Indoor spaces accommodate large numbers of spatial objects, e.g., points of interest (POIs), and moving populations. A variety of services, e.g., location-based services and security control, are relevant to indoor spaces. Such services can be improved substantially if they are capable of utilizing indoor distances. However, existing indoor space models do not account well for indoor distances. To address this shortcoming, we propose a data management infrastructure that captures indoor distance and facilitates distance-aware query processing. In particular, we propose a distance-aware indoor space model that integrates indoor distance seamlessly. To enable the use of the model as a foundation for query processing, we develop accompanying, efficient algorithms that compute indoor distances for different indoor entities like doors as well as locations. We also propose an indexing framework that accommodates indoor distances that are pre-computed using the proposed algorithms. On top of this foundation, we develop efficient algorithms for typical indoor, distance-aware queries. The results of an extensive experimental evaluation demonstrate the efficacy of the proposals.
{"title":"A Foundation for Efficient Indoor Distance-Aware Query Processing","authors":"Hua Lu, Xin Cao, Christian S. Jensen","doi":"10.1109/ICDE.2012.44","DOIUrl":"https://doi.org/10.1109/ICDE.2012.44","url":null,"abstract":"Indoor spaces accommodate large numbers of spatial objects, e.g., points of interest (POIs), and moving populations. A variety of services, e.g., location-based services and security control, are relevant to indoor spaces. Such services can be improved substantially if they are capable of utilizing indoor distances. However, existing indoor space models do not account well for indoor distances. To address this shortcoming, we propose a data management infrastructure that captures indoor distance and facilitates distance-aware query processing. In particular, we propose a distance-aware indoor space model that integrates indoor distance seamlessly. To enable the use of the model as a foundation for query processing, we develop accompanying, efficient algorithms that compute indoor distances for different indoor entities like doors as well as locations. We also propose an indexing framework that accommodates indoor distances that are pre-computed using the proposed algorithms. On top of this foundation, we develop efficient algorithms for typical indoor, distance-aware queries. The results of an extensive experimental evaluation demonstrate the efficacy of the proposals.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132954012","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}
B. Gufler, Nikolaus Augsten, Angelika Reiser, A. Kemper
MapReduce has emerged as a popular tool for distributed and scalable processing of massive data sets and is being used increasingly in e-science applications. Unfortunately, the performance of MapReduce systems strongly depends on an even data distribution while scientific data sets are often highly skewed. The resulting load imbalance, which raises the processing time, is even amplified by high runtime complexity of the reducer tasks. An adaptive load balancing strategy is required for appropriate skew handling. In this paper, we address the problem of estimating the cost of the tasks that are distributed to the reducers based on a given cost model. An accurate cost estimation is the basis for adaptive load balancing algorithms and requires to gather statistics from the mappers. This is challenging: (a) Since the statistics from all mappers must be integrated, the mapper statistics must be small. (b) Although each mapper sees only a small fraction of the data, the integrated statistics must capture the global data distribution. (c) The mappers terminate after sending the statistics to the controller, and no second round is possible. Our solution to these challenges consists of two components. First, a monitoring component executed on every mapper captures the local data distribution and identifies its most relevant subset for cost estimation. Second, an integration component aggregates these subsets approximating the global data distribution.
{"title":"Load Balancing in MapReduce Based on Scalable Cardinality Estimates","authors":"B. Gufler, Nikolaus Augsten, Angelika Reiser, A. Kemper","doi":"10.1109/ICDE.2012.58","DOIUrl":"https://doi.org/10.1109/ICDE.2012.58","url":null,"abstract":"MapReduce has emerged as a popular tool for distributed and scalable processing of massive data sets and is being used increasingly in e-science applications. Unfortunately, the performance of MapReduce systems strongly depends on an even data distribution while scientific data sets are often highly skewed. The resulting load imbalance, which raises the processing time, is even amplified by high runtime complexity of the reducer tasks. An adaptive load balancing strategy is required for appropriate skew handling. In this paper, we address the problem of estimating the cost of the tasks that are distributed to the reducers based on a given cost model. An accurate cost estimation is the basis for adaptive load balancing algorithms and requires to gather statistics from the mappers. This is challenging: (a) Since the statistics from all mappers must be integrated, the mapper statistics must be small. (b) Although each mapper sees only a small fraction of the data, the integrated statistics must capture the global data distribution. (c) The mappers terminate after sending the statistics to the controller, and no second round is possible. Our solution to these challenges consists of two components. First, a monitoring component executed on every mapper captures the local data distribution and identifies its most relevant subset for cost estimation. Second, an integration component aggregates these subsets approximating the global data distribution.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132587368","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}
We demonstrate SWITCH, a deep embedding of relational queries into Ruby and Ruby on Rails. With SWITCH, there is no syntactic or stylistic difference between Ruby programs that operate over in-memory array objects or database-resident tables, even if these programs rely on array order or nesting. SWITCH's built-in compiler and SQL code generator guarantee to emit few queries, addressing long-standing performance problems that trace back to Rails' Active Record database binding. "Looks likes Ruby, but performs like handcrafted SQL, " is the ideal that drives the research and development effort behind SWITCH.
我们将演示SWITCH，一个在Ruby和Ruby on Rails中深度嵌入关系查询的工具。使用SWITCH，在操作内存中的数组对象或数据库驻留表的Ruby程序之间没有语法或风格上的区别，即使这些程序依赖于数组顺序或嵌套。SWITCH的内置编译器和SQL代码生成器保证发出很少的查询，解决了长期存在的性能问题，这些问题可以追溯到Rails的Active Record数据库绑定。“看起来像Ruby，但执行起来像手工制作的SQL”是驱动SWITCH背后研究和开发工作的理想。
{"title":"A Deep Embedding of Queries into Ruby","authors":"Torsten Grust, Manuel Mayr","doi":"10.1109/ICDE.2012.121","DOIUrl":"https://doi.org/10.1109/ICDE.2012.121","url":null,"abstract":"We demonstrate SWITCH, a deep embedding of relational queries into Ruby and Ruby on Rails. With SWITCH, there is no syntactic or stylistic difference between Ruby programs that operate over in-memory array objects or database-resident tables, even if these programs rely on array order or nesting. SWITCH's built-in compiler and SQL code generator guarantee to emit few queries, addressing long-standing performance problems that trace back to Rails' Active Record database binding. \"Looks likes Ruby, but performs like handcrafted SQL, \" is the ideal that drives the research and development effort behind SWITCH.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131362926","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}
Most objects and data in the real world are interconnected, forming complex, heterogeneous but often semistructured information networks. However, many database researchers consider a database merely as a data repository that supports storage and retrieval rather than an information-rich, inter-related and multi-typed information network that supports comprehensive data analysis, whereas many network researchers focus on homogeneous networks. Departing from both, we view interconnected, semi-structured datasets as heterogeneous, information-rich networks and study how to uncover hidden knowledge in such networks. For example, a university database can be viewed as a heterogeneous information network, where objects of multiple types, such as students, professors, courses, departments, and multiple typed relationships, such as teach and advise are intertwined together, providing abundant information. In this tutorial, we present an organized picture on mining heterogeneous information networks and introduce a set of interesting, effective and scalable network mining methods. The topics to be covered include (i) database as an information network, (ii) mining information networks: clustering, classification, ranking, similarity search, and meta path-guided analysis, (iii) construction of quality, informative networks by data mining, (iv) trend and evolution analysis in heterogeneous information networks, and (v) research frontiers. We show that heterogeneous information networks are informative, and link analysis on such networks is powerful at uncovering critical knowledge hidden in large semi-structured datasets. Finally, we also present a few promising research directions.
{"title":"Mining Knowledge from Data: An Information Network Analysis Approach","authors":"Jiawei Han, Yizhou Sun, Xifeng Yan, Philip S. Yu","doi":"10.1109/ICDE.2012.145","DOIUrl":"https://doi.org/10.1109/ICDE.2012.145","url":null,"abstract":"Most objects and data in the real world are interconnected, forming complex, heterogeneous but often semistructured information networks. However, many database researchers consider a database merely as a data repository that supports storage and retrieval rather than an information-rich, inter-related and multi-typed information network that supports comprehensive data analysis, whereas many network researchers focus on homogeneous networks. Departing from both, we view interconnected, semi-structured datasets as heterogeneous, information-rich networks and study how to uncover hidden knowledge in such networks. For example, a university database can be viewed as a heterogeneous information network, where objects of multiple types, such as students, professors, courses, departments, and multiple typed relationships, such as teach and advise are intertwined together, providing abundant information. In this tutorial, we present an organized picture on mining heterogeneous information networks and introduce a set of interesting, effective and scalable network mining methods. The topics to be covered include (i) database as an information network, (ii) mining information networks: clustering, classification, ranking, similarity search, and meta path-guided analysis, (iii) construction of quality, informative networks by data mining, (iv) trend and evolution analysis in heterogeneous information networks, and (v) research frontiers. We show that heterogeneous information networks are informative, and link analysis on such networks is powerful at uncovering critical knowledge hidden in large semi-structured datasets. Finally, we also present a few promising research directions.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134367970","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}
The Web has enabled the availability of a vast amount of useful information in recent years. However, the web technologies that have enabled sources to share their information have also made it easy for sources to copy from each other and often publish without proper attribution. Understanding the copying relationships between sources has many benefits, including helping data providers protect their own rights, improving various aspects of data integration, and facilitating in-depth analysis of information flow. The importance of copy detection has led to a substantial amount of research in many disciplines of Computer Science, based on the type of information considered, such as text, images, videos, software code, and structured data. This seminar explores the similarities and differences between the techniques proposed for copy detection across the different types of information. We also examine the computational challenges associated with large-scale copy detection, indicating how they could be detected efficiently, and identify a range of open problems for the community.
{"title":"Detecting Clones, Copying and Reuse on the Web","authors":"X. Dong, D. Srivastava","doi":"10.1109/ICDE.2012.146","DOIUrl":"https://doi.org/10.1109/ICDE.2012.146","url":null,"abstract":"The Web has enabled the availability of a vast amount of useful information in recent years. However, the web technologies that have enabled sources to share their information have also made it easy for sources to copy from each other and often publish without proper attribution. Understanding the copying relationships between sources has many benefits, including helping data providers protect their own rights, improving various aspects of data integration, and facilitating in-depth analysis of information flow. The importance of copy detection has led to a substantial amount of research in many disciplines of Computer Science, based on the type of information considered, such as text, images, videos, software code, and structured data. This seminar explores the similarities and differences between the techniques proposed for copy detection across the different types of information. We also examine the computational challenges associated with large-scale copy detection, indicating how they could be detected efficiently, and identify a range of open problems for the community.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132930884","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}
A limitation of XQuery is that a programmer has to be familiar with the shape of the data to query it effectively. And if that shape changes, or if the shape is other than what the programmer expects, the query may fail. One way to avoid this limitation is to transform the data into a desired shape. A data transformation is a rearrangement of data into a new shape. In this paper, we present the semantics and implementation of XMorph 2.0, a shape-polymorphic data transformation language for XML. An XMorph program can act as a query guard. The guard both transforms data to the shape needed by the query and determines whether and how the transformation potentially loses information, a transformation that loses information may lead to a query yielding an inaccurate result. This paper describes how to use XMorph as a query guard, gives a formal semantics for shape-to-shape transformations, documents how XMorph determines how a transformation potentially loses information, and describes the XMorph implementation.
{"title":"Querying XML Data: As You Shape It","authors":"C. Dyreson, S. Bhowmick","doi":"10.1109/ICDE.2012.65","DOIUrl":"https://doi.org/10.1109/ICDE.2012.65","url":null,"abstract":"A limitation of XQuery is that a programmer has to be familiar with the shape of the data to query it effectively. And if that shape changes, or if the shape is other than what the programmer expects, the query may fail. One way to avoid this limitation is to transform the data into a desired shape. A data transformation is a rearrangement of data into a new shape. In this paper, we present the semantics and implementation of XMorph 2.0, a shape-polymorphic data transformation language for XML. An XMorph program can act as a query guard. The guard both transforms data to the shape needed by the query and determines whether and how the transformation potentially loses information, a transformation that loses information may lead to a query yielding an inaccurate result. This paper describes how to use XMorph as a query guard, gives a formal semantics for shape-to-shape transformations, documents how XMorph determines how a transformation potentially loses information, and describes the XMorph implementation.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133327907","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}
Vector wise is a new entrant in the analytical database marketplace whose technology comes straight from innovations in the database research community in the past years. The product has since made waves due to its excellent performance in analytical customer workloads as well as benchmarks. We describe the history of Vectorwise, as well as its basic architecture and the experiences in turning a technology developed in an academic context into a commercial-grade product. Finally, we turn our attention to recent performance results, most notably on the TPC-H benchmark at various sizes.
{"title":"Vectorwise: A Vectorized Analytical DBMS","authors":"M. Zukowski, M. V. D. Wiel, P. Boncz","doi":"10.1109/ICDE.2012.148","DOIUrl":"https://doi.org/10.1109/ICDE.2012.148","url":null,"abstract":"Vector wise is a new entrant in the analytical database marketplace whose technology comes straight from innovations in the database research community in the past years. The product has since made waves due to its excellent performance in analytical customer workloads as well as benchmarks. We describe the history of Vectorwise, as well as its basic architecture and the experiences in turning a technology developed in an academic context into a commercial-grade product. Finally, we turn our attention to recent performance results, most notably on the TPC-H benchmark at various sizes.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116094819","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}
Ahmed M. Aly, Asmaa Sallam, B. Gnanasekaran, Long-Van Nguyen-Dinh, Walid G. Aref, M. Ouzzani, A. Ghafoor
The continuous growth of social web applications along with the development of sensor capabilities in electronic devices is creating countless opportunities to analyze the enormous amounts of data that is continuously steaming from these applications and devices. To process large scale data on large scale computing clusters, MapReduce has been introduced as a framework for parallel computing. However, most of the current implementations of the MapReduce framework support only the execution of fixed-input jobs. Such restriction makes these implementations inapplicable for most streaming applications, in which queries are continuous in nature, and input data streams are continuously received at high arrival rates. In this demonstration, we showcase M3, a prototype implementation of the MapReduce framework in which continuous queries over streams of data can be efficiently answered. M3 extends Hadoop, the open source implementation of MapReduce, bypassing the Hadoop Distributed File System (HDFS) to support main-memory-only processing. Moreover, M3 supports continuous execution of the Map and Reduce phases where individual Mappers and Reducers never terminate.
{"title":"M3: Stream Processing on Main-Memory MapReduce","authors":"Ahmed M. Aly, Asmaa Sallam, B. Gnanasekaran, Long-Van Nguyen-Dinh, Walid G. Aref, M. Ouzzani, A. Ghafoor","doi":"10.1109/ICDE.2012.120","DOIUrl":"https://doi.org/10.1109/ICDE.2012.120","url":null,"abstract":"The continuous growth of social web applications along with the development of sensor capabilities in electronic devices is creating countless opportunities to analyze the enormous amounts of data that is continuously steaming from these applications and devices. To process large scale data on large scale computing clusters, MapReduce has been introduced as a framework for parallel computing. However, most of the current implementations of the MapReduce framework support only the execution of fixed-input jobs. Such restriction makes these implementations inapplicable for most streaming applications, in which queries are continuous in nature, and input data streams are continuously received at high arrival rates. In this demonstration, we showcase M3, a prototype implementation of the MapReduce framework in which continuous queries over streams of data can be efficiently answered. M3 extends Hadoop, the open source implementation of MapReduce, bypassing the Hadoop Distributed File System (HDFS) to support main-memory-only processing. Moreover, M3 supports continuous execution of the Map and Reduce phases where individual Mappers and Reducers never terminate.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125749650","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}
In recent years, due to the appealing features of cloud computing, large amount of data have been stored in the cloud. Although cloud based services offer many advantages, privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data against illegal access, but it complicates some basic, yet important functionality such as the search on the data. To achieve search over encrypted data without compromising the privacy, considerable amount of searchable encryption schemes have been proposed in the literature. However, almost all of them handle exact query matching but not similarity matching, a crucial requirement for real world applications. Although some sophisticated secure multi-party computation based cryptographic techniques are available for similarity tests, they are computationally intensive and do not scale for large data sources. In this paper, we propose an efficient scheme for similarity search over encrypted data. To do so, we utilize a state-of-the-art algorithm for fast near neighbor search in high dimensional spaces called locality sensitive hashing. To ensure the confidentiality of the sensitive data, we provide a rigorous security definition and prove the security of the proposed scheme under the provided definition. In addition, we provide a real world application of the proposed scheme and verify the theoretical results with empirical observations on a real dataset.
{"title":"Efficient Similarity Search over Encrypted Data","authors":"Mehmet Kuzu, M. S. Islam, Murat Kantarcioglu","doi":"10.1109/ICDE.2012.23","DOIUrl":"https://doi.org/10.1109/ICDE.2012.23","url":null,"abstract":"In recent years, due to the appealing features of cloud computing, large amount of data have been stored in the cloud. Although cloud based services offer many advantages, privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data against illegal access, but it complicates some basic, yet important functionality such as the search on the data. To achieve search over encrypted data without compromising the privacy, considerable amount of searchable encryption schemes have been proposed in the literature. However, almost all of them handle exact query matching but not similarity matching, a crucial requirement for real world applications. Although some sophisticated secure multi-party computation based cryptographic techniques are available for similarity tests, they are computationally intensive and do not scale for large data sources. In this paper, we propose an efficient scheme for similarity search over encrypted data. To do so, we utilize a state-of-the-art algorithm for fast near neighbor search in high dimensional spaces called locality sensitive hashing. To ensure the confidentiality of the sensitive data, we provide a rigorous security definition and prove the security of the proposed scheme under the provided definition. In addition, we provide a real world application of the proposed scheme and verify the theoretical results with empirical observations on a real dataset.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127560794","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}
Numerous applications such as wireless communication and telematics need to keep track of evolution of spatio-temporal data for a limited past. Limited retention may even be required by regulations. In general, each data entry can have its own user specified lifetime. It is desired that expired entries are automatically removed by the system through some garbage collection mechanism. This kind of limited retention can be achieved by using a sliding window semantics similar to that from stream data processing. However, due to the large volume and relatively long lifetime of data in the aforementioned applications (in contrast to the real-time transient streaming data), the sliding window here needs to be maintained for data on disk rather than in memory. It is a new challenge to provide fast access to the information from the recent past and, at the same time, facilitate efficient deletion of the expired entries. In this paper, we propose a disk based, two-layered, sliding window indexing scheme for discretely moving spatio-temporal data. Our index can support efficient processing of standard time slice and interval queries and delete expired entries with almost no overhead. In existing historical spatio-temporal indexing techniques, deletion is either infeasible or very inefficient. Our sliding window based processing model can support both current and past entries, while many existing historical spatio-temporal indexing techniques cannot keep these two types of data together in the same index. Our experimental comparison with the best known historical index (i.e., the MV3R tree) for discretely moving spatio-temporal data shows that our index is about five times faster in terms of insertion time and comparable in terms of search performance. MV3R follows a partial persistency model, whereas our index can support very efficient deletion and update.
{"title":"SWST: A Disk Based Index for Sliding Window Spatio-Temporal Data","authors":"Manish Singh, Qiang Zhu, H. Jagadish","doi":"10.1109/ICDE.2012.98","DOIUrl":"https://doi.org/10.1109/ICDE.2012.98","url":null,"abstract":"Numerous applications such as wireless communication and telematics need to keep track of evolution of spatio-temporal data for a limited past. Limited retention may even be required by regulations. In general, each data entry can have its own user specified lifetime. It is desired that expired entries are automatically removed by the system through some garbage collection mechanism. This kind of limited retention can be achieved by using a sliding window semantics similar to that from stream data processing. However, due to the large volume and relatively long lifetime of data in the aforementioned applications (in contrast to the real-time transient streaming data), the sliding window here needs to be maintained for data on disk rather than in memory. It is a new challenge to provide fast access to the information from the recent past and, at the same time, facilitate efficient deletion of the expired entries. In this paper, we propose a disk based, two-layered, sliding window indexing scheme for discretely moving spatio-temporal data. Our index can support efficient processing of standard time slice and interval queries and delete expired entries with almost no overhead. In existing historical spatio-temporal indexing techniques, deletion is either infeasible or very inefficient. Our sliding window based processing model can support both current and past entries, while many existing historical spatio-temporal indexing techniques cannot keep these two types of data together in the same index. Our experimental comparison with the best known historical index (i.e., the MV3R tree) for discretely moving spatio-temporal data shows that our index is about five times faster in terms of insertion time and comparable in terms of search performance. MV3R follows a partial persistency model, whereas our index can support very efficient deletion and update.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127841086","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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