Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498343
Jiaqi Gu, Jin Wang, C. Zaniolo
There is a growing interest in pattern matching over complex event streams. While many bodies of techniques were proposed to search complex patterns and enhance the expressive power of query language, no previous work focused on supporting a well-defined ranking mechanism over answers using semantic ordering. To satisfy this need, we proposed CEPR, a CEP system capable of ranking matchings and emitting ordered results based on users' intentions via a novel query language. In this demo, we will (i) demonstrate language features, system architecture and functionalities, (ii) show examples of CEPR in various application domains and (iii) present a user-friendly interface to monitor query results and interact with the system in real time.
{"title":"Ranking support for matched patterns over complex event streams: The CEPR system","authors":"Jiaqi Gu, Jin Wang, C. Zaniolo","doi":"10.1109/ICDE.2016.7498343","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498343","url":null,"abstract":"There is a growing interest in pattern matching over complex event streams. While many bodies of techniques were proposed to search complex patterns and enhance the expressive power of query language, no previous work focused on supporting a well-defined ranking mechanism over answers using semantic ordering. To satisfy this need, we proposed CEPR, a CEP system capable of ranking matchings and emitting ordered results based on users' intentions via a novel query language. In this demo, we will (i) demonstrate language features, system architecture and functionalities, (ii) show examples of CEPR in various application domains and (iii) present a user-friendly interface to monitor query results and interact with the system in real time.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"4 1","pages":"1354-1357"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74856799","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 : 2016-05-16DOI: 10.1109/ICDE.2016.7498255
X. Yi, E. Bertino, Fang-Yu Rao, A. Bouguettaya
In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to find out some users whose profiles are similar to the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online data site, Ashley Madison, was hacked, which results in disclosure of a large number of dating user profiles. This serious data breach has urged researchers to explore practical privacy protection for user profiles in online dating. In this paper, we give a privacy-preserving solution for user profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out some matching users with the help of the multiple servers without revealing to anyone privacy of the query and the queried user profiles. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our implementation and experiments demonstrate that our solution is practical.
{"title":"Practical privacy-preserving user profile matching in social networks","authors":"X. Yi, E. Bertino, Fang-Yu Rao, A. Bouguettaya","doi":"10.1109/ICDE.2016.7498255","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498255","url":null,"abstract":"In this paper, we consider a scenario where a user queries a user profile database, maintained by a social networking service provider, to find out some users whose profiles are similar to the profile specified by the querying user. A typical example of this application is online dating. Most recently, an online data site, Ashley Madison, was hacked, which results in disclosure of a large number of dating user profiles. This serious data breach has urged researchers to explore practical privacy protection for user profiles in online dating. In this paper, we give a privacy-preserving solution for user profile matching in social networks by using multiple servers. Our solution is built on homomorphic encryption and allows a user to find out some matching users with the help of the multiple servers without revealing to anyone privacy of the query and the queried user profiles. Our solution achieves user profile privacy and user query privacy as long as at least one of the multiple servers is honest. Our implementation and experiments demonstrate that our solution is practical.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"92 1","pages":"373-384"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91259803","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 : 2016-05-16DOI: 10.1109/ICDE.2016.7498253
Won-Seok Hwang, J. Parc, Sang-Wook Kim, Jongwuk Lee, Dongwon Lee
We study how to improve the accuracy and running time of top-N recommendation with collaborative filtering (CF). Unlike existing works that use mostly rated items (which is only a small fraction in a rating matrix), we propose the notion of pre-use preferences of users toward a vast amount of unrated items. Using this novel notion, we effectively identify uninteresting items that were not rated yet but are likely to receive very low ratings from users, and impute them as zero. This simple-yet-novel zero-injection method applied to a set of carefully-chosen uninteresting items not only addresses the sparsity problem by enriching a rating matrix but also completely prevents uninteresting items from being recommended as top-N items, thereby improving accuracy greatly. As our proposed idea is method-agnostic, it can be easily applied to a wide variety of popular CF methods. Through comprehensive experiments using the Movielens dataset and MyMediaLite implementation, we successfully demonstrate that our solution consistently and universally improves the accuracies of popular CF methods (e.g., item-based CF, SVD-based CF, and SVD++) by two to five orders of magnitude on average. Furthermore, our approach reduces the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in experiments are available at: https://goo.gl/KUrmip.
{"title":"“Told you i didn't like it”: Exploiting uninteresting items for effective collaborative filtering","authors":"Won-Seok Hwang, J. Parc, Sang-Wook Kim, Jongwuk Lee, Dongwon Lee","doi":"10.1109/ICDE.2016.7498253","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498253","url":null,"abstract":"We study how to improve the accuracy and running time of top-N recommendation with collaborative filtering (CF). Unlike existing works that use mostly rated items (which is only a small fraction in a rating matrix), we propose the notion of pre-use preferences of users toward a vast amount of unrated items. Using this novel notion, we effectively identify uninteresting items that were not rated yet but are likely to receive very low ratings from users, and impute them as zero. This simple-yet-novel zero-injection method applied to a set of carefully-chosen uninteresting items not only addresses the sparsity problem by enriching a rating matrix but also completely prevents uninteresting items from being recommended as top-N items, thereby improving accuracy greatly. As our proposed idea is method-agnostic, it can be easily applied to a wide variety of popular CF methods. Through comprehensive experiments using the Movielens dataset and MyMediaLite implementation, we successfully demonstrate that our solution consistently and universally improves the accuracies of popular CF methods (e.g., item-based CF, SVD-based CF, and SVD++) by two to five orders of magnitude on average. Furthermore, our approach reduces the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in experiments are available at: https://goo.gl/KUrmip.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"49 1","pages":"349-360"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89247949","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 : 2016-05-16DOI: 10.1109/ICDE.2016.7498310
S. Böttcher, Rita Hartel, T. Jacobs, S. Maneth
XML tree structures can be effectively compressed using straight-line grammars. It has been an open problem how to update straight-line grammars, while keeping them compressed. Therefore, the best previous known methods resort to periodic decompression followed by compression from scratch. The decompression step is expensive, potentially with exponential running time. We present a method that avoids this expensive step. Our method recompresses the updated grammar directly, without prior decompression; it thus greatly outperforms the decompress-compress approach, in terms of both space and time. Our experiments show that the obtained grammars are similar or even smaller than those of the decompress-compress method.
{"title":"Incremental updates on compressed XML","authors":"S. Böttcher, Rita Hartel, T. Jacobs, S. Maneth","doi":"10.1109/ICDE.2016.7498310","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498310","url":null,"abstract":"XML tree structures can be effectively compressed using straight-line grammars. It has been an open problem how to update straight-line grammars, while keeping them compressed. Therefore, the best previous known methods resort to periodic decompression followed by compression from scratch. The decompression step is expensive, potentially with exponential running time. We present a method that avoids this expensive step. Our method recompresses the updated grammar directly, without prior decompression; it thus greatly outperforms the decompress-compress approach, in terms of both space and time. Our experiments show that the obtained grammars are similar or even smaller than those of the decompress-compress method.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"66 1","pages":"1026-1037"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85072227","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 : 2016-05-16DOI: 10.1109/ICDE.2016.7498297
Bolong Zheng, Kai Zheng, Xiaokui Xiao, Han Su, Hongzhi Yin, Xiaofang Zhou, Guohui Li
It is nowadays quite common for road networks to have textual contents on the vertices, which describe auxiliary information (e.g., business, traffic, etc.) associated with the vertex. In such road networks, which are modelled as weighted undirected graphs, each vertex is associated with one or more keywords, and each edge is assigned with a weight, which can be its physical length or travelling time. In this paper, we study the problem of keyword-aware continuous k nearest neighbour (KCkNN) search on road networks, which computes the k nearest vertices that contain the query keywords issued by a moving object and maintains the results continuously as the object is moving on the road network. Reducing the query processing costs in terms of computation and communication has attracted considerable attention in the database community with interesting techniques proposed. This paper proposes a framework, called a Labelling AppRoach for Continuous kNN query (LARC), on road networks to cope with KCkNN query efficiently. First we build a pivot-based reverse label index and a keyword-based pivot tree index to improve the efficiency of keyword-aware k nearest neighbour (KkNN) search by avoiding massive network traversals and sequential probe of keywords. To reduce the frequency of unnecessary result updates, we develop the concepts of dominance interval and region on road network, which share the similar intuition with safe region for processing continuous queries in Euclidean space but are more complicated and thus require more dedicated design. For high frequency keywords, we resolve the dominance interval when the query results changed. In addition, a path-based dominance updating approach is proposed to compute the dominance region efficiently when the query keywords are of low frequency. We conduct extensive experiments by comparing our algorithms with the state-of-the-art methods on real data sets. The empirical observations have verified the superiority of our proposed solution in all aspects of index size, communication cost and computation time.
{"title":"Keyword-aware continuous kNN query on road networks","authors":"Bolong Zheng, Kai Zheng, Xiaokui Xiao, Han Su, Hongzhi Yin, Xiaofang Zhou, Guohui Li","doi":"10.1109/ICDE.2016.7498297","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498297","url":null,"abstract":"It is nowadays quite common for road networks to have textual contents on the vertices, which describe auxiliary information (e.g., business, traffic, etc.) associated with the vertex. In such road networks, which are modelled as weighted undirected graphs, each vertex is associated with one or more keywords, and each edge is assigned with a weight, which can be its physical length or travelling time. In this paper, we study the problem of keyword-aware continuous k nearest neighbour (KCkNN) search on road networks, which computes the k nearest vertices that contain the query keywords issued by a moving object and maintains the results continuously as the object is moving on the road network. Reducing the query processing costs in terms of computation and communication has attracted considerable attention in the database community with interesting techniques proposed. This paper proposes a framework, called a Labelling AppRoach for Continuous kNN query (LARC), on road networks to cope with KCkNN query efficiently. First we build a pivot-based reverse label index and a keyword-based pivot tree index to improve the efficiency of keyword-aware k nearest neighbour (KkNN) search by avoiding massive network traversals and sequential probe of keywords. To reduce the frequency of unnecessary result updates, we develop the concepts of dominance interval and region on road network, which share the similar intuition with safe region for processing continuous queries in Euclidean space but are more complicated and thus require more dedicated design. For high frequency keywords, we resolve the dominance interval when the query results changed. In addition, a path-based dominance updating approach is proposed to compute the dominance region efficiently when the query keywords are of low frequency. We conduct extensive experiments by comparing our algorithms with the state-of-the-art methods on real data sets. The empirical observations have verified the superiority of our proposed solution in all aspects of index size, communication cost and computation time.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"85 1","pages":"871-882"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84001472","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 : 2016-05-16DOI: 10.1109/ICDE.2016.7498323
Xin Tang, R. Wehrmeister, J. Shau, Abhirup Chakraborty, Daley Alex, A. A. Omari, Feven Atnafu, Jeff Davis, Litao Deng, Deepak Jaiswal, C. Keswani, Yafeng Lu, Chao Ren, T. Reyes, Kashif Siddiqui, David E. Simmen, D. Vidhani, Ling Wang, Shuai Yang, Daniel Yu
There is increasing demand to integrate big data analytic systems using SQL. Given the vast ecosystem of SQL applications, enabling SQL capabilities allows big data platforms to expose their analytic potential to a wide variety of end users, accelerating discovery processes and providing significant business value. Most existing big data frameworks are based on one particular programming model such as MapReduce or Graph. However, data scientists are often forced to manually create adhoc data pipelines to connect various big data tools and platforms to serve their analytic needs. When the analytic tasks change, these data pipelines may be costly to modify and maintain. In this paper we present SQL-SA, a polymorphic and parallelizable SQL scalar and aggregate infrastructure in Aster 6.20. This infrastructure extends Aster 6's MapReduce and Graph capabilities to support polymorphic user-defined scalar and aggregate functions using flexible SQL syntax. The implementation enhances main Aster components including query syntax, API, planning and execution extensively. Integrating these new user-defined scalar and aggregate functions with Aster MapReduce and Graph functions, Aster 6.20 enables data scientists to integrate diverse programming models in a single SQL statement. The statement is automatically converted to an optimal data pipeline and executed in parallel. Using a real world business problem and data, Aster 6.20 demonstrates a significant performance advantage (25%+) over Hadoop Pig and Hive.
{"title":"SQL-SA for big data discovery polymorphic and parallelizable SQL user-defined scalar and aggregate infrastructure in Teradata Aster 6.20","authors":"Xin Tang, R. Wehrmeister, J. Shau, Abhirup Chakraborty, Daley Alex, A. A. Omari, Feven Atnafu, Jeff Davis, Litao Deng, Deepak Jaiswal, C. Keswani, Yafeng Lu, Chao Ren, T. Reyes, Kashif Siddiqui, David E. Simmen, D. Vidhani, Ling Wang, Shuai Yang, Daniel Yu","doi":"10.1109/ICDE.2016.7498323","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498323","url":null,"abstract":"There is increasing demand to integrate big data analytic systems using SQL. Given the vast ecosystem of SQL applications, enabling SQL capabilities allows big data platforms to expose their analytic potential to a wide variety of end users, accelerating discovery processes and providing significant business value. Most existing big data frameworks are based on one particular programming model such as MapReduce or Graph. However, data scientists are often forced to manually create adhoc data pipelines to connect various big data tools and platforms to serve their analytic needs. When the analytic tasks change, these data pipelines may be costly to modify and maintain. In this paper we present SQL-SA, a polymorphic and parallelizable SQL scalar and aggregate infrastructure in Aster 6.20. This infrastructure extends Aster 6's MapReduce and Graph capabilities to support polymorphic user-defined scalar and aggregate functions using flexible SQL syntax. The implementation enhances main Aster components including query syntax, API, planning and execution extensively. Integrating these new user-defined scalar and aggregate functions with Aster MapReduce and Graph functions, Aster 6.20 enables data scientists to integrate diverse programming models in a single SQL statement. The statement is automatically converted to an optimal data pipeline and executed in parallel. Using a real world business problem and data, Aster 6.20 demonstrates a significant performance advantage (25%+) over Hadoop Pig and Hive.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"92 1","pages":"1182-1193"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82685359","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 : 2016-05-16DOI: 10.1109/ICDE.2016.7498333
Niloy J. Mukherjee, S. Chavan, Maria Colgan, M. Gleeson, Xiaoming He, Allison L. Holloway, J. Kamp, Kartik Kulkarni, T. Lahiri, Juan R. Loaiza, N. MacNaughton, Atrayee Mullick, S. Muthulingam, V. Raja, Raunak Rungta
Modern data management systems are required to address new breeds of OLTAP applications. These applications demand real time analytical insights over massive data volumes not only on dedicated data warehouses but also on live mainstream production environments where data gets continuously ingested and modified. Oracle introduced the Database In-memory Option (DBIM) in 2014 as a unique dual row and column format architecture aimed to address the emerging space of mixed OLTAP applications along with traditional OLAP workloads. The architecture allows both the row format and the column format to be maintained simultaneously with strict transactional consistency. While the row format is persisted in underlying storage, the column format is maintained purely in-memory without incurring additional logging overheads in OLTP. Maintenance of columnar data purely in memory creates the need for distributed data management architectures. Performance of analytics incurs severe regressions in single server architectures during server failures as it takes non-trivial time to recover and rebuild terabytes of in-memory columnar format. A distributed and distribution aware architecture therefore becomes necessary to provide real time high availability of the columnar format for glitch-free in-memory analytic query execution across server failures and additions, besides providing scale out of capacity and compute to address real time throughput requirements over large volumes of in-memory data. In this paper, we will present the high availability aspects of the distributed architecture of Oracle DBIM that includes extremely scaled out application transparent column format duplication mechanism, distributed query execution on duplicated in-memory columnar format, and several scenarios of fault tolerant analytic query execution across the in-memory column format at various stages of redistribution of columnar data during cluster topology changes.
需要现代数据管理系统来解决新型OLTAP应用。这些应用程序需要对大量数据进行实时分析，不仅需要在专用数据仓库中，还需要在数据不断被摄取和修改的实时主流生产环境中。Oracle在2014年推出了数据库内存选项(Database in -memory Option, DBIM)，作为一种独特的双行双列格式架构，旨在解决混合OLTAP应用程序和传统OLAP工作负载的新兴空间。该体系结构允许同时维护行格式和列格式，并具有严格的事务一致性。虽然行格式在底层存储中持久化，但列格式完全在内存中维护，不会在OLTP中产生额外的日志开销。纯粹在内存中维护列数据需要分布式数据管理架构。在服务器故障期间，单服务器架构中的分析性能会导致严重的退化，因为恢复和重建内存中tb的列格式需要花费大量时间。因此，除了提供超出容量的扩展和计算来解决大量内存数据的实时吞吐量需求外，还需要分布式和分布感知架构来提供柱状格式的实时高可用性，以便跨服务器故障和添加执行无故障的内存分析查询。在本文中，我们将介绍Oracle DBIM分布式架构的高可用性方面，包括高度向外扩展的应用透明列格式复制机制，在重复的内存列格式上执行分布式查询，以及在集群拓扑变化期间，在列数据重新分配的各个阶段跨内存列格式执行容错分析查询的几个场景。
{"title":"Fault-tolerant real-time analytics with distributed Oracle Database In-memory","authors":"Niloy J. Mukherjee, S. Chavan, Maria Colgan, M. Gleeson, Xiaoming He, Allison L. Holloway, J. Kamp, Kartik Kulkarni, T. Lahiri, Juan R. Loaiza, N. MacNaughton, Atrayee Mullick, S. Muthulingam, V. Raja, Raunak Rungta","doi":"10.1109/ICDE.2016.7498333","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498333","url":null,"abstract":"Modern data management systems are required to address new breeds of OLTAP applications. These applications demand real time analytical insights over massive data volumes not only on dedicated data warehouses but also on live mainstream production environments where data gets continuously ingested and modified. Oracle introduced the Database In-memory Option (DBIM) in 2014 as a unique dual row and column format architecture aimed to address the emerging space of mixed OLTAP applications along with traditional OLAP workloads. The architecture allows both the row format and the column format to be maintained simultaneously with strict transactional consistency. While the row format is persisted in underlying storage, the column format is maintained purely in-memory without incurring additional logging overheads in OLTP. Maintenance of columnar data purely in memory creates the need for distributed data management architectures. Performance of analytics incurs severe regressions in single server architectures during server failures as it takes non-trivial time to recover and rebuild terabytes of in-memory columnar format. A distributed and distribution aware architecture therefore becomes necessary to provide real time high availability of the columnar format for glitch-free in-memory analytic query execution across server failures and additions, besides providing scale out of capacity and compute to address real time throughput requirements over large volumes of in-memory data. In this paper, we will present the high availability aspects of the distributed architecture of Oracle DBIM that includes extremely scaled out application transparent column format duplication mechanism, distributed query execution on duplicated in-memory columnar format, and several scenarios of fault tolerant analytic query execution across the in-memory column format at various stages of redistribution of columnar data during cluster topology changes.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"03 1","pages":"1298-1309"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86523050","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 : 2016-05-16DOI: 10.1109/ICDE.2016.7498334
F. Chirigati, Jérôme Siméon, Martin Hirzel, J. Freire
Increasingly, applications that deal with big data need to run analytics concurrently with updates. But bridging the gap between big and fast data is challenging: most of these applications require analytics' results that are fresh and consistent, but without impacting system latency and throughput. We propose virtual lightweight snapshots (VLS), a mechanism that enables consistent analytics without blocking incoming updates in NoSQL stores. VLS requires neither native support for database versioning nor a transaction manager. Besides, it is storage-efficient, keeping additional versions of records only when needed to guarantee consistency, and sharing versions across multiple concurrent snapshots. We describe an implementation of VLS in MongoDB and present a detailed experimental evaluation which shows that it supports consistency for analytics with small impact on query evaluation time, update throughput, and latency.
{"title":"Virtual lightweight snapshots for consistent analytics in NoSQL stores","authors":"F. Chirigati, Jérôme Siméon, Martin Hirzel, J. Freire","doi":"10.1109/ICDE.2016.7498334","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498334","url":null,"abstract":"Increasingly, applications that deal with big data need to run analytics concurrently with updates. But bridging the gap between big and fast data is challenging: most of these applications require analytics' results that are fresh and consistent, but without impacting system latency and throughput. We propose virtual lightweight snapshots (VLS), a mechanism that enables consistent analytics without blocking incoming updates in NoSQL stores. VLS requires neither native support for database versioning nor a transaction manager. Besides, it is storage-efficient, keeping additional versions of records only when needed to guarantee consistency, and sharing versions across multiple concurrent snapshots. We describe an implementation of VLS in MongoDB and present a detailed experimental evaluation which shows that it supports consistency for analytics with small impact on query evaluation time, update throughput, and latency.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"31 1","pages":"1310-1321"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86876369","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 : 2016-05-16DOI: 10.1109/ICDE.2016.7498263
Prateek Tandon, Faissal M. Sleiman, Michael J. Cafarella, T. Wenisch
Rapidly processing high-velocity text data is critical for many technical and business applications. Widely used software solutions for processing these large text corpora target disk-resident data and rely on pre-computed indexes and large clusters to achieve high performance. However, greater capacity and falling costs are enabling a shift to RAM-resident data sets. The enormous bandwidth of RAM can facilitate scan operations that are competitive with pre-computed indexes for interactive, ad-hoc queries. However, software approaches for processing these large text corpora fall far short of saturating available bandwidth and meeting peak scan rates possible on modern memory systems. In this paper, we present HAWK, a hardware accelerator for ad hoc queries against large in-memory logs. HAWK comprises a stall-free hardware pipeline that scans input data at a constant rate, examining multiple input characters in parallel during a single accelerator clock cycle. We describe a 1GHz 32-characterwide HAWK design targeting ASIC implementation, designed to process data at 32GB/s (up to two orders of magnitude faster than software solutions), and demonstrate a scaled-down FPGA prototype that operates at 100MHz with 4-wide parallelism, which processes at 400MB/s (13× faster than software grep for large multi-pattern scans).
{"title":"HAWK: Hardware support for unstructured log processing","authors":"Prateek Tandon, Faissal M. Sleiman, Michael J. Cafarella, T. Wenisch","doi":"10.1109/ICDE.2016.7498263","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498263","url":null,"abstract":"Rapidly processing high-velocity text data is critical for many technical and business applications. Widely used software solutions for processing these large text corpora target disk-resident data and rely on pre-computed indexes and large clusters to achieve high performance. However, greater capacity and falling costs are enabling a shift to RAM-resident data sets. The enormous bandwidth of RAM can facilitate scan operations that are competitive with pre-computed indexes for interactive, ad-hoc queries. However, software approaches for processing these large text corpora fall far short of saturating available bandwidth and meeting peak scan rates possible on modern memory systems. In this paper, we present HAWK, a hardware accelerator for ad hoc queries against large in-memory logs. HAWK comprises a stall-free hardware pipeline that scans input data at a constant rate, examining multiple input characters in parallel during a single accelerator clock cycle. We describe a 1GHz 32-characterwide HAWK design targeting ASIC implementation, designed to process data at 32GB/s (up to two orders of magnitude faster than software solutions), and demonstrate a scaled-down FPGA prototype that operates at 100MHz with 4-wide parallelism, which processes at 400MB/s (13× faster than software grep for large multi-pattern scans).","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"4 4 1","pages":"469-480"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75939254","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 : 2016-05-16DOI: 10.1109/ICDE.2016.7498276
Jianhua Yin, Jianyong Wang
Text clustering is a challenging problem due to the high-dimensional and large-volume characteristics of text datasets. In this paper, we propose a collapsed Gibbs Sampling algorithm for the Dirichlet Process Multinomial Mixture model for text clustering (abbr. to GSDPMM) which does not need to specify the number of clusters in advance and can cope with the high-dimensional problem of text clustering. Our extensive experimental study shows that GSDPMM can achieve significantly better performance than three other clustering methods and can achieve high consistency on both long and short text datasets. We found that GSDPMM has low time and space complexity and can scale well with huge text datasets. We also propose some novel and effective methods to detect the outliers in the dataset and obtain the representative words of each cluster.
{"title":"A model-based approach for text clustering with outlier detection","authors":"Jianhua Yin, Jianyong Wang","doi":"10.1109/ICDE.2016.7498276","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498276","url":null,"abstract":"Text clustering is a challenging problem due to the high-dimensional and large-volume characteristics of text datasets. In this paper, we propose a collapsed Gibbs Sampling algorithm for the Dirichlet Process Multinomial Mixture model for text clustering (abbr. to GSDPMM) which does not need to specify the number of clusters in advance and can cope with the high-dimensional problem of text clustering. Our extensive experimental study shows that GSDPMM can achieve significantly better performance than three other clustering methods and can achieve high consistency on both long and short text datasets. We found that GSDPMM has low time and space complexity and can scale well with huge text datasets. We also propose some novel and effective methods to detect the outliers in the dataset and obtain the representative words of each cluster.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"116 1","pages":"625-636"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79797791","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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