Entity Matching is a classic research problem in any data analytics pipeline, aiming to identify records referring to the same real-world entity. It plays an important role in data cleansing and integration. Advanced entity matching techniques focus on extracting syntactic or semantic features from record pairs via complex neural architectures or pre-trained language models. However, the performances always suffer from noisy or missing attribute values in the records. We observe that comparing one record with several relevant records in a collective manner allows each pairwise matching decision to be made by borrowing valuable insights from other pairs, which is beneficial to the overall matching performance. In this paper, we propose a generic one-to-set neural framework named GNEM for entity matching. GNEM predicts matching labels between one record and a set of relevant records simultaneously. It constructs a record pair graph with weighted edges and adopts the graph neural network to propagate information among pairs. We further show that GNEM can be interpreted as an extension and generalization of the existing pairwise matching techniques. Extensive experiments on real-world data sets demonstrate that GNEM consistently outperforms the existing pairwise entity matching techniques and achieves up to 8.4% improvement on F1-Score compared with the state-of-the-art neural methods.
{"title":"GNEM: A Generic One-to-Set Neural Entity Matching Framework","authors":"Runjin Chen, Yanyan Shen, Dongxiang Zhang","doi":"10.1145/3442381.3450119","DOIUrl":"https://doi.org/10.1145/3442381.3450119","url":null,"abstract":"Entity Matching is a classic research problem in any data analytics pipeline, aiming to identify records referring to the same real-world entity. It plays an important role in data cleansing and integration. Advanced entity matching techniques focus on extracting syntactic or semantic features from record pairs via complex neural architectures or pre-trained language models. However, the performances always suffer from noisy or missing attribute values in the records. We observe that comparing one record with several relevant records in a collective manner allows each pairwise matching decision to be made by borrowing valuable insights from other pairs, which is beneficial to the overall matching performance. In this paper, we propose a generic one-to-set neural framework named GNEM for entity matching. GNEM predicts matching labels between one record and a set of relevant records simultaneously. It constructs a record pair graph with weighted edges and adopts the graph neural network to propagate information among pairs. We further show that GNEM can be interpreted as an extension and generalization of the existing pairwise matching techniques. Extensive experiments on real-world data sets demonstrate that GNEM consistently outperforms the existing pairwise entity matching techniques and achieves up to 8.4% improvement on F1-Score compared with the state-of-the-art neural methods.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134101967","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}
Jinze Bai, Jialin Wang, Zhao Li, Donghui Ding, Ji Zhang, Jun Gao
A relational database, consisting of multiple tables, provides heterogeneous information across various entities, widely used in real-world services. This paper studies the supervised learning task on multiple tables, aiming to predict one label column with the help of multiple-tabular data. However, classical ML techniques mainly focus on single-tabular data. Multiple-tabular data refers to many-to-many mapping among joinable attributes and n-ary relations, which cannot be utilized directly by classical ML techniques. Besides, current graph techniques, like heterogeneous information network (HIN) and graph neural networks (GNN), are infeasible to be deployed directly and automatically in a multi-table environment, which limits the learning on databases. For automatic learning on relational databases, we propose an auto-table-join network (ATJ-Net). Multiple tables with relationships are considered as a hypergraph, where vertices are joinable attributes and hyperedges are tuples of tables. Then, ATJ-Net builds a graph neural network on the heterogeneous hypergraph, which samples and aggregates the vertices and hyperedges on n-hop sub-graphs as the receptive field. In order to enable ATJ-Net to be automatically deployed to different datasets and avoid the ”no free lunch” dilemma, we use random architecture search to select optimal aggregators and prune redundant paths in the network. For verifying the effectiveness of our methods across various tasks and schema, we conduct extensive experiments on 4 tasks, 8 various schemas, and 19 sub-datasets w.r.t. citing prediction, review classification, recommendation, and task-blind challenge. ATJ-Net achieves the best performance over state-of-the-art approaches on three tasks and is competitive with KddCup Winner solution on task-blind challenge.
{"title":"ATJ-Net: Auto-Table-Join Network for Automatic Learning on Relational Databases","authors":"Jinze Bai, Jialin Wang, Zhao Li, Donghui Ding, Ji Zhang, Jun Gao","doi":"10.1145/3442381.3449980","DOIUrl":"https://doi.org/10.1145/3442381.3449980","url":null,"abstract":"A relational database, consisting of multiple tables, provides heterogeneous information across various entities, widely used in real-world services. This paper studies the supervised learning task on multiple tables, aiming to predict one label column with the help of multiple-tabular data. However, classical ML techniques mainly focus on single-tabular data. Multiple-tabular data refers to many-to-many mapping among joinable attributes and n-ary relations, which cannot be utilized directly by classical ML techniques. Besides, current graph techniques, like heterogeneous information network (HIN) and graph neural networks (GNN), are infeasible to be deployed directly and automatically in a multi-table environment, which limits the learning on databases. For automatic learning on relational databases, we propose an auto-table-join network (ATJ-Net). Multiple tables with relationships are considered as a hypergraph, where vertices are joinable attributes and hyperedges are tuples of tables. Then, ATJ-Net builds a graph neural network on the heterogeneous hypergraph, which samples and aggregates the vertices and hyperedges on n-hop sub-graphs as the receptive field. In order to enable ATJ-Net to be automatically deployed to different datasets and avoid the ”no free lunch” dilemma, we use random architecture search to select optimal aggregators and prune redundant paths in the network. For verifying the effectiveness of our methods across various tasks and schema, we conduct extensive experiments on 4 tasks, 8 various schemas, and 19 sub-datasets w.r.t. citing prediction, review classification, recommendation, and task-blind challenge. ATJ-Net achieves the best performance over state-of-the-art approaches on three tasks and is competitive with KddCup Winner solution on task-blind challenge.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133039576","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}
Entity candidate retrieval plays a critical role in cross-lingual entity linking (XEL). In XEL, entity candidate retrieval needs to retrieve a list of plausible candidate entities from a large knowledge graph in a target language given a piece of text in a sentence or question, namely a mention, in a source language. Existing works mainly fall into two categories: lexicon-based and semantic-based approaches. The lexicon-based approach usually creates cross-lingual and mention-entity lexicons, which is effective but relies heavily on bilingual resources (e.g. inter-language links in Wikipedia). The semantic-based approach maps mentions and entities in different languages to a unified embedding space, which reduces dependence on large-scale bilingual dictionaries. However, its effectiveness is limited by the representation capacity of fixed-length vectors. In this paper, we propose a pivot-based approach which inherits the advantages of the aforementioned two approaches while avoiding their limitations. It takes an intermediary set of plausible target-language mentions as pivots to bridge the two types of gaps: cross-lingual gap and mention-entity gap. Specifically, it first converts mentions in the source language into an intermediary set of plausible mentions in the target language by cross-lingual semantic retrieval and a selective mechanism, and then retrieves candidate entities based on the generated mentions by lexical retrieval. The proposed approach only relies on a small bilingual word dictionary, and fully exploits the benefits of both lexical and semantic matching. Experimental results on two challenging cross-lingual entity linking datasets spanning over 11 languages show that the pivot-based approach outperforms both the lexicon-based and semantic-based approach by a large margin.
{"title":"Pivot-based Candidate Retrieval for Cross-lingual Entity Linking","authors":"Qian Liu, Xiubo Geng, Jie Lu, Daxin Jiang","doi":"10.1145/3442381.3449852","DOIUrl":"https://doi.org/10.1145/3442381.3449852","url":null,"abstract":"Entity candidate retrieval plays a critical role in cross-lingual entity linking (XEL). In XEL, entity candidate retrieval needs to retrieve a list of plausible candidate entities from a large knowledge graph in a target language given a piece of text in a sentence or question, namely a mention, in a source language. Existing works mainly fall into two categories: lexicon-based and semantic-based approaches. The lexicon-based approach usually creates cross-lingual and mention-entity lexicons, which is effective but relies heavily on bilingual resources (e.g. inter-language links in Wikipedia). The semantic-based approach maps mentions and entities in different languages to a unified embedding space, which reduces dependence on large-scale bilingual dictionaries. However, its effectiveness is limited by the representation capacity of fixed-length vectors. In this paper, we propose a pivot-based approach which inherits the advantages of the aforementioned two approaches while avoiding their limitations. It takes an intermediary set of plausible target-language mentions as pivots to bridge the two types of gaps: cross-lingual gap and mention-entity gap. Specifically, it first converts mentions in the source language into an intermediary set of plausible mentions in the target language by cross-lingual semantic retrieval and a selective mechanism, and then retrieves candidate entities based on the generated mentions by lexical retrieval. The proposed approach only relies on a small bilingual word dictionary, and fully exploits the benefits of both lexical and semantic matching. Experimental results on two challenging cross-lingual entity linking datasets spanning over 11 languages show that the pivot-based approach outperforms both the lexicon-based and semantic-based approach by a large margin.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122940370","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}
Weakly-typed languages such as PHP support loosely comparing two operands by implicitly converting their types and values. Such a language feature is widely used but can also pose severe security threats. In certain conditions, loose comparisons can cause unexpected results, leading to authentication bypass and other functionality problems. In this paper, we present the first in-depth study of such loose comparison bugs. We develop LChecker, a system to statically detect PHP loose comparison bugs. It employs a context-sensitive inter-procedural data-flow analysis together with several new techniques. We also enhance the PHP interpreter to help dynamically validate the detected bugs. Our evaluation shows that LChecker can both effectively and efficiently detect PHP loose comparison bugs with a reasonably low false-positive rate. It also successfully detected all previously known bugs in our evaluation dataset with no false negative. Using LChecker, we discovered 42 new loose comparison bugs and were assigned 9 new CVE IDs.
{"title":"LChecker: Detecting Loose Comparison Bugs in PHP","authors":"Penghui Li, W. Meng","doi":"10.1145/3442381.3449826","DOIUrl":"https://doi.org/10.1145/3442381.3449826","url":null,"abstract":"Weakly-typed languages such as PHP support loosely comparing two operands by implicitly converting their types and values. Such a language feature is widely used but can also pose severe security threats. In certain conditions, loose comparisons can cause unexpected results, leading to authentication bypass and other functionality problems. In this paper, we present the first in-depth study of such loose comparison bugs. We develop LChecker, a system to statically detect PHP loose comparison bugs. It employs a context-sensitive inter-procedural data-flow analysis together with several new techniques. We also enhance the PHP interpreter to help dynamically validate the detected bugs. Our evaluation shows that LChecker can both effectively and efficiently detect PHP loose comparison bugs with a reasonably low false-positive rate. It also successfully detected all previously known bugs in our evaluation dataset with no false negative. Using LChecker, we discovered 42 new loose comparison bugs and were assigned 9 new CVE IDs.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123847170","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}
Agathe Balayn, Panagiotis Soilis, C. Lofi, Jie Yang, A. Bozzon
Global interpretability is a vital requirement for image classification applications. Existing interpretability methods mainly explain a model behavior by identifying salient image patches, which require manual efforts from users to make sense of, and also do not typically support model validation with questions that investigate multiple visual concepts. In this paper, we introduce a scalable human-in-the-loop approach for global interpretability. Salient image areas identified by local interpretability methods are annotated with semantic concepts, which are then aggregated into a tabular representation of images to facilitate automatic statistical analysis of model behavior. We show that this approach answers interpretability needs for both model validation and exploration, and provides semantically more diverse, informative, and relevant explanations while still allowing for scalable and cost-efficient execution.
{"title":"What do You Mean? Interpreting Image Classification with Crowdsourced Concept Extraction and Analysis","authors":"Agathe Balayn, Panagiotis Soilis, C. Lofi, Jie Yang, A. Bozzon","doi":"10.1145/3442381.3450069","DOIUrl":"https://doi.org/10.1145/3442381.3450069","url":null,"abstract":"Global interpretability is a vital requirement for image classification applications. Existing interpretability methods mainly explain a model behavior by identifying salient image patches, which require manual efforts from users to make sense of, and also do not typically support model validation with questions that investigate multiple visual concepts. In this paper, we introduce a scalable human-in-the-loop approach for global interpretability. Salient image areas identified by local interpretability methods are annotated with semantic concepts, which are then aggregated into a tabular representation of images to facilitate automatic statistical analysis of model behavior. We show that this approach answers interpretability needs for both model validation and exploration, and provides semantically more diverse, informative, and relevant explanations while still allowing for scalable and cost-efficient execution.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124056670","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}
Qinghai Zhou, Liangyue Li, Xintao Wu, Nan Cao, Lei Ying, Hanghang Tong
Network alignment finds node correspondences across multiple networks, where the alignment accuracy is of crucial importance because of its profound impact on downstream applications. The vast majority of existing works focus on how to best utilize the topology and attribute information of the input networks as well as the anchor links when available. Nonetheless, it has not been well studied on how to boost the alignment performance through actively obtaining high-quality and informative anchor links, with a few exceptions. The sparse literature on active network alignment introduces the human in the loop to label some seed node correspondence (i.e., anchor links), which are informative from the perspective of querying the most uncertain node given few potential matchings. However, the direct influence of the intrinsic network attribute information on the alignment results has largely remained unknown. In this paper, we tackle this challenge and propose an active network alignment method (Attent) to identify the best nodes to query. The key idea of the proposed method is to leverage effective and efficient influence functions defined over the alignment solution to evaluate the goodness of the candidate nodes for query. Our proposed query strategy bears three distinct advantages, including (1) effectiveness, being able to accurately quantify the influence of the candidate nodes on the alignment results; (2) efficiency, scaling linearly with 15 − 17 × speed-up over the straight-forward implementation without any quality loss; (3) generality, consistently improving alignment performance of a variety of network alignment algorithms.
{"title":"Attent: Active Attributed Network Alignment","authors":"Qinghai Zhou, Liangyue Li, Xintao Wu, Nan Cao, Lei Ying, Hanghang Tong","doi":"10.1145/3442381.3449886","DOIUrl":"https://doi.org/10.1145/3442381.3449886","url":null,"abstract":"Network alignment finds node correspondences across multiple networks, where the alignment accuracy is of crucial importance because of its profound impact on downstream applications. The vast majority of existing works focus on how to best utilize the topology and attribute information of the input networks as well as the anchor links when available. Nonetheless, it has not been well studied on how to boost the alignment performance through actively obtaining high-quality and informative anchor links, with a few exceptions. The sparse literature on active network alignment introduces the human in the loop to label some seed node correspondence (i.e., anchor links), which are informative from the perspective of querying the most uncertain node given few potential matchings. However, the direct influence of the intrinsic network attribute information on the alignment results has largely remained unknown. In this paper, we tackle this challenge and propose an active network alignment method (Attent) to identify the best nodes to query. The key idea of the proposed method is to leverage effective and efficient influence functions defined over the alignment solution to evaluate the goodness of the candidate nodes for query. Our proposed query strategy bears three distinct advantages, including (1) effectiveness, being able to accurately quantify the influence of the candidate nodes on the alignment results; (2) efficiency, scaling linearly with 15 − 17 × speed-up over the straight-forward implementation without any quality loss; (3) generality, consistently improving alignment performance of a variety of network alignment algorithms.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124555927","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}
Conventional sketching methods on counting stream item frequencies use hash functions for mapping data items to a concise structure, e.g., a two-dimensional array, at the expense of overcounting due to hashing collisions. Despite the popularity, however, the accumulated errors originated in hashing collisions deteriorate the sketching accuracies at the rapid pace of data increasing, which poses a great challenge to sketch big data streams at web scale. In this paper, we propose a novel structure, called XY-sketch, which estimates the frequency of a data item by estimating the probability of this item appearing in the data stream. The framework associated with XY-sketch consists of two phases, namely decomposition and recomposition phases. A data item is split into a set of compactly stored basic elements, which can be stringed up in a probabilistic manner for query evaluation during the recomposition phase. Throughout, we conduct optimization under space constraints and detailed theoretical analysis. Experiments on both real and synthetic datasets are done to show the superior scalability on sketching large-scale streams. Remarkably, XY-sketch is orders of magnitudes more accurate than existing solutions, when the space budget is small.
{"title":"XY-Sketch: on Sketching Data Streams at Web Scale","authors":"Yongqiang Liu, Xike Xie","doi":"10.1145/3442381.3449984","DOIUrl":"https://doi.org/10.1145/3442381.3449984","url":null,"abstract":"Conventional sketching methods on counting stream item frequencies use hash functions for mapping data items to a concise structure, e.g., a two-dimensional array, at the expense of overcounting due to hashing collisions. Despite the popularity, however, the accumulated errors originated in hashing collisions deteriorate the sketching accuracies at the rapid pace of data increasing, which poses a great challenge to sketch big data streams at web scale. In this paper, we propose a novel structure, called XY-sketch, which estimates the frequency of a data item by estimating the probability of this item appearing in the data stream. The framework associated with XY-sketch consists of two phases, namely decomposition and recomposition phases. A data item is split into a set of compactly stored basic elements, which can be stringed up in a probabilistic manner for query evaluation during the recomposition phase. Throughout, we conduct optimization under space constraints and detailed theoretical analysis. Experiments on both real and synthetic datasets are done to show the superior scalability on sketching large-scale streams. Remarkably, XY-sketch is orders of magnitudes more accurate than existing solutions, when the space budget is small.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127807576","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}
One of the major obstacles that currently prevents the Semantic Web from exploiting its full potential is that the data it provides access to is sometimes not available or outdated. The reason is rooted deep within its architecture that relies on data providers to keep the data available, queryable, and up-to-date at all times – an expectation that many data providers in reality cannot live up to for an extended (or infinite) period of time. Hence, decentralized architectures have recently been proposed that use replication to keep the data available in case the data provider fails. Although this increases availability, it does not help keeping the data up-to-date or allow users to query and access previous versions of a dataset. In this paper, we therefore propose ColChain (COLlaborative knowledge CHAINs), a novel decentralized architecture based on blockchains that not only lowers the burden for the data providers but at the same time also allows users to propose updates to faulty or outdated data, trace updates back to their origin, and query older versions of the data. Our extensive experiments show that ColChain reaches these goals while achieving query processing performance comparable to the state of the art.
{"title":"ColChain: Collaborative Linked Data Networks","authors":"Christian Aebeloe, Gabriela Montoya, K. Hose","doi":"10.1145/3442381.3450037","DOIUrl":"https://doi.org/10.1145/3442381.3450037","url":null,"abstract":"One of the major obstacles that currently prevents the Semantic Web from exploiting its full potential is that the data it provides access to is sometimes not available or outdated. The reason is rooted deep within its architecture that relies on data providers to keep the data available, queryable, and up-to-date at all times – an expectation that many data providers in reality cannot live up to for an extended (or infinite) period of time. Hence, decentralized architectures have recently been proposed that use replication to keep the data available in case the data provider fails. Although this increases availability, it does not help keeping the data up-to-date or allow users to query and access previous versions of a dataset. In this paper, we therefore propose ColChain (COLlaborative knowledge CHAINs), a novel decentralized architecture based on blockchains that not only lowers the burden for the data providers but at the same time also allows users to propose updates to faulty or outdated data, trace updates back to their origin, and query older versions of the data. Our extensive experiments show that ColChain reaches these goals while achieving query processing performance comparable to the state of the art.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128938699","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}
Paul Murley, Zane Ma, Joshua Mason, Michael Bailey, Amin Kharraz
Developers are increasingly deploying web applications which require real-time bidirectional updates, a use case which does not naturally align with the traditional client-server architecture of the web. Many solutions have arisen to address this need over the preceding decades, including HTTP polling, Server-Sent Events, and WebSockets. This paper investigates this ecosystem and reports on the prevalence, benefits, and drawbacks of these technologies, with a particular focus on the adoption of WebSockets. We crawl the Tranco Top 1 Million websites to build a dataset for studying real-time updates in the wild. We find that HTTP Polling remains significantly more common than WebSockets, and WebSocket adoption appears to have stagnated in the past two to three years. We investigate some of the possible reasons for this decrease in the rate of adoption, and we contrast the adoption process to that of other web technologies. Our findings further suggest that even when WebSockets are employed, the prescribed best practices for securing them are often disregarded. The dataset is made available in the hopes that it may help inform the development of future real-time solutions for the web.
{"title":"WebSocket Adoption and the Landscape of the Real-Time Web","authors":"Paul Murley, Zane Ma, Joshua Mason, Michael Bailey, Amin Kharraz","doi":"10.1145/3442381.3450063","DOIUrl":"https://doi.org/10.1145/3442381.3450063","url":null,"abstract":"Developers are increasingly deploying web applications which require real-time bidirectional updates, a use case which does not naturally align with the traditional client-server architecture of the web. Many solutions have arisen to address this need over the preceding decades, including HTTP polling, Server-Sent Events, and WebSockets. This paper investigates this ecosystem and reports on the prevalence, benefits, and drawbacks of these technologies, with a particular focus on the adoption of WebSockets. We crawl the Tranco Top 1 Million websites to build a dataset for studying real-time updates in the wild. We find that HTTP Polling remains significantly more common than WebSockets, and WebSocket adoption appears to have stagnated in the past two to three years. We investigate some of the possible reasons for this decrease in the rate of adoption, and we contrast the adoption process to that of other web technologies. Our findings further suggest that even when WebSockets are employed, the prescribed best practices for securing them are often disregarded. The dataset is made available in the hopes that it may help inform the development of future real-time solutions for the web.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128815546","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}
With over 42 billion USD market capitalization (October 2020), Ethereum is the largest public blockchain that supports smart contracts. Recent works have modeled transactions, tokens, and other interactions in the Ethereum blockchain as static graphs to provide new observations and insights by conducting relevant graph analysis. Surprisingly, there is much less study on the evolution and temporal properties of these networks. In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graphs perspective. We study the growth rate and model of four Ethereum blockchain networks, active lifespan and update rate of high-degree vertices. We detect anomalies based on temporal changes in global network properties, and forecast the survival of network communities in succeeding months leveraging on the relevant graph features and machine learning models.
{"title":"Temporal Analysis of the Entire Ethereum Blockchain Network","authors":"Lin Zhao, Sourav Sengupta, Arijit Khan, Robby Luo","doi":"10.1145/3442381.3449916","DOIUrl":"https://doi.org/10.1145/3442381.3449916","url":null,"abstract":"With over 42 billion USD market capitalization (October 2020), Ethereum is the largest public blockchain that supports smart contracts. Recent works have modeled transactions, tokens, and other interactions in the Ethereum blockchain as static graphs to provide new observations and insights by conducting relevant graph analysis. Surprisingly, there is much less study on the evolution and temporal properties of these networks. In this paper, we investigate the evolutionary nature of Ethereum interaction networks from a temporal graphs perspective. We study the growth rate and model of four Ethereum blockchain networks, active lifespan and update rate of high-degree vertices. We detect anomalies based on temporal changes in global network properties, and forecast the survival of network communities in succeeding months leveraging on the relevant graph features and machine learning models.","PeriodicalId":106672,"journal":{"name":"Proceedings of the Web Conference 2021","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128752210","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：