Pub Date : 2014-05-19DOI: 10.1109/ICDE.2014.6816707
W. Kang, A. Tung, Feng Zhao, Xinyu Li
In recent years, much effort has been invested in analyzing social network data. However, it remains a great challenge to support interactive exploration of such huge amounts of data. In this paper, we propose Vesta, a system that enables visual exploration of social network data via tag clouds. Under Vesta, users can interactively explore and extract summaries of social network contents published in a certain spatial region during a certain period of time. These summaries are represented using a novel concept called hierarchical tag clouds, which allows users to zoom in/out to explore more specific/general tag summaries. In Vesta, the spatiotemporal data is split into partitions. A novel biclustering approach is applied for each partition to extract summaries, which are then used to construct a hierarchical latent Dirichlet allocation model to generate a topic hierarchy. At runtime, the topic hierarchies in the relevant partitions of the user-specified region are merged in a probabilistic manner to form tag hierarchies, which are used to construct interactive hierarchical tag clouds for visualization. The result of an extensive experimental study verifies the efficiency and effectiveness of Vesta.
{"title":"Interactive hierarchical tag clouds for summarizing spatiotemporal social contents","authors":"W. Kang, A. Tung, Feng Zhao, Xinyu Li","doi":"10.1109/ICDE.2014.6816707","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816707","url":null,"abstract":"In recent years, much effort has been invested in analyzing social network data. However, it remains a great challenge to support interactive exploration of such huge amounts of data. In this paper, we propose Vesta, a system that enables visual exploration of social network data via tag clouds. Under Vesta, users can interactively explore and extract summaries of social network contents published in a certain spatial region during a certain period of time. These summaries are represented using a novel concept called hierarchical tag clouds, which allows users to zoom in/out to explore more specific/general tag summaries. In Vesta, the spatiotemporal data is split into partitions. A novel biclustering approach is applied for each partition to extract summaries, which are then used to construct a hierarchical latent Dirichlet allocation model to generate a topic hierarchy. At runtime, the topic hierarchies in the relevant partitions of the user-specified region are merged in a probabilistic manner to form tag hierarchies, which are used to construct interactive hierarchical tag clouds for visualization. The result of an extensive experimental study verifies the efficiency and effectiveness of Vesta.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117107664","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 : 2014-05-19DOI: 10.1109/ICDE.2014.6816747
Ruilin Liu, Guan Wang, Wendy Hui Wang, Flip Korn
The completeness of data is vital to data quality. In this demo, we present iCoDA, a system that supports interactive, exploratory data completeness analysis. iCoDA provides algorithms and tools to generate tableau patterns that concisely summarize the incomplete data under various configuration settings. During the demo, the audience can use iCoDA to interactively explore the tableau patterns generated from incomplete data, with the flexibility of filtering and navigating through different granularity of these patterns. iCoDA supports various visualization methods to the audience for the display of tableau patterns. Overall, we will demonstrate that iCoDA provides sophisticated analysis of data completeness.
{"title":"iCoDA: Interactive and exploratory data completeness analysis","authors":"Ruilin Liu, Guan Wang, Wendy Hui Wang, Flip Korn","doi":"10.1109/ICDE.2014.6816747","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816747","url":null,"abstract":"The completeness of data is vital to data quality. In this demo, we present iCoDA, a system that supports interactive, exploratory data completeness analysis. iCoDA provides algorithms and tools to generate tableau patterns that concisely summarize the incomplete data under various configuration settings. During the demo, the audience can use iCoDA to interactively explore the tableau patterns generated from incomplete data, with the flexibility of filtering and navigating through different granularity of these patterns. iCoDA supports various visualization methods to the audience for the display of tableau patterns. Overall, we will demonstrate that iCoDA provides sophisticated analysis of data completeness.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115807205","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 : 2014-05-19DOI: 10.1109/ICDE.2014.6816673
Sofiane Abbar, Habibur Rahman, Saravanan Thirumuruganathan, Carlos Castillo, Gautam Das
We assume a database of items in which each item is described by a set of attributes, some of which could be multi-valued. We refer to each of the distinct attribute values as a feature. We also assume that we have information about the interactions (such as visits or likes) between a set of users and those items. In our paper, we would like to rank the features of an item using user-item interactions. For instance, if the items are movies, features could be actors, directors or genres, and user-item interaction could be user liking the movie. These information could be used to identify the most important actors for each movie. While users are drawn to an item due to a subset of its features, a user-item interaction only provides an expression of user preference over the entire item, and not its component features. We design algorithms to rank the features of an item depending on whether interaction information is available at aggregated or individual level granularity and extend them to rank composite features (set of features). Our algorithms are based on constrained least squares, network flow and non-trivial adaptations to non-negative matrix factorization. We evaluate our algorithms using both real-world and synthetic datasets.
{"title":"Ranking item features by mining online user-item interactions","authors":"Sofiane Abbar, Habibur Rahman, Saravanan Thirumuruganathan, Carlos Castillo, Gautam Das","doi":"10.1109/ICDE.2014.6816673","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816673","url":null,"abstract":"We assume a database of items in which each item is described by a set of attributes, some of which could be multi-valued. We refer to each of the distinct attribute values as a feature. We also assume that we have information about the interactions (such as visits or likes) between a set of users and those items. In our paper, we would like to rank the features of an item using user-item interactions. For instance, if the items are movies, features could be actors, directors or genres, and user-item interaction could be user liking the movie. These information could be used to identify the most important actors for each movie. While users are drawn to an item due to a subset of its features, a user-item interaction only provides an expression of user preference over the entire item, and not its component features. We design algorithms to rank the features of an item depending on whether interaction information is available at aggregated or individual level granularity and extend them to rank composite features (set of features). Our algorithms are based on constrained least squares, network flow and non-trivial adaptations to non-negative matrix factorization. We evaluate our algorithms using both real-world and synthetic datasets.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125136179","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 : 2014-05-19DOI: 10.1109/ICDE.2014.6816758
V. Slavov, A. Katib, P. Rao
We present a novel tool called XGossip for Internet-scale cardinality estimation of XPath queries over distributed XML data. XGossip relies on the principle of gossip, is scalable, decentralized, and can cope with network churn and failures. It employs a novel divide-and-conquer strategy for load balancing and reducing the overall network bandwidth consumption. It has a strong theoretical underpinning and provides provable guarantees on the accuracy of cardinality estimates, the number of messages exchanged, and the total bandwidth usage. In this demonstration, users will experience three engaging scenarios: In the first scenario, they can set up, configure, and deploy XGossip on Amazon Elastic Compute Cloud (EC2). In the second scenario, they can execute XGossip, pose XPath queries, observe in real-time the convergence speed of XGossip, the accuracy of cardinality estimates, the bandwidth usage, and the number of messages exchanged. In the third scenario, they can introduce network churn and failures during the execution of XGossip and observe how these impact the behavior of XGossip.
{"title":"A tool for Internet-scale cardinality estimation of XPath queries over distributed semistructured data","authors":"V. Slavov, A. Katib, P. Rao","doi":"10.1109/ICDE.2014.6816758","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816758","url":null,"abstract":"We present a novel tool called XGossip for Internet-scale cardinality estimation of XPath queries over distributed XML data. XGossip relies on the principle of gossip, is scalable, decentralized, and can cope with network churn and failures. It employs a novel divide-and-conquer strategy for load balancing and reducing the overall network bandwidth consumption. It has a strong theoretical underpinning and provides provable guarantees on the accuracy of cardinality estimates, the number of messages exchanged, and the total bandwidth usage. In this demonstration, users will experience three engaging scenarios: In the first scenario, they can set up, configure, and deploy XGossip on Amazon Elastic Compute Cloud (EC2). In the second scenario, they can execute XGossip, pose XPath queries, observe in real-time the convergence speed of XGossip, the accuracy of cardinality estimates, the bandwidth usage, and the number of messages exchanged. In the third scenario, they can introduce network churn and failures during the execution of XGossip and observe how these impact the behavior of XGossip.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132494008","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 : 2014-05-19DOI: 10.1109/ICDE.2014.6816649
A. Magdy, M. Mokbel, S. Elnikety, Suman Nath, Yuxiong He
This paper presents Mercury; a system for real-time support of top-k spatio-temporal queries on microblogs, where users are able to browse recent microblogs near their locations. With high arrival rates of microblogs, Mercury ensures real-time query response within a tight memory-constrained environment. Mercury bounds its search space to include only those microblogs that have arrived within certain spatial and temporal boundaries, in which only the top-k microblogs, according to a spatio-temporal ranking function, are returned in the search results. Mercury employs: (a) a scalable dynamic in-memory index structure that is capable of digesting all incoming microblogs, (b) an efficient query processor that exploits the in-memory index through spatio-temporal pruning techniques that reduce the number of visited microblogs to return the final answer, (c) an index size tuning module that dynamically finds and adjusts the minimum index size to ensure that incoming queries will be answered accurately, and (d) a load shedding technique that trades slight decrease in query accuracy for significant storage savings. Extensive experimental results based on a real-time Twitter Firehose feed and actual locations of Bing search queries show that Mercury supports high arrival rates of up to 64K microblogs/second and average query latency of 4 msec.
{"title":"Mercury: A memory-constrained spatio-temporal real-time search on microblogs","authors":"A. Magdy, M. Mokbel, S. Elnikety, Suman Nath, Yuxiong He","doi":"10.1109/ICDE.2014.6816649","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816649","url":null,"abstract":"This paper presents Mercury; a system for real-time support of top-k spatio-temporal queries on microblogs, where users are able to browse recent microblogs near their locations. With high arrival rates of microblogs, Mercury ensures real-time query response within a tight memory-constrained environment. Mercury bounds its search space to include only those microblogs that have arrived within certain spatial and temporal boundaries, in which only the top-k microblogs, according to a spatio-temporal ranking function, are returned in the search results. Mercury employs: (a) a scalable dynamic in-memory index structure that is capable of digesting all incoming microblogs, (b) an efficient query processor that exploits the in-memory index through spatio-temporal pruning techniques that reduce the number of visited microblogs to return the final answer, (c) an index size tuning module that dynamically finds and adjusts the minimum index size to ensure that incoming queries will be answered accurately, and (d) a load shedding technique that trades slight decrease in query accuracy for significant storage savings. Extensive experimental results based on a real-time Twitter Firehose feed and actual locations of Bing search queries show that Mercury supports high arrival rates of up to 64K microblogs/second and average query latency of 4 msec.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133459913","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 : 2014-05-19DOI: 10.1109/ICDE.2014.6816700
D. Anastasiu, G. Karypis
The All-Pairs similarity search, or self-similarity join problem, finds all pairs of vectors in a high dimensional sparse dataset with a similarity value higher than a given threshold. The problem has been classically solved using a dynamically built inverted index. The search time is reduced by early pruning of candidates using size and value-based bounds on the similarity. In the context of cosine similarity and weighted vectors, leveraging the Cauchy-Schwarz inequality, we propose new ℓ2-norm bounds for reducing the inverted index size, candidate pool size, and the number of full dot-product computations. We tighten previous candidate generation and verification bounds and introduce several new ones to further improve our algorithm's performance. Our new pruning strategies enable significant speedups over baseline approaches, most times outperforming even approximate solutions. We perform an extensive evaluation of our algorithm, L2AP, and compare against state-of-the-art exact and approximate methods, AllPairs, MMJoin, and BayesLSH, across a variety of real-world datasets and similarity thresholds.
all - pair相似性搜索，或自相似性连接问题，在一个高维稀疏数据集中寻找相似性值高于给定阈值的所有向量对。这个问题已经用动态建立的倒排索引经典地解决了。通过在相似性上使用大小和基于值的界限对候选对象进行早期修剪，减少了搜索时间。在余弦相似度和加权向量的背景下，利用Cauchy-Schwarz不等式，我们提出了新的2-范数界限，以减少倒索引大小，候选池大小和完整点积计算的数量。我们收紧了之前的候选生成和验证边界，并引入了几个新的边界来进一步提高算法的性能。我们的新修剪策略可以显著提高基线方法的速度，大多数情况下甚至优于近似解决方案。我们对L2AP算法进行了广泛的评估，并在各种真实数据集和相似阈值上与最先进的精确和近似方法AllPairs、MMJoin和BayesLSH进行了比较。
{"title":"L2AP: Fast cosine similarity search with prefix L-2 norm bounds","authors":"D. Anastasiu, G. Karypis","doi":"10.1109/ICDE.2014.6816700","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816700","url":null,"abstract":"The All-Pairs similarity search, or self-similarity join problem, finds all pairs of vectors in a high dimensional sparse dataset with a similarity value higher than a given threshold. The problem has been classically solved using a dynamically built inverted index. The search time is reduced by early pruning of candidates using size and value-based bounds on the similarity. In the context of cosine similarity and weighted vectors, leveraging the Cauchy-Schwarz inequality, we propose new ℓ2-norm bounds for reducing the inverted index size, candidate pool size, and the number of full dot-product computations. We tighten previous candidate generation and verification bounds and introduce several new ones to further improve our algorithm's performance. Our new pruning strategies enable significant speedups over baseline approaches, most times outperforming even approximate solutions. We perform an extensive evaluation of our algorithm, L2AP, and compare against state-of-the-art exact and approximate methods, AllPairs, MMJoin, and BayesLSH, across a variety of real-world datasets and similarity thresholds.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130134378","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 : 2014-05-19DOI: 10.1109/ICDE.2014.6816683
Viktor Leis, A. Kemper, Thomas Neumann
So far, transactional memory-although a promising technique-suffered from the absence of an efficient hardware implementation. The upcoming Haswell microarchitecture from Intel introduces hardware transactional memory (HTM) in mainstream CPUs. HTM allows for efficient concurrent, atomic operations, which is also highly desirable in the context of databases. On the other hand HTM has several limitations that, in general, prevent a one-to-one mapping of database transactions to HTM transactions. In this work we devise several building blocks that can be used to exploit HTM in main-memory databases. We show that HTM allows to achieve nearly lock-free processing of database transactions by carefully controlling the data layout and the access patterns. The HTM component is used for detecting the (infrequent) conflicts, which allows for an optimistic, and thus very low-overhead execution of concurrent transactions.
{"title":"Exploiting hardware transactional memory in main-memory databases","authors":"Viktor Leis, A. Kemper, Thomas Neumann","doi":"10.1109/ICDE.2014.6816683","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816683","url":null,"abstract":"So far, transactional memory-although a promising technique-suffered from the absence of an efficient hardware implementation. The upcoming Haswell microarchitecture from Intel introduces hardware transactional memory (HTM) in mainstream CPUs. HTM allows for efficient concurrent, atomic operations, which is also highly desirable in the context of databases. On the other hand HTM has several limitations that, in general, prevent a one-to-one mapping of database transactions to HTM transactions. In this work we devise several building blocks that can be used to exploit HTM in main-memory databases. We show that HTM allows to achieve nearly lock-free processing of database transactions by carefully controlling the data layout and the access patterns. The HTM component is used for detecting the (infrequent) conflicts, which allows for an optimistic, and thus very low-overhead execution of concurrent transactions.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130202737","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 : 2014-05-19DOI: 10.1109/ICDE.2014.6816745
G. Mecca, Paolo Papotti, Donatello Santoro
We call a data-transformation system any system that maps, translates and exchanges data across different representations. Nowadays, data architects are faced with a large variety of transformation tasks, and there is huge number of different approaches and systems that were conceived to solve them. As a consequence, it is very important to be able to evaluate such alternative solutions, in order to pick up the right ones for the problem at hand. To do this, we introduce IQ-Meter, the first comprehensive tool for the evaluation of data-transformation systems. IQ-Meter can be used to benchmark, test, and even learn the best usage of data-transformation tools. It builds on a number of novel algorithms to measure the quality of outputs and the human effort required by a given system, and ultimately measures “how much intelligence” the system brings to the solution of a data-translation task.
{"title":"IQ-METER - An evaluation tool for data-transformation systems","authors":"G. Mecca, Paolo Papotti, Donatello Santoro","doi":"10.1109/ICDE.2014.6816745","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816745","url":null,"abstract":"We call a data-transformation system any system that maps, translates and exchanges data across different representations. Nowadays, data architects are faced with a large variety of transformation tasks, and there is huge number of different approaches and systems that were conceived to solve them. As a consequence, it is very important to be able to evaluate such alternative solutions, in order to pick up the right ones for the problem at hand. To do this, we introduce IQ-Meter, the first comprehensive tool for the evaluation of data-transformation systems. IQ-Meter can be used to benchmark, test, and even learn the best usage of data-transformation tools. It builds on a number of novel algorithms to measure the quality of outputs and the human effort required by a given system, and ultimately measures “how much intelligence” the system brings to the solution of a data-translation task.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132618215","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 : 2014-05-19DOI: 10.1109/ICDE.2014.6816719
P. Wang, Wenbo He, Xue Liu
Recently map services (e.g., Google maps) and location-based online social networks (e.g., Foursquare) attract a lot of attention and businesses. With the increasing popularity of these location-based services, exploring and characterizing points of interests (PoIs) such as restaurants and hotels on maps provides valuable information for applications such as start-up marketing research. Due to the lack of a direct fully access to PoI databases, it is infeasible to exhaustively search and collect all PoIs within a large area using public APIs, which usually impose a limit on the maximum query rate. In this paper, we propose an effective and efficient method to sample PoIs on maps, and give unbiased estimators to calculate PoI statistics such as sum and average aggregates. Experimental results based on real datasets show that our method is efficient, and requires six times less queries than state-of-the-art methods to achieve the same accuracy.
{"title":"An efficient sampling method for characterizing points of interests on maps","authors":"P. Wang, Wenbo He, Xue Liu","doi":"10.1109/ICDE.2014.6816719","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816719","url":null,"abstract":"Recently map services (e.g., Google maps) and location-based online social networks (e.g., Foursquare) attract a lot of attention and businesses. With the increasing popularity of these location-based services, exploring and characterizing points of interests (PoIs) such as restaurants and hotels on maps provides valuable information for applications such as start-up marketing research. Due to the lack of a direct fully access to PoI databases, it is infeasible to exhaustively search and collect all PoIs within a large area using public APIs, which usually impose a limit on the maximum query rate. In this paper, we propose an effective and efficient method to sample PoIs on maps, and give unbiased estimators to calculate PoI statistics such as sum and average aggregates. Experimental results based on real datasets show that our method is efficient, and requires six times less queries than state-of-the-art methods to achieve the same accuracy.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124915749","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 : 2014-05-19DOI: 10.1109/ICDE.2014.6816635
Pei Lee, L. Lakshmanan, E. Milios
Dynamic networks are commonly found in the current web age. In scenarios like social networks and social media, dynamic networks are noisy, are of large-scale and evolve quickly. In this paper, we focus on the cluster evolution tracking problem on highly dynamic networks, with clear application to event evolution tracking. There are several previous works on data stream clustering using a node-by-node approach for maintaining clusters. However, handling of bulk updates, i.e., a subgraph at a time, is critical for achieving acceptable performance over very large highly dynamic networks. We propose a subgraph-by-subgraph incremental tracking framework for cluster evolution in this paper. To effectively illustrate the techniques in our framework, we consider the event evolution tracking task in social streams as an application, where a social stream and an event are modeled as a dynamic post network and a dynamic cluster respectively. By monitoring through a fading time window, we introduce a skeletal graph to summarize the information in the dynamic network, and formalize cluster evolution patterns using a group of primitive evolution operations and their algebra. Two incremental computation algorithms are developed to maintain clusters and track evolution patterns as time rolls on and the network evolves. Our detailed experimental evaluation on large Twitter datasets demonstrates that our framework can effectively track the complete set of cluster evolution patterns from highly dynamic networks on the fly.
{"title":"Incremental cluster evolution tracking from highly dynamic network data","authors":"Pei Lee, L. Lakshmanan, E. Milios","doi":"10.1109/ICDE.2014.6816635","DOIUrl":"https://doi.org/10.1109/ICDE.2014.6816635","url":null,"abstract":"Dynamic networks are commonly found in the current web age. In scenarios like social networks and social media, dynamic networks are noisy, are of large-scale and evolve quickly. In this paper, we focus on the cluster evolution tracking problem on highly dynamic networks, with clear application to event evolution tracking. There are several previous works on data stream clustering using a node-by-node approach for maintaining clusters. However, handling of bulk updates, i.e., a subgraph at a time, is critical for achieving acceptable performance over very large highly dynamic networks. We propose a subgraph-by-subgraph incremental tracking framework for cluster evolution in this paper. To effectively illustrate the techniques in our framework, we consider the event evolution tracking task in social streams as an application, where a social stream and an event are modeled as a dynamic post network and a dynamic cluster respectively. By monitoring through a fading time window, we introduce a skeletal graph to summarize the information in the dynamic network, and formalize cluster evolution patterns using a group of primitive evolution operations and their algebra. Two incremental computation algorithms are developed to maintain clusters and track evolution patterns as time rolls on and the network evolves. Our detailed experimental evaluation on large Twitter datasets demonstrates that our framework can effectively track the complete set of cluster evolution patterns from highly dynamic networks on the fly.","PeriodicalId":159130,"journal":{"name":"2014 IEEE 30th International Conference on Data Engineering","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122006405","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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