Pub Date : 2019-04-01DOI: 10.1109/ICDEW.2019.000-2
Zhengmin Lai, Zhengyi Yang, Longbin Lai
The subgraph matching problem is defined to find all subgraphs of a data graph that are isomorphic to a given query graph. Subgraph matching plays a vital role in the fields of e-commerce, social media and biological science. CliqueJoin is a distributed subgraph matching algorithm that is designed to be efficient and scalable. However, CliqueJoin is originally developed on MapReduce, thus the performance of the algorithm can be affected by the notorious I/O issue of MapReduce while processing multi-round join tasks. Meanwhile, CliqueJoin does not propose a cost evaluation strategy for labelled graphs, which limits its application in practice where most real-world graphs are labelled. Targeting the limitations of CliqueJoin, we propose CliqueJoin++ to improve CliqueJoin in two aspects. Firstly, we implement CliqueJoin++ on the Timely dataflow system instead of MapReduce to avoid considerable I/O cost. Secondly, we extend the cost evaluation function in CliqueJoin to compute optimal join plans for labelled graphs in the distributed context. Extensive experiments have been conducted to show that the proposed method is up to 10 times faster than the MapReduce version for unlabelled matching, and it achieves good performance and scalability for labelled matching.
{"title":"Improving Distribued Subgraph Matching Algorithm on Timely Dataflow","authors":"Zhengmin Lai, Zhengyi Yang, Longbin Lai","doi":"10.1109/ICDEW.2019.000-2","DOIUrl":"https://doi.org/10.1109/ICDEW.2019.000-2","url":null,"abstract":"The subgraph matching problem is defined to find all subgraphs of a data graph that are isomorphic to a given query graph. Subgraph matching plays a vital role in the fields of e-commerce, social media and biological science. CliqueJoin is a distributed subgraph matching algorithm that is designed to be efficient and scalable. However, CliqueJoin is originally developed on MapReduce, thus the performance of the algorithm can be affected by the notorious I/O issue of MapReduce while processing multi-round join tasks. Meanwhile, CliqueJoin does not propose a cost evaluation strategy for labelled graphs, which limits its application in practice where most real-world graphs are labelled. Targeting the limitations of CliqueJoin, we propose CliqueJoin++ to improve CliqueJoin in two aspects. Firstly, we implement CliqueJoin++ on the Timely dataflow system instead of MapReduce to avoid considerable I/O cost. Secondly, we extend the cost evaluation function in CliqueJoin to compute optimal join plans for labelled graphs in the distributed context. Extensive experiments have been conducted to show that the proposed method is up to 10 times faster than the MapReduce version for unlabelled matching, and it achieves good performance and scalability for labelled matching.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132826279","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 : 2019-04-01DOI: 10.1109/ICDEW.2019.00-14
Yang Li, Yadan Luo, Zheng Zhang, S. Sadiq, Peng Cui
With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendation, a natural extension of POI recommendation, has attracted much attention. It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications. Existing approaches mainly model the spatial and temporal information, and memorise historical patterns through the user's trajectories for the recommendation. However, they suffer from the negative impact of missing and irregular check-in data, which significantly influences model performance. In this paper, we propose an attention-based sequence-to-sequence generative model, namely POI-Augmentation Seq2Seq (PA-Seq2Seq), to address the sparsity of training set by making check-in records to be evenly-spaced. Specifically, the encoder summarises each checkin sequence and the decoder predicts the possible missing checkins based on the encoded information. In order to learn timeaware correlation among user history, we employ local attention mechanism to help the decoder focus on a specific range of context information when predicting a certain missing check-in point. Extensive experiments have been conducted on two realworld check-in datasets, Gowalla and Brightkite, for performance and effectiveness evaluation.
{"title":"Context-Aware Attention-Based Data Augmentation for POI Recommendation","authors":"Yang Li, Yadan Luo, Zheng Zhang, S. Sadiq, Peng Cui","doi":"10.1109/ICDEW.2019.00-14","DOIUrl":"https://doi.org/10.1109/ICDEW.2019.00-14","url":null,"abstract":"With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendation, a natural extension of POI recommendation, has attracted much attention. It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications. Existing approaches mainly model the spatial and temporal information, and memorise historical patterns through the user's trajectories for the recommendation. However, they suffer from the negative impact of missing and irregular check-in data, which significantly influences model performance. In this paper, we propose an attention-based sequence-to-sequence generative model, namely POI-Augmentation Seq2Seq (PA-Seq2Seq), to address the sparsity of training set by making check-in records to be evenly-spaced. Specifically, the encoder summarises each checkin sequence and the decoder predicts the possible missing checkins based on the encoded information. In order to learn timeaware correlation among user history, we employ local attention mechanism to help the decoder focus on a specific range of context information when predicting a certain missing check-in point. Extensive experiments have been conducted on two realworld check-in datasets, Gowalla and Brightkite, for performance and effectiveness evaluation.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133680995","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 : 2019-04-01DOI: 10.1109/ICDEW.2019.00-11
Lei Li, Ruihai Dong, Li Chen
Context-aware recommender systems are able to produce more accurate recommendations by harnessing contextual information, such as consuming time and location. Further, user reviews as an important information resource, providing valuable information about users' preferences, items' aspects, and implicit contextual features, could be used to enhance the embeddings of users, items, and contexts. However, few works attempt to incorporate these two types of information, i.e., contexts and reviews, into their models. Recent state-of-the-art context-aware methods only characterize relations between two types of entities among users, items and contexts, which may be insufficient, as the final prediction is closely related to all the three types of entities. In this paper, we propose a novel model, named Context-aware Co-Attention Neural Network (CCANN), to dynamically infer relations between contexts and users/items, and subsequently to model the degree of matching between users' contextual preferences and items' context-aware aspects via co-attention mechanism. To better leverage the information from reviews, we propose an embedding method, named Entity2Vec, to jointly learn embeddings of different entities (users, items and contexts) with words in a textual review. Experimental results, on three datasets composed of millions of review records crawled from TripAdvisor, demonstrate that our CCANN significantly outperforms state-of-the-art recommendation methods, and Entity2Vec can further boost the model's performance.
{"title":"Context-Aware Co-attention Neural Network for Service Recommendations","authors":"Lei Li, Ruihai Dong, Li Chen","doi":"10.1109/ICDEW.2019.00-11","DOIUrl":"https://doi.org/10.1109/ICDEW.2019.00-11","url":null,"abstract":"Context-aware recommender systems are able to produce more accurate recommendations by harnessing contextual information, such as consuming time and location. Further, user reviews as an important information resource, providing valuable information about users' preferences, items' aspects, and implicit contextual features, could be used to enhance the embeddings of users, items, and contexts. However, few works attempt to incorporate these two types of information, i.e., contexts and reviews, into their models. Recent state-of-the-art context-aware methods only characterize relations between two types of entities among users, items and contexts, which may be insufficient, as the final prediction is closely related to all the three types of entities. In this paper, we propose a novel model, named Context-aware Co-Attention Neural Network (CCANN), to dynamically infer relations between contexts and users/items, and subsequently to model the degree of matching between users' contextual preferences and items' context-aware aspects via co-attention mechanism. To better leverage the information from reviews, we propose an embedding method, named Entity2Vec, to jointly learn embeddings of different entities (users, items and contexts) with words in a textual review. Experimental results, on three datasets composed of millions of review records crawled from TripAdvisor, demonstrate that our CCANN significantly outperforms state-of-the-art recommendation methods, and Entity2Vec can further boost the model's performance.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129096412","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 : 2019-04-01DOI: 10.1109/ICDEW.2019.000-3
Wanqi Liu, Dong Wen, Hanchen Wang, Fan Zhang, Xubo Wang
Nearest neighbor search is a fundamental problem in graph theory. In real-world applications, the multi-layer graph model is extensively studied to reveal the multi-dimensional relations between the graph entities. In this paper, we formulate a new problem named skyline nearest neighbor search on multi-layer graphs. Given a query vertex u, we aim to compute a set of skyline vertices that are not dominated by other vertices in terms of the shortest distance on all graph layers. We propose an early-termination algorithm instead of naively adopting the traditional skyline procedure as a subroutine. We also investigate the rule to optimize search order in the algorithm and further improve the algorithmic efficiency. The experimental results demonstrate that the optimization strategies work well on different graphs and can speed up the algorithm significantly.
{"title":"Skyline Nearest Neighbor Search on Multi-layer Graphs","authors":"Wanqi Liu, Dong Wen, Hanchen Wang, Fan Zhang, Xubo Wang","doi":"10.1109/ICDEW.2019.000-3","DOIUrl":"https://doi.org/10.1109/ICDEW.2019.000-3","url":null,"abstract":"Nearest neighbor search is a fundamental problem in graph theory. In real-world applications, the multi-layer graph model is extensively studied to reveal the multi-dimensional relations between the graph entities. In this paper, we formulate a new problem named skyline nearest neighbor search on multi-layer graphs. Given a query vertex u, we aim to compute a set of skyline vertices that are not dominated by other vertices in terms of the shortest distance on all graph layers. We propose an early-termination algorithm instead of naively adopting the traditional skyline procedure as a subroutine. We also investigate the rule to optimize search order in the algorithm and further improve the algorithmic efficiency. The experimental results demonstrate that the optimization strategies work well on different graphs and can speed up the algorithm significantly.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124068434","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 : 2019-04-01DOI: 10.1109/ICDEW.2019.00-19
Yifan Chen, Xiang Zhao, Jin-Yuan Liu, Bin Ge, Weiming Zhang
Recommending new items to users remains a challenge due to the absence of user's past preferences for these items. Item features from side information are typically leveraged to tackle the problem. Existing methods formulate regression models, taking as input item features and as output user ratings. Availing of high dimensional item features, these methods are confronted with the issue of overfitting, which greatly impedes recommendation experience. In this work, we opt for feature selection to solve the problem of recommending top-N new items with high-dimensional side information. Existing feature selection methods find a common set of features for all users, which fails to differentiate user preferences over item features. To achieve personalization for feature selection, we propose to select item features specifically for users. The refined features filtered out the dimensions that are irrelevant to recommendations or unappealing to users. The experiment results on real-life datasets with high-dimensional side information reveal that the proposed method is effective in singling out features crucial to top-N recommendations and hence boosting the performance.
{"title":"Learning to Select User-Specific Features for Top-N Recommendation of New Items","authors":"Yifan Chen, Xiang Zhao, Jin-Yuan Liu, Bin Ge, Weiming Zhang","doi":"10.1109/ICDEW.2019.00-19","DOIUrl":"https://doi.org/10.1109/ICDEW.2019.00-19","url":null,"abstract":"Recommending new items to users remains a challenge due to the absence of user's past preferences for these items. Item features from side information are typically leveraged to tackle the problem. Existing methods formulate regression models, taking as input item features and as output user ratings. Availing of high dimensional item features, these methods are confronted with the issue of overfitting, which greatly impedes recommendation experience. In this work, we opt for feature selection to solve the problem of recommending top-N new items with high-dimensional side information. Existing feature selection methods find a common set of features for all users, which fails to differentiate user preferences over item features. To achieve personalization for feature selection, we propose to select item features specifically for users. The refined features filtered out the dimensions that are irrelevant to recommendations or unappealing to users. The experiment results on real-life datasets with high-dimensional side information reveal that the proposed method is effective in singling out features crucial to top-N recommendations and hence boosting the performance.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132878574","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 : 2019-04-01DOI: 10.1109/ICDEW.2019.00-37
Hassan Mehmood, Ekaterina Gilman, Marta Cortés, Panos Kostakos, A. Byrne, K. Valta, Stavros Tekes, J. Riekki
Modern connected cities are more and more leveraging advances in ICT to improve their services and the quality of life of their inhabitants. The data generated from different sources, such as environmental sensors, social networking platforms, traffic counters, are harnessed to achieve these end goals. However, collecting, integrating, and analyzing all the heterogeneous data sources available from the cities is a challenge. This article suggests a data lake approach built on Big Data technologies, to gather all the data together for further analysis. The platform, described here, enables data collection, storage, integration, and further analysis and visualization of the results. This solution is the first attempt to integrate a diverse set of data sources from four pilot cities as part of the CUTLER project (Coastal urban development through the lenses of resiliency). The design and implementation details, as well as usage scenarios are presented in this paper.
{"title":"Implementing Big Data Lake for Heterogeneous Data Sources","authors":"Hassan Mehmood, Ekaterina Gilman, Marta Cortés, Panos Kostakos, A. Byrne, K. Valta, Stavros Tekes, J. Riekki","doi":"10.1109/ICDEW.2019.00-37","DOIUrl":"https://doi.org/10.1109/ICDEW.2019.00-37","url":null,"abstract":"Modern connected cities are more and more leveraging advances in ICT to improve their services and the quality of life of their inhabitants. The data generated from different sources, such as environmental sensors, social networking platforms, traffic counters, are harnessed to achieve these end goals. However, collecting, integrating, and analyzing all the heterogeneous data sources available from the cities is a challenge. This article suggests a data lake approach built on Big Data technologies, to gather all the data together for further analysis. The platform, described here, enables data collection, storage, integration, and further analysis and visualization of the results. This solution is the first attempt to integrate a diverse set of data sources from four pilot cities as part of the CUTLER project (Coastal urban development through the lenses of resiliency). The design and implementation details, as well as usage scenarios are presented in this paper.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"7 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121311524","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 : 2019-04-01DOI: 10.1109/ICDEW.2019.00-33
Ziang Zhao, Yunfan Kang, A. Magdy, Win Colton Cowger, A. Gray
In the recent years, human litter, such as food waste, diapers, construction materials, used motor oil, hypodermic needles, etc, is causing growing problems for the environment and quality of life in modern cities. Data about this waste has a significant importance in the field of environmental sciences due to its important use cases that span saving marine life, reducing the risk from natural hazards, community cleaning efforts, etc. In addition, such litter spreads several diseases in urban areas with high populations such as undeveloped neighborhoods in large modern cities. In this paper, we introduce a data-driven approach that enables environmental scientists and organizations to track, manage, and model human litter data at a large scale through smart technologies. We make a major on-going effort to collect and maintain this data worldwide from different sources through a community of environmental scientists and partner organizations. With the increasing volume of collected datasets, existing software packages, such as GIS software, do not scale to process, query, and visualize such data. To overcome this, we provide a scalable data management and visualization framework that digests datasets from different sources, with different formats, in a scalable backend that cleans, integrates, and unifies them in a structured form. On top of this backend, frontend applications are built to visualize litter data at multiple spatial levels, from continents and oceans to street level, to enable new opportunities for both environmental scientists and organizations to track, model, and clean up litter data. The framework is currently managing thirty real datasets and provide different interfaces for different kinds of users.
{"title":"A Data-Driven Approach for Tracking Human Litter in Modern Cities","authors":"Ziang Zhao, Yunfan Kang, A. Magdy, Win Colton Cowger, A. Gray","doi":"10.1109/ICDEW.2019.00-33","DOIUrl":"https://doi.org/10.1109/ICDEW.2019.00-33","url":null,"abstract":"In the recent years, human litter, such as food waste, diapers, construction materials, used motor oil, hypodermic needles, etc, is causing growing problems for the environment and quality of life in modern cities. Data about this waste has a significant importance in the field of environmental sciences due to its important use cases that span saving marine life, reducing the risk from natural hazards, community cleaning efforts, etc. In addition, such litter spreads several diseases in urban areas with high populations such as undeveloped neighborhoods in large modern cities. In this paper, we introduce a data-driven approach that enables environmental scientists and organizations to track, manage, and model human litter data at a large scale through smart technologies. We make a major on-going effort to collect and maintain this data worldwide from different sources through a community of environmental scientists and partner organizations. With the increasing volume of collected datasets, existing software packages, such as GIS software, do not scale to process, query, and visualize such data. To overcome this, we provide a scalable data management and visualization framework that digests datasets from different sources, with different formats, in a scalable backend that cleans, integrates, and unifies them in a structured form. On top of this backend, frontend applications are built to visualize litter data at multiple spatial levels, from continents and oceans to street level, to enable new opportunities for both environmental scientists and organizations to track, model, and clean up litter data. The framework is currently managing thirty real datasets and provide different interfaces for different kinds of users.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115024302","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 : 2019-02-18DOI: 10.1109/ICDEW.2019.00-43
Chao Li, Balaji Palanisamy, Runhua Xu
The rise of smart contract systems such as Ethereum has resulted in a proliferation of blockchain-based decentralized applications including applications that store and manage a wide range of data. Current smart contracts are designed to be executed solely by miners and are revealed entirely on-chain, resulting in reduced scalability and privacy. In this paper, we discuss that scalability and privacy of smart contracts can be enhanced by splitting a given contract into an off-chain contract and an on-chain contract. Specifically, functions of the contract that involve high-cost computation or sensitive information can be split and included as the off-chain contract, that is signed and executed by only the interested participants. The proposed approach allows the participants to reach unanimous agreement off-chain when all of them are honest, allowing computing resources of miners to be saved and content of the off-chain contract to be hidden from the public. In case of a dispute caused by any dishonest participants, a signed copy of the off-chain contract can be revealed so that a verified instance can be created to make miners enforce the true execution result. Thus, honest participants have the ability to redress and penalize any fraudulent or dishonest behavior, which incentivizes all participants to honestly follow the agreed off-chain contract. We discuss techniques for splitting a contract into a pair of on/off-chain contracts and propose a mechanism to address the challenges of handling dishonest participants in the system. Our implementation and evaluation of the proposed approach using an example smart contract demonstrate the effectiveness of the proposed approach in Ethereum.
{"title":"Scalable and Privacy-Preserving Design of On/Off-Chain Smart Contracts","authors":"Chao Li, Balaji Palanisamy, Runhua Xu","doi":"10.1109/ICDEW.2019.00-43","DOIUrl":"https://doi.org/10.1109/ICDEW.2019.00-43","url":null,"abstract":"The rise of smart contract systems such as Ethereum has resulted in a proliferation of blockchain-based decentralized applications including applications that store and manage a wide range of data. Current smart contracts are designed to be executed solely by miners and are revealed entirely on-chain, resulting in reduced scalability and privacy. In this paper, we discuss that scalability and privacy of smart contracts can be enhanced by splitting a given contract into an off-chain contract and an on-chain contract. Specifically, functions of the contract that involve high-cost computation or sensitive information can be split and included as the off-chain contract, that is signed and executed by only the interested participants. The proposed approach allows the participants to reach unanimous agreement off-chain when all of them are honest, allowing computing resources of miners to be saved and content of the off-chain contract to be hidden from the public. In case of a dispute caused by any dishonest participants, a signed copy of the off-chain contract can be revealed so that a verified instance can be created to make miners enforce the true execution result. Thus, honest participants have the ability to redress and penalize any fraudulent or dishonest behavior, which incentivizes all participants to honestly follow the agreed off-chain contract. We discuss techniques for splitting a contract into a pair of on/off-chain contracts and propose a mechanism to address the challenges of handling dishonest participants in the system. Our implementation and evaluation of the proposed approach using an example smart contract demonstrate the effectiveness of the proposed approach in Ethereum.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125143100","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 : 2018-12-23DOI: 10.1109/ICDEW.2019.00006
Indraneil Paul, Abhinav Khattar, P. Kumaraguru, Manish Gupta, Shaan Chopra
Social network and publishing platforms, such as Twitter, support the concept of verification. Verified accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been repeated assertions by these platforms about verification not being tantamount to endorsement. However, a significant body of prior work suggests that possessing a verified status symbolizes enhanced credibility in the eyes of the platform audience. As a result, such a status is highly coveted among public figures and influencers. Hence, we attempt to characterize the network of verified users on Twitter and compare the results to similar analysis performed for the entire Twitter network. We extracted the entire network of verified users on Twitter (as of July 2018) and obtained 231,246 English user profiles and 79,213,811 connections. Subsequently, in the network analysis, we found that the sub-graph of verified users mirrors the full Twitter users graph in some aspects such as possessing a short diameter. However, our findings contrast with earlier findings on multiple aspects, such as the possession of a power law out-degree distribution, slight dissortativity, and a significantly higher reciprocity rate, as elucidated in the paper. Moreover, we attempt to gauge the presence of salient components within this sub-graph and detect the absence of homophily with respect to popularity, which again is in stark contrast to the full Twitter graph. Finally, we demonstrate stationarity in the time series of verified user activity levels. To the best of our knowledge, this work represents the first quantitative attempt at characterizing verified users on Twitter.
{"title":"Elites Tweet? Characterizing the Twitter Verified User Network","authors":"Indraneil Paul, Abhinav Khattar, P. Kumaraguru, Manish Gupta, Shaan Chopra","doi":"10.1109/ICDEW.2019.00006","DOIUrl":"https://doi.org/10.1109/ICDEW.2019.00006","url":null,"abstract":"Social network and publishing platforms, such as Twitter, support the concept of verification. Verified accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been repeated assertions by these platforms about verification not being tantamount to endorsement. However, a significant body of prior work suggests that possessing a verified status symbolizes enhanced credibility in the eyes of the platform audience. As a result, such a status is highly coveted among public figures and influencers. Hence, we attempt to characterize the network of verified users on Twitter and compare the results to similar analysis performed for the entire Twitter network. We extracted the entire network of verified users on Twitter (as of July 2018) and obtained 231,246 English user profiles and 79,213,811 connections. Subsequently, in the network analysis, we found that the sub-graph of verified users mirrors the full Twitter users graph in some aspects such as possessing a short diameter. However, our findings contrast with earlier findings on multiple aspects, such as the possession of a power law out-degree distribution, slight dissortativity, and a significantly higher reciprocity rate, as elucidated in the paper. Moreover, we attempt to gauge the presence of salient components within this sub-graph and detect the absence of homophily with respect to popularity, which again is in stark contrast to the full Twitter graph. Finally, we demonstrate stationarity in the time series of verified user activity levels. To the best of our knowledge, this work represents the first quantitative attempt at characterizing verified users on Twitter.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123806374","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}
Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Data in traditional "caching" data systems resides on secondary storage, and is read into main memory only when operated on. This limits system performance. Main memory data stores with data always in main memory are much faster. But this performance comes at a cost. In this paper, we analyze the costs of both in-memory operations and secondary storage operations where data is not "in cache". We study the performance impact of cache misses on caching system performance. The analysis considers both execution and storage costs. Based on our analysis, we derive cost/performance results for a data caching system [Deuteronomy and its Bw-tree] and a main memory system [MassTree] to understand where each demonstrates the best cost per operation, what is driving the cost differences, and the scale of the differences. This analysis (1) provides insight into why data caching systems continue to dominate the market; (2) points to higher performance that does not rely on simply increasing main memory cache size; and (3) suggests a path to lower costs and hence better cost/performance.
{"title":"Cost/Performance in Modern Data Stores: How Data Caching Systems Succeed","authors":"D. Lomet","doi":"10.1145/3211922.3211927","DOIUrl":"https://doi.org/10.1145/3211922.3211927","url":null,"abstract":"Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Data in traditional \"caching\" data systems resides on secondary storage, and is read into main memory only when operated on. This limits system performance. Main memory data stores with data always in main memory are much faster. But this performance comes at a cost. In this paper, we analyze the costs of both in-memory operations and secondary storage operations where data is not \"in cache\". We study the performance impact of cache misses on caching system performance. The analysis considers both execution and storage costs. Based on our analysis, we derive cost/performance results for a data caching system [Deuteronomy and its Bw-tree] and a main memory system [MassTree] to understand where each demonstrates the best cost per operation, what is driving the cost differences, and the scale of the differences. This analysis (1) provides insight into why data caching systems continue to dominate the market; (2) points to higher performance that does not rely on simply increasing main memory cache size; and (3) suggests a path to lower costs and hence better cost/performance.","PeriodicalId":186190,"journal":{"name":"2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134599190","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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