Pub Date : 2010-03-01DOI: 10.1109/ICDEW.2010.5452762
Christoph Böhm, Felix Naumann, Ziawasch Abedjan, D. Fenz, Toni Grütze, Daniel Hefenbrock, M. Pohl, David Sonnabend
Linked open data (LOD), as provided by a quickly growing number of sources constitutes a wealth of easily accessible information. However, this data is not easy to understand. It is usually provided as a set of (RDF) triples, often enough in the form of enormous files covering many domains. What is more, the data usually has a loose structure when it is derived from end-user generated sources, such as Wikipedia. Finally, the quality of the actual data is also worrisome, because it may be incomplete, poorly formatted, inconsistent, etc. To understand and profile such linked open data, traditional data profiling methods do not suffice. With ProLOD, we propose a suite of methods ranging from the domain level (clustering, labeling), via the schema level (matching, disambiguation), to the data level (data type detection, pattern detection, value distribution). Packaged into an interactive, web-based tool, they allow iterative exploration and discovery of new LOD sources. Thus, users can quickly gauge the relevance of the source for the problem at hand (e.g., some integration task), focus on and explore the relevant subset.
{"title":"Profiling linked open data with ProLOD","authors":"Christoph Böhm, Felix Naumann, Ziawasch Abedjan, D. Fenz, Toni Grütze, Daniel Hefenbrock, M. Pohl, David Sonnabend","doi":"10.1109/ICDEW.2010.5452762","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452762","url":null,"abstract":"Linked open data (LOD), as provided by a quickly growing number of sources constitutes a wealth of easily accessible information. However, this data is not easy to understand. It is usually provided as a set of (RDF) triples, often enough in the form of enormous files covering many domains. What is more, the data usually has a loose structure when it is derived from end-user generated sources, such as Wikipedia. Finally, the quality of the actual data is also worrisome, because it may be incomplete, poorly formatted, inconsistent, etc. To understand and profile such linked open data, traditional data profiling methods do not suffice. With ProLOD, we propose a suite of methods ranging from the domain level (clustering, labeling), via the schema level (matching, disambiguation), to the data level (data type detection, pattern detection, value distribution). Packaged into an interactive, web-based tool, they allow iterative exploration and discovery of new LOD sources. Thus, users can quickly gauge the relevance of the source for the problem at hand (e.g., some integration task), focus on and explore the relevant subset.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116100373","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 : 2010-03-01DOI: 10.1109/ICDEW.2010.5452741
Karsten Schmidt, T. Härder
Autonomous index management in native XML DBMSs has to address XML's flexibility and storage mapping features, which provide a rich set of indexing options. Change of workload characteristics, indexes selected by the query optimizer's "magic", subtle differences in the expressiveness of indexes, and tailor-made index properties ask-in addition to (long-range) manual index selection-for rapid autonomic reactions and self-tuning options by the DBMS. Hence, when managing an existing set of indexes (i.e., a configuration), its cost trade-off has to be steadily controlled by observing query runtimes, index creation and maintenance, and space constraints.
{"title":"On the use of query-driven XML auto-indexing","authors":"Karsten Schmidt, T. Härder","doi":"10.1109/ICDEW.2010.5452741","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452741","url":null,"abstract":"Autonomous index management in native XML DBMSs has to address XML's flexibility and storage mapping features, which provide a rich set of indexing options. Change of workload characteristics, indexes selected by the query optimizer's \"magic\", subtle differences in the expressiveness of indexes, and tailor-made index properties ask-in addition to (long-range) manual index selection-for rapid autonomic reactions and self-tuning options by the DBMS. Hence, when managing an existing set of indexes (i.e., a configuration), its cost trade-off has to be steadily controlled by observing query runtimes, index creation and maintenance, and space constraints.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"446 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122485378","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 : 2010-03-01DOI: 10.1109/ICDEW.2010.5452743
G. Graefe, Harumi A. Kuno
Adaptive indexing schemes such as database cracking and adaptive merging have been investigated to-date only in the context of range queries. These are typical for non-key columns in relational databases. For complete self-managing indexing, adaptive indexing must also apply to key columns. The present paper proposes a design and offers a first performance evaluation in the context of keys. Adaptive merging for keys also enables further improvements in B-tree indexes. First, partitions can be matched to levels in the memory hierarchy such as a CPU cache and an in-memory buffer pool. Second, adaptive merging in merged B-trees enables automatic master-detail clustering.
{"title":"Adaptive indexing for relational keys","authors":"G. Graefe, Harumi A. Kuno","doi":"10.1109/ICDEW.2010.5452743","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452743","url":null,"abstract":"Adaptive indexing schemes such as database cracking and adaptive merging have been investigated to-date only in the context of range queries. These are typical for non-key columns in relational databases. For complete self-managing indexing, adaptive indexing must also apply to key columns. The present paper proposes a design and offers a first performance evaluation in the context of keys. Adaptive merging for keys also enables further improvements in B-tree indexes. First, partitions can be matched to levels in the memory hierarchy such as a CPU cache and an in-memory buffer pool. Second, adaptive merging in merged B-trees enables automatic master-detail clustering.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129674130","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 : 2010-03-01DOI: 10.1109/ICDEW.2010.5452722
Ruilin Liu, Wendy Hui Wang
Data publishing has generated much concern on individual privacy. Recent work has focused on different background knowledge and their various threats to the privacy of published data. However, there still exist a few types of adversary knowledge waiting to be investigated. In this paper, I explain my research on privacy-preserving data publishing (PPDP) by using full functional dependencies (FFDs) as part of adversary knowledge. I also briefly explain my research plan.
{"title":"Privacy-preserving data publishing","authors":"Ruilin Liu, Wendy Hui Wang","doi":"10.1109/ICDEW.2010.5452722","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452722","url":null,"abstract":"Data publishing has generated much concern on individual privacy. Recent work has focused on different background knowledge and their various threats to the privacy of published data. However, there still exist a few types of adversary knowledge waiting to be investigated. In this paper, I explain my research on privacy-preserving data publishing (PPDP) by using full functional dependencies (FFDs) as part of adversary knowledge. I also briefly explain my research plan.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128615387","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 : 2010-03-01DOI: 10.1109/ICDEW.2010.5452726
Gabriele Tolomei, S. Orlando, F. Silvestri
Nowadays, people have been increasingly interested in exploiting Web Search Engines (WSEs) not only for having access to simple Web pages, but mainly for carrying out even complex activities, namely Web-mediated processes (or taskflows). Therefore, users' information needs will become more complex, and (Web) search and recommender systems should change accordingly for dealing with this shift. We claim that such taskflows and their composing tasks are implicitly present in users' minds when they interact, thus, with a WSE to access the Web. Our first research challenge is thus to evaluate this belief by analyzing a very large, longterm log of queries submitted to a WSE, and associating meaningful semantic labels with the extracted tasks (i.e., clusters of task-related queries) and taskflows. This large knowledge base constitutes a good starting point for building a model of users' behaviors. The second research challenge is to devise a novel recommender system that goes beyond the simple query suggestion of modern WSEs. Our system has to exploit the knowledge base of Webmediated processes and the learned model of users' behaviors, to generate complex insights and task-based suggestions to incoming users while they interact with a WSE.
{"title":"Towards a task-based search and recommender systems","authors":"Gabriele Tolomei, S. Orlando, F. Silvestri","doi":"10.1109/ICDEW.2010.5452726","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452726","url":null,"abstract":"Nowadays, people have been increasingly interested in exploiting Web Search Engines (WSEs) not only for having access to simple Web pages, but mainly for carrying out even complex activities, namely Web-mediated processes (or taskflows). Therefore, users' information needs will become more complex, and (Web) search and recommender systems should change accordingly for dealing with this shift. We claim that such taskflows and their composing tasks are implicitly present in users' minds when they interact, thus, with a WSE to access the Web. Our first research challenge is thus to evaluate this belief by analyzing a very large, longterm log of queries submitted to a WSE, and associating meaningful semantic labels with the extracted tasks (i.e., clusters of task-related queries) and taskflows. This large knowledge base constitutes a good starting point for building a model of users' behaviors. The second research challenge is to devise a novel recommender system that goes beyond the simple query suggestion of modern WSEs. Our system has to exploit the knowledge base of Webmediated processes and the learned model of users' behaviors, to generate complex insights and task-based suggestions to incoming users while they interact with a WSE.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121074736","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 : 2010-03-01DOI: 10.1109/ICDEW.2010.5452767
M. Yakout, A. Elmagarmid, Jennifer Neville
Improving data quality is a time-consuming, laborintensive and often domain specific operation. A recent principled approach for repairing dirty database is to use data quality rules in the form of database constraints to identify dirty tuples and then use the rules to derive data repairs. Most of existing data repair approaches focus on providing fully automated solutions, which could be risky to depend upon especially for critical data. To guarantee the optimal quality repairs applied to the database, users should be involved to confirm each repair. This highlights the need for an interactive approach that combines the best of both; automatically generating repairs, while efficiently employing user's efforts to verify the repairs. In such approach, the user will guide an online repairing process to incrementally generate repairs. A key challenge in this approach is the response time within the user's interactive sessions, because the process of generating the repairs is time consuming due to the large search space of possible repairs. To this end, we present in this paper a mechanism to continuously generate repairs only to the current top k important violated data quality rules. Moreover, the repairs are grouped and ranked such that the most beneficial in terms of improving data quality comes first to consult the user for verification and feedback. Our experiments on real-world dataset demonstrate the effectiveness of our ranking mechanism to provide a fast response time for the user while improving the data quality as quickly as possible.
{"title":"Ranking for data repairs","authors":"M. Yakout, A. Elmagarmid, Jennifer Neville","doi":"10.1109/ICDEW.2010.5452767","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452767","url":null,"abstract":"Improving data quality is a time-consuming, laborintensive and often domain specific operation. A recent principled approach for repairing dirty database is to use data quality rules in the form of database constraints to identify dirty tuples and then use the rules to derive data repairs. Most of existing data repair approaches focus on providing fully automated solutions, which could be risky to depend upon especially for critical data. To guarantee the optimal quality repairs applied to the database, users should be involved to confirm each repair. This highlights the need for an interactive approach that combines the best of both; automatically generating repairs, while efficiently employing user's efforts to verify the repairs. In such approach, the user will guide an online repairing process to incrementally generate repairs. A key challenge in this approach is the response time within the user's interactive sessions, because the process of generating the repairs is time consuming due to the large search space of possible repairs. To this end, we present in this paper a mechanism to continuously generate repairs only to the current top k important violated data quality rules. Moreover, the repairs are grouped and ranked such that the most beneficial in terms of improving data quality comes first to consult the user for verification and feedback. Our experiments on real-world dataset demonstrate the effectiveness of our ranking mechanism to provide a fast response time for the user while improving the data quality as quickly as possible.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116768519","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 : 2010-03-01DOI: 10.1109/ICDEW.2010.5452706
Carlos Eduardo S. Pires, Paulo Orlando Queiroz-Sousa, Zoubida Kedad, A. Salgado
Quickly understanding the content of a data source is very useful in several contexts. In a Peer Data Management System (PDMS), peers can be semantically clustered, each cluster being represented by a schema obtained by merging the local schemas of the peers in this cluster. In this paper, we present a process for summarizing schemas of peers participating in a PDMS. We assume that all the schemas are represented by ontologies and we propose a summarization algorithm which produces a summary containing the maximum number of relevant concepts and the minimum number of non-relevant concepts of the initial ontology. The relevance of a concept is determined using the notions of centrality and frequency. Since several possible candidate summaries can be identified during the summarization process, classical Information Retrieval metrics are employed to determine the best summary.
{"title":"Summarizing ontology-based schemas in PDMS","authors":"Carlos Eduardo S. Pires, Paulo Orlando Queiroz-Sousa, Zoubida Kedad, A. Salgado","doi":"10.1109/ICDEW.2010.5452706","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452706","url":null,"abstract":"Quickly understanding the content of a data source is very useful in several contexts. In a Peer Data Management System (PDMS), peers can be semantically clustered, each cluster being represented by a schema obtained by merging the local schemas of the peers in this cluster. In this paper, we present a process for summarizing schemas of peers participating in a PDMS. We assume that all the schemas are represented by ontologies and we propose a summarization algorithm which produces a summary containing the maximum number of relevant concepts and the minimum number of non-relevant concepts of the initial ontology. The relevance of a concept is determined using the notions of centrality and frequency. Since several possible candidate summaries can be identified during the summarization process, classical Information Retrieval metrics are employed to determine the best summary.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121095924","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 : 2010-03-01DOI: 10.1109/ICDEW.2010.5452724
Hilmi Yildirim, Mohammed J. Zaki
Reachability queries appear very frequently in many important applications that work with graph structured data. In some of them, testing reachability between two nodes corresponds to an important problem. For example, in proteinprotein interaction networks one can use it to answer whether two proteins are related, whereas in ontological databases such queries might correspond to the question of whether a concept subsumes another one. Given the huge databases that are often tested with reachability queries, it is important problem to come up with a scalable indexing scheme that has almost constant query time. In this paper, we bring a new dimension to the well-known interval labeling approach. Our approach labels each node with multiple intervals instead of a single interval so that each labeling represents a hyper-rectangle. Our new approach BOX can index dags in linear time and space while retaining the querying time admissible. In experiments, we show that BOX is not vulnerable to increasing edge to node ratios which is a problem for the existing approaches.
{"title":"Graph indexing for reachability queries","authors":"Hilmi Yildirim, Mohammed J. Zaki","doi":"10.1109/ICDEW.2010.5452724","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452724","url":null,"abstract":"Reachability queries appear very frequently in many important applications that work with graph structured data. In some of them, testing reachability between two nodes corresponds to an important problem. For example, in proteinprotein interaction networks one can use it to answer whether two proteins are related, whereas in ontological databases such queries might correspond to the question of whether a concept subsumes another one. Given the huge databases that are often tested with reachability queries, it is important problem to come up with a scalable indexing scheme that has almost constant query time. In this paper, we bring a new dimension to the well-known interval labeling approach. Our approach labels each node with multiple intervals instead of a single interval so that each labeling represents a hyper-rectangle. Our new approach BOX can index dags in linear time and space while retaining the querying time admissible. In experiments, we show that BOX is not vulnerable to increasing edge to node ratios which is a problem for the existing approaches.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116410351","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 : 2010-03-01DOI: 10.1109/ICDEW.2010.5452714
I. Assent
Recommender systems provide an automatic means of filtering out interesting items, usually based on past similarity of user ratings. In previous work, we have suggested a model that allows users to actively build a recommender network. Users express trust, obtain transparency, and grow (anonymous) recommender connections. In this work, we propose mining such active systems to generate easily understandable representations of the recommender network. Users may review these representations to provide active feedback. This approach further enhances the quality of recommendations, especially as topics of interest change over time. Most notably, it extends the amount of control users have over the model that the recommender network builds of their interests.
{"title":"Mining and representing recommendations in actively evolving recommender systems","authors":"I. Assent","doi":"10.1109/ICDEW.2010.5452714","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452714","url":null,"abstract":"Recommender systems provide an automatic means of filtering out interesting items, usually based on past similarity of user ratings. In previous work, we have suggested a model that allows users to actively build a recommender network. Users express trust, obtain transparency, and grow (anonymous) recommender connections. In this work, we propose mining such active systems to generate easily understandable representations of the recommender network. Users may review these representations to provide active feedback. This approach further enhances the quality of recommendations, especially as topics of interest change over time. Most notably, it extends the amount of control users have over the model that the recommender network builds of their interests.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"82 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123234774","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 : 2010-03-01DOI: 10.1109/ICDEW.2010.5452738
M. Holze, A. Haschimi, N. Ritter
The workloads of enterprise DBS often show periodic patterns, e.g. because there are mainly OLTP transactions during day-time and analysis operations at night. However, current DBS self-management functions do not consider these periodic patterns in their analysis. Instead, they either adapt the DBS configuration to an overall “average” workload, or they reactively try to adapt the DBS configuration after every periodic change as if the workload had never been observed before. In this paper we propose a periodicity detection approach, which allows the prediction of workload changes for DBS self-management functions. For this purpose, we first describe how recurring DBS workloads, i.e. workloads that are similar to workloads that have been observed in the past, can be identified. We then propose two different approaches for detecting periodic patterns in the history of recurring DBS workloads. Finally we show how this knowledge on periodic patterns can be used to predict workload changes, and how it can be adapted to changes in the periodic patterns over time.
{"title":"Towards workload-aware self-management: Predicting significant workload shifts","authors":"M. Holze, A. Haschimi, N. Ritter","doi":"10.1109/ICDEW.2010.5452738","DOIUrl":"https://doi.org/10.1109/ICDEW.2010.5452738","url":null,"abstract":"The workloads of enterprise DBS often show periodic patterns, e.g. because there are mainly OLTP transactions during day-time and analysis operations at night. However, current DBS self-management functions do not consider these periodic patterns in their analysis. Instead, they either adapt the DBS configuration to an overall “average” workload, or they reactively try to adapt the DBS configuration after every periodic change as if the workload had never been observed before. In this paper we propose a periodicity detection approach, which allows the prediction of workload changes for DBS self-management functions. For this purpose, we first describe how recurring DBS workloads, i.e. workloads that are similar to workloads that have been observed in the past, can be identified. We then propose two different approaches for detecting periodic patterns in the history of recurring DBS workloads. Finally we show how this knowledge on periodic patterns can be used to predict workload changes, and how it can be adapted to changes in the periodic patterns over time.","PeriodicalId":442345,"journal":{"name":"2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128218865","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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