The abundance of Web 2.0 resources in various media formats calls for better resource integration to enrich user experience. This naturally leads to a new cross-modal resource search requirement, in which a query is a resource in one modal and the results are closely related resources in other modalities. With cross-modal search, we can better exploit existing resources. Tags associated with Web 2.0 resources are intuitive medium to link resources with different modality together. However, tagging is by nature an ad hoc activity. They often contain noises and are affected by the subjective inclination of the tagger. Consequently, linking resources simply by tags will not be reliable. In this paper, we propose an approach for linking tagged resources to concepts extracted from Wikipedia, which has become a fairly reliable reference over the last few years. Compared to the tags, the concepts are therefore of higher quality. We develop effective methods for cross-modal search based on the concepts associated with resources. Extensive experiments were conducted, and the results show that our solution achieves good performance.
{"title":"Cross Domain Search by Exploiting Wikipedia","authors":"Chen Liu, Sai Wu, Shouxu Jiang, A. Tung","doi":"10.1109/ICDE.2012.13","DOIUrl":"https://doi.org/10.1109/ICDE.2012.13","url":null,"abstract":"The abundance of Web 2.0 resources in various media formats calls for better resource integration to enrich user experience. This naturally leads to a new cross-modal resource search requirement, in which a query is a resource in one modal and the results are closely related resources in other modalities. With cross-modal search, we can better exploit existing resources. Tags associated with Web 2.0 resources are intuitive medium to link resources with different modality together. However, tagging is by nature an ad hoc activity. They often contain noises and are affected by the subjective inclination of the tagger. Consequently, linking resources simply by tags will not be reliable. In this paper, we propose an approach for linking tagged resources to concepts extracted from Wikipedia, which has become a fairly reliable reference over the last few years. Compared to the tags, the concepts are therefore of higher quality. We develop effective methods for cross-modal search based on the concepts associated with resources. Extensive experiments were conducted, and the results show that our solution achieves good performance.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"4 5 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":"116627303","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}
"Big Data" in map-reduce (M-R) clusters is often fundamentally temporal in nature, as are many analytics tasks over such data. For instance, display advertising uses Behavioral Targeting (BT) to select ads for users based on prior searches, page views, etc. Previous work on BT has focused on techniques that scale well for offline data using M-R. However, this approach has limitations for BT-style applications that deal with temporal data: (1) many queries are temporal and not easily expressible in M-R, and moreover, the set-oriented nature of M-R front-ends such as SCOPE is not suitable for temporal processing, (2) as commercial systems mature, they may need to also directly analyze and react to real-time data feeds since a high turnaround time can result in missed opportunities, but it is difficult for current solutions to naturally also operate over real-time streams. Our contributions are twofold. First, we propose a novel framework called TiMR (pronounced timer), that combines a time-oriented data processing system with a M-R framework. Users write and submit analysis algorithms as temporal queries - these queries are succinct, scale-out-agnostic, and easy to write. They scale well on large-scale offline data using TiMR, and can work unmodified over real-time streams. We also propose new cost-based query fragmentation and temporal partitioning schemes for improving efficiency with TiMR. Second, we show the feasibility of this approach for BT, with new temporal algorithms that exploit new targeting opportunities. Experiments using real data from a commercial ad platform show that TiMR is very efficient and incurs orders-of-magnitude lower development effort. Our BT solution is easy and succinct, and performs up to several times better than current schemes in terms of memory, learning time, and click-through-rate/coverage.
{"title":"Temporal Analytics on Big Data for Web Advertising","authors":"B. Chandramouli, J. Goldstein, S. Duan","doi":"10.1109/ICDE.2012.55","DOIUrl":"https://doi.org/10.1109/ICDE.2012.55","url":null,"abstract":"\"Big Data\" in map-reduce (M-R) clusters is often fundamentally temporal in nature, as are many analytics tasks over such data. For instance, display advertising uses Behavioral Targeting (BT) to select ads for users based on prior searches, page views, etc. Previous work on BT has focused on techniques that scale well for offline data using M-R. However, this approach has limitations for BT-style applications that deal with temporal data: (1) many queries are temporal and not easily expressible in M-R, and moreover, the set-oriented nature of M-R front-ends such as SCOPE is not suitable for temporal processing, (2) as commercial systems mature, they may need to also directly analyze and react to real-time data feeds since a high turnaround time can result in missed opportunities, but it is difficult for current solutions to naturally also operate over real-time streams. Our contributions are twofold. First, we propose a novel framework called TiMR (pronounced timer), that combines a time-oriented data processing system with a M-R framework. Users write and submit analysis algorithms as temporal queries - these queries are succinct, scale-out-agnostic, and easy to write. They scale well on large-scale offline data using TiMR, and can work unmodified over real-time streams. We also propose new cost-based query fragmentation and temporal partitioning schemes for improving efficiency with TiMR. Second, we show the feasibility of this approach for BT, with new temporal algorithms that exploit new targeting opportunities. Experiments using real data from a commercial ad platform show that TiMR is very efficient and incurs orders-of-magnitude lower development effort. Our BT solution is easy and succinct, and performs up to several times better than current schemes in terms of memory, learning time, and click-through-rate/coverage.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"43 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":"123415442","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}
This paper addresses the challenges faced by everyday Web users, who interact with inherently heterogeneous and distributed information. Managing such data is currently beyond the skills of casual users. We describe ongoing work that has as its goal the development of foundations for declarative distributed data management. In this approach, we see the Web as a knowledge base consisting of distributed logical facts and rules. Our objective is to enable automated reasoning over this knowledge base, ultimately improving the quality of service and of data. For this, we use Webdamlog, a Datalog-style language with rule delegation. We outline ongoing efforts on the Web dam Exchange platform that combines Webdamlog evaluation with communication and security protocols.
{"title":"Viewing the Web as a Distributed Knowledge Base","authors":"S. Abiteboul, Émilien Antoine, Julia Stoyanovich","doi":"10.1109/ICDE.2012.150","DOIUrl":"https://doi.org/10.1109/ICDE.2012.150","url":null,"abstract":"This paper addresses the challenges faced by everyday Web users, who interact with inherently heterogeneous and distributed information. Managing such data is currently beyond the skills of casual users. We describe ongoing work that has as its goal the development of foundations for declarative distributed data management. In this approach, we see the Web as a knowledge base consisting of distributed logical facts and rules. Our objective is to enable automated reasoning over this knowledge base, ultimately improving the quality of service and of data. For this, we use Webdamlog, a Datalog-style language with rule delegation. We outline ongoing efforts on the Web dam Exchange platform that combines Webdamlog evaluation with communication and security protocols.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"64 3 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":"126087540","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}
C. Doulkeridis, Akrivi Vlachou, K. Nørvåg, Y. Kotidis, N. Polyzotis
In this paper, we study efficient processing of rank joins in highly distributed systems, where servers store fragments of relations in an autonomous manner. Existing rank-join algorithms exhibit poor performance in this setting due to excessive communication costs or high latency. We propose a novel distributed rank-join framework that employs data statistics, maintained as histograms, to determine the subset of each relational fragment that needs to be fetched to generate the top-k join results. At the heart of our framework lies a distributed score bound estimation algorithm that produces sufficient score bounds for each relation, that guarantee the correctness of the rank-join result set, when the histograms are accurate. Furthermore, we propose a generalization of our framework that supports approximate statistics, in the case that the exact statistical information is not available. An extensive experimental study validates the efficiency of our framework and demonstrates its advantages over existing methods.
{"title":"Processing of Rank Joins in Highly Distributed Systems","authors":"C. Doulkeridis, Akrivi Vlachou, K. Nørvåg, Y. Kotidis, N. Polyzotis","doi":"10.1109/ICDE.2012.108","DOIUrl":"https://doi.org/10.1109/ICDE.2012.108","url":null,"abstract":"In this paper, we study efficient processing of rank joins in highly distributed systems, where servers store fragments of relations in an autonomous manner. Existing rank-join algorithms exhibit poor performance in this setting due to excessive communication costs or high latency. We propose a novel distributed rank-join framework that employs data statistics, maintained as histograms, to determine the subset of each relational fragment that needs to be fetched to generate the top-k join results. At the heart of our framework lies a distributed score bound estimation algorithm that produces sufficient score bounds for each relation, that guarantee the correctness of the rank-join result set, when the histograms are accurate. Furthermore, we propose a generalization of our framework that supports approximate statistics, in the case that the exact statistical information is not available. An extensive experimental study validates the efficiency of our framework and demonstrates its advantages over existing methods.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"9 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":"126019518","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 facility for merging equivalent data streams can support multiple capabilities in a data stream management system (DSMS), such as query-plan switching and high availability. One can logically view a data stream as a temporal table of events, each associated with a lifetime (time interval) over which the event contributes to output. In many applications, the "same" logical stream may present itself physically in multiple physical forms, for example, due to disorder arising in transmission or from combining multiple sources, and modifications of earlier events. Merging such streams correctly is challenging when the streams may differ physically in timing, order, and composition. This paper introduces a new stream operator called Logical Merge (LMerge) that takes multiple logically consistent streams as input and outputs a single stream that is compatible with all of them. LMerge can handle the dynamic attachment and detachment of input streams. We present a range of algorithms for LMerge that can exploit compile-time stream properties for efficiency. Experiments with Stream Insight, a commercial DSMS, show that LMerge is sometimes orders-of-magnitude more efficient than enforcing determinism on inputs, and that there is benefit to using specialized algorithms when stream variability is limited. We also show that LMerge and its extensions can provide performance benefits in several real-world applications.
{"title":"Physically Independent Stream Merging","authors":"B. Chandramouli, D. Maier, J. Goldstein","doi":"10.1109/ICDE.2012.25","DOIUrl":"https://doi.org/10.1109/ICDE.2012.25","url":null,"abstract":"A facility for merging equivalent data streams can support multiple capabilities in a data stream management system (DSMS), such as query-plan switching and high availability. One can logically view a data stream as a temporal table of events, each associated with a lifetime (time interval) over which the event contributes to output. In many applications, the \"same\" logical stream may present itself physically in multiple physical forms, for example, due to disorder arising in transmission or from combining multiple sources, and modifications of earlier events. Merging such streams correctly is challenging when the streams may differ physically in timing, order, and composition. This paper introduces a new stream operator called Logical Merge (LMerge) that takes multiple logically consistent streams as input and outputs a single stream that is compatible with all of them. LMerge can handle the dynamic attachment and detachment of input streams. We present a range of algorithms for LMerge that can exploit compile-time stream properties for efficiency. Experiments with Stream Insight, a commercial DSMS, show that LMerge is sometimes orders-of-magnitude more efficient than enforcing determinism on inputs, and that there is benefit to using specialized algorithms when stream variability is limited. We also show that LMerge and its extensions can provide performance benefits in several real-world applications.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"18 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":"117140483","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}
Query plan caching eliminates the need for repeated query optimization, hence, it has strong practical implications for relational database management systems (RDBMSs). Unfortunately, existing approaches consider only the query plan generated at the expected values of parameters that characterize the query, data and the current state of the system, while these parameters may take different values during the lifetime of a cached plan. A better alternative is to harvest the optimizer's plan choice for different parameter values, populate the cache with promising query plans, and select a cached plan based upon current parameter values. To address this challenge, we propose a parametric plan caching (PPC) framework that uses an online plan space clustering algorithm. The clustering algorithm is density-based, and it exploits locality-sensitive hashing as a pre-processing step so that clusters in the plan spaces can be efficiently stored in database histograms and queried in constant time. We experimentally validate that our approach is precise, efficient in space-and-time and adaptive, requiring no eager exploration of the plan spaces of the optimizer.
{"title":"Parametric Plan Caching Using Density-Based Clustering","authors":"Günes Aluç, David DeHaan, Ivan T. Bowman","doi":"10.1109/ICDE.2012.57","DOIUrl":"https://doi.org/10.1109/ICDE.2012.57","url":null,"abstract":"Query plan caching eliminates the need for repeated query optimization, hence, it has strong practical implications for relational database management systems (RDBMSs). Unfortunately, existing approaches consider only the query plan generated at the expected values of parameters that characterize the query, data and the current state of the system, while these parameters may take different values during the lifetime of a cached plan. A better alternative is to harvest the optimizer's plan choice for different parameter values, populate the cache with promising query plans, and select a cached plan based upon current parameter values. To address this challenge, we propose a parametric plan caching (PPC) framework that uses an online plan space clustering algorithm. The clustering algorithm is density-based, and it exploits locality-sensitive hashing as a pre-processing step so that clusters in the plan spaces can be efficiently stored in database histograms and queried in constant time. We experimentally validate that our approach is precise, efficient in space-and-time and adaptive, requiring no eager exploration of the plan spaces of the optimizer.","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":"130731254","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}
Michael Busch, Krishna Gade, B. Larson, Patrick Lok, Samuel B. Luckenbill, Jimmy J. Lin
The web today is increasingly characterized by social and real-time signals, which we believe represent two frontiers in information retrieval. In this paper, we present Early bird, the core retrieval engine that powers Twitter's real-time search service. Although Early bird builds and maintains inverted indexes like nearly all modern retrieval engines, its index structures differ from those built to support traditional web search. We describe these differences and present the rationale behind our design. A key requirement of real-time search is the ability to ingest content rapidly and make it searchable immediately, while concurrently supporting low-latency, high-throughput query evaluation. These demands are met with a single-writer, multiple-reader concurrency model and the targeted use of memory barriers. Early bird represents a point in the design space of real-time search engines that has worked well for Twitter's needs. By sharing our experiences, we hope to spur additional interest and innovation in this exciting space.
{"title":"Earlybird: Real-Time Search at Twitter","authors":"Michael Busch, Krishna Gade, B. Larson, Patrick Lok, Samuel B. Luckenbill, Jimmy J. Lin","doi":"10.1109/ICDE.2012.149","DOIUrl":"https://doi.org/10.1109/ICDE.2012.149","url":null,"abstract":"The web today is increasingly characterized by social and real-time signals, which we believe represent two frontiers in information retrieval. In this paper, we present Early bird, the core retrieval engine that powers Twitter's real-time search service. Although Early bird builds and maintains inverted indexes like nearly all modern retrieval engines, its index structures differ from those built to support traditional web search. We describe these differences and present the rationale behind our design. A key requirement of real-time search is the ability to ingest content rapidly and make it searchable immediately, while concurrently supporting low-latency, high-throughput query evaluation. These demands are met with a single-writer, multiple-reader concurrency model and the targeted use of memory barriers. Early bird represents a point in the design space of real-time search engines that has worked well for Twitter's needs. By sharing our experiences, we hope to spur additional interest and innovation in this exciting space.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"55 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":"132296812","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}
Zhitao Shen, M. A. Cheema, Xuemin Lin, W. Zhang, Haixun Wang
Top-k pairs queries have received significant attention by the research community. k-closest pairs queries, k-furthest pairs queries and their variants are among the most well studied special cases of the top-k pairs queries. In this paper, we present the first approach to answer a broad class of top-k pairs queries over sliding windows. Our framework handles multiple top-k pairs queries and each query is allowed to use a different scoring function, a different value of k and a different size of the sliding window. Although the number of possible pairs in the sliding window is quadratic to the number of objects N in the sliding window, we efficiently answer the top-k pairs query by maintaining a small subset of pairs called K-sky band which is expected to consist of O(K log(N/K)) pairs. For all the queries that use the same scoring function, we need to maintain only one K-sky band. We present efficient techniques for the K-sky band maintenance and query answering. We conduct a detailed complexity analysis and show that the expected cost of our approach is reasonably close to the lower bound cost. We experimentally verify this by comparing our approach with a specially designed supreme algorithm that assumes the existence of an oracle and meets the lower bound cost.
{"title":"Efficiently Monitoring Top-k Pairs over Sliding Windows","authors":"Zhitao Shen, M. A. Cheema, Xuemin Lin, W. Zhang, Haixun Wang","doi":"10.1109/ICDE.2012.89","DOIUrl":"https://doi.org/10.1109/ICDE.2012.89","url":null,"abstract":"Top-k pairs queries have received significant attention by the research community. k-closest pairs queries, k-furthest pairs queries and their variants are among the most well studied special cases of the top-k pairs queries. In this paper, we present the first approach to answer a broad class of top-k pairs queries over sliding windows. Our framework handles multiple top-k pairs queries and each query is allowed to use a different scoring function, a different value of k and a different size of the sliding window. Although the number of possible pairs in the sliding window is quadratic to the number of objects N in the sliding window, we efficiently answer the top-k pairs query by maintaining a small subset of pairs called K-sky band which is expected to consist of O(K log(N/K)) pairs. For all the queries that use the same scoring function, we need to maintain only one K-sky band. We present efficient techniques for the K-sky band maintenance and query answering. We conduct a detailed complexity analysis and show that the expected cost of our approach is reasonably close to the lower bound cost. We experimentally verify this by comparing our approach with a specially designed supreme algorithm that assumes the existence of an oracle and meets the lower bound cost.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"365 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":"132875048","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}
Sequence analysis is very important in our daily life. Typically, each sequence is associated with an ordered list of elements. For example, in a movie rental application, a customer's movie rental record containing an ordered list of movies is a sequence example. Most studies about sequence analysis focus on subsequence matching which finds all sequences stored in the database such that a given query sequence is a subsequence of each of these sequences. In many applications, elements are associated with properties or attributes. For example, each movie is associated with some attributes like "Director" and "Actors". Unfortunately, to the best of our knowledge, all existing studies about sequence analysis do not consider the attributes of elements. In this paper, we propose two problems. The first problem is: given a query sequence and a set of sequences, considering the attributes of elements, we want to find all sequences which are matched by this query sequence. This problem is called attribute-based subsequence matching (ASM). All existing applications for the traditional subsequence matching problem can also be applied to our new problem provided that we are given the attributes of elements. We propose an efficient algorithm for problem ASM. The key idea to the efficiency of this algorithm is to compress each whole sequence with potentially many associated attributes into just a triplet of numbers. By dealing with these very compressed representations, we greatly speed up the attribute-based subsequence matching. The second problem is to find all frequent attribute-based subsequence. We also adapt an existing efficient algorithm for this second problem to show we can use the algorithm developed for the first problem. Empirical studies show that our algorithms are scalable in large datasets. In particular, our algorithms run at least an order of magnitude faster than a straightforward method in most cases. This work can stimulate a number of existing data mining problems which are fundamentally based on subsequence matching such as sequence classification, frequent sequence mining, motif detection and sequence matching in bioinformatics.
{"title":"Attribute-Based Subsequence Matching and Mining","authors":"Yu Peng, R. C. Wong, Liangliang Ye, Philip S. Yu","doi":"10.1109/ICDE.2012.81","DOIUrl":"https://doi.org/10.1109/ICDE.2012.81","url":null,"abstract":"Sequence analysis is very important in our daily life. Typically, each sequence is associated with an ordered list of elements. For example, in a movie rental application, a customer's movie rental record containing an ordered list of movies is a sequence example. Most studies about sequence analysis focus on subsequence matching which finds all sequences stored in the database such that a given query sequence is a subsequence of each of these sequences. In many applications, elements are associated with properties or attributes. For example, each movie is associated with some attributes like \"Director\" and \"Actors\". Unfortunately, to the best of our knowledge, all existing studies about sequence analysis do not consider the attributes of elements. In this paper, we propose two problems. The first problem is: given a query sequence and a set of sequences, considering the attributes of elements, we want to find all sequences which are matched by this query sequence. This problem is called attribute-based subsequence matching (ASM). All existing applications for the traditional subsequence matching problem can also be applied to our new problem provided that we are given the attributes of elements. We propose an efficient algorithm for problem ASM. The key idea to the efficiency of this algorithm is to compress each whole sequence with potentially many associated attributes into just a triplet of numbers. By dealing with these very compressed representations, we greatly speed up the attribute-based subsequence matching. The second problem is to find all frequent attribute-based subsequence. We also adapt an existing efficient algorithm for this second problem to show we can use the algorithm developed for the first problem. Empirical studies show that our algorithms are scalable in large datasets. In particular, our algorithms run at least an order of magnitude faster than a straightforward method in most cases. This work can stimulate a number of existing data mining problems which are fundamentally based on subsequence matching such as sequence classification, frequent sequence mining, motif detection and sequence matching in bioinformatics.","PeriodicalId":321608,"journal":{"name":"2012 IEEE 28th International Conference on Data Engineering","volume":"84 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":"131725192","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}
Tobias Emrich, H. Kriegel, N. Mamoulis, M. Renz, Andreas Züfle
The problem of modeling and managing uncertain data has received a great deal of interest, due to its manifold applications in spatial, temporal, multimedia and sensor databases. There exists a wide range of work covering spatial uncertainty in the static (snapshot) case, where only one point of time is considered. In contrast, the problem of modeling and querying uncertain spatio-temporal data has only been treated as a simple extension of the spatial case, disregarding time dependencies between consecutive timestamps. In this work, we present a framework for efficiently modeling and querying uncertain spatio-temporal data. The key idea of our approach is to model possible object trajectories by stochastic processes. This approach has three major advantages over previous work. First it allows answering queries in accordance with the possible worlds model. Second, dependencies between object locations at consecutive points in time are taken into account. And third it is possible to reduce all queries on this model to simple matrix multiplications. Based on these concepts we propose efficient solutions for different probabilistic spatio-temporal queries. In an experimental evaluation we show that our approaches are several order of magnitudes faster than state-of-the-art competitors.
{"title":"Querying Uncertain Spatio-Temporal Data","authors":"Tobias Emrich, H. Kriegel, N. Mamoulis, M. Renz, Andreas Züfle","doi":"10.1109/ICDE.2012.94","DOIUrl":"https://doi.org/10.1109/ICDE.2012.94","url":null,"abstract":"The problem of modeling and managing uncertain data has received a great deal of interest, due to its manifold applications in spatial, temporal, multimedia and sensor databases. There exists a wide range of work covering spatial uncertainty in the static (snapshot) case, where only one point of time is considered. In contrast, the problem of modeling and querying uncertain spatio-temporal data has only been treated as a simple extension of the spatial case, disregarding time dependencies between consecutive timestamps. In this work, we present a framework for efficiently modeling and querying uncertain spatio-temporal data. The key idea of our approach is to model possible object trajectories by stochastic processes. This approach has three major advantages over previous work. First it allows answering queries in accordance with the possible worlds model. Second, dependencies between object locations at consecutive points in time are taken into account. And third it is possible to reduce all queries on this model to simple matrix multiplications. Based on these concepts we propose efficient solutions for different probabilistic spatio-temporal queries. In an experimental evaluation we show that our approaches are several order of magnitudes faster than state-of-the-art competitors.","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":"133992827","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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