ corrects the bias by temporarily hiding some results. The framework dynamically estimates the distribution of the complete result and outputs progressive results with a similar distribution to the estimated complete result. This demo presents QPJVis, which is a progressive query processing system designed to inherently process the progressive queries using the QPJ frame- work. Additionally, we also implement an input control framework, Prism, in QPJVis so that users can compare the difference between the input&output framework and a purely input framework.
more »
« less
- Award ID(s):
- 1924694
- PAR ID:
- 10550229
- Publisher / Repository:
- VLDB Endowment
- Date Published:
- Journal Name:
- Proceedings of the VLDB Endowment
- ISSN:
- 2150-8097
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
With the requirements to enable data analytics and exploration interactively and efficiently, progressive data processing, especially progressive join, became essential to data science. Join queries are particularly challenging due to the correlation between input datasets which causes the results to be biased towards some join keys. Existing methods carefully control which parts of the input to process in order to improve the quality of progressive results. If the quality is not satisfactory, they will process more data to improve the result. In this paper, we propose an alternative approach that initially seems counter-intuitive but surprisingly works very well. After query processing, we intentionally report fewer results to the user with the goal of improving the quality. The key idea is that if the output is deviated from the correct distribution, we temporarily hide some results to correct the bias. As we process more data, the hidden results are inserted back until the full dataset is processed. The main challenge is that we do not know the correct output distribution while the progressive query is running. In this work, we formally define the progressive join problem with quality and progressive result rate constraints. We propose an input&output quality-aware progressive join framework (QPJ) that (1) provides input control that decides which parts of the input to process; (2) estimates the final result distribution progressively; (3) automatically controls the quality of the progressive output rate; and (4) combines input&output control to enable quality control of the progressive results. We compare QPJ with existing methods and show QPJ can provide the progressive output that can represent the final answer better than existing methods.more » « less
-
With the requirements to enable data analytics and exploration interactively and efficiently, progressive data processing, especially progressive join, became essential to data science. Join queries are particularly challenging due to the correlation between input datasets which causes the results to be biased towards some join keys. Existing methods carefully control which parts of the input to process in order to improve the quality of progressive results. If the quality is not satisfactory, they will process more data to improve the result. In this paper, we propose an alternative approach that initially seems counter-intuitive but surprisingly works very well. After query processing, we intentionally report fewer results to the user with the goal of improving the quality. The key idea is that if the output is deviated from the correct distribution, we temporarily hide some results to correct the bias. As we process more data, the hidden results are inserted back until the full dataset is processed. The main challenge is that we do not know the correct output distribution while the progressive query is running. In this work, we formally define the progressive join problem with quality and progressive result rate constraints. We propose an input&output quality-aware progressive join framework (QPJ) that (1) provides input control that decides which parts of the input to process; (2) estimates the final result distribution progressively; (3) automat- ically controls the quality of the progressive output rate; and (4) combines input&output control to enable quality control of the progressive results. We compare QPJ with existing methods and show QPJ can provide the progressive output that can represent the final answer better than existing methods.more » « less
-
Progressive visual analytics enable data scientists to efficiently explore large datasets and examine progressive results with low latency. Most progressive visualization frameworks use a progressive query processing module that controls the quality of the results and then feeds these results into a visualization module. The goal is to avoid poor-quality progressive results which could mislead data scientists. This method misses some optimization opportunities as it improves the quality of the intermediate result while ignoring how this result affects the final visualization. This work presents a work-in-progress quality-aware progressive visualization input control component, named QPV. The key idea of the proposed framework is to integrate the visualization module into the progressive query results so that the quality control takes into account the final visualization. With limited computational resources, QPV solves an optimization problem to allocate resources and alleviate the misleading effects in the progressive plots.more » « less
-
As new laws governing management of personal data are introduced, e.g., the European Union’s General Data Protection Regulation of 2016 and the California Consumer Privacy Act of 2018, compliance with data governance legislation is becoming an increasingly important aspect of data management. An important component of many data privacy laws is that they require companies to only use an individual’s data for a purpose the individual has explicitly consented to. Prior methods for enforcing consent for aggregate queries either use access control to eliminate data without consent from query evaluation or apply differential privacy algorithms to inject synthetic noise into the outcomes of queries (or input data) to ensure that the anonymity of non-consenting individuals is preserved with high probability. Both approaches return query results that differ from the ground truth results corresponding to the full input containing data from both consenting and non-consenting individuals. We present an alternative frame- work for group-by aggregate queries, tailored for applications, e.g., medicine, where even a small deviation from the correct answer to a query cannot be tolerated. Our approach uses provenance to determine, for each output tuple of a group-by aggregate query, which individual’s data was used to derive the result for this group. We then use statistical tests to determine how likely it is that the presence of data for a non-consenting individual will be revealed by such an output tuple. We filter out tuples for which this test fails, i.e., which are deemed likely to reveal non-consenting data. Thus, our approach always returns a subset of the ground truth query answers. Our experiments successfully return only 100% accurate results in instances where access control or differential privacy would have either returned less total or less accurate results.more » « less