Pub Date : 2020-04-01DOI: 10.1109/ICDE48307.2020.00142
Wolfram Wingerath, Felix Gessert, Erik Witt, Hannes Kuhlmann, Florian Bücklers, Benjamin Wollmer, N. Ritter
Users leave when page loads take too long. This simple fact has complex implications for virtually all modern businesses, because accelerating content delivery through caching is not as simple as it used to be. As a fundamental technical challenge, the high degree of personalization in today’s Web has seemingly outgrown the capabilities of traditional content delivery networks (CDNs) which have been designed for distributing static assets under fixed caching times. As an additional legal challenge for services with personalized content, an increasing number of regional data protection laws constrain the ways in which CDNs can be used in the first place. In this paper, we present Speed Kit as a radically different approach for content distribution that combines (1) a polyglot architecture for efficiently caching personalized content with (2) a natively GDPR-compliant client proxy that handles all sensitive information within the user device. We describe the system design and implementation, explain the custom cache coherence protocol to avoid data staleness and achieve Δ-atomicity, and we share field experiences from over a year of productive use in the e-commerce industry.
{"title":"Speed Kit: A Polyglot & GDPR-Compliant Approach For Caching Personalized Content","authors":"Wolfram Wingerath, Felix Gessert, Erik Witt, Hannes Kuhlmann, Florian Bücklers, Benjamin Wollmer, N. Ritter","doi":"10.1109/ICDE48307.2020.00142","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00142","url":null,"abstract":"Users leave when page loads take too long. This simple fact has complex implications for virtually all modern businesses, because accelerating content delivery through caching is not as simple as it used to be. As a fundamental technical challenge, the high degree of personalization in today’s Web has seemingly outgrown the capabilities of traditional content delivery networks (CDNs) which have been designed for distributing static assets under fixed caching times. As an additional legal challenge for services with personalized content, an increasing number of regional data protection laws constrain the ways in which CDNs can be used in the first place. In this paper, we present Speed Kit as a radically different approach for content distribution that combines (1) a polyglot architecture for efficiently caching personalized content with (2) a natively GDPR-compliant client proxy that handles all sensitive information within the user device. We describe the system design and implementation, explain the custom cache coherence protocol to avoid data staleness and achieve Δ-atomicity, and we share field experiences from over a year of productive use in the e-commerce industry.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"28 1","pages":"1603-1608"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90906910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/ICDE48307.2020.00133
Haitao Yuan, Guoliang Li, Ling Feng, Ji Sun, Yue Han
Materializing views is an important method to reduce redundant computations in DBMS, especially for processing large scale analytical queries. However, many existing methods still need DBAs to manually generate materialized views, which are not scalable to a large number of database instances, especially on the cloud database. To address this problem, we propose an automatic view generation method which judiciously selects "highly beneficial" subqueries to generate materialized views. However, there are two challenges. (1) How to estimate the benefit of using a materialized view for a queryƒ (2) How to select optimal subqueries to generate materialized viewsƒ To address the first challenge, we propose a neural network based method to estimate the benefit of using a materialized view to answer a query. In particular, we extract significant features from different perspectives and design effective encoding models to transform these features into hidden representations. To address the second challenge, we model this problem to an ILP (Integer Linear Programming) problem, which aims to maximize the utility by selecting optimal subqueries to materialize. We design an iterative optimization method to select subqueries to materialize. However, this method cannot guarantee the convergence of the solution. To address this issue, we model the iterative optimization process as an MDP (Markov Decision Process) and use the deep reinforcement learning model to solve the problem. Extensive experiments show that our method outperforms existing solutions by 28.4%, 8.8% and 31.7% on three real-world datasets.
{"title":"Automatic View Generation with Deep Learning and Reinforcement Learning","authors":"Haitao Yuan, Guoliang Li, Ling Feng, Ji Sun, Yue Han","doi":"10.1109/ICDE48307.2020.00133","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00133","url":null,"abstract":"Materializing views is an important method to reduce redundant computations in DBMS, especially for processing large scale analytical queries. However, many existing methods still need DBAs to manually generate materialized views, which are not scalable to a large number of database instances, especially on the cloud database. To address this problem, we propose an automatic view generation method which judiciously selects \"highly beneficial\" subqueries to generate materialized views. However, there are two challenges. (1) How to estimate the benefit of using a materialized view for a queryƒ (2) How to select optimal subqueries to generate materialized viewsƒ To address the first challenge, we propose a neural network based method to estimate the benefit of using a materialized view to answer a query. In particular, we extract significant features from different perspectives and design effective encoding models to transform these features into hidden representations. To address the second challenge, we model this problem to an ILP (Integer Linear Programming) problem, which aims to maximize the utility by selecting optimal subqueries to materialize. We design an iterative optimization method to select subqueries to materialize. However, this method cannot guarantee the convergence of the solution. To address this issue, we model the iterative optimization process as an MDP (Markov Decision Process) and use the deep reinforcement learning model to solve the problem. Extensive experiments show that our method outperforms existing solutions by 28.4%, 8.8% and 31.7% on three real-world datasets.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"37 1","pages":"1501-1512"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91210970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/ICDE48307.2020.00205
Xiaodong Qi, Zhao Zhang, Cheqing Jin, Aoying Zhou
The full-replication data storage mechanism, as commonly utilized in existing blockchain systems, is lack of sufficient storage scalability, since it reserves a copy of the whole block data in each node so that the overall storage consumption per block is O(n) with n nodes. Moreover, due to the existence of Byzantine nodes, existing partitioning methods, though widely adopted in distributed systems for decades, cannot suit for blockchain systems directly, thereby it is critical to devise a new storage mechanism. This paper proposes a novel storage engine, called BFT-Store, to enhance storage scalability by integrating erasure coding with Byzantine Fault Tolerance (BFT) consensus protocol. First, the storage consumption per block can be reduced to O(1), which enlarges overall storage capability when more nodes join blockchain. Second, an efficient online re-encoding protocol is designed for storage scale-out and a hybrid replication scheme is employed to improve reading performance. Last, extensive experimental results illustrate the scalability, availability and efficiency of BFT-Store, which is implemented on an open-source permissioned blockchain Tendermint.
{"title":"BFT-Store: Storage Partition for Permissioned Blockchain via Erasure Coding","authors":"Xiaodong Qi, Zhao Zhang, Cheqing Jin, Aoying Zhou","doi":"10.1109/ICDE48307.2020.00205","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00205","url":null,"abstract":"The full-replication data storage mechanism, as commonly utilized in existing blockchain systems, is lack of sufficient storage scalability, since it reserves a copy of the whole block data in each node so that the overall storage consumption per block is O(n) with n nodes. Moreover, due to the existence of Byzantine nodes, existing partitioning methods, though widely adopted in distributed systems for decades, cannot suit for blockchain systems directly, thereby it is critical to devise a new storage mechanism. This paper proposes a novel storage engine, called BFT-Store, to enhance storage scalability by integrating erasure coding with Byzantine Fault Tolerance (BFT) consensus protocol. First, the storage consumption per block can be reduced to O(1), which enlarges overall storage capability when more nodes join blockchain. Second, an efficient online re-encoding protocol is designed for storage scale-out and a hybrid replication scheme is employed to improve reading performance. Last, extensive experimental results illustrate the scalability, availability and efficiency of BFT-Store, which is implemented on an open-source permissioned blockchain Tendermint.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"89 1","pages":"1926-1929"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73216128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/ICDE48307.2020.00089
Bolong Zheng, Chenze Huang, Christian S. Jensen, Lu Chen, Nguyen Quoc Viet Hung, Guanfeng Liu, Guohui Li, Kai Zheng
In Pickup-and-Delivery problems (PDP), mobile workers are employed to pick up and deliver items with the goal of reducing travel and fuel consumption. Unlike most existing efforts that focus on finding a schedule that enables the delivery of as many items as possible at the lowest cost, we consider trichromatic (worker-item-task) utility that encompasses worker reliability, item quality, and task profitability. Moreover, we allow customers to specify keywords for desired items when they submit tasks, which may result in multiple pickup options, thus further increasing the difficulty of the problem. Specifically, we formulate the problem of Online Trichromatic Pickup and Delivery Scheduling (OTPD) that aims to find optimal delivery schedules with highest overall utility. In order to quickly respond to submitted tasks, we propose a greedy solution that finds the schedule with the highest utility-cost ratio. Next, we introduce a skyline kinetic tree-based solution that materializes intermediate results to improve the result quality. Finally, we propose a density-based grouping solution that partitions streaming tasks and efficiently assigns them to the workers with high overall utility. Extensive experiments with real and synthetic data offer evidence that the proposed solutions excel over baselines with respect to both effectiveness and efficiency.
{"title":"Online Trichromatic Pickup and Delivery Scheduling in Spatial Crowdsourcing","authors":"Bolong Zheng, Chenze Huang, Christian S. Jensen, Lu Chen, Nguyen Quoc Viet Hung, Guanfeng Liu, Guohui Li, Kai Zheng","doi":"10.1109/ICDE48307.2020.00089","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00089","url":null,"abstract":"In Pickup-and-Delivery problems (PDP), mobile workers are employed to pick up and deliver items with the goal of reducing travel and fuel consumption. Unlike most existing efforts that focus on finding a schedule that enables the delivery of as many items as possible at the lowest cost, we consider trichromatic (worker-item-task) utility that encompasses worker reliability, item quality, and task profitability. Moreover, we allow customers to specify keywords for desired items when they submit tasks, which may result in multiple pickup options, thus further increasing the difficulty of the problem. Specifically, we formulate the problem of Online Trichromatic Pickup and Delivery Scheduling (OTPD) that aims to find optimal delivery schedules with highest overall utility. In order to quickly respond to submitted tasks, we propose a greedy solution that finds the schedule with the highest utility-cost ratio. Next, we introduce a skyline kinetic tree-based solution that materializes intermediate results to improve the result quality. Finally, we propose a density-based grouping solution that partitions streaming tasks and efficiently assigns them to the workers with high overall utility. Extensive experiments with real and synthetic data offer evidence that the proposed solutions excel over baselines with respect to both effectiveness and efficiency.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"10 1","pages":"973-984"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74223517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/ICDE48307.2020.00243
Yifan Qiao
This thesis advocates a novel DRAM-only strategy to reduce the computing system memory cost for the first time, and investigates its applications to database systems. This thesis envisions a low-cost DRAM module called block-protected DRAM, which reduces bit cost by significantly relaxing the DRAM raw reliability and meanwhile employs long error correction code (ECC) to ensure data integrity at small coding redundancy. Built upon the exactly same DRAM technology, today’s byte-accessible DRAM and envisioned block-protected DRAM strike at different trade-offs between memory bit cost and native data access granularity, and naturally form a heterogeneous DRAM-only memory system. The practical feasibility of such heterogeneous memory systems is further strengthened by the new media-agnostic and latency-oblivious CPU-memory interfaces such as IBM’s OpenCAPI/OMI and Intel’s CXL. This DRAM-only design approach perfectly leverages the existing DRAM manufacturing infrastructure and is not subject to any fundamental technology risk and uncertainty. Hence, before NVM technologies could eventually fulfill their long-awaited promises (i.e., DRAM-grade speed at flash-grade cost), this DRAM-only design framework can fill the gap to empower continuous progress and advances of computing systems. This thesis aims to develop techniques that enable relational and NoSQL databases to take full advantage of the envisioned low-cost heterogeneous DRAM system. As the first step, we studied how one could employ heterogeneous DRAM to implement a low-cost tiered caching solution for relational database, and obtained encouraging results using MySQL as a test vehicle.
{"title":"Design of Database Systems with DRAM-only Heterogeneous Memory Architecture","authors":"Yifan Qiao","doi":"10.1109/ICDE48307.2020.00243","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00243","url":null,"abstract":"This thesis advocates a novel DRAM-only strategy to reduce the computing system memory cost for the first time, and investigates its applications to database systems. This thesis envisions a low-cost DRAM module called block-protected DRAM, which reduces bit cost by significantly relaxing the DRAM raw reliability and meanwhile employs long error correction code (ECC) to ensure data integrity at small coding redundancy. Built upon the exactly same DRAM technology, today’s byte-accessible DRAM and envisioned block-protected DRAM strike at different trade-offs between memory bit cost and native data access granularity, and naturally form a heterogeneous DRAM-only memory system. The practical feasibility of such heterogeneous memory systems is further strengthened by the new media-agnostic and latency-oblivious CPU-memory interfaces such as IBM’s OpenCAPI/OMI and Intel’s CXL. This DRAM-only design approach perfectly leverages the existing DRAM manufacturing infrastructure and is not subject to any fundamental technology risk and uncertainty. Hence, before NVM technologies could eventually fulfill their long-awaited promises (i.e., DRAM-grade speed at flash-grade cost), this DRAM-only design framework can fill the gap to empower continuous progress and advances of computing systems. This thesis aims to develop techniques that enable relational and NoSQL databases to take full advantage of the envisioned low-cost heterogeneous DRAM system. As the first step, we studied how one could employ heterogeneous DRAM to implement a low-cost tiered caching solution for relational database, and obtained encouraging results using MySQL as a test vehicle.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"60 1","pages":"2054-2058"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75053755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/ICDE48307.2020.00215
Yongjiang Liang, Tingting Hu, Peixiang Zhao
Clustering uncertain graphs based on the probabilistic graph model has sparked extensive research and widely varying applications. Existing structural clustering methods rely heavily on the computation of pairwise reliable structural similarity between vertices, which has proven to be extremely costly, especially in large uncertain graphs. In this paper, we develop a new, decomposition-based method, ProbSCAN, for efficient reliable structural similarity computation with theoretically improved complexity. We further design a cost-effective index structure UCNO-Index, and a series of powerful pruning strategies to expedite reliable structural similarity computation in uncertain graphs. Experimental studies on eight real-world uncertain graphs demonstrate the effectiveness of our proposed solutions, which achieves orders of magnitude improvement of clustering efficiency, compared with the state-of-the-art structural clustering methods in large uncertain graphs.
{"title":"Efficient Structural Clustering in Large Uncertain Graphs","authors":"Yongjiang Liang, Tingting Hu, Peixiang Zhao","doi":"10.1109/ICDE48307.2020.00215","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00215","url":null,"abstract":"Clustering uncertain graphs based on the probabilistic graph model has sparked extensive research and widely varying applications. Existing structural clustering methods rely heavily on the computation of pairwise reliable structural similarity between vertices, which has proven to be extremely costly, especially in large uncertain graphs. In this paper, we develop a new, decomposition-based method, ProbSCAN, for efficient reliable structural similarity computation with theoretically improved complexity. We further design a cost-effective index structure UCNO-Index, and a series of powerful pruning strategies to expedite reliable structural similarity computation in uncertain graphs. Experimental studies on eight real-world uncertain graphs demonstrate the effectiveness of our proposed solutions, which achieves orders of magnitude improvement of clustering efficiency, compared with the state-of-the-art structural clustering methods in large uncertain graphs.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"32 1","pages":"1966-1969"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79769453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/ICDE48307.2020.00177
Chengliang Chai, Guoliang Li, Ju Fan, Yuyu Luo
Visualization charts are widely utilized for presenting structured data. Under many circumstances, people want to explore the data in the charts collected from various sources, such as papers and websites, so as to further analyzing the data or creating new charts. However, the existing automatic and semi-automatic approaches are not always effective due to the variety of charts. In this paper, we introduce a crowdsourcing approach that leverages human ability to extract data from visualization charts. There are several challenges. The first one is how to avoid tedious human interaction with charts and design simple crowdsourcing tasks. Second, it is challenging to evaluate worker’s quality for truth inference, because workers may not only provide inaccurate values but also misalign values to wrong data series. To address the challenges, we design an effective crowdsourcing task scheme that splits a chart into simple micro-tasks. We introduce a novel worker quality model by considering worker’s accuracy and task difficulty. We also devise an effective early-stopping mechanisms to save the cost. We have conducted experiments on a real crowdsourcing platform, and the results show that our framework outperforms state-of-the-art approaches on both cost and quality.
{"title":"Crowdsourcing-based Data Extraction from Visualization Charts","authors":"Chengliang Chai, Guoliang Li, Ju Fan, Yuyu Luo","doi":"10.1109/ICDE48307.2020.00177","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00177","url":null,"abstract":"Visualization charts are widely utilized for presenting structured data. Under many circumstances, people want to explore the data in the charts collected from various sources, such as papers and websites, so as to further analyzing the data or creating new charts. However, the existing automatic and semi-automatic approaches are not always effective due to the variety of charts. In this paper, we introduce a crowdsourcing approach that leverages human ability to extract data from visualization charts. There are several challenges. The first one is how to avoid tedious human interaction with charts and design simple crowdsourcing tasks. Second, it is challenging to evaluate worker’s quality for truth inference, because workers may not only provide inaccurate values but also misalign values to wrong data series. To address the challenges, we design an effective crowdsourcing task scheme that splits a chart into simple micro-tasks. We introduce a novel worker quality model by considering worker’s accuracy and task difficulty. We also devise an effective early-stopping mechanisms to save the cost. We have conducted experiments on a real crowdsourcing platform, and the results show that our framework outperforms state-of-the-art approaches on both cost and quality.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"1814-1817"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82983461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Getting high quality XML schemas to avoid or reduce application risks is an important problem in practice, for which some important aspects have yet to be addressed satisfactorily in existing work. In this paper, we propose a tool FlashSchema for high quality XML schema design, which supports both one-pass and interactive schema design and schema recommendation. To the best of our knowledge, no other existing tools support interactive schema design and schema recommendation. One salient feature of our work is the design of algorithms to infer k-occurrence interleaving regular expressions, which are not only more powerful in model capacity, but also more efficient. Additionally, such algorithms form the basis of our interactive schema design. The other feature is that, starting from large-scale schema data that we have harvested from the Web, we devise a new solution for type inference, as well as propose schema recommendation for schema design. Finally, we conduct a series of experiments on two XML datasets, comparing with 9 state-of-the-art algorithms and open-source tools in terms of running time, preciseness, and conciseness. Experimental results show that our work achieves the highest level of preciseness and conciseness within only a few seconds. Experimental results and examples also demonstrate the effectiveness of our type inference and schema recommendation methods.
{"title":"FlashSchema: Achieving High Quality XML Schemas with Powerful Inference Algorithms and Large-scale Schema Data","authors":"Yeting Li, Jialun Cao, H. Chen, Tingjian Ge, Zhiwu Xu, Qiancheng Peng","doi":"10.1109/ICDE48307.2020.00214","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00214","url":null,"abstract":"Getting high quality XML schemas to avoid or reduce application risks is an important problem in practice, for which some important aspects have yet to be addressed satisfactorily in existing work. In this paper, we propose a tool FlashSchema for high quality XML schema design, which supports both one-pass and interactive schema design and schema recommendation. To the best of our knowledge, no other existing tools support interactive schema design and schema recommendation. One salient feature of our work is the design of algorithms to infer k-occurrence interleaving regular expressions, which are not only more powerful in model capacity, but also more efficient. Additionally, such algorithms form the basis of our interactive schema design. The other feature is that, starting from large-scale schema data that we have harvested from the Web, we devise a new solution for type inference, as well as propose schema recommendation for schema design. Finally, we conduct a series of experiments on two XML datasets, comparing with 9 state-of-the-art algorithms and open-source tools in terms of running time, preciseness, and conciseness. Experimental results show that our work achieves the highest level of preciseness and conciseness within only a few seconds. Experimental results and examples also demonstrate the effectiveness of our type inference and schema recommendation methods.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"820 1","pages":"1962-1965"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80995124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/ICDE48307.2020.00092
Min Xie, R. C. Wong, Peng Peng, V. Tsotras
When faced with a database containing millions of products, a user may be only interested in a (typically much) smaller representative subset. Various approaches were proposed to create a good representative subset that fits the user’s needs which are expressed in the form of a utility function (e.g., the top-k and diversification query). Recently, a regret minimization query was proposed: it does not require users to provide their utility functions and returns a small set of tuples such that any user’s favorite tuple in this subset is guaranteed to be not much worse than his/her favorite tuple in the whole database. In a sense, this query finds a small set of tuples that makes the user happy (i.e., not regretful) even if s/he gets the best tuple in the selected set but not the best tuple among all tuples in the database.In this paper, we study the min-size version of the regret minimization query; that is, we want to determine the least tuples needed to keep users happy at a given level. We term this problem as the α-happiness query where we quantify the user’s happiness level by a criterion, called the happiness ratio, and guarantee that each user is at least α happy with the set returned (i.e., the happiness ratio is at least α) where α is a real number from 0 to 1. As this is an NP-hard problem, we derive an approximate solution with theoretical guarantee by considering the problem from a geometric perspective. Since in practical scenarios, users are interested in achieving higher happiness levels (i.e., α is closer to 1), we performed extensive experiments for these scenarios, using both real and synthetic datasets. Our evaluations show that our algorithm outperforms the best-known previous approaches in two ways: (i) it answers the α-happiness query by returning fewer tuples to users and, (ii) it answers much faster (up to two orders of magnitude times improvement for large α).
{"title":"Being Happy with the Least: Achieving α-happiness with Minimum Number of Tuples","authors":"Min Xie, R. C. Wong, Peng Peng, V. Tsotras","doi":"10.1109/ICDE48307.2020.00092","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00092","url":null,"abstract":"When faced with a database containing millions of products, a user may be only interested in a (typically much) smaller representative subset. Various approaches were proposed to create a good representative subset that fits the user’s needs which are expressed in the form of a utility function (e.g., the top-k and diversification query). Recently, a regret minimization query was proposed: it does not require users to provide their utility functions and returns a small set of tuples such that any user’s favorite tuple in this subset is guaranteed to be not much worse than his/her favorite tuple in the whole database. In a sense, this query finds a small set of tuples that makes the user happy (i.e., not regretful) even if s/he gets the best tuple in the selected set but not the best tuple among all tuples in the database.In this paper, we study the min-size version of the regret minimization query; that is, we want to determine the least tuples needed to keep users happy at a given level. We term this problem as the α-happiness query where we quantify the user’s happiness level by a criterion, called the happiness ratio, and guarantee that each user is at least α happy with the set returned (i.e., the happiness ratio is at least α) where α is a real number from 0 to 1. As this is an NP-hard problem, we derive an approximate solution with theoretical guarantee by considering the problem from a geometric perspective. Since in practical scenarios, users are interested in achieving higher happiness levels (i.e., α is closer to 1), we performed extensive experiments for these scenarios, using both real and synthetic datasets. Our evaluations show that our algorithm outperforms the best-known previous approaches in two ways: (i) it answers the α-happiness query by returning fewer tuples to users and, (ii) it answers much faster (up to two orders of magnitude times improvement for large α).","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"95 1","pages":"1009-1020"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82782539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/ICDE48307.2020.00096
Yan Li, Tingjian Ge, Cindy X. Chen
Knowledge graphs have seen increasingly broad applications. However, they are known to be incomplete. We define the notion of a virtual knowledge graph which extends a knowledge graph with predicted edges and their probabilities. We focus on two important types of queries: top-k entity queries and aggregate queries. To improve query processing efficiency, we propose an incremental index on top of low dimensional entity vectors transformed from network embedding vectors. We also devise query processing algorithms with the index. Moreover, we provide theoretical guarantees of accuracy, and conduct a systematic experimental evaluation. The experiments show that our approach is very efficient and effective. In particular, with the same or better accuracy guarantees, it is one to two orders of magnitude faster in query processing than the closest previous work which can only handle one relationship type.
{"title":"Online Indices for Predictive Top-k Entity and Aggregate Queries on Knowledge Graphs","authors":"Yan Li, Tingjian Ge, Cindy X. Chen","doi":"10.1109/ICDE48307.2020.00096","DOIUrl":"https://doi.org/10.1109/ICDE48307.2020.00096","url":null,"abstract":"Knowledge graphs have seen increasingly broad applications. However, they are known to be incomplete. We define the notion of a virtual knowledge graph which extends a knowledge graph with predicted edges and their probabilities. We focus on two important types of queries: top-k entity queries and aggregate queries. To improve query processing efficiency, we propose an incremental index on top of low dimensional entity vectors transformed from network embedding vectors. We also devise query processing algorithms with the index. Moreover, we provide theoretical guarantees of accuracy, and conduct a systematic experimental evaluation. The experiments show that our approach is very efficient and effective. In particular, with the same or better accuracy guarantees, it is one to two orders of magnitude faster in query processing than the closest previous work which can only handle one relationship type.","PeriodicalId":6709,"journal":{"name":"2020 IEEE 36th International Conference on Data Engineering (ICDE)","volume":"20 1","pages":"1057-1068"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89430975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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